Abdollahi, M, Ashtari, S, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2022, 'Dynamic Routing Protocol Selection in Multi-Hop Device-to-Device Wireless Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8796-8809.
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Abughalwa, M, Tuan, HD, Nguyen, DN, Poor, HV & Hanzo, L 2022, 'Finite-Blocklength RIS-Aided Transmit Beamforming', IEEE Transactions on Vehicular Technology, vol. 71, no. 11, pp. 12374-12379.
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This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution.
Afroz, F & Braun, R 2022, 'Empirical Analysis of Extended QX-MAC for IOT-Based WSNS', Electronics, vol. 11, no. 16, pp. 2543-2543.
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The Internet of Things (IoT) connects our world in more ways than we imagine. Wireless sensor network (WSN) technology is at the core of implementing IoT architectures. Although WSN applications give us enormous opportunities, their deployment is challenging because of the energy constraint in sensor nodes. The primary design objective of WSNs is therefore to maximize energy efficiency. Enhancing network quality of service (QoS), such as latency, is another crucial factor, particularly for different delay-sensitive applications. Medium access control (MAC) protocols are of paramount importance to achieve these targets. Over the years, several duty-cycled MAC protocols were proposed. Among them, the strobed preamble approach introduced in X-MAC has gained much interest in IoT field because of its several theoretical advantages. However, X-MAC is highly efficient only under light traffic. Under heavy traffic, X-MAC incurs high per-packet overhead and extra delay. In addition, X-MAC has several design flaws that can significantly degrade network performance. In this paper, we point out some specific malfunctions in the original X-MAC design and propose alternatives to reduce their impact. We present an energy-efficient, traffic-adaptive MAC protocol called QX-MAC that addresses the foreseen shortcomings in X-MAC. QX-MAC integrates Q-learning and the more bit scheme to enable the nodes to adapt the active period and duty cycle in accordance with incoming traffic. Finally, the performance of QX-MAC is thoroughly analyzed compared with other reference protocols to validate its efficacy. Our QX-MAC simulation results demonstrate substantial improvements in overall network performance in terms of energy consumption, packet loss, delay, or throughput.
Afroz, F, Braun, R & Chaczko, Z 2022, 'XX-MAC and EX-MAC: Two Variants of X-MAC Protocol for Low Power Wireless Sensor Networks', Ad-Hoc and Sensor Wireless Networks, vol. 51, no. 4, pp. 285-314.
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The strobed preamble approach introduced in the X-MAC protocol minimises long preamble duration, overhearing, and per-hop latency in conventional wireless sensor networks (WSNs). However, it incurs high per-packet overhead and extra delay under high traffic scenarios as it operates only in the unsynchronised state. In this paper, we model a variant of X-MAC, namely XX-MAC, which employs an adaptive dutycycling algorithm to address this issue in low data rate WSNs with short, fixed inter-packet arrival time. Furthermore, we identify the shortcoming of XX-MAC as well as propose a request-based MAC protocol, namely EX-MAC, targeting WSNs in dynamic traffic scenarios. Simulations show that at optimum slot duration, XX-MAC reduces the per-packet delay by 13.53% and 48.86% than the delay experienced by X-MAC and B-MAC, respectively. XX-MAC, on average, can deliver 92.5% of packets to the receiver, whereas X-MAC and B-MAC respectively support 91.66% and 82.91% packet delivery. XX-MAC also reduces the energy consumption per received packet by 2.61% than X-MAC, and by 65.31% than the B-MAC protocol. Experimental results also demonstrate that under variable traffic conditions, EX-MAC offers the lowest packet loss (8.55%), whilst XX-MAC and X-MAC experience 13.1% and 18.3% packet loss, respectively. EX-MAC decreases per-packet network energy consumption (3.056mJ/packet) compared with XX-MAC (3.107mJ/ packet) and X-MAC (3.424mJ/packet). Furthermore, EX-MAC minimises the mean delay per received packet by 5.758% and 10.457% (approximately) than that of XX-MAC and X-MAC, respectively.
Alsawwaf, M, Chaczko, Z, Kulbacki, M & Sarathy, N 2022, 'In Your Face: Person Identification Through Ratios and Distances Between Facial Features', Vietnam Journal of Computer Science, vol. 09, no. 02, pp. 187-202.
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These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.
Alsenwi, M, Abolhasan, M & Lipman, J 2022, 'Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21582-21592.
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Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.
Alsufyani, N & Gill, AQ 2022, 'Digitalisation performance assessment: A systematic review', Technology in Society, vol. 68, pp. 101894-101894.
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Organisations are showing a keen interest in digitalisation. However, they are uncertain about how to determine the impact of digitalisation on organisation performance outcomes. This places decision-makers in a challenging position to assess the feasibility and intended performance outcomes of digitalisation. This paper aims to address this important research need and provides the performance indicators, measures, metrics and scales based on a systematic review of 30 selected papers. The results from this review were synthesised using the “adaptive enterprise architecture”, and “results and determinants” frameworks as theoretical lenses. This work will benefit researchers and practitioners interested in studying the impact of digitalisation on organisational performance.
Alzoubi, YI & Gill, AQ 2022, 'Can Agile Enterprise Architecture be Implemented Successfully in Distributed Agile Development? Empirical Findings', Global Journal of Flexible Systems Management, vol. 23, no. 2, pp. 221-235.
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A potential solution to the high failure rate in distributed agile development and enhance the success of projects is through implementing agile enterprise architecture, though the success is still to be established. The present paper empirically investigates the gap, by defining the role and commitment of implementing agile enterprise architecture on distributed agile development. The data were collected by interviewing 12 key team members and observing four team meetings over 2 months and analyzing using thematic analysis. The present study suggests that implementing agile enterprise architecture is possible in distributed agile development and may have a positive impact on project success. However, many questions demand further investigation.
Alzoubi, YI, Gill, A & Mishra, A 2022, 'A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues', Journal of Cloud Computing, vol. 11, no. 1, p. 80.
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AbstractThe fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs.
Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Remote Water Salinity Sensor Using Metamaterial Perfect Absorber', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6785-6794.
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Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote-sensing scenarios. In this article, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of the absorbed signals. Hence, there is no need for electrical connections, which enables remote-sensing applications. Square-shaped channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity levels. A 7 × 7 array with a total size of 140 mm × 160 mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 to 3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.
Ansari, M, Jones, B & Guo, YJ 2022, 'Spherical Luneburg Lens of Layered Structure With Low Anisotropy and Low Cost', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4307-4318.
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A spherical Luneburg lens made of parallel planar layers of lightweight foam with embedded conducting cylindrical inserts on a uniform hexagonal grid centered in each layer is presented. This work draws on the authors' previous paper (Ansari et al., 2020) describing a Luneburg lens that uses cubic conducting inserts on a uniform cubic grid. This previous lens, while being of lightweight and economical construction, suffered from anisotropy resulting in a focal length that varied with the inclination of the beam relative to the orientation of the cubic grid. The lens described here largely overcomes this problem and allows for simpler and more economical construction. A prototype lens designed for the 3.3-3.8 GHz band with a diameter of 400 mm and a beamwidth of 14° was tested. Radiation patterns at wide scanning angles were nearly identical, and cross-polarization for slant incident polarization was below -25 dB on boresight and below -18 dB for all angles. A characteristic of this lens construction is its extremely high efficiency. The measured gain at the mid-band was 21.6 dBi, agreeing with simulated gain based on lossless materials to within measurement error. It is shown that wider bandwidths are obtainable if the thickness of the layers is reduced.
Anwar, MJ, Gill, AQ, Fitzgibbon, AD & Gull, I 2022, 'PESTLE+ risk analysis model to assess pandemic preparedness of digital ecosystems.', Secur. Priv., vol. 5, no. 1.
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AbstractCOVID‐19 pandemic has affected every country in many ways. Its substantial economic impacts are causing businesses to fade, pushing many nations into an economic downturn. This exposes organizations worldwide to unique risks which cannot be foreseen with conventional methods of risk analysis. This research is part of a broader action design research project conducted in collaboration with industry partner to answer an important research question: How to extend PESTLE risk analysis model to assess pandemic preparedness? In this context, the health factor is added to extend the traditional PESTLE risk analysis model. Furthermore, the interdependence between PESTLE factors has also been investigated, which has not been discussed before. The contribution of this research is the novel PESTLE+ risk analysis model that will help individuals and businesses to improve their understanding of the health crisis, such as the COVID‐19, adjust accordingly and eventually endure the ongoing crisis, which is driving most businesses into liquidation.
Arnaz, A, Lipman, J, Abolhasan, M & Hiltunen, M 2022, 'Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey', IEEE Access, vol. 10, pp. 67747-67770.
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Open RAN is an emerging vision and an advancement of the Radio Access Network (RAN). Its purpose is to implement a vendor and network-generation agnostic RAN, provide networking solutions across all service requests, and implement artificial intelligence solutions in different stages of an end-to-end communication path. The 5th Generation (5G) and beyond the 5th Generation (B5G) of networking introduce and support new use cases, such as tactile internet and autonomous driving. The complexity and innovative nature of these use cases require continuous innovation at a high pace in the RAN. The traditional approach of building end-to-end RAN solutions by only one vendor hampers the speed of innovation - furthermore, the lack of a standard approach to implementing artificial intelligence complicates the compatibility of products with the RAN ecosystem. O-RAN Alliance, a community of industry and academic experts in RAN, works on writing Open RAN specifications on top of the 3rd Generation Partnership Project (3GPP) standards. Founded on these specifications, the aim of this paper is to introduce open research topics in Open RAN that overlap the interests of both AI and telecommunication researchers. The paper provides an overview of the architecture and components of Open RAN, then explores AI use cases in Open RAN. Also, this survey includes some plausible AI deployment scenarios that the specifications have not covered. Open RAN in future cities creates opportunities for various use cases across different sectors, including engineering, operations, and research that this paper addresses.
Ashtari, S, Abdollahi, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Performance analysis of multi-hop routing protocols in SDN-based wireless networks', Computers & Electrical Engineering, vol. 97, pp. 107393-107393.
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Wireless cellular networks have rapidly evolved to be software-defined in nature. This has created opportunities to improve their performance. One such opportunity is through enabling programming and integration of multi-hop device-to-device (MD2D) at the edge. However, efficient integration of MD2D at the edge requires a highly adaptable and scalable routing protocol, where its development is underpinned through understanding of which type of current routing characteristics and architectures are suitable over dynamic networking conditions. To develop such understanding, we conducted a detailed analysis and performance study on three routing protocols, namely virtual ad-hoc routing protocol-source based (VARP-S) Abolhasan et al. (2018), SDN-based multi-hop device-to-device routing protocol (SMDRP) Abdollahi et al. (2019) and hybrid SDN architecture for wireless distributed networks (HSAW) Abolhasan et al. (2015). Our investigations illustrate that VARP-S and SMDRP perform best in terms of energy consumption and cellular routing overhead. However, HSAW shows better performance in terms of end-to-end (E2E) delay and packet loss over lower network and traffic densities.
ashtari, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Joint Mobile Node Participation and Multihop Routing for Emerging Open Radio-Based Intelligent Transportation System', IEEE Access, vol. 10, pp. 85228-85242.
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This paper proposes joint mobile node participation and routing protocol for multi-hop device-to-device (MD2D) networking in intelligent transportation systems, called fuzzy-based participation and routing protocol for MD2D (FPRM). Our proposed protocol is designed to operate over future open-radio access networks (O-RANs). We introduce a sub-layer at the network layer that can determine nodes with the highest participation probability in routing using a fuzzy logic system, thus building a framework to create more stable routes. To ensure the participating nodes are capable of handling the data traffic, two constraints are proposed, mobility and coverage constraints. The former enables the creation of sustainable communication links, and the latter enforces the communication service to the entire MD2D network. Simulation results show that our approach can increase the network lifetime, decrease the end-to-end (E2E) delay, and increase the packet delivery ratio (PDR) compared to the existing proactive routing protocol. Our protocol outperforms the benchmarked MD2D protocols and other investigated ad hoc protocols.
Ashtari, S, Zhou, I, Abolhasan, M, Shariati, N, Lipman, J & Ni, W 2022, 'Knowledge-defined networking: Applications, challenges and future work', Array, vol. 14, pp. 100136-100136.
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Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.
Babakian, A, Monclus, P, Braun, R & Lipman, J 2022, 'A Retrospective on Workload Identifiers: From Data Center to Cloud-Native Networks', IEEE Access, vol. 10, pp. 105518-105527.
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As applications move to multiple clouds, the network has become a reactive element to support cloud consumption and application needs. Through each generation of network architectures, identifiers and the use of dynamic locators evolved in different levels of the protocol stack. The identifiers and locators type is defined by the isolation boundary and how the architecture considers semantic overload in the IP address. Each solution is an outcome of incrementalism, resulting in application delivery outgrowing the underlying network. This paper contributes an industrial retrospective of how the schemes and mechanisms for identification and location of network entities have evolved in traditional data centers and how they match cloud-native application requirements. Specifically, there is an evaluation of each application artifact that forced necessary changes in the identifiers and locators. Finally, the common themes are highlighted from observations to determine the investigation areas that may play an essential role in the future of cloud-native networking.
Bashir, MR, Gill, AQ & Beydoun, G 2022, 'A Reference Architecture for IoT-Enabled Smart Buildings.', SN Comput. Sci., vol. 3, pp. 493-493.
Bashir, MR, Gill, AQ & Beydoun, G 2022, 'A Reference Architecture for IoT-Enabled Smart Buildings', SN Computer Science, vol. 3, no. 6, p. 493.
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AbstractThe management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.
Benedict, G & Gill, AQ 2022, 'A regulatory control framework for decentrally governed DLT systems: Action design research', Information & Management, vol. 59, no. 7, pp. 103555-103555.
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Benedict, G & Gill, AQ 2022, 'A regulatory control framework for decentrally governed DLT systems: Action design research.', Inf. Manag., vol. 59, pp. 103555-103555.
Bour, H, Abolhasan, M, Jafarizadeh, S, Lipman, J & Makhdoom, I 2022, 'A multi-layered intrusion detection system for software defined networking', Computers and Electrical Engineering, vol. 101, pp. 108042-108042.
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The majority of existing DDoS defense mechanisms in SDN impose a significant computational burden on the controller and employ limited flow statistics and packet features. Tackling these issues, this paper presents a multi-layer defense mechanism that detects and mitigates three distinct types of flooding DDoS attacks. In the proposed framework, the detection process consists of flow-based and packet-based attack detection mechanisms employing Extreme Learning Machine-based Single-hidden Layer Feedforward Networks (ELM-SLFNs) and Case-based Information Entropy (C-IE), respectively. Moreover, the affected switches are avoided in the optimal path determined by the Floyd-Warshall algorithm, where the switches are classified based on the Hidden Markov Model (HMM) using the extracted packet features. Our simulation demonstrates the improved performance of our framework compared to similar schemes proposed in the literature in terms of different metrics, including attack detection rate, detection accuracy, false-positive rate, switch failure ratio, packet loss rate, response time, and CPU utilization.
Canning, J, Guo, Y & Chaczko, Z 2022, '(INVITED)Sustainability, livability and wellbeing in a bionic internet-of-things', Optical Materials: X, vol. 16, pp. 100204-100204.
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Chakraborty, S, Milner, LE, Zhu, X, Parker, A & Heimlich, M 2022, 'Analysis and Comparison of Marchand and Transformer Baluns Applied in GaAs', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 11, pp. 4278-4282.
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Chen, D, Liu, Y, Li, M, Guo, P, Zeng, Z, Hu, J & Guo, YJ 2022, 'A Polarization Programmable Antenna Array', Engineering, vol. 16, pp. 100-114.
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Chen, L, Chen, L, Ge, Z, Sun, Y, Hamilton, T & Zhu, X 2022, 'A W-Band SPDT Switch With 15-dBm P1dB in 55-nm Bulk CMOS', IEEE Microwave and Wireless Components Letters, vol. 32, no. 7, pp. 879-882.
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Power-handling capability of bulk CMOS-based single-pole double-throw (SPDT) switch operating in millimeter-wave (mm-wave) and subterahertz region is significantly limited by the reduced threshold voltage of deeply scaled transistors. A unique design technique based on impedance transformation network (ITN) is presented in this work, which improves 1-dB compression point, namely P1dB, without deteriorating other performance. To prove the presented solution is valid, a 70-100-GHz switch is designed and implemented in a 55-nm bulk CMOS technology. At 90 GHz, it achieves a measured P1dB of 15 dBm, an insertion loss (IL) of 3.5 dB, and an isolation (ISO) of 18 dB. The total area of the chip is only 0.14 mm2.
Chen, L, Liu, Y, Ren, Y, Zhu, C, Yang, S & Guo, YJ 2022, 'Synthesizing Wideband Frequency-Invariant Shaped Patterns by Linear Phase Response-Based Iterative Spatiotemporal Fourier Transform', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10378-10390.
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Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2022, 'Miniaturized On-Chip Notch Filter With Sharp Selectivity and >35-dB Attenuation in 0.13-μm Bulk CMOS Technology', IEEE Electron Device Letters, vol. 43, no. 8, pp. 1175-1178.
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Chen, S-L, Liu, Y, Zhu, H, Chen, D & Guo, YJ 2022, 'Millimeter-Wave Cavity-Backed Multi-Linear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2531-2542.
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Chen, S-L, Liu, Y, Ziolkowski, RW, Li, Z & Guo, YJ 2022, 'High-Gain Single-Feed Overmoded Cavity Antenna With Closely Spaced Phased Patch Surface', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 229-239.
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Chen, S-L, Wu, G-B, Wong, H, Chen, B-J, Chan, CH & Guo, YJ 2022, 'Millimeter-Wave Slot-Based Cavity Antennas With Flexibly-Chosen Linear Polarization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6604-6616.
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Slot-based cavity antennas are hailed as promising candidates for millimeter-wave applications. Nevertheless, the linear-polarization (LP) angle of their broadside main beam is limited by the slots etched on the cavity’s top surface. In this work, an innovative technique is developed to significantly improve the selection flexibility of their LP inclination angle. It is attained by an integration of a single-layer, closely-spaced C-shaped patch surface. A TE710-mode slot-based cavity antenna is employed as the base configuration, which radiates a broadside beam with its LP along ϕ=90°. To effectively predict and monitor the polarization conversion of the surface-integrated TE710-mode cavity antenna, an analysis method using a unit cavity extracted from its original cavity antenna is presented. A subsequent surface-integrated system with the specified 45°-LP was then simulated, fabricated, and measured. The measured results validate that a 45°-LP state is achieved with an operating bandwidth from 33.3 to 36.5 GHz. Further investigation is conducted to flexibly choose the LP direction from ϕ=15° to 165°. Two more examples with the fabricated antenna prototypes successfully radiate the specified ϕ=15° and 75° LP beam, respectively. This near-field polarization conversion surface can be generalized to cavities with different resonant modes.
Chen, S-L, Ziolkowski, RW, Jones, B & Guo, YJ 2022, 'Analysis, Design, and Measurement of Directed-Beam Toroidal Waveguide-Based Leaky-Wave Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10141-10155.
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Chen, T, Xie, G-S, Yao, Y, Wang, Q, Shen, F, Tang, Z & Zhang, J 2022, 'Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation', IEEE Transactions on Multimedia, vol. 24, pp. 968-980.
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Dang, TD, Hoang, D & Nguyen, DN 2022, 'Trust-Based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 279-293.
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Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data "in processing". For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7% of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.
Dang-Ngoc, H, Nguyen, DN, Ho-Van, K, Hoang, DT, Dutkiewicz, E, Pham, Q-V & Hwang, W-J 2022, 'Secure Swarm UAV-Assisted Communications With Cooperative Friendly Jamming', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25596-25611.
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This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. In particular, we consider a swarm of hovering UAVs that relays information from a terrestrial base station to a distant mobile user and simultaneously generates friendly jamming signals to interfere/obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate in harvesting wireless energy, relay information, and jam the eavesdropper. To evaluate the performance, we derive the secrecy outage probability (SOP) for two popular detection techniques at the eavesdropper, i.e., selection combining and maximum-ratio combining. Monte Carlo simulations are then used to validate the theoretical SOP derivation. Using the derived SOP, one can obtain engineering insights to optimize the energy harvesting time and the number of UAVs in the swarm to achieve a given secrecy protection level. Furthermore, simulations show the effectiveness of the proposed framework in terms of SOP compared to the conventional AF relaying system. The analytical SOP derived in this work can also be helpful in future UAV secure-communications optimizations (e.g., trajectory, locations of UAVs). As an example, we present a case study to find the optimal corridor to locate the swarm so as to minimize the system SOP. Our proposed framework helps secure communications for various applications that require large coverage, e.g., industrial IoT, smart city, intelligent transportation systems, and critical IoT infrastructures like energy and water.
Dhull, P, Guevara, AP, Ansari, M, Pollin, S, Shariati, N & Schreurs, D 2022, 'Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer', IEEE Microwave Magazine, vol. 23, no. 3, pp. 39-54.
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Dinh, TH, Singh, AK, Linh Trung, N, Nguyen, DN & Lin, C-T 2022, 'EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1548-1556.
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Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).
Gao, S, Guo, YJ, Safavi-Naeini, SA, Hong, W & Yang, X-X 2022, 'Guest Editorial Low-Cost Wide-Angle Beam-Scanning Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7378-7383.
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Gong, S, Zou, Y, Xu, J, Hoang, DT, Lyu, B & Niyato, D 2022, 'Optimization-Driven Hierarchical Learning Framework for Wireless Powered Backscatter-Aided Relay Communications', IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 1378-1391.
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In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays' radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep $Q$ -network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.
Gong, Y, Li, Z, Zhang, J, Liu, W & Zheng, Y 2022, 'Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 865-880.
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Goudarzi, S, Ahmad Soleymani, S, Hossein Anisi, M, Ciuonzo, D, Kama, N, Abdullah, S, Abdollahi Azgomi, M, Chaczko, Z & Azmi, A 2022, 'Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network', Computers, Materials & Continua, vol. 70, no. 1, pp. 715-738.
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Guo, CA & Guo, YJ 2022, 'A General Approach for Synthesizing Multibeam Antenna Arrays Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7556-7564.
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Hieu, NQ, Hoang, DT, Niyato, D, Wang, P, Kim, DI & Yuen, C 2022, 'Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications', IEEE Transactions on Communications, vol. 70, no. 8, pp. 5164-5180.
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Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.
Hoang, LM, Andrew Zhang, J, Nguyen, DN & Thai Hoang, D 2022, 'Frequency Hopping Joint Radar-Communications With Hybrid Sub-Pulse Frequency and Duration Modulation', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2300-2304.
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Frequency-hopping (FH) joint radar-communications (JRC) can offer excellent security for integrated sensing and communication systems. However, existing JRC schemes mainly embed information using only the sub-pulse frequencies and hence the data rate is limited. In this letter, we propose to use both sub-pulse frequencies and durations for information modulation, leading to higher communication data rates. For information demodulation, we propose a novel scheme by using the time-frequency analysis (TFA) technique and a 'you only look once' (YOLO)-based detection system. As such, our system does not require channel estimation, simplifying the transmission signal frame design. Simulation results demonstrate the effectiveness of our scheme, and show that it is robust against the Doppler shift and timing offset between the transceiver and the communication receiver.
Huang, H, Zhang, J, Yu, L, Zhang, J, Wu, Q & Xu, C 2022, 'TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization With Few Labeled Samples', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 853-866.
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Huang, T, Ben, X, Gong, C, Zhang, B, Yan, R & Wu, Q 2022, 'Enhanced Spatial-Temporal Salience for Cross-View Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 10, pp. 6967-6980.
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Gait recognition can be used in person identification and re-identification by itself or in conjunction with other biometrics. Although gait has both spatial and temporal attributes, and it has been observed that decoupling spatial feature and temporal feature can better exploit the gait feature on the fine-grained level. However, the spatial-temporal correlations of gait video signals are also lost in the decoupling process. Direct 3D convolution approaches can retain such correlations, but they also introduce unnecessary interferences. Instead of common 3D convolution solutions, this paper proposes an integration of decoupling process into a 3D convolution framework for cross-view gait recognition. In particular, a novel block consisting of a Parallel-insight Convolution layer integrated with a Spatial-Temporal Dual-Attention (STDA) unit is proposed as the basic block for global spatial-temporal information extraction. Under the guidance of the STDA unit, this block can well integrate spatial-temporal information extracted by two decoupled models and at the same time retain the spatial-temporal correlations. In addition, a Multi-Scale Salient Feature Extractor is proposed to further exploit the fine-grained features through context awareness extension of part-based features and adaptively aggregating the spatial features. Extensive experiments on three popular gait datasets, namely CASIA-B, OULP and OUMVLP, demonstrate that the proposed method outperforms state-of-the-art methods.
Huang, X, Li, S, Zuo, Y, Fang, Y, Zhang, J & Zhao, X 2022, 'Unsupervised Point Cloud Registration by Learning Unified Gaussian Mixture Models', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7028-7035.
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Huang, X, Nan, Y & Guo, YJ 2022, 'Radio Frequency Camera: A Noncoherent Circular Array SAR With Uncoordinated Illuminations', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14.
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A novel noncoherent microwave imaging principle with periodical or random radio frequency (RF) illumination is proposed in this article. Implemented with circular array synthetic aperture radar (SAR) frontend and low-complexity signal processing algorithms, the imaging device, called RF camera, achieves some desired properties similar to an optical camera, such as the capability to operate with multiple uncoordinated illuminators. Different from conventional multistatic imaging, the RF camera does not require any knowledge about an illuminator's location or signal waveform. A static illumination sensor (IS) can be used to provide a reference signal for image reconstruction. With periodical illumination, the RF camera can even operate without IS, but the imaging performance can be improved with IS. With random illumination, the IS is necessary for the RF camera operation, and the imaging distortion can be described by a point blur function. Theoretical analyses on the imaging signal-to-noise ratios are performed under different RF camera operation modes. Simulation and experimental tests are conducted using 77-GHz millimeter wave frequency to verify the noncoherent imaging principle and its performance.
Huang, X, Wang, Y, Li, S, Mei, G, Xu, Z, Wang, Y, Zhang, J & Bennamoun, M 2022, 'Robust real-world point cloud registration by inlier detection', Computer Vision and Image Understanding, vol. 224, pp. 103556-103556.
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Real-world point cloud registration is challenging because of large outliers in correspondence search. The mixture variations, such as partial overlap, noise and cross sources, are the root cause of these large outliers. Existing methods face challenges in effectively removing the large outliers. We propose a novel coarse-to-fine framework to remove the outliers by detecting the accurate inlier correspondences. Specifically, our coarse module predicts the top-K accurate correspondences. The coarse module is trained by jointly leveraging global and local structured information. Then, our refinement module checks the correspondences further using our proposed novel higher-order filter, which enables the structure conformity of correspondences to improve the quality of inlier correspondences. The final transformation matrix is calculated by using the refined inlier correspondences. Furthermore, a new cross-source point cloud dataset is proposed to further demonstrate the robustness in real-world point clouds. Experimental results demonstrate that our algorithm achieves the state-of-the-art accuracy on both indoor and outdoor, same-source and newly proposed cross-source real-world point clouds.
Huang, Y, Li, Y, Heyes, T, Jourjon, G, Cheng, A, Seneviratne, S, Thilakarathna, K, Webb, D & Xu, RYD 2022, 'Task adaptive siamese neural networks for open-set recognition of encrypted network traffic with bidirectional dropout', Pattern Recognition Letters, vol. 159, pp. 132-139.
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Existing deep learning approaches have achieved high performance in encrypted network traffic analysis tasks. However, practical requirements such as open-set recognition on dynamically changing tasks (e.g., changes in the target website list), challenge existing methods. While few-shot learning and open-set recognition methods have been proposed for domains such as computer vision, few-shot open-set recognition for encrypted network traffic remains an unexplored area. This paper proposes a task adaptive siamese neural network for open-set recognition of encrypted network traffic with bidirectional dropout data augmentation. Our contributions are three-fold: First, we introduce generated positive and negative pairs into the siamese neural network training process to shape a more precise similarity boundary through bidirectional dropout data augmentation. Second, we utilize Dirichlet Process Gaussian Mixture Model (DPGMM) distribution to fit the similarity scores of the negative pairs constructed by the support set of each query task, and create a new open-set recognition metric. Third, by leveraging the extracted features at coarse and fine granular levels, we construct a hierarchical cross entropy loss to improve the confidence of the similarity score. Extensive experiments on a network traffic dataset and the Omniglot dataset demonstrate the superiority and generalizability of our proposed approach.
Huang, Y, Wu, Q, Xu, J, Zhong, Y, Zhang, P & Zhang, Z 2022, 'Alleviating Modality Bias Training for Infrared-Visible Person Re-Identification', IEEE Transactions on Multimedia, vol. 24, pp. 1570-1582.
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The task of infrared-visible person re-identification (IV-reID) is to recognize people across two modalities (i.e., RGB and IR). Existing cutting-edge approaches normally use a pair of images that have the same IDs (i.e., ID-tied cross-modality image pairs) and input them into an ImageNet-trained ResNet50. The ResNet50 backbone model can learn shared features across modalities to tolerate modality discrepancies between RGB and IR. This work will unveil a Modality Bias Training (MBT) problem that is less discussed in IV-reID, which will demonstrate that MBT significantly compromises the performance of IVreID. Due to MBT, IR information can be overwhelmed by RGB information during training when the ResNet50 model is pretrained based on a large amount of RGB images from ImageNet. Thus, the trained models are more inclined to RGB information. Accordingly, the cross-modality generalization ability of the model is also compromised. To tackle this issue, we present a Dual-level Learning Strategy (DLS) that 1) enforces the focus of the network on ID-exclusive (rather than ID-tied) labels of cross-modality image pairs to mitigate the problem of MBT and 2) introduces third modality data that contain both RGB and IR information to further prevent the information from the IR modality from being overwhelmed during training. Our third modality images are generated by a generative adversarial network. A dynamic ID-exclusive Smooth (dIDeS) label is proposed for the generated third modality data. In experiments, without adopting a fancy network architecture, the effectiveness of the proposed DLS is verified by using the classic ID-discriminative Embedding (IDE) model. Comprehensive experiments are carried out to demonstrate the success of DLS in tackling the MBT issue exposed in IV-reID.
Ilahi, I, Usama, M, Qadir, J, Janjua, MU, Al-Fuqaha, A, Hoang, DT & Niyato, D 2022, 'Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning', IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 90-109.
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Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. To address this problem, we provide a comprehensive survey that discusses emerging attacks on DRL-based systems and the potential countermeasures to defend against these attacks. We first review the fundamental background on DRL and present emerging adversarial attacks on machine learning techniques. We then investigate the vulnerabilities that an adversary can exploit to attack DRL along with state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems.
Jayawickrama, BA & He, Y 2022, 'Improved Layered Normalized Min-Sum Algorithm for 5G NR LDPC', IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 2015-2018.
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Jiang, M, Wu, T, Wang, Z, Gong, Y, Zhang, L & Liu, RP 2022, 'A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2459-2471.
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Jiang, S, Li, K & Da Xu, RY 2022, 'Magnitude Bounded Matrix Factorisation for Recommender Systems', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1856-1869.
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Keshavarz, R & Shariati, N 2022, 'Highly Sensitive and Compact Quad-Band Ambient RF Energy Harvester', IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3609-3621.
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Khaliliboroujeni, S, He, X, Jia, W & Amirgholipour, S 2022, 'End-to-end metastasis detection of breast cancer from histopathology whole slide images', Computerized Medical Imaging and Graphics, vol. 102, pp. 102136-102136.
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Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.
Kumar, A, Esmaili, N & Piccardi, M 2022, 'Neural Topic Model Training with the REBAR Gradient Estimator', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 5, pp. 1-18.
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Topic modelling is an important approach of unsupervised machine learning that allows automatically extracting the main “topics” from large collections of documents. In addition, topic modelling is able to identify the topic proportions of each individual document, which can be helpful for organizing the collections. Many topic modelling algorithms have been proposed to date, including several that leverage advanced techniques such as variational inference and deep autoencoders. However, to date topic modelling has made limited use of reinforcement learning, a framework that has obtained vast success in many other unsupervised learning tasks. For this reason, in this article we propose training a neural topic model using a reinforcement learning objective and minimizing the objective with the recently-proposed REBAR gradient estimator. Experiments performed over two probing datasets have shown that the proposed model has achieved improvements over all the compared models in terms of both model perplexity and topic coherence, and produced topics that appear qualitatively informative and consistent.
Le, AT, Huang, X & Guo, YJ 2022, 'A Two-Stage Analog Self-Interference Cancelation Structure for High Transmit Power In-Band Full-Duplex Radios', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2425-2429.
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Le, AT, Huang, X, Tran, LC & Guo, YJ 2022, 'On the Impacts of I/Q Imbalance in Analog Least Mean Square Adaptive Filter for Self-Interference Cancellation in Full-Duplex Radios', IEEE Transactions on Vehicular Technology, vol. 71, no. 10, pp. 10683-10693.
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Li, H, Huang, X, Zhang, JA, Zhang, H & Cheng, Z 2022, 'Dual pulse shaping transmission with sinc‐function based complementary Nyquist pulses', IET Communications, vol. 16, no. 17, pp. 2091-2104.
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Due to difficulties in manufacturing, data conversion devices with extremely high sampling rate are becoming the bottleneck in realising high-speed communication systems with a large bandwidth. Dual pulse shaping (DPS) transmission allows half-symbol-rate conversion devices to be used for two parallel data streams to achieve full-rate transmission, and is proved to be an effective solution. Here, two sets of ideal sinc-function based complementary Nyquist pulses for DPS transmission are proposed. Theoretically, it is shown that the proposed pulses satisfy the inter-symbol and cross-symbol interference-free conditions, and can achieve full-Nyquist-rate transmission with half of the sampling rate. With reference to commercially available D/As, two sets of practical dual spectral shaping pulses are further proposed, and the close relationship between the ideal and practical pulses are disclosed. Performance analysis for linear equalisation is provided in the presence of both timing offset between dual shaping pulses and carrier-frequency offset. Two approaches are then proposed to improve the system robustness by adjusting the clock phase of the D/As and A/Ds. Simulation results are presented to provide a comparison between the proposed DPS transmission schemes and the state of the art, in terms of the performance metrics of peak-to-average power ratio and bit error rate.
Li, M, Liu, Y, Chen, S-L, Hu, J & Guo, YJ 2022, 'Synthesizing Shaped-Beam Cylindrical Conformal Array Considering Mutual Coupling Using Refined Rotation/Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10543-10553.
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Li, X, Zhang, JA, Wu, K, Cui, Y & Jing, X 2022, 'CSI-Ratio-Based Doppler Frequency Estimation in Integrated Sensing and Communications', IEEE Sensors Journal, vol. 22, no. 21, pp. 20886-20895.
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Estimating the Doppler frequency is an important part of sensing moving targets in integrated sensing and communications (ISAC) systems, such as human tracking and activity recognition. However, it can be highly challenging when there is clock asynchronism between the transmitter (Tx) and the receiver (Rx), in bistatic setups that are common nowadays. In this article, we propose three algorithms for Doppler frequency estimation based on the ratio of channel state information (CSI). These algorithms explore different properties of the CSI ratio, including the circle-preserving property of the Mobius transform, the periodicity of the CSI ratio, and the difference (or correlation) between segments of CSI-ratio signals. Experimental results demonstrate that the proposed algorithms can estimate Doppler frequency accurately, outperforming the commonly used approach based on cross-antenna cross correlation (CACC).
Li, Y, Huang, Y, Seneviratne, S, Thilakarathna, K, Cheng, A, Jourjon, G, Webb, D, Smith, DB & Xu, RYD 2022, 'From traffic classes to content: A hierarchical approach for encrypted traffic classification', Computer Networks, vol. 212, pp. 109017-109017.
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The vast majority of Internet traffic is now end-to-end encrypted, and while encryption provides user privacy and security, it has made network surveillance an impossible task. Various parties are using this limitation to distribute problematic content such as fake news, copy-righted material, and propaganda videos. Recent advances in machine learning techniques have shown great promise in extracting content fingerprints from encrypted traffic captured at the various points in IP core networks. Nonetheless, content fingerprinting from listening to encrypted wireless traffic remains a challenging task due to the difficulty in distinguishing re-transmissions and multiple flows on the same link. In this paper, we show the potential of fingerprinting internet traffic by passively sniffing WiFi frames in air, without connecting to the WiFi network by leveraging deep learning methods. First, we show the possibility of building a generic traffic classifier using a hierarchical approach that is able to identity most common traffic types in the Internet and reveal fine-granular details such as identifying the exact content of the traffic. Second, we demonstrate the possibility of using Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) to identify streaming traffic, such as video and music, from a closed set, by sniffing WiFi traffic that is encrypted at both Media Access Control (MAC) and Transport layers. Overall, our results demonstrate that we can achieve over 95% accuracy in identifying traffic types such as web, video streaming, and audio streaming as well as identifying the exact content consumed by the user.
Liao, Q, Wang, D & Xu, M 2022, 'Category attention transfer for efficient fine-grained visual categorization', Pattern Recognition Letters, vol. 153, pp. 10-15.
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Fine-Grained Visual Categorization (FGVC) aims at distinguishing subordinate-level categories with subtle interclass differences. Although previous research shows the impressive effectiveness of the recurrent multi-attention models and the second-order feature encoding, they often require an enormous amount of both computation and memory space, making them inadequate for mobile applications. This paper proposed a Category Attention Transfer CNN (CAT-CNN) to address the efficiency issue in solving FGVC problems. We transfer part attention knowledge from a very large-scale FGVC network to a small but efficient network to significantly improve its presentation ability. Using the proposed CAT-CNN, the accuracy of the efficient networks, such as ShuffleNet, MobilieNet, and EfficientNet, can be improved by up to 5.7% on the CUB-2011-200 dataset without increasing computation complexity or memory cost. Our experiments show that the proposed CAT-CNN can be applied to multiple structures to enhance their performance. With a single efficient network structure and single inference, the proposed CAT-MobileNet-large-1.0 and the CAT-EfficientNet-b0 can achieve accuracies of 86.5% and 86.7%, respectively, on the CUB-2011-200 dataset, which is close to or better than the results from state-of-the-art methods using large scale networks and multiple inferences, and make FGVC feasible on mobile devices.
Liu, B, Ni, W, Liu, RP, Zhu, Q, Guo, YJ & Zhu, H 2022, 'Novel Integrated Framework of Unmanned Aerial Vehicle and Road Traffic for Energy-Efficient Delay-Sensitive Delivery', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10692-10707.
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Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.
Liu, H, Zhang, C, Yao, Y, Wei, X-S, Shen, F, Tang, Z & Zhang, J 2022, 'Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 546-557.
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Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. To this end, in this paper, we propose a novel approach to remove irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods.
Liu, T, Hu, X, Xu, H, Shu, T & Nguyen, DN 2022, 'High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation', Journal of Information Security and Applications, vol. 70, pp. 103309-103309.
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Lu, X, Cong Luong, N, Hoang, DT, Niyato, D, Xiao, Y & Wang, P 2022, 'Secure Wirelessly Powered Networks at the Physical Layer: Challenges, Countermeasures, and Road Ahead', Proceedings of the IEEE, vol. 110, no. 1, pp. 193-209.
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Harvesting wireless power to energize miniature devices has been envisioned as a promising solution to sustain future-generation energy-sensitive networks, e.g., Internet-of-Things systems. However, due to the limited computing and communication capabilities, wirelessly powered networks (WPNs) may be incapable of employing complex security practices, e.g., encryption, which may incur considerable computation and communication overheads. This challenge makes securing energy harvesting communications an arduous task and, thus, limits the use of WPNs in many high-security applications. In this context, security at the physical layer (PHY) that exploits the intrinsic properties of the wireless medium to achieve secure communication has emerged as an alternative paradigm. This article first introduces the fundamental principles of primary PHY attacks, covering jamming, eavesdropping, and detection of covert, and then presents an overview of the prevalent countermeasures to secure both active and passive communications in WPNs. Furthermore, a number of open research issues are identified to inspire possible future research.
Lyu, B, Ramezani, P, Hoang, DT & Jamalipour, A 2022, 'IRS-Assisted Downlink and Uplink NOMA in Wireless Powered Communication Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 1083-1088.
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This paper studies the integration of the newly-emerged intelligent reflecting surface (IRS) technology into non-orthogonal multiple access (NOMA)-based wireless powered communication networks (WPCNs). We consider two WPCNs which communicate with a common hybrid access point (HAP), where there exists two types of devices in each WPCN, namely information receiving device (IRD) and harvest-then-transmit device (HTTD). Downlink communication from the HAP to IRDs, downlink energy transfer (ET) from the HAP to HTTDs, and uplink information transmission (IT) from the HTTDs to the HAP are assisted by two IRSs, one in each WPCN. Under this setup, we propose efficient algorithms to optimize reflection coefficients, beamforming vectors, and resource allocation for the sake of uplink sum-rate maximization, taking into account the minimum rate requirement at the IRDs. Numerical results show the considerable performance gain of the proposed NOMA-based scheme as compared to the conventional orthogonal multiple access (OMA)-based counterpart.
Ma, B, Wang, X, Ni, W & Liu, RP 2022, 'Personalized Location Privacy With Road Network-Indistinguishability', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20860-20872.
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The proliferation of location-based services (LBS) leads to increasing concern about location privacy. Location obfuscation is a promising privacy-preserving technique but yet to be adequately tailored for vehicles in road networks. Existing obfuscation schemes are based primarily on the Euclidean distances and can lead to infeasible results, e.g., off-road locations. In this paper, we define Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based location privacy-preserving schemes in road networks. To protect drivers' location privacy in road networks, we propose a Personalized Location Privacy-Preserving (PLPP) scheme and prove it achieves RN-I. The PLPP scheme employs a dual-obfuscation algorithm, consisting of a connection perturbation and an interval perturbation, to obfuscate on-road locations. An efficient personalization algorithm is designed for the PLPP scheme to fine-tune location privacy budgets for capturing drivers' sensitive locations and privacy requirements. Experiments upon two real-world datasets confirm the location privacy-preserving capability, data utility, and efficiency of the proposed PLPP scheme.
Ma, Y, Wu, N, Zhang, JA, Li, B & Hanzo, L 2022, 'Generalized Approximate Message Passing Equalization for Multi-Carrier Faster-Than-Nyquist Signaling', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3309-3314.
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Multi-carrier faster-than-Nyquist (MFTN) signaling constitutes a promising spectrally efficient non-orthogonal physical layer waveform. In this correspondence, we propose a pair of low-complexity generalized approximate message passing (GAMP)-based frequency-domain equalization (FDE) algorithms for MFTN systems operating in multipath channels. To mitigate the ill-condition of the resultant equivalent channel matrix, we construct block circulant interference matrices by inserting a few cyclic postfixes, followed by truncating the duration of the inherent two-dimensional interferences. Based on the decomposition of the block circulant matrices, we develop a novel frequency-domain received signal model using the two-dimensional fast Fourier transform for mitigating the colored noise imposed by the non-orthogonal matched filter. Moreover, we derive a GAMP-based FDE algorithm and its refined version, where the latter relies on approximations for circumventing the emergence of the ill-conditioned matrices. Our simulation results demonstrate that, for a fixed spectral efficiency, MFTN signaling can significantly improve the bit error rate (BER) performance by jointly optimizing the time- and frequency-domain packing factors. Compared to its Nyquist-signaling counterpart, our proposed MFTN systems employing the refined GAMP equalizer can achieve about 39% higher transmission rates at a negligible BER performance degradation.
Makhdoom, I, Abolhasan, M & Lipman, J 2022, 'A comprehensive survey of covert communication techniques, limitations and future challenges', Computers & Security, vol. 120, pp. 102784-102784.
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Data encryption aims to protect the confidentiality of data at storage, during transmission, or while in processing. However, it is not always the optimum choice as attackers know the existence of the ciphertext. Hence, they can exploit various weaknesses in the implementation of encryption algorithms and can thus decrypt or guess the related cryptographic primitives. Moreover, in the case of proprietary applications such as online social networks, users are at the mercy of the vendor's security measures. Therefore, users are vulnerable to various security and privacy threats. Contrary to this, covert communication techniques hide the existence of communication and thus achieve security through obscurity and hidden communication channels. Over the period, there has been a significant advancement in this field. However, existing literature fails to encompass all the aspects of covert communications in a single document. This survey thus endeavors to highlight the latest trends in covert communication techniques, related challenges, and future directions.
Makhdoom, I, Lipman, J, Abolhasan, M & Challen, D 2022, 'Science and Technology Parks: A Futuristic Approach', IEEE Access, vol. 10, pp. 31981-32021.
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Most of the existing science and technology parks resort to various conventional ways to attract different stakeholders to the park. Some of these traditional measures include business support, workspaces, laboratories, networking events, accommodation, and essential commodities. Besides, with rampantly changing multidisciplinary technologies and increased data-oriented business models, the classic science and technology park value-creation strategies may not be instrumental in the near future. Hence, we foresee that future science and a technology parks should be fully integrated, sustainable, and innovative living science cities. Where park tenants can actively interact and contribute to emerging technologies. Therefore, this paper carries out an in-depth study of world s best practices in smart cities and science and technology parks, their characteristics, and value-added contributions that excite the prospective tenants. Developing on the detailed survey, we propose a unique feature of Autonomous Systems as a Service to bestow a futuristic look to the science and technology parks. It is envisaged that autonomous systems will not only provide value-added services to the park tenants but will also provide an infrastructure for testing new technologies within park premises. Furthermore, this study evaluates security and privacy challenges associated with autonomous systems and data-oriented services and recommends appropriate security measures. The role of universities in the success of a science and technology park is also delineated. Finally, the components deemed essential for the attainment of science and technology parks objectives are highlighted.
Mishra, A, Alzoubi, YI, Anwar, MJ & Gill, AQ 2022, 'Attributes impacting cybersecurity policy development: An evidence from seven nations', Computers & Security, vol. 120, pp. 102820-102820.
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Cyber threats have risen as a result of the growing usage of the Internet. Organizations must have effective cybersecurity policies in place to respond to escalating cyber threats. Individual users and corporations are not the only ones who are affected by cyber-attacks; national security is also a serious concern. Different nations' cybersecurity rules make it simpler for cybercriminals to carry out damaging actions while making it tougher for governments to track them down. Hence, a comprehensive cybersecurity policy is needed to enable governments to take a proactive approach to all types of cyber threats. This study investigates cybersecurity regulations and attributes used in seven nations in an attempt to fill this research gap. This paper identified fourteen common cybersecurity attributes such as telecommunication, network, Cloud computing, online banking, E-commerce, identity theft, privacy, and smart grid. Some nations seemed to focus, based on the study of key available policies, on certain cybersecurity attributes more than others. For example, the USA has scored the highest in terms of online banking policy, but Canada has scored the highest in terms of E-commerce and spam policies. Identifying the common policies across several nations may assist academics and policymakers in developing cybersecurity policies. A survey of other nations' cybersecurity policies might be included in the future research.
Mishra, A, Alzoubi, YI, Anwar, MJ & Gill, AQ 2022, 'Attributes impacting cybersecurity policy development: An evidence from seven nations.', Comput. Secur., vol. 120, pp. 102820-102820.
Mishra, A, Alzoubi, YI, Gill, AQ & Anwar, MJ 2022, 'Cybersecurity Enterprises Policies: A Comparative Study', Sensors, vol. 22, no. 2, pp. 538-538.
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Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization’s cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises’ security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.
Mishra, A, Alzoubi, YI, Gill, AQ & Anwar, MJ 2022, 'Cybersecurity Enterprises Policies: A Comparative Study.', Sensors, vol. 22, pp. 538-538.
Nahar, K & Gill, AQ 2022, 'Integrated identity and access management metamodel and pattern system for secure enterprise architecture', Data & Knowledge Engineering, vol. 140, pp. 102038-102038.
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Identity and access management (IAM) is one of the key components of the secure enterprise architecture for protecting the digital assets of the information systems. The challenge is: How to model an integrated IAM for a secure enterprise architecture to protect digital assets? This research aims to address this question and develops an ontology based integrated IAM metamodel for the secure digital enterprise architecture (EA). Business domain and technology agnostic characteristics of the developed IAM metamodel will allow it to develop IAM models for different types of information systems. Well-known design science research (DSR) methodology was adopted to conduct this research. The developed IAM metamodel is evaluated by using the demonstration method. Furthermore, as a part of the evaluation, a pattern system has been developed, consisting of eight IAM patterns. Each pattern offers a solution to a specific IAM related problem. The outcome of this research indicates that enterprise, IAM and information systems architects and academic researchers can use the proposed IAM metamodel and the pattern system to design and implement situation-specific IAM models within the overall context of a secure EA for information systems.
Nahar, K & Gill, AQ 2022, 'Integrated identity and access management metamodel and pattern system for secure enterprise architecture.', Data Knowl. Eng., vol. 140, pp. 102038-102038.
Nan, Y, Huang, X & Guo, YJ 2022, '3-D Millimeter-Wave Helical Imaging', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 4, pp. 2499-2511.
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This article proposes a low-cost three-dimensional (3-D) millimeter-wave (MMW) holographic imaging system using helical scanning with multiple receivers to achieve a fast continuous scanning over a large two-dimensional (2-D) cylindrical surface. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The corresponding imaging point spread function (PSF) and resolutions are analyzed accordingly. To reduce the computational cost significantly, a novel 3-D helical imaging algorithm is then proposed based on the piecewise constant Doppler (PCD) principle. The slant range difference resulting from the helical scanning can be compensated jointly along angular and vertical directions. The proposed imaging is prototyped using the AWR1843 radar sensor from Texas Instruments (TIs) and a moving platform composed of step motors and a micro-controller unit (MCU). The digital imaging process and the number of the required complex multiplications are also discussed in detail. Finally, simulation and experimental results are provided to validate the accuracy and efficiency of the proposed imaging system.
Nan, Y, Huang, X & Guo, YJ 2022, 'A Panoramic Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13.
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This article proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360° panoramic view of the observed scene can be reconstructed. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a 2-D synthetic aperture, and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency-modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm.
Nan, Y, Huang, X & Guo, YJ 2022, 'An Universal Circular Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15.
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This article presents an universal circular synthetic aperture radar (SAR) (UCSAR) by which the targets to be observed at any radial distance can be imaged, thus making SAR imaging possible in a more general scenario with a circular movement of the radar platform. The UCSAR point spread function (PSF) is firstly analyzed based on the time-domain correlation imaging approach, and thus a three-dimension (3-D) spatial variant PSF of the target can be formulated. The closed-form PSF expressions with single-frequency and frequency-modulated continuous wave (FMCW) transmitted signals are derived respectively to quantify the imaging resolutions, showing that the PSF is a product of a sinc function and a zeroth-order Bessel function when using a wideband FMCW signal. Secondly, a fast UCSAR imaging algorithm and its further simplified version are proposed to reduce the computational cost significantly based on the piecewise constant Doppler (PCD) principle. To quantify the imaging performance, we derive an error function of the slant range approximation for the proposed algorithm, serving as a practical guideline for the UCSAR parameter selection. Finally, the simulation and experimental results are provided to validate the PSF analysis, the fast imaging algorithm, and the implementation of the proposed UCSAR.
Nasir, AA, Tuan, HD, Dutkiewicz, E & Hanzo, L 2022, 'Finite-Resolution Digital Beamforming for Multi-User Millimeter-Wave Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9647-9662.
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Recent studies have shown that low-resolution analog-to-digital-converters and digital-to-analog-converters (ADCs and DACs) can make fully-digital beamforming more power efficient than its analog or hybrid beamforming counterpart over wide-band millimeter-wave (mmWave) channels. Inspired by this, we propose a computationally efficient fully-digital beamformer relying on low-resolution ADCs/DACs for multi-user mmWave communication networks. Both a generalized (unstructured) beamformer (GB) and a structured zero-forcing beamformer (ZFB) are proposed. For maintaining fairness among all users in the network, specifically tailored objective functions are considered under sum-power constraints, namely that of maximizing the geometric mean (GM) of users' rate and their max-min rate. These computationally challenging beamforming design problems are tackled by developing computationally efficient steep ascent algorithms, which have the radical benefit of relying on a closed-form solution at each iteration. Moreover, to facilitate the employment of low-cost amplifiers at each antenna, the GB design problem subject to the equal-gain transmission constraint is considered, which assigns equal transmit power to each transmit antenna. The proposed algorithms promise a user-rate distribution having a reduced deviation among the user-rates, i.e., improved rate-fairness. Our extensive simulation results show an approximately upto 45% reduction for the GM-rate of a 2-bit ADC (4-bin quantization) compared to the $\infty$-resolution ADC.
Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Low-Resolution RIS-Aided Multiuser MIMO Signaling', IEEE Transactions on Communications, vol. 70, no. 10, pp. 6517-6531.
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A multi-antenna aided base station (BS) supporting several multi-antenna downlink users with the aid of a reconfigurable intelligent surface (RIS) of programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by a set of sparse discrete values are used for reasons of cost-efficiency. Our challenging objective is to jointly design the beamformers at the BS and the RIS's PREs for improving the throughput of all users by maximizing their geometric-mean, under a variety of different access schemes. This constitutes a computationally challenging problem of mixed continuous-discrete optimization, because each user's throughput is a complicated function of both the continuous-valued beamformer weights and of the discrete-valued PREs. We develop low-complexity algorithms, which iterate by directly evaluating low-complexity closed-form expressions. Our simulation results show the advantages of non-orthogonal multiple access-aided signaling, which allows the users to decode a part of the multi-user interference for enhancing their throughput.
Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Relay-Aided Multi-User OFDM Relying on Joint Wireless Power Transfer and Self-Interference Recycling', IEEE Transactions on Communications, vol. 70, no. 1, pp. 291-305.
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Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network's sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.
Nguyen, CT, Van Huynh, N, Chu, NH, Saputra, YM, Hoang, DT, Nguyen, DN, Pham, Q-V, Niyato, D, Dutkiewicz, E & Hwang, W-J 2022, 'Transfer Learning for Wireless Networks: A Comprehensive Survey', Proceedings of the IEEE, vol. 110, no. 8, pp. 1073-1115.
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With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
Nguyen, M-D, Lee, S-M, Pham, Q-V, Hoang, DT, Nguyen, DN & Hwang, W-J 2022, 'HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-13.
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Nguyen, NHT, Perry, S, Bone, D, Le Thanh, H, Xu, M & Nguyen, TT 2022, 'Combination of Images and Point Clouds in a Generative Adversarial Network for Upsampling Crack Point Clouds', IEEE Access, vol. 10, pp. 67198-67209.
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Nguyen, TG, Phan, TV, Hoang, DT, Nguyen, HH & Le, DT 2022, 'DeepPlace: Deep reinforcement learning for adaptive flow rule placement in Software-Defined IoT Networks', Computer Communications, vol. 181, pp. 156-163.
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In this paper, we propose a novel and adaptive flow rule placement system based on deep reinforcement learning, namely DeepPlace, in Software-Defined Internet of Things (SDIoT) networks. DeepPlace can provide a fine-grained traffic analysis capability while assuring QoS of traffic flows and proactively avoiding the flow-table overflow issue in the data plane. Specifically, we first investigate the traffic forwarding process in an SDIoT network, i.e., routing and flow rule placement tasks. We design a cost function for the routing to set up traffic flow paths in the data plane. Next, we propose an adaptive flow rule placement approach to maximize the number of match-fields in a flow rule at SDN switches. To deal with the dynamics of IoT traffic flows, we model the system operation by using the Markov decision process (MDP) with a continuous action space and formulate its optimization problem. Subsequently, we develop a deep deterministic policy gradient-based algorithm to help the system obtain the optimal policy. The evaluation results demonstrate that DeepPlace can efficiently maintain a significant number of match-fields in a flow rule, i.e., approximately 86% of the maximum level, while minimizing the QoS violation ratio of traffic flows, i.e., 6.7%, in a highly dynamic traffic scenario, which outperforms three other existing solutions, i.e., FlowMan, FlowStat, and DeepMatch.
Ni, Z, Zhang, JA, Yang, K, Huang, X & Tsiftsis, TA 2022, 'Multi-Metric Waveform Optimization for Multiple-Input Single-Output Joint Communication and Radar Sensing', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1276-1289.
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Pearce, A, Zhang, JA & Xu, R 2022, 'A Combined mmWave Tracking and Classification Framework Using a Camera for Labeling and Supervised Learning', Sensors, vol. 22, no. 22, pp. 8859-8859.
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Millimeter wave (mmWave) radar poses prosperous opportunities surrounding multiple-object tracking and sensing as a unified system. One of the most challenging aspects of exploiting sensing opportunities with mmWave radar is the labeling of mmWave data so that, in turn, a respective model can be designed to achieve the desired tracking and sensing goals. The labeling of mmWave datasets usually involves a domain expert manually associating radar frames with key events of interest. This is a laborious means of labeling mmWave data. This paper presents a framework for training a mmWave radar with a camera as a means of labeling the data and supervising the radar model. The methodology presented in this paper is compared and assessed against existing frameworks that aim to achieve a similar goal. The practicality of the proposed framework is demonstrated through experimentation in varying environmental conditions. The proposed framework is applied to design a mmWave multi-object tracking system that is additionally capable of classifying individual human motion patterns, such as running, walking, and falling. The experimental findings demonstrate a reliably trained radar model that uses a camera for labeling and supervision that can consistently produce high classification accuracy across environments beyond those in which the model was trained against. The research presented in this paper provides a foundation for future research in unified tracking and sensing systems by alleviating the labeling and training challenges associated with designing a mmWave classification model.
Peng, Y, Liu, Y, Li, M, Liu, H & Guo, YJ 2022, 'Synthesizing Circularly Polarized Multi-Beam Planar Dipole Arrays With Sidelobe and Cross-Polarization Control by Two-Step Element Rotation and Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4379-4391.
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Poostchi, H & Piccardi, M 2022, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 203-212.
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Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss. The typical loss functions used for evaluating NER are entity-level variants of the F1 score such as the CoNLL and MUC losses. Unfortunately, the common loss function used for training NER - the cross entropy - is only loosely related to the evaluation losses. For this reason, in this paper we propose a training approach for the BiLSTM-CRF that leverages a hinge loss bounding the CoNLL loss from above. In addition, we present a mixed hinge loss that bounds either the CoNLL loss or the Hamming loss based on the density of entity tokens in each sentence. The experimental results over four benchmark languages (English, German, Spanish and Dutch) show that training with the mixed hinge loss has led to small but consistent improvements over the cross entropy across all languages and four different evaluation measures
Qin, P-Y, Song, L-Z & Guo, YJ 2022, 'Conformal Transmitarrays for Unmanned Aerial Vehicles Aided 6G Networks', IEEE Communications Magazine, vol. 60, no. 1, pp. 14-20.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5492-5506.
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Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.
Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2022, 'Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 597-609.
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In this paper, we propose a novel mobility model providing train traffic traces essential for train-to-train communication models. As the proposed mobility model works only based on trip timetables and train timetables are currently available in real-time, the produced mobility traces will be also in real-time. Additionally, as no GPS module is used in this method, our proposed model can provide a practical solution when signal from GPS or Assisted GPS is poor or unavailable such as in urban area or inside tunnels. Furthermore, as we used an energy optimization function, the proposed mobility model will provide a guidance trajectory for trains to have an energy-optimized operation. We also develop an algorithm that can determine the specifications of contacts between trains based on the traffic traces obtained from the mobility model. Such specifications includes duration, rate and location of train contacts used for estimation of data exchange capacity between trains through train-to-train communications. We validate our proposed model using data collected from Sydney Trains of Australia. The results obtained from our proposed model show over 98 percent accuracy in comparison with the real data collected via a GPS module from Sydney Trains.
Saputra, YM, Nguyen, D, Dinh, HT, Pham, Q-V, Dutkiewicz, E & Hwang, W-J 2022, 'Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-1.
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This work proposes a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, considering limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, the selected MU can propose a contract to the MAP according to its expected local and encrypted data. To find optimal contracts that can maximize utilities while maintaining high learning quality of the system, we develop a multi-principal one-agent contract-based problem considering the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare up to 114% under the privacy cost consideration compared with those of baseline methods.
Shi, J, Chu, L, Ma, C & Braun, R 2022, 'The Uncertainty Propagation for Carbon Atomic Interactions in Graphene under Resonant Vibration Based on Stochastic Finite Element Model', Materials, vol. 15, no. 10, pp. 3679-3679.
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Graphene is one of the most promising two-dimensional nanomaterials with broad applications in many fields. However, the variations and fluctuations in the material and geometrical properties are challenging issues that require more concern. In order to quantify uncertainty and analyze the impacts of uncertainty, a stochastic finite element model (SFEM) is proposed to propagate uncertainty for carbon atomic interactions under resonant vibration. Compared with the conventional truss or beam finite element models, both carbon atoms and carbon covalent bonds are considered by introducing plane elements. In addition, the determined values of the material and geometrical parameters are expanded into the related interval ranges with uniform probability density distributions. Based on the SFEM, the uncertainty propagation is performed by the Monte Carlo stochastic sampling process, and the resonant frequencies of graphene are provided by finite element computation. Furthermore, the correlation coefficients of characteristic parameters are computed based on the database of SFEM. The vibration modes of graphene with the extreme geometrical values are also provided and analyzed. According to the computed results, the minimum and maximum values of the first resonant frequency are 0.2131 and 16.894 THz, respectively, and the variance is 2.5899 THz. The proposed SFEM is an effective method to propagate uncertainty and analyze the impacts of uncertainty in the carbon atomic interactions of graphene. The work in this paper provides an important supplement to the atomic interaction modeling in nanomaterials.
Shi, Q, Wu, N, Nguyen, DN, Huang, X, Wang, H & Hanzo, L 2022, 'Low-Complexity Iterative Detection for Dual-Mode Index Modulation in Dispersive Nonlinear Satellite Channels', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1261-1275.
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Shi, Z, Cheng, Q, Zhang, JA & Yi Da Xu, R 2022, 'Environment-Robust WiFi-Based Human Activity Recognition Using Enhanced CSI and Deep Learning', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 24643-24654.
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Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence has attracted increasing attention in both the industry and academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying the previous-trained ones to a new/unseen environment. This issue has limited their practical usabilities. In order to overcome this challenge, this article proposes an innovative scheme, which combines an activity-related feature extraction and enhancement (AFEE) method and matching network (AFEE-MatNet). The proposed scheme is 'one-fits-all,' meaning that the trained model can be directly applied in new/unseen environments without any retraining. We introduce the AFEE method to enhance CSI quality by eliminating noise. Specifically, the approach mitigates environmental noises unrelated to activity while better compressing and preserving the behavior-related information. Moreover, the size of feature signals generated by AFEE are reduced, which in turn significantly shortens the training time. For effective feature extraction, we propose to use the MatNet architecture to learn transferable features shared among source environments. To further improve the recognition performance, we introduce a prediction checking and correction scheme to rectify some classification errors that do not abide by the state transition of human behaviors. Extensive experimental results demonstrate that our proposed AFEE-MatNet significantly outperforms existing state-of-the-art HAR methods, in terms of both recognition accuracy and training time.
Shi, Z, Zhang, JA, Xu, RY & Cheng, Q 2022, 'Environment-Robust Device-Free Human Activity Recognition With Channel-State-Information Enhancement and One-Shot Learning', IEEE Transactions on Mobile Computing, vol. 21, no. 2, pp. 540-554.
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Song, L-Z, Qin, P-Y, Zhu, H & Du, J 2022, 'Wideband Conformal Transmitarrays for E-Band Multi-Beam Applications', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10417-10425.
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Wideband conformal transmitarrays at E-band are developed for multi-beam applications in this paper. A triple-layer element with double split rings is presented for wideband transmissions, achieving a 360° continuous phase variation range at 74 GHz with less than 2.3-dB transmission loss. A comprehensive design methodology of multi-beam conformal transmitarrays is demonstrated for various platforms with different curvatures. To validate the theoretical analysis, conformal transmitarrays with two different curvatures are designed, fabricated, and measured. Multiple radiation beams are realized between ±30° and ±45° for the two prototypes, respectively. Good agreement is obtained between simulation and measurement. The 3-dB gain bandwidths are 30% from 66.5 GHz to 90 GHz, and 27.8% from 68 GHz to 90 GHz for the two designs, respectively, covering the entire E-band.
Song, L-Z, Wang, X & Qin, P-Y 2022, 'Single-Feed Multibeam Conformal Transmitarrays With Phase and Amplitude Modulations', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 8, pp. 1669-1673.
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Single-feed multibeam conformal transmitarrays using a superposition method are presented in this letter. The arrays consist of ultrathin Huygens elements with independent amplitude and phase manipulations. Three cylindrical conformal transmitarrays with dual-beam radiation patterns are designed at 10 GHz, producing dual beams at ±30°, +30°, and -20°, +30° and -10°, respectively. As an experimental validation, the prototype with symmetrical dual beams is fabricated and measured. Two beams at +29° and -28° along the H-plane are achieved with a measured 18.29 dBi peak gain. The gain difference between the two beams is 0.13 dB. Good agreement between simulation and measurement is observed.
Song, Z, Lu, J, Yao, Y & Zhang, J 2022, 'Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 11654-11665.
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Tuan, HD, Nasir, AA, Ngo, HQ, Dutkiewicz, E & Poor, HV 2022, 'Scalable User Rate and Energy-Efficiency Optimization in Cell-Free Massive MIMO', IEEE Transactions on Communications, vol. 70, no. 9, pp. 6050-6065.
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This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users' rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2022, 'Low-profile dual-band pixelated defected ground antenna for multistandard IoT devices', Scientific Reports, vol. 12, no. 1, p. 11479.
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AbstractA low-profile dual-band pixelated defected ground antenna has been proposed at 3.5 GHz and 5.8 GHz bands. This work presents a flexible design guide for achieving single-band and dual-band antenna using pixelated defected ground (PDG). The unique pixelated defected ground has been designed using the binary particle swarm optimization (BPSO) algorithm. Computer Simulation Technology Microwave Studio incorporated with Matlab has been utilized in the antenna design process. The PDG configuration provides freedom of exploration to achieve the desired antenna performance. Compact antenna design can be achieved by making the best use of designated design space on the defected ground (DG) plane. Further, a V-shaped transfer function based on BPSO with fast convergence allows us to efficiently implement the PDG technique. In the design procedure, pixelization is applied to a small rectangular region of the ground plane. The square pixels on the designated defected ground area of the antenna have been formed using a binary bit string, consisting of 512 bits taken during each iteration of the algorithm. The PDG method is concerned with the shape of the DG and does not rely on the geometrical dimension analysis used in traditional defected ground antennas. Initially, three single band antennas have been designed at 3.5 GHz, 5.2 GHz and 5.8 GHz using PDG technique. Finally, same PDG area has been used to design a dual-band antenna at 3.5 GHz and 5.8 GHz. The proposed antenna exhibits almost omnidirectional radiation performance with nearly 90% efficiency. It also shows dual radiation pattern property with similar patterns having different polarizations at each operational band. The antenna is fabricated on a ROGERS RO4003 substrate with 1.52 mm thickness. Reflection coefficient and radiation patterns are measured to validate its performance. The simulated and measured results of the antenna are closely correlated. The propos...
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J, Esselle, KP & Shariati, N 2022, 'A Review on Antenna Technologies for Ambient RF Energy Harvesting and Wireless Power Transfer: Designs, Challenges and Applications', IEEE Access, vol. 10, pp. 17231-17267.
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Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna's performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.
Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2022, 'Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks', IEEE Journal on Selected Areas in Communications, vol. 40, no. 2, pp. 484-498.
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Unlike theoretical analysis of distributed learning (DL) in the literature, DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks. This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes’ straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
Van Huynh, N, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E & Chatzinotas, S 2022, 'Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis', IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7374-7390.
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This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.
Veitch, D, Mani, SK, Cao, Y & Barford, P 2022, 'iHorology: Lowering the Barrier to Microsecond-Level Internet Time', IEEE/ACM Transactions on Networking, vol. 30, no. 6, pp. 2544-2558.
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High accuracy, synchronized clocks are essential to a growing number of Internet applications. Standard protocols and their associated server infrastructure typically enable client clocks to synchronize to the order of tens of milliseconds. We address one of the key challenges to high precision Internet timekeeping - the intrinsic contribution to clock error of underlying path asymmetry between client and time server, a fundamental barrier to microsecond level accuracy. We first exploit results of a unique measurement study to reliably quantify asymmetry by taking routing changes into account for the first time, and then to infer the impacts on timing. We then describe three approaches to addressing the path asymmetry problem: LBBE, SBBE and K-SBBE, each based on timestamp exchange with multiple servers, with the goal of tightening bounds on asymmetry for each client. We explore their capabilities and limitations through simulation and model-based argument. We show that substantial improvements are possible, and discuss whether, and how, the goal of microsecond accuracy might be attained.
Vu, L, Cao, VL, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2022, 'Learning Latent Representation for IoT Anomaly Detection', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3769-3782.
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Wang, N, Liu, ZX, Ding, C, Zhang, J-N, Sui, G-R, Jia, H-Z & Gao, X-M 2022, 'High Efficiency Thermoelectric Temperature Control System With Improved Proportional Integral Differential Algorithm Using Energy Feedback Technique', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 5225-5234.
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This paper proposes an efficient thermoelectric temperature control system based on an improved proportional integral differential algorithm in which energy feedback technology is used to enhance thermoelectric cooling. In the proposed power management system, two groups of batteries are efficiently and alternatingly charged and discharged such that the information of the circuit can be monitored in real time. The PID algorithm is improved by using the idea of a state machine to control the thermoelectric coolers through an H-bridge circuit with pulse-width modulation. Finally, the energy feedback circuit combined with improved synchronous switching technology is designed to recycle the energy to drive the sensor. By inputting current of 3.1A, a wide range of temperature control from 1.437 to 60.187 was implemented. While targeting a temperature of 10 at an ambient temperature of 22, the proposed temperature control system had a control time of 30.5s, compared with 287s when using the conventional method, with an accuracy of 0.1, and an error of only 0.35. The results confirm that electric energy at a peak voltage of 1.2V and current of 24A can be recovered. The proposed energy feedback system can thus improve the efficiency of energy utilization of TEC from peripheral circuits.
Wang, N, Zhang, J-N, Ni, H, Jia, H-Z & Ding, C 2022, 'Improved MPPT System Based on FTSMC for Thermoelectric Generator Array Under Dynamic Temperature and Impedance', IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 10715-10723.
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The thermoelectric generator (TEG) is typically used as a clean power supply to harvest waste heat energy in applications involving a large thermal gradient, such as industrial heat removal and power electronic equipment systems. However, it is often difficult to achieve the optimal output power in the loop of the array system of the TEG owing to different output loads. This study proposes an improved fast terminal sliding-mode variable-structure control algorithm (FTSMC) to maximize power point tracking. The variable-structure sliding-mode control function used in the nonlinear sliding-mode surface of the algorithm allows us to obtain the characteristics of global stability that can enable it to converge to the sliding-mode surface at any position to reduce chatter. Digital modeling and simulation as well as experimental developmental Field Programmable Gate Array (FPGA) platforms were built to verify the effectiveness of the proposed FTSMC. It can attain the nonlinear sliding mode more quickly than the traditional sliding-mode algorithm. The results of experiments show that it can reach a tracking response speed of 0.08 s and a maximum conversion efficiency of 99.91%. The work here provides a new way for the efficient use of the TEG array for waste heat recovery.
Wang, X, Fei, Z, Zhang, JA & Xu, J 2022, 'Partially-Connected Hybrid Beamforming Design for Integrated Sensing and Communication Systems', IEEE Transactions on Communications, vol. 70, no. 10, pp. 6648-6660.
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Beamforming design is an important technique for enhancing the performance of integrated sensing and communication (ISAC) systems. However, related research based on the hybrid analog-digital (HAD) architecture is still limited. In this paper, we investigate the partially-connected hybrid beamforming design for multi-user ISAC systems. Instead of the commonly used beampattern related metric, the Cramér-Rao bound (CRB) is employed as the sensing performance metric for direction of arrival (DOA) estimation. We aim to minimize the CRB while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for individual communication users by jointly optimizing the digital and analog beamformers. Subsequently, we propose an alternating optimization based framework, which is significantly different from the conventional methods based on the approximation of the optimal fully-digital beamformer with a hybrid one. We also consider an alternative formulation of optimizing the SINR of radar echo signals. Based on optimal receive beamformer design, we transform the SINR based joint transmitter and receiver optimization problem to a series of problems sharing a similar form with the CRB based transmitter optimization problem, which can be efficiently solved via the proposed algorithm. Simulation results show that the proposed designs provide significant performance gains in DOA estimation over the existing beampattern approximation based design.
Wang, X, Qin, P-Y, Tuyen Le, A, Zhang, H, Jin, R & Guo, YJ 2022, 'Beam Scanning Transmitarray Employing Reconfigurable Dual-Layer Huygens Element', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7491-7500.
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A Ku-band electronic 2-dimensional (2-D) beam-scanning transmitarray employing a new reconfigurable dual-layer Huygens element is developed in this article. The Huygens element consists of two metallic crosses printed on two layers of a dielectric substrate, which enables a near nonreflection Huygens resonance. A 1 bit phase compensation with low transmission loss is realized by controlling two p-i-n diodes on the element. Compared with many other reconfigurable transmitarray elements using multilayer structures with metallic vias, the proposed reconfigurable Huygens element has a much simpler configuration with a simpler biasing network, and it is not affected by multilayer alignment errors. This particularly facilitates large aperture array development at higher frequencies. To validate the design concept, an electronically reconfigurable transmitarray with the proposed element is fabricated at 13 GHz. Good agreement between the measured and simulated results is found, showing 2-D scanning beams within ±50° in the E-plane and ±40° in the H-plane with a maximum realized gain of 18.4 dBi.
Wang, X, Yu, G, Liu, RP, Zhang, J, Wu, Q, Su, SW, He, Y, Zhang, Z, Yu, L, Liu, T, Zhang, W, Loneragan, P, Dutkiewicz, E, Poole, E & Paton, N 2022, 'Blockchain-Enabled Fish Provenance and Quality Tracking System', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8130-8142.
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Wang, Z, Zhang, JA, Xiao, F & Xu, M 2022, 'Accurate AoA Estimation for RFID Tag Array With Mutual Coupling', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12954-12972.
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Angle-of-Arrival (AoA) estimation is an important problem in passive radio-frequency identification (RFID) systems. Affixing an RFID tag array to an object enables to acquire its orientation information. However, the electromagnetic interaction between the tags can induce mutual coupling interference, distorting the RFID fingerprint measurements used for AoA estimation. Moreover, RFID reader modes with radio-frequency (RF) noise-tolerant Miller encoding can induce π-radians phase jump. In this article, we propose a scheme called RF-Mirror that can resolve the mutual coupling and phase jump problems and achieve accurate AoA estimation for an array with two or more tags. First, we characterize the impact of mutual coupling on a tag's signal fingerprint and develop novel RSSI/phase-distance models. We then develop new experimental methods and signal processing techniques to verify the effectiveness of the proposed models. Based on the validated models, we develop new AoA estimation algorithms for tag arrays that deal with the mutual coupling effect explicitly. We provide extensive experimental results, which demonstrate that RF-Mirror can achieve significantly improved performance compared to baseline schemes, with median AoA estimation errors of 11.65° and 6.29° for two- and four-tag arrays, respectively.
Wen, Y, Qin, P-Y, Wei, G-M & Ziolkowski, RW 2022, 'Circular Array of Endfire Yagi-Uda Monopoles With a Full 360° Azimuthal Beam Scanning', IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 6042-6047.
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Wisanmongkol, J, Taparugssanagorn, A, Tran, LC, Le, AT, Huang, X, Ritz, C, Dutkiewicz, E & Phung, SL 2022, 'An ensemble approach to deep‐learning‐based wireless indoor localization', IET Wireless Sensor Systems, vol. 12, no. 2, pp. 33-55.
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The authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range-based (e.g. trilateration and triangulation) and range-free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand-picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose an ensemble method where DNN models obtained from multiple training sessions are combined to locate the target. In the authors' evaluation, several DNN models are trained on the data, which consists of the received signal strength (RSS), angle of arrival (AOA), and channel state information (CSI), used in the existing hybrid RSS/AOA and RSS/CSI fingerprinting, and their root-mean-square error (RMSE) values are compared accordingly. The results show that the proposed method achieves the lower RMSE than the existing methods, and the RMSE can be lowered by up to 1.47 m compared with the ones obtained from a single model. Moreover, for some DNN models, the RMSE values are even lower than the minimum RMSE obtained by their single-model counterparts.
Wong, SYK, Chan, JSK, Azizi, L & Xu, RYD 2022, 'Time‐varying neural network for stock return prediction', Intelligent Systems in Accounting, Finance and Management, vol. 29, no. 1, pp. 3-18.
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AbstractWe consider the problem of neural network training in a time‐varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly US stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators exhibit time‐varying predictive power on stock returns. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.
Wu, K, Beydoun, G, Sohaib, O & Gill, A 2022, 'The Co-construct/ Co-evolving Process between Organization's Absorptive Capacity and Enterprise System Practice under Changing Context: The Case of ERP Practice.', Inf. Syst. Frontiers, vol. 24, no. 6, pp. 2123-2138.
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AbstractLong term sustainability in a competitive and changing environment requires an organization to continuously learn and adapt. The ability to access and use new knowledge is contingent on the organisational absorptive capacity (AC). In this paper, we focus on how an organization’s absorptive capacity and its enterprise system practices develop and co-evolve over time. Analysing a fifteen years’ ERP practice in an organisational context, this study synthesizes a new AC analysis framework that takes into account the dynamic nature of AC. This high-level analysis coupled with a longitudinal view resolves inconsistent results between current AC studies and suggest further directions for organisational AC research.
Wu, K, Zhang, JA & Guo, YJ 2022, 'Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail [Tips & Tricks]', IEEE Signal Processing Magazine, vol. 39, no. 6, pp. 76-84.
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Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, the Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, the Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1], and the Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in physics [2]; moreover, the Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing, and in artificial intelligence for defining neural networks.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Frequency-Hopping MIMO Radar-Based Communications: An Overview', IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 4, pp. 42-54.
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Abstract—Enabled by the advancement in radio frequency technologies, the convergence of radar and communication systems becomes increasingly promising and is envisioned as a key feature of future sixth-generation networks. Recently, the frequency-hopping (FH) MIMO radar is introduced to underlay dual-function radar-communication (DFRC) systems. Superior to many previous radar-centric DFRC designs, the symbol rate of FH-MIMO radar-based DFRC (FH-MIMO DFRC) can exceed the radar pulse repetition frequency. However, many practical issues, particularly those crucial to achieving effective data communications, are unexplored or unsolved. To promote the awareness and general understanding of the novel DFRC, this article is devoted to providing a timely introduction of FH-MIMO DFRC. We comprehensively review many essential aspects of the novel DFRC: channel/signal models, signaling strategies, modulation/demodulation processing and channel estimation methods, to name a few. We also highlight major remaining issues in FHMIMO DFRC and suggest potential solutions to shed light on future research directions.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Integrating Low-Complexity and Flexible Sensing Into Communication Systems', IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1873-1889.
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Integrating sensing into standardized communication systems can potentially benefit many consumer applications that require both radio frequency functions. However, without an effective sensing method, such integration may not achieve the expected gains of cost and energy efficiency. Existing sensing methods, which use communication payload signals, either have limited sensing performance or suffer from high complexity. In this paper, we develop a novel and flexible sensing framework which has a complexity only dominated by a Fourier transform and also provides the flexibility in adapting to different sensing needs. We propose to segment a whole block of echo signal evenly into sub-blocks; adjacent ones are allowed to overlap. We design a virtual cyclic prefix (VCP) for each sub-block that allows us to employ two common ways of removing communication data symbols and generate two types of range-Doppler maps (RDMs) for sensing. We perform a comprehensive analysis of the signal components in the RDMs, proving that their interference-plus-noise (IN) terms are approximately Gaussian distributed. The statistical properties of the distributions are derived, which leads to the analytical comparisons between the two RDMs as well as between the prior and our sensing methods. Moreover, the impact of the lengths of sub-block, VCP and overlapping signal on sensing performance is analyzed. Criteria for designing these lengths for better sensing performance are also provided. Extensive simulations validate the superiority of the proposed sensing framework over prior methods in terms of signal-to-IN ratios in RDMs, detecting performance and flexibility.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Integrating Secure Communications Into Frequency Hopping MIMO Radar With Improved Data Rate', IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5392-5405.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Joint Communications and Sensing Employing Multi- or Single-Carrier OFDM Communication Signals: A Tutorial on Sensing Methods, Recent Progress and a Novel Design', Sensors, vol. 22, no. 4, pp. 1613-1613.
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Joint communications and sensing (JCAS) has recently attracted extensive attention due to its potential in substantially improving the cost, energy and spectral efficiency of Internet of Things (IoT) systems that need both radio frequency functions. Given the wide applicability of orthogonal frequency division multiplexing (OFDM) in modern communications, OFDM sensing has become one of the major research topics of JCAS. To raise the awareness of some critical yet long-overlooked issues that restrict the OFDM sensing capability, a comprehensive overview of OFDM sensing is provided first in this paper, and then a tutorial on the issues is presented. Moreover, some recent research efforts for addressing the issues are reviewed, with interesting designs and results highlighted. In addition, the redundancy in OFDM sensing signals is unveiled, on which, a novel method is based and developed in order to remove the redundancy by introducing efficient signal decimation. Corroborated by analysis and simulation results, the new method further reduces the sensing complexity over one of the most efficient methods to date, with a minimal impact on the sensing performance.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging', Sensors, vol. 22, no. 22, pp. 9015-9015.
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Employing a cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework was developed recently, enabling CP-OFDM sensing to surpass the conventional limits imposed by underlying communications. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveforms. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to remove false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution. In particular, our design can reduce the false alarm rate caused by false targets by over 50% compared with the prior art.
Wu, K, Zhang, JA, Huang, X, Guo, YJ, Nguyen, DN, Kekirigoda, A & Hui, K-P 2022, 'Analog-Domain Suppression of Strong Interference Using Hybrid Antenna Array', Sensors, vol. 22, no. 6, pp. 2417-2417.
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The proliferation of wireless applications, the ever-increasing spectrum crowdedness, as well as cell densification makes the issue of interference increasingly severe in many emerging wireless applications. Most interference management/mitigation methods in the literature are problem-specific and require some cooperation/coordination between different radio frequency systems. Aiming to seek a more versatile solution to counteracting strong interference, we resort to the hybrid array of analog subarrays and suppress interference in the analog domain so as to greatly reduce the required quantization bits of the analog-to-digital converters and their power consumption. To this end, we design a real-time algorithm to steer nulls towards the interference directions and maintain flat in non-interference directions, solely using constant-modulus phase shifters. To ensure sufficient null depth for interference suppression, we also develop a two-stage method for accurately estimating interference directions. The proposed solution can be applicable to most (if not all) wireless systems as neither training/reference signal nor cooperation/coordination is required. Extensive simulations show that more than 65 dB of suppression can be achieved for 3 spatially resolvable interference signals yet with random directions.
Xi, Y, Jia, W, Miao, Q, Liu, X, Fan, X & Li, H 2022, 'FiFoNet: Fine-Grained Target Focusing Network for Object Detection in UAV Images', Remote Sensing, vol. 14, no. 16, pp. 3919-3919.
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Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.
Xi, Y, Jia, W, Miao, Q, Liu, X, Fan, X & Lou, J 2022, 'DyCC-Net: Dynamic Context Collection Network for Input-Aware Drone-View Object Detection', Remote Sensing, vol. 14, no. 24, pp. 6313-6313.
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Benefiting from the advancement of deep neural networks (DNNs), detecting objects from drone-view images has achieved great success in recent years. It is a very challenging task to deploy such DNN-based detectors on drones in real-life applications due to their excessive computational costs and limited onboard computational resources. Large redundant computation exists because existing drone-view detectors infer all inputs with nearly identical computation. Detectors with less complexity can be sufficient for a large portion of inputs, which contain a small number of sparse distributed large-size objects. Therefore, a drone-view detector supporting input-aware inference, i.e., capable of dynamically adapting its architecture to different inputs, is highly desirable. In this work, we present a Dynamic Context Collection Network (DyCC-Net), which can perform input-aware inference by dynamically adapting its structure to inputs of different levels of complexities. DyCC-Net can significantly improve inference efficiency by skipping or executing a context collector conditioned on the complexity of the input images. Furthermore, since the weakly supervised learning strategy for computational resource allocation lacks of supervision, models may execute the computationally-expensive context collector even for easy images to minimize the detection loss. We present a Pseudo-label-based semi-supervised Learning strategy (Pseudo Learning), which uses automatically generated pseudo labels as supervision signals, to determine whether to perform context collector according to the input. Extensive experiment results on VisDrone2021 and UAVDT, show that our DyCC-Net can detect objects in drone-captured images efficiently. The proposed DyCC-Net reduces the inference time of state-of-the-art (SOTA) drone-view detectors by over 30 percent, and DyCC-Net outperforms them by 1.94% in AP75.
Xia, J, Zhang, H, Wen, S, Yang, S & Xu, M 2022, 'An efficient multitask neural network for face alignment, head pose estimation and face tracking', Expert Systems with Applications, vol. 205, pp. 117368-117368.
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Xiao, D, Chen, S, Ni, W, Zhang, J, Zhang, A & Liu, R 2022, 'A sub-action aided deep reinforcement learning framework for latency-sensitive network slicing', Computer Networks, vol. 217, pp. 109279-109279.
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Network slicing is a core technique of fifth-generation (5G) systems and beyond. To maximize the number of accepted network slices with limited hardware resources, service providers must avoid over-provisioning of quality-of-service (QoS), which could prevent them from lowering capital expenditures (CAPEX)/operating expenses (OPEX) for 5G infrastructure. In this paper, we propose a sub-action aided double deep Q-network (SADDQN)-based network slicing algorithm for latency-aware services. Specifically, we model network slicing as a Markov decision process (MDP), where we consider virtual network function (VNF) placements to be the actions of the MDP, and define a reward function based on cost and service priority. Furthermore, we adopt the Dijkstra algorithm to determine the forwarding graph (FG) embedding for a given VNF placement and design a resource allocation algorithm – binary search assisted gradient descent (BSAGD) – to allocate resources to VNFs given the VNF-FG placement. For every service request, we first use the DDQN to choose an MDP action to determine the VNF placement (main action). Next, we employ the Dijkstra algorithm (first-phase sub-action) to find the shortest path for each pair of adjacent VNFs in the given VNF chain. Finally, we implement the BSAGD (second-phase sub-action) to realize this service with the minimum cost. The joint action results in an MDP reward that can be utilized to train the DDQN. Numerical evaluations show that, compared to state-of-the-art algorithms, the proposed algorithm can improve the cost-efficiency while giving priority to higher-priority services and maximizing the acceptance ratio.
Xu, M, Hoang, DT, Kang, J, Niyato, D, Yan, Q & Kim, DI 2022, 'Secure and Reliable Transfer Learning Framework for 6G-Enabled Internet of Vehicles', IEEE Wireless Communications, vol. 29, no. 4, pp. 132-139.
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In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Artificial Intelligence (AI). Transfer Learning (TL) has great potential to empower promising 6G-enabled IoV, such as smart driving assistance, with its outstanding features including enhancing the quality and quantity of training data, speeding up learning processes, and reducing computing demands. Although TL had been widely adopted in wireless applications (e.g., spectrum management and caching), its reliability and security in 6G-enabled IoV were still not well investigated. For instance, malicious vehicles in source domains may transfer and share untrustworthy models (i.e., knowledge) about connection availability to target domains, thus adversely affecting the performance of learning processes. Therefore, it is important to select and also incentivize trustworthy vehicles to participate in TL. In this article, we first introduce the integration of TL and 6G-enabled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to evaluate the reliability of pre-trained models and utilizing the consortium blockchain to achieve secure and efficient decentralized reputation management. Moreover, a deep learning-based auction scheme for the TL model market is designed to motivate high-reputation vehicles to participate in model sharing. Finally, the simulation results demonstrate that the proposed framework is secure and reliable with well-designed incentives for TL in 6G-enabled IoV.
Yan, B, Zhao, Q, Li, M, Zhang, J, Zhang, JA & Yao, X 2022, 'Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications', Applied Soft Computing, vol. 131, pp. 109725-109725.
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Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.
Yang, S, Wu, S, Liu, T & Xu, M 2022, 'Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9830-9843.
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A major gap between few-shot and many-shot learning is the data distribution empirically observed by the model during training. In few-shot learning, the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples, while the ground-truth data distribution is more accurately uncovered in many-shot learning to learn a well-generalized model. In this paper, we propose to calibrate the distribution of these few-sample classes to be more unbiased to alleviate such an over-fitting problem. The distribution calibration is achieved by transferring statistics from the classes with sufficient examples to those few-sample classes. After calibration, an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. Extensive experiments on three datasets, miniImageNet, tieredImageNet, and CUB, show that a simple linear classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy by a large margin. We also establish a generalization error bound for the proposed distribution-calibration-based few-shot learning, which consists of the distribution assumption error, the distribution approximation error, and the estimation error. This generalization error bound theoretically justifies the effectiveness of the proposed method.
Yang, X, Wang, S, Xing, Y, Li, L, Xu, RYD, Friston, KJ & Guo, Y 2022, 'Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19', PLOS Computational Biology, vol. 18, no. 2, pp. e1009807-e1009807.
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Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art ‘DARt’ system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Collaborative Feature Learning for Gait Recognition Under Cloth Changes', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 6, pp. 3615-3629.
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Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset.
Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Recognizing Gaits Across Walking and Running Speeds', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 3, pp. 1-22.
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For decades, very few methods were proposed for cross-mode (i.e., walking vs. running) gait recognition. Thus, it remains largely unexplored regarding how to recognize persons by the way they walk and run. Existing cross-mode methods handle the walking-versus-running problem in two ways, either by exploring the generic mapping relation between walking and running modes or by extracting gait features which are non-/less vulnerable to the changes across these two modes. However, for the first approach, a mapping relation fit for one person may not be applicable to another person. There is no generic mapping relation given that walking and running are two highly self-related motions. The second approach does not give more attention to the disparity between walking and running modes, since mode labels are not involved in their feature learning processes. Distinct from these existing cross-mode methods, in our method, mode labels are used in the feature learning process, and a mode-invariant gait descriptor is hybridized for cross-mode gait recognition to handle this walking-versus-running problem. Further research is organized in this article to investigate the disparity between walking and running. Running is different from walking not only in the speed variances but also, more significantly, in prominent gesture/motion changes. According to these rationales, in our proposed method, we give more attention to the differences between walking and running modes, and a robust gait descriptor is developed to hybridize the mode-invariant spatial and temporal features. Two multi-task learning-based networks are proposed in this method to explore these mode-invariant features. Spatial features describe the body parts non-/less affected by mode changes, and temporal features depict the instinct motion relation of each person. Mode labels are also adopted in the training phase to guide the network to give more attention to the disparity across walking and run...
Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Maximizing the Geometric Mean of User-Rates to Improve Rate-Fairness: Proper vs. Improper Gaussian Signaling', IEEE Transactions on Wireless Communications, vol. 21, no. 1, pp. 295-309.
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This paper considers a reconfigurable intelligent surface (RIS)-aided network, which relies on a multiple antenna array aided base station (BS) and an RIS for serving multiple single antenna downlink users. To provide reliable links to all users over the same bandwidth and same time-slot, the paper proposes the joint design of linear transmit beamformers and the programmable reflecting coefficients of an RIS to maximize the geometric mean (GM) of the users' rates. A new computationally efficient alternating descent algorithm is developed, which is based on closed-forms only for generating improved feasible points of this nonconvex problem. We also consider the joint design of widely linear transmit beamformers and the programmable reflecting coefficients to further improve the GM of the users' rates. Hence another alternating descent algorithm is developed for its solution, which is also based on closed forms only for generating improved feasible points. Numerical examples are provided to demonstrate the efficiency of the proposed approach.
Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'RIS-Aided Zero-Forcing and Regularized Zero-Forcing Beamforming in Integrated Information and Energy Delivery', IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5500-5513.
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This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs' harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs' harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs' throughput.
Yu, L, Li, Z, Xu, M, Gao, Y, Luo, J & Zhang, J 2022, 'Distribution-Aware Margin Calibration for Semantic Segmentation in Images', International Journal of Computer Vision, vol. 130, no. 1, pp. 95-110.
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Yu, L, Zhang, J & Wu, Q 2022, 'Dual Attention on Pyramid Feature Maps for Image Captioning', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 1775-1786.
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Yu, P, Ni, W, Liu, RP, Zhang, Z, Zhang, H & Wen, Q 2022, 'Efficient Encrypted Range Query on Cloud Platforms', ACM Transactions on Cyber-Physical Systems, vol. 6, no. 3, pp. 1-23.
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In the Internet of Things (IoT) era, various IoT devices are equipped with sensing capabilities and employed to support clinical applications. The massive electronic health records (EHRs) are expected to be stored in the cloud, where the data are usually encrypted, and the encrypted data can be used for disease diagnosis. There exist some numeric health indicators, such as blood pressure and heart rate. These numeric indicators can be classified into multiple ranges, and each range may represent an indication of normality or abnormity. Once receiving encrypted IoT data, the CS maps it to one of the ranges, achieving timely monitoring and diagnosis of health indicators. This article presents a new approach to identify the range that an encrypted numeric value corresponds to without exposing the explicit value. We establish the sufficient and necessary condition to convert a range query to matchings of encrypted binary sequences with the minimum number of matching operations. We further apply the minimization of range queries to design and implement a secure range query system, where numeric health indicators encrypted independently by multiple IoT devices can be cohesively stored and efficiently queried by using Lagrange polynomial interpolation. Comprehensive performance studies show that the proposed approach can protect both the health records and range query against untrusted cloud platforms and requires less computational and communication cost than existing techniques.
Yu, P, Ni, W, Zhang, H, Ping Liu, R, Wen, Q, Li, W & Gao, F 2022, 'Secure and Differentiated Fog-Assisted Data Access for Internet of Things', The Computer Journal, vol. 65, no. 8, pp. 1948-1963.
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Abstract The ability of Fog computing to admit and process huge volumes of heterogeneous data is the catalyst for the fast expansion of Internet of things (IoT). The critical challenge is secure and differentiated access to the data, given limited computation capability and trustworthiness in typical IoT devices and Fog servers, respectively. This paper designs and develops a new approach for secure, efficient and differentiated data access. Secret sharing is decoupled to allow the Fog servers to assist the IoT devices with attribute-based encryption of data while preventing the Fog servers from tampering with the data and the access structure. The proposed encryption supports direct revocation and can be decoupled among multiple Fog servers for acceleration. Based on the decisional $q$-parallel bilinear Diffie–Hellman exponent assumption, we propose a new extended $q$-parallel bilinear Diffie–Hellman exponent (E$q$-PBDHE) assumption and prove that the proposed approach provides ‘indistinguishably chosen-plaintext attacks secure’ data access for legitimate data subscribers. As numerically and experimentally verified, the proposed approach is able to reduce the encryption time by 20% at the IoT devices and by 50% at the Fog network using parallel computing as compared to the state of the art .
Yu, X, Li, H, Zhang, JA, Huang, X & Cheng, Z 2022, 'Enhanced Angle-of-Arrival and Polarization Parameter Estimation Using Localized Hybrid Dual-Polarized Arrays', Sensors, vol. 22, no. 14, pp. 5207-5207.
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The millimeter wave (mmWave) channel is dominated by line-of-sight propagation. Therefore, the acquisition of angle-of-arrival (AoA) and polarization state of the wave is of great significance to the receiver. In this paper, we investigate AoA and polarization estimation in a mmWave system employing dual-polarized antenna arrays. We propose an enhanced AoA estimation method using a localized hybrid dual-polarized array for a polarized mmWave signal. The use of dual-polarized arrays greatly improves the calibration of differential signals and the signal-to-noise ratio (SNR) of the phase offset estimation between adjacent subarrays. Given the estimated phase offset, an initial AoA estimate can be obtained, and is then used to update the phase offset estimation. This leads to a recursive estimation with improved accuracy. We further propose an enhanced polarization estimation method, which uses the power of total received signals at dual-polarized antennas to compute the cross-correlation-to-power ratio instead of using only one axis dipole. Thus the accuracy of polarization parameter estimation is improved. We also derive a closed-form expression for mean square error lower bounds of AoA estimation and present an average SNR analysis for polarization estimation performance. Simulation results demonstrate the superiority of the enhanced AoA and polarization parameter estimation methods compared to the state of the art.
Zeng, J, Xu, Q, Fan, X, Ye, N, Ni, W & Guo, YJ 2022, 'Achieving URLLC by MU-MIMO With Imperfect CSI: Under κ–μ Shadowed Fading', IEEE Wireless Communications Letters, vol. 11, no. 12, pp. 2560-2564.
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Zhang, C, Meng, G, Xu, RYD, Xiang, S & Pan, C 2022, 'Learning adversarial point-wise domain alignment for stereo matching', Neurocomputing, vol. 491, pp. 564-574.
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Zhang, J, Hanjalic, A, Jain, R, Hua, X, Satoh, S, Yao, Y & Zeng, D 2022, 'Guest Editorial: Learning From Noisy Multimedia Data', IEEE Transactions on Multimedia, vol. 24, pp. 1247-1252.
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Zhang, JA, Rahman, ML, Wu, K, Huang, X, Guo, YJ, Chen, S & Yuan, J 2022, 'Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey', IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 306-345.
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Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, tracking, and object recognition of traditional radar. In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing unde...
Zhang, JA, Wu, K, Huang, X, Guo, YJ, Zhang, D & Heath, RW 2022, 'Integration of Radar Sensing into Communications with Asynchronous Transceivers', IEEE Communications Magazine, vol. 60, no. 11, pp. 106-112.
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Clock asynchronism is a critical issue in integrating radar sensing into communication networks. It can cause ranging ambiguity and prevent coherent processing of discontinuous measurements in integration with asynchronous transceivers. Should it be resolved, sensing can be efficiently realized in communication networks, requiring few network infrastructure and hardware changes. This article provides a systematic overview of existing and potential new techniques for tackling this fundamental problem. We first review existing solutions, including using a finetuned global reference clock, and single-node-based and network-based techniques. We then examine open problems and research opportunities, offering insights into what may be better realized in each of the three solution areas.
Zhang, L, Shen, J, Zhang, J, Xu, J, Li, Z, Yao, Y & Yu, L 2022, 'Multimodal Marketing Intent Analysis for Effective Targeted Advertising', IEEE Transactions on Multimedia, vol. 24, pp. 1830-1843.
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Zhang, L, Xu, J, Gong, Y, Yu, L, Zhang, J & Shen, J 2022, 'Unsupervised Image and Text Fusion for Travel Information Enhancement', IEEE Transactions on Multimedia, vol. 24, pp. 1415-1425.
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Zhang, R, Xu, L, Yu, Z, Shi, Y, Mu, C & Xu, M 2022, 'Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation', IEEE Transactions on Multimedia, vol. 24, pp. 1735-1749.
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Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels of the thermal imagery restrict the feature extraction ability of CNNs. In order to deal with these difficulties in the feature extraction, we propose a novel backbone network named Deep-IRTarget, composing of a frequency feature extractor, a spatial feature extractor and a dual-domain feature resource allocation model. Hypercomplex Infrared Fourier Transform is developed to calculate the infrared intensity saliency by designing hypercomplex representations in the frequency domain, while a convolutional neural network is invoked to extract feature maps in the spatial domain. Features from the frequency domain and spatial domain are stacked to construct Dual-domain features. To efficiently integrate and recalibrate them, we propose a Resource Allocation model for Features (RAF). The well-designed channel attention block and position attention block are used in RAF to respectively extract interdependent relationships among channel and position dimensions, and capture channel-wise and position-wise contextual information. Extensive experiments are conducted on three challenging infrared imagery databases. We achieve 10.14%, 9.1% and 8.05% improvement on mAP scores, compared to the current state of the art method on MWIR, BITIR and WCIR respectively.
Zhang, T, Du, J & Guo, YJ 2022, 'High-Tc Superconducting Microwave and Millimeter Devices and Circuits—An Overview', IEEE Journal of Microwaves, vol. 2, no. 3, pp. 374-388.
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Zhang, T, Jin, B & Jia, W 2022, 'An anchor-free object detector based on soften optimized bi-directional FPN', Computer Vision and Image Understanding, vol. 218, pp. 103410-103410.
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We propose an anchor-free object detector that combines a weighted bi-directional Feature Pyramid Network (BiFPN) and Soft Anchor Point Detector to address the object detection problem in a pixel-wise paradigm. The current mainstream object detection methods are anchor-based, which require to set hyper parameters such as scale and aspect ratio. This requires strong prior knowledge and can be difficult to design. Therefore, we propose an anchor-free detector that completely avoids the complex calculations and all the hyper parameters related to the anchor box by eliminating the predefined set of anchor boxes in an anchor-free way. Anchor-free detectors are essentially dense prediction methods. Although the huge solution space can yield high recall, simple anchor-free methods tend to return too many false positives, which leads to the problem of semantic ambiguity caused by the high overlap of object centers. Therefore, we propose BiFPN to alleviate the impact of high overlap which also effectively addresses the problems related to multi-scale features. Moreover, in order to utilize the power of feature pyramid better, we tackle the issues with a novel training strategy that involves two soften optimization techniques, i.e., soft-weighted anchor points and soft-selected pyramid levels. This training strategy further re-weights the quality of the detection results to make our detection results more stable.
Zhang, T, Zhang, H, Huang, X, Suzuki, H, Pathikulangara, J, Smart, K, Du, J & Guo, J 2022, 'A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate', Photonics, vol. 9, no. 10, pp. 683-683.
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This paper presents a 245 GHz wireless communications system with a data rate of 30 Giga bits per second (Gbps) at a 1.2 m distance, which proves the potential for future high-speed communications beyond 5G technology. The system consists of low-complexity and real-time base-band modules to provide the high-speed wideband signal processing capability. Multi-channel base-band signals are combined and converted to 15.65 ± 6.25 GHz wideband intermediate frequency (IF) signals. A novel 245 GHz waveguide bandpass filter (BPF) with low loss and high selectivity is designed and applied to a terahertz (THz) front-end for image rejection and noise suppression. Configuration of the base-band, IF, and THz front-end modules is also given in detail. The 245 GHz wireless communication link is demonstrated over a distance of 1.2 m.
Zhang, T, Zhu, T, Liu, R & Zhou, W 2022, 'Correlated data in differential privacy: Definition and analysis', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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SummaryDifferential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real‐world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: (1) using parameters to describe data correlation in differential privacy, (2) using models to describe data correlation in differential privacy, and (3) describing data correlation based on the framework of Pufferfish. First, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.
Zhang, X, Liu, J, Li, Y, Cui, Q, Tao, X, Liu, RP & Li, W 2022, 'Vehicle-oriented ridesharing package delivery in blockchain system', Digital Communications and Networks.
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Zhang, X, Xia, W, Wang, X, Liu, J, Cui, Q, Tao, X & Liu, RP 2022, 'The Block Propagation in Blockchain-Based Vehicular Networks', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8001-8011.
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Zhang, Y-F, Zheng, J, Jia, W, Huang, W, Li, L, Liu, N, Li, F & He, X 2022, 'Deep RGB-D Saliency Detection Without Depth', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 755-767.
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The existing saliency detection models based on RGB colors only leverage appearance cues to detect salient objects. Depth information also plays a very important role in visual saliency detection and can supply complementary cues for saliency detection. Although many RGB-D saliency models have been proposed, they require to acquire depth data, which is expensive and not easy to get. In this paper, we propose to estimate depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Specically, we rst use an encoder network to extract common features from each RGB image and then build two decoder networks for depth estimation and saliency detection, respectively. The depth decoder features can be fused with the RGB saliency features to enhance their capability. Furthermore, we also propose a novel dense multiscale fusion model to densely fuse multiscale depth and RGB features based on the dense ASPP model. A new global context branch is also added to boost the multiscale features. Experimental results demonstrate that the added depth cues and the proposed fusion model can both improve the saliency detection performance. Finally, our model not only outperforms state-of-the-art RGB saliency models, but also achieves comparable results compared with state-of-the-art RGB-D saliency models.
Zhang, Z, Jiang, S, Huang, C & Da Xu, RY 2022, 'Unsupervised Clothing Change Adaptive Person ReID', IEEE Signal Processing Letters, vol. 29, pp. 304-308.
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Zhang, Z, Wu, Q, Wang, Y & Chen, F 2022, 'Exploring Pairwise Relationships Adaptively From Linguistic Context in Image Captioning', IEEE Transactions on Multimedia, vol. 24, pp. 3101-3113.
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For image captioning, recent works start to focus on exploring visual relationships for generating high-quality interactive words (i.e. verbs and prepositions). However, many existing works only focus on semantic level by analysing the feature similarity between objects in the visual domain but ignore the linguistic context included in the caption decoder. When captioning is being carried out, the entity words can be inferred based on visual information of objects. The interactive words representing the relationships between entity words can only be inferred based on high-level language meaning generated in the process of captioning decoding. Such high-level language meaning is called linguistic context, which refers to the relational context between words or phrases in the caption sentences. The linguistic context can be used as strong guidance to explore related visual relationships between different objects effectively. To achieve this, we propose a novel context-adaptive attention module that is strongly driven by the linguistic context from the caption decoder. In this module, a novel design of visual relationship attention is proposed based on a bilinear self-attention model to explore related visual relationships and encode more discriminative features under the linguistic context. It works parallelly with visual region attention. To achieve the adaptive process of attending to related visual relationships for generating interactive words or related visual objects for entity words, an attention modulator is integrated as an attention channel controller responding to the changing linguistic context of the caption decoder dynamically. To take full advantage of the linguistic context in the caption, an additional interaction dataset is extracted from the COCO caption datasets and COCO Entities dataset to supervise the training of the proposed context-adaptive attention module explicitly. Demonstrated by experiments on MSCOCO caption dataset, it is e...
Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive constrained generalized maximum correntropy algorithms for adaptive filtering', Signal Processing, vol. 199, pp. 108611-108611.
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Thanks to the ability of preventing the accumulation of errors, constrained adaptive filtering (CAF) algorithms have been widely applied. However, in practice, non-Gaussian noise may significantly degrade the filtering performance of CAFs derived from the second-order signal statistics. In this paper, we propose several constrained generalized maximum correntropy (CGMC) algorithms to overcome this problem, inspired by the robustness and flexibility of GMC to non-Gaussian noises. We first introduce a CGMC algorithm based on the gradient method. To improve its convergence rate with correlated inputs, we further propose a recursive CGMC (RCGMC) algorithm. For RCGMC, we conduct the convergence analysis, and characterize the theoretical transient mean square deviation (MSD) performance. Furthermore, we derive a low-complexity version of RCGMC by using the weighting method and the leading dichotomous coordinate descent (DCD) algorithm. Simulation results demonstrate the effectiveness of our proposed algorithms in non-Gaussian noise environment, and the consistency between the analytical and simulation results.
Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 4, pp. 2336-2340.
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As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.
Zhao, L-H, Wen, S, Xu, M, Shi, K, Zhu, S & Huang, T 2022, 'PID Control for Output Synchronization of Multiple Output Coupled Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1553-1566.
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This article attempts to address output synchronization and $\mathcal {H}_{\infty }$ output synchronization problems for multiple output coupled complex networks (MOCCNs) under proportional-derivative (PD) and proportional-integral (PI) controllers. Firstly, two classes of MOCCNs without and with external disturbances are separately put forward. Secondly, based on the PD and PI control schemes, several output synchronization criteria for MOCCNs are formulated by using the Lyapunov functional method and inequality techniques. Thirdly, $\mathcal {H}_{\infty }$ output synchronization for MOCCNs is also studied with the help of the PD and PI controllers. Finally, two numerical examples are separately presented to demonstrate the validity of acquired theoretical results.
Zhong, Y, Bi, T, Wang, J, Zeng, J, Huang, Y, Jiang, T, Wu, Q & Wu, S 2022, 'Empowering the V2X Network by Integrated Sensing and Communications: Background, Design, Advances, and Opportunities', IEEE Network, vol. 36, no. 4, pp. 54-60.
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To enable next-generation connected autonomous vehicles (CAVs), the future Vehicle-to-everything (V2X) network is expected to provide centimeter-accurate localization service while attaining low-latency transmissions in high-mobility environments. Nevertheless, these unprecedented requirements are far beyond the capabilities of 5G vehicular networks. Given the above evolution trend, a natural idea is thus to design a joint system architecture that combines both communications and sensing subsystems. To this end, research efforts toward integrated sensing and communications (ISAC) for the V2X network are well underway. It is our belief that ISAC should facilitate both sensing and communication via a single system in a spectrum-/energy-/cost-efficient way. Moreover, it can also improve the performance of both functionalities with mutual assistance, which is also essential to enable CAV's mission-critical services for 6G and beyond V2X. In this article, we first provide a brief historical overview of V2X and ISAC. In particular, we analyze the forces driving the usage of ISAC in V2X. Then we introduce three ISAC design schemes based on their underlying systems. We also survey state-of-the-art enabling technologies by reviewing recent developments of ISAC-assisted beamforming technologies in vehicular networks. Finally, we shed light on some potential challenges and research directions.
Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2022, 'Minute-wise frost prediction: An approach of recurrent neural networks', Array, vol. 14, pp. 100158-100158.
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Frost events incur substantial economic losses to farmers. These events could induce damage to plants and crops by damaging the cells. In this article, a recurrent neural network-based method, automating the frost prediction process, is proposed. The recurrent neural network-based models leveraged in this article include the standard recurrent neural network, long short-term memory, and gated recurrent unit. The proposed method aims to increase the prediction frequency from once per 12–24 h for the next day or night events to minute-wise predictions for the next hour events. To achieve this goal, datasets from NSW and ACT of Australia are obtained. The experiments are designed considering the scene of deploying the model to the Internet of Things systems. Factors such as model processing speed, long-term error and data availability are reviewed. After model construction, there are three experiments. The first experiment tests the errors between different model types. The second and third experiments test the effect of sequence length on error and performance for recurrent neural network-based models. All tests introduce artificial neural network models as the baseline. Also, all tests for model error are conducted in two rounds with testing datasets from the current year (2016) and next year (2017). As a result, recurrent neural network-based models are more suitable for short-term deployment with a smaller sequence length. In contrast, artificial neural network models demonstrate a lower error over the long term with faster processing time. With the results presented, the limitations of the proposed method are discussed.
Zhu, H, Ansari, M & Guo, YJ 2022, 'Wideband Beam-Forming Networks Utilizing Planar Hybrid Couplers and Phase Shifters', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7592-7602.
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Zhu, H, Zhang, T & Guo, YJ 2022, 'Wideband Hybrid Couplers With Unequal Power Division/Arbitrary Output Phases and Applications to Miniaturized Nolen Matrices', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 6, pp. 3040-3053.
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Zhu, W, Tuan, HD, Dutkiewicz, E & Hanzo, L 2022, 'Collaborative Beamforming Aided Fog Radio Access Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 7805-7820.
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The success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs' caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms.
Zou, Y, Long, Y, Gong, S, Hoang, DT, Liu, W, Cheng, W & Niyato, D 2022, 'Robust Beamforming Optimization for Self-Sustainable Intelligent Reflecting Surface Assisted Wireless Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 856-870.
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We focus on an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system where the IRS sustains its operations by harvesting energy from the access point (AP) in the power splitting (PS) protocol. We aim to minimize the AP's transmit power subject to the receivers' signal-to-noise ratio (SNR) and the IRS's energy budget constraints. A two-stage optimization framework is proposed to jointly optimize the AP's active beamforming, the IRS's passive beamforming, and the reflection amplitude. Given the reflection amplitude, we employ alternating optimization to update the beamforming strategies. Then, we determine the lower and upper bounds of the reflection amplitude in closed-form expressions, which help to update the reflection amplitude in a bisection method. We further extend our study to the robust case with uncertain channels. Our analysis reveals that the robust counterpart can be solved by the same optimization framework. Extensive simulations reveal that our algorithm is efficacy to balance the IRS's energy budget and the receiver's SNR performance. With uncertain channel information, a larger size of the IRS does not always ensure a higher performance improvement to information transmissions.
Zuo, Y, Wang, H, Fang, Y, Huang, X, Shang, X & Wu, Q 2022, 'MIG-Net: Multi-Scale Network Alternatively Guided by Intensity and Gradient Features for Depth Map Super-Resolution', IEEE Transactions on Multimedia, vol. 24, pp. 3506-3519.
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The studies of previous decades have shown that the quality of depth maps can be significantly lifted by introducing the guidance from intensity images describing the same scenes. With the rising of deep convolutional neural network, the performance of guided depth map super-resolution is further improved. The variants always consider deep structure, optimized gradient flow and feature reusing. Nevertheless, it is difficult to obtain sufficient and appropriate guidance from intensity features without any prior. In fact, the features in gradient domain, e.g., edges, present strong correlations between the intensity image and the corresponding depth map. Therefore, the guidance in gradient domain can be more efficiently explored. In this paper, the depth features are iteratively upsampled by 2$\times$. In each upsampling stage, the low-quality depth features and the corresponding gradient features are iteratively refined by the guidance from the intensity features via two parallel streams. Then, to make full use of depth features in pixel and gradient domains, the depth features and gradient features are alternatively complemented with each other. Compared with state-of-the-art counterparts, the sufficient experimental results show improvements according to the objective and subjective assessments.
Altaf, T & Braun, R 1970, 'A Roadmap to Smart Homes Security Aided SDN and ML', 2022 5th Conference on Cloud and Internet of Things (CIoT), 2022 5th Conference on Cloud and Internet of Things (CIoT), IEEE, pp. 129-136.
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The smart home is one of those significant technology trends which enhances comfort and allows the integration of environment-friendly smart applications in daily life. It contains a large pool of internet-enabled devices some of which have a limited capability and operate in a specific manner. Due to heterogeneity and operational complexity, home networks pose extremely challenging vulnerabilities concerning privacy and security. This paper is a review of recent attempts to provide privacy and security in IoT smart homes. We summarize the research efforts in the past few years, discuss and classify them based on technologies deployed i.e. SDN, ML. We then propose an approach for the integration of SDN with ML in the home network for an automatic and reconfigurable network security mechanism. We emphasize that the proposed approach addresses the heterogeneity and scalability issues (by implementing SDN) as well as pave ways to prevent harmful attacks efficiently and in a timely manner.
Ang, JD & Zhu, X 1970, 'Recent Advances in On-Chip Silicon-based Passive Components for RF and Millimeter-Wave Applications', 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE.
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Ang, JD, Hora, JA & Zhu, X 1970, 'Design of Millimetre-Wave Passive Mixer in 45-nm SOI CMOS Technology', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Ansari, M, Jones, B & Jay Guo, Y 1970, 'A Wide Angle Scanning Spherical Luneburg Lens Antenna Employing Metamaterial', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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Anwar, MJ, Gill, AQ & Proper, HA 1970, 'A Conceptual Model to Assess the Maturity Of Information Security Audit Process.', PoEM Workshops, Practice of Enterprise Modelling 2022 Workshops and Models at Work, CEUR-WS.org, London, UK.
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One of the critical aspects of information security management is the security audit, both internal and external audits. The fundamental challenge for organisations is the effective design and implementation of the information security audits to better understand their information security capability. In this paper, we present insights from an action design research (ADR) project and propose a conceptual model to assess the maturity of security audit processes. The results of this research can be used to create an improvement plan, which will guide organisations to reach their target process maturity level. The maturity model proposed in this paper was evaluated by way of feedback workshops in the target organization. The model forms the basis for future work for generalising the research into a formal reference architecture (involving models and principles) for audit process maturity.
Benedict, G, Sullivan, C & Gill, A 1970, 'Governance Challenges of AI-enabled Decentralized Autonomous Organizations: Toward a Research Agenda', https://aisel.aisnet.org/icis2022/blockchain/blockchain/2/, International Conference on Information Systems, AIS, Copenhagen, Denmark, pp. 1-9.
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The emergence of novel applications using distributed ledger technologies (DLTs) has gathered pace since the introduction of Bitcoin and the subsequent release of the Ethereum platform for decentralized applications (dApps). Such decentrally governed DLT systems are accelerating the displacement of intermediaries in regulated contexts such as the financial system and challenging the efficacy of governance regimes that have conventionally levered governance controls on identifiable, accountable decision-makers. The governance challenges of DLT systems are exacerbated by the arrival of digital autonomous organizations (DAOs) that use on-ledger decision-making mechanisms to further displace or eliminate human decision-makers. When DAOs are augmented with artificial intelligence (AI), their potent combination of computational power and access to large on-platform data sets and resources, signals a significant disruption to conventional institutional, regulatory, and legal governance regimes. This paper discusses the governance challenges of AI-enabled DAOs and presents a research agenda to address these challenges.
Chemalamarri, VD, Abolhasan, M & Braun, R 1970, 'An agent-based approach to disintegrate and modularise Software Defined Networks controller', 2022 IEEE 47th Conference on Local Computer Networks (LCN), 2022 IEEE 47th Conference on Local Computer Networks (LCN), IEEE, Edmonton, CANADA, pp. 407-413.
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The Software Defined Network paradigm deviates from traditional networks by logically centralising and physically separating the control plane from the data plane. In this work, we present the idea of a modular, agent-based SDN controller. We first highlight issues with current SDN controller designs, followed by a description of the proposed framework. We present a prototype for our design to demonstrate the controller in action using a few common use-cases. We continue the discussion by highlighting areas that require further research.
Chen, S-L, Liu, Y, Chen, D & Guo, YJ 1970, 'High-Gain Multi-Linear Polarization Reconfigurable Antenna in the Millimeter-Wave Band', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Chen, S-L, Ziolkowski, RW, Jones, B & Guo, YJ 1970, 'Closed-Path Toroidal-Waveguide Leaky-Wave Antenna with Directive Beam', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Chen, Y, Ding, C, Zhu, H & Guo, YJ 1970, 'A Dual-Slant-Polarized Differentially-Fed In-band Full-duplex (IBFD) Antenna', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 221-222.
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In this paper, a dual-polarized antenna is developed with high isolation between its transmitting (TX) and receiving (RX) ports for in-band full-duplex (IBFD) applications. A square patch antenna with horizontal and vertical polarizations is adopted as the antenna element. A new self-interference cancellation (SIC) feed network is proposed to differentially feed the antenna and combine the horizontal/vertical polarizations into ±45° polarizations. By making use of the symmetry of the antenna configuration and differential feeding, the proposed network can cancel out the coupled and reflected signals, leading to high isolation between the TX and RX ports. A high isolation of 46 dB is realized within the working band from 3.31 to 4 GHz (18.5%) and the gain is above 7.5 dBi. In addition, across the operation band, the radiation patterns show a good stability with the frequency variation.
Cui, L, Long, Y, Hoang, DT & Gong, S 1970, 'Hierarchical Learning Approach for Age-of-Information Minimization in Wireless Sensor Networks', 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), IEEE, pp. 130-136.
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In this paper, we focus on a multi-user wireless network coordinated by a multi-antenna access point (AP). Each user can generate the sensing information randomly and report it to the AP. The freshness of information is measured by the age of information (AoI). We formulate the AoI minimization problem by jointly optimizing the users' scheduling and transmission control strategies. Moreover, we employ the intelligent reflecting surface (IRS) to enhance the channel conditions and thus reduce the transmission delay by controlling the AP's beamforming vector and the IRS's phase shifting matrices. The resulting AoI minimization becomes a mixed-integer program and difficult to solve due to uncertain information of the sensing data arrivals at individual users. By exploiting the problem structure, we devised a hierarchical deep reinforcement learning (DRL) framework to search for optimal solution in two iterative steps. Specifically, the users' scheduling strategy is firstly determined by the outer-loop DRL approach, and then the inner-loop optimization adapts either the uplink information transmission or downlink energy transfer to all users. Our numerical results verify that the proposed algorithm can outperform typical baselines in terms of the average AoI performance.
Dang-Ngoc, H, Nguyen, DN, Hoang, DT, Ho-Van, K & Dutkiewicz, E 1970, 'Cooperative Friendly Jamming in Swarm UAV-assisted Communications with Wireless Energy Harvesting', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland.
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This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. We consider a swarm of hovering UAVs that relays information from a terrestrial source to a distant mobile user and simultaneously generates jamming signals to obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate to harvest wireless energy, relay information, and jam the eavesdropper. To evaluate the secrecy rate, we derive the expressions of the secrecy outage probability (SOP) in the integral form for two popular detection techniques used by the eavesdropper, i.e., selection combining and maximum-ratio combining in high signal-to-noise ratio regime. Monte Carlo simulations validate the derived SOP and show that the proposed framework outperforms the conventional AF relaying system, in terms of SOP. The insights from SOP and analysis in this work sheds light on optimizing the energy harvesting time, the number of UAVs in the swarm as well as their placements, to achieve the required secrecy protection level.
Dinh, PV, Nguyen, DN, Hoang, DT, Uy, NQ, Bao, SP & Dutkiewicz, E 1970, 'Balanced Twin Auto-Encoder for IoT Intrusion Detection', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 3387-3392.
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Intrusion detection systems (IDSs) provide an ef-fective solution for protecting loT systems. However, due to the massive number of loT devices (in billions) and their heterogeneity, IDSs face challenges posed by the complexity of loT data such as correlation-based features, high dimensions, and imbalance. To address these problems, this paper proposes a novel neural network architecture, called Balanced Twin Auto-Encoder (BTAE) which consists of three components, i.e., an encoder, a hermaphrodite, and a decoder. The encoder of BTAE first aims to transfer the input data into the latent space before data samples (pre-images) are translated into this space by different translation vectors. In addition, the data of the skewed labels are also generated in the latent space to address the problem of imbalanced data in which the number of attack samples is often significantly lower than those of the benign samples. Second, the hermaphrodite component serves as a bridge to move the data from the encoder to the decoder. Third, the decoder tries to copy the distribution of the samples in the latent space. BTAE is trained by a supervised learning technique, and its data representation extracted from the decoder can well distinguish the attack from the normal data. The experiments on five loT botnet datasets show that BTAE outperforms three existing groups of methods, e.g., the typical supervised learning, the well-known sampling, and the state-of-the-art representation learning. In addition, the false alarm rate (FAR) of BTAE applied for loT intrusion detection is less than equal to 1.2%.
Dinh, PV, Quang Uy, N, Nguyen, DN, Thai Hoang, D, Bao, SP & Dutkiewicz, E 1970, 'Twin Variational Auto-Encoder for Representation Learning in IoT Intrusion Detection', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 848-853.
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Intrusion detection systems (IDSs) play a pivotal role in defending IoT systems. However, developing a robust and efficient IDS is challenging due to the rapid and continuing evolving of various forms of cyber-attacks as well as a massive number of low-end IoT devices. In this paper, we introduce a novel deep learning architecture based on auto-encoders that allows to develop a robust intrusion detection system. Specifically, we propose a novel neural network architecture called Twin Variational Auto-Encoder (TVAE) for representation learning. TVAE includes a variational Auto-Encoder (VAE) and an Auto-Encoder (AE) that share a common stage where the decoder of the VAE is used as the encoder of the AE. The TVAE is trained in an unsupervised manner to effectively transform the original representation of data at the input of the VAE into a new representation at the output of the AE. In the new representation space, the difference between normal and attack data is more distinguishable. A variant of TVAE, namely Twin Sparse Variational Auto-Encoder (TSVAE) is also introduced by imposing a sparsity constraint on the representation units. The effectiveness of TVAE and TSVAE is evaluated using popular IDS and IoT botnet datasets. The simulation results show that the accuracy of TVAE and TSVAE can achieve the best results on six datasets, which is higher than those of state-of-the-art AE and VAE variants. We also investigate various characteristics of TVAE in the latent space as well as in the data extraction process. Besides applications on the IoT IDS, TVAE can also be applicable to all conventional network IDSs.
Dinh, TH, Doan, QM, Trung, NL, Nguyen, DN & Lin, C-T 1970, 'Masked Face Detection with Illumination Awareness', 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), IEEE, pp. 1-6.
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Mask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.
Dutkiewicz, E & Nguyen, D 1970, 'Keynote Speaker 2: Enabling Metaverse with Secure and Smart Network Resource Slicing', 2022 9th NAFOSTED Conference on Information and Computer Science (NICS), 2022 9th NAFOSTED Conference on Information and Computer Science (NICS), IEEE.
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Grigorev, A, Mihaita, A-S, Saleh, K & Piccardi, M 1970, 'Traffic incident duration prediction via a deep learning framework for text description encoding', 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 1770-1777.
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Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents. This paper proposes a new fusion framework for predicting the incident duration from limited information by using an integration of machine learning with traffic flow/speed and incident description as features, encoded via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN sentiment classifier). The paper constructs a cross-disciplinary modelling approach in transport and data science. The approach improves the incident duration prediction accuracy over the top-performing ML models applied to baseline incident reports. Results show that our proposed method can improve the accuracy by 60% when compared to standard linear or support vector regression models, and a further 7% improvement with respect to the hybrid deep learning auto-encoded GBDT model which seems to outperform all other models. The application area is the city of San Francisco, rich in both traffic incident logs (Countrywide Traffic Accident Data set) and past historical traffic congestion information (5-minute precision measurements from Caltrans Performance Measurement System).
Gu, Z, Yang, X, Jia, W, Xu, C, Yu, P, He, X, Chen, H & Lin, Y 1970, 'StrokePEO: Construction of a Clinical Ontology for Physical Examination of Stroke', 2022 9th International Conference on Digital Home (ICDH), 2022 9th International Conference on Digital Home (ICDH), IEEE, pp. 218-223.
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Clinical ontology is a standardized medical knowledge representation model that facilitates the integration and analysis of a large amount of heterogeneous electronic health record (EHR) data. Using ontologies to represent clinical terms can improve data integration to build robust and interoperable medical information systems. To date, there is no ontology existing to represent the medical knowledge for physical examination of stroke, which has inhibited the stroke physicians to make full use of clinical information captured in EHR data to understand stroke patient's health status and plan effective medication and rehabilitation treatment. In this research, we co-design with two stroke clinical specialists a stroke clinical ontology 'StrokePEO'using advanced natural language processing and deep learning techniques to extract terms and their relationships from real clinical case records provided by a tertiary hospital in China. We apply the W3C Resource Description Framework (RDF) data model to represent these clinical terms and relationships, and successfully store all case data in a graph database with StrokePEO. Our experiment results suggest that our methods and the output of StrokePEO can be applied in various medical contexts that require extraction of medical knowledge from free text for decision making. These include, but not limited to, physical assessment, drug and rehabilitation treatment outcome evaluation, medication effect analysis, and patient risk prediction.
Hakim, G & Braun, R 1970, 'Wireless Sensor Network Routing for Energy Efficiency', Springer Proceedings in Mathematics and Statistics, International Conference On Systems Engineering, Springer International Publishing, Wrocław, Poland, pp. 329-343.
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In this paper we present a new ABM model of Directed Diffusion using NetLogo that allows to study the impact of energy usage for various transmission activities. Furthermore we developed two new derivatives of this model of Directed Diffusion that could lead to energy saving in practical applications. We call these Lazy Diffusion and Gradient Diffusion. We exercise the models to produce results that show significant reduction in energy consumption over Directed Diffusion. We conclude that Wireless Sensor Networks with their routing protocols are complex systems, which can yield to Agent Based Modelling.
He, Y, Ding, C, Wei, G & Guo, YJ 1970, 'An Embedded Dual-Band Base Station Antenna Array Employing Choked Bowl-Shaped Antenna for Cross-Band Scattering Mitigation', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, Madrid, SPAIN.
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An embedded dual-band dual-polarized base station antenna (BSA) array is proposed in this paper. The array consists of two low-scattering bowl-shaped antenna elements working at the lower band (LB) and five cross-dipoles operating at the higher band (HB). Such an array configuration is intended to mitigate the negative effect on the HB antennas' radiation pattern caused by the presence of adjacent LB antennas. In this paper, a new LB antenna loaded with metal chokes is proposed to further reduce its scattering to the HB radiation. The results obtained with conventional bowl-shaped LB antenna and with choked LB antenna are compared to demonstrate the superiority of this de-scattering method. The simulation results show that the HB performance is significantly improved with the help of metal chokes while the LB performance remains nearly unchanged.
Hoang, LM, Nguyen, D, Zhang, JA & Thai Hoang, D 1970, 'Multiple Correlated Jammers Suppression: A Deep Dueling Q-Learning Approach', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 998-1003.
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For wireless networks under jamming attacks, suppressing the jammer is essential to guarantee a rehable communication link. However, it can be problematic to nullify the jamming signal when the correlations between transmitted jamming signals are deliberately varied over tone. Specifically recent studies reveal that the time-varying correlations create a 'virtual change'm the jamming channel and thus their nullspace, even when the physical channels remain unchanged Unlike existing studies that only consider unchanged correlations or merely propose a heuristic solution to the 'virtual change'problem by continuously monitoring the residual jamming signal then updating the beam-forming matrix, we develop a deep dueling Q-learning technique to minimize the magnitude of the 'virtual change'by choosing a suitable allocated time for different phases of each communication frame. Extensive simulations show that the proposed techniques can suppress the jamming signal, even when the correlations vary over time, and the correlations' trajectory is unrevealed. Moreover, our techniques do not require monitoring the residual jamming signals then updating the beam-forming matrix. Therefore, our technique can improve the system's spectral efficiency and reduce the outage probability.
Huang, J, Zhang, L, Gong, Y, Zhang, J, Nie, X & Yin, Y 1970, 'Series Photo Selection via Multi-View Graph Learning', 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022 IEEE International Conference on Multimedia and Expo (ICME), IEEE.
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Huang, Y, Kang, D, Chen, L, Zhe, X, Jia, W, Bao, L & He, X 1970, 'CAR: Class-Aware Regularizations for Semantic Segmentation', Computer Vision – ECCV 2022, Springer Nature Switzerland, pp. 518-534.
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Huang, Y, Kang, D, Jia, W, Liu, L & He, X 1970, 'Channelized Axial Attention - Considering Channel Relation within Spatial Attention for Semantic Segmentation', THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, ELECTR NETWORK, pp. 1016-1025.
Huang, Y, Kang, D, Jia, W, Liu, L & He, X 1970, 'Channelized Axial Attention – considering Channel Relation within Spatial Attention for Semantic Segmentation', Proceedings of the AAAI Conference on Artificial Intelligence, The Thirty-Sixth AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Online virtual conference, pp. 1016-1025.
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Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we propose Channelized Axial Attention (CAA) to seamlessly integrate channel attention and spatial attention into a single operation with negligible computation overhead. Specifically, we break down the dot-product operation of the spatial attention into two parts and insert channel relation in between, allowing for independently optimized channel attention on each spatial location. We further develop grouped vectorization, which allows our model to run with very little memory consumption without slowing down the running speed. Comparative experiments conducted on multiple benchmark datasets, including Cityscapes, PASCAL Context, and COCO-Stuff, demonstrate that our CAA outperforms many state-of-the-art segmentation models (including dual attention) on all tested datasets.
Khoi Tran, N, Sabir, B, Babar, MA, Cui, N, Abolhasan, M & Lipman, J 1970, 'ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16th European Conference on Software Architecture (ECSA), Springer International Publishing, Prague, CZECH REPUBLIC, pp. 49-65.
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Large-scale Machine Learning (ML) based Software Systems are increasingly developed by distributed teams situated in different trust domains. Insider threats can launch attacks from any domain to compromise ML assets (models and datasets). Therefore, practitioners require information about how and by whom ML assets were developed to assess their quality attributes such as security, safety, and fairness. Unfortunately, it is challenging for ML teams to access and reconstruct such historical information of ML assets (ML provenance) because it is generally fragmented across distributed ML teams and threatened by the same adversaries that attack ML assets. This paper proposes ProML, a decentralised platform that leverages blockchain and smart contracts to empower distributed ML teams to jointly manage a single source of truth about circulated ML assets’ provenance without relying on a third party, which is vulnerable to insider threats and presents a single point of failure. We propose a novel architectural approach called Artefact-as-a-State-Machine to leverage blockchain transactions and smart contracts for managing ML provenance information and introduce a user-driven provenance capturing mechanism to integrate existing scripts and tools to ProML without compromising participants’ control over their assets and toolchains. We evaluate the performance and overheads of ProML by benchmarking a proof-of-concept system on a global blockchain. Furthermore, we assessed ProML’s security against a threat model of a distributed ML workflow.
Kluwak, K, Klempous, R, Ito, A, Górski, T, Nikodem, J, Wojciechowski, K, Rozenblit, J, Borowik, G, Chaczko, Z, Bożejko, W & Kulbacki, M 1970, 'Reference Datasets for Analysis of Traditional Japanese and German Martial Arts', Springer Nature Switzerland, pp. 504-511.
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Kumar, A, Esmaili, N & Piccardi, M 1970, 'A Temperature-Modified Dynamic Embedded Topic Model', Communications in Computer and Information Science, Springer Nature Singapore, pp. 15-27.
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Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and Abstract dataset show that the proposed model – nicknamed DETM-tau after the temperature parameter – has been able to improve the model’s perplexity and topic coherence for all datasets.
Li, Z, Zeng, J, Zhang, W, Zhou, S & Liu, RP 1970, '6G mURLLC over Cell-Free Massive MIMO Systems in the Finite Blocklength Regime', Springer International Publishing, pp. 425-437.
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Liang, L, Lin, X, Ma, B, Wang, X, He, Y, Liu, RP & Ni, W 1970, 'Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, Rio de Janeiro, Brazil.
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Lin, L-X, Tu, Z-H & Zhu, H 1970, 'Isolation Enhancement in Millimeter-wave MIMO Array Base on Array-Antenna Decoupling Surface', 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE.
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Lin, X, Ma, B, Wang, X, He, Y, Liu, RP & Ni, W 1970, 'Multi-layer Reverse Engineering System for Vehicular Controller Area Network Messages', 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 1185-1190.
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The undisclosed Controller Area Network (CAN) decoding specification is important to the in-vehicle network (IVN) research for both industry and academia. Researchers have developed several CAN reverse engineering systems to predict signal boundaries and labels in order to map out CAN signal decoding specifications. Existing works mainly use one parameter (i.e., bit flip rate) to determine CAN signals boundary, which results in biased slicing and labelling of CAN signals. In this paper, we propose a multi-layer CAN reverse engineering system to cluster signal boundary at byte-level and label sliced CAN signal blocks at bit-level. The proposed system avoids biased signal slicing and labelling by introducing multiple parameters in signal classification, while existing works only use the bit flip rate and the number of unique value. The feasibility and adaptability of the proposed system is assessed by deploying it into a web application as a functionality module. We evaluate the proposed system with CAN messages from real cars. Compared with existing reverse engineering models, the proposed system introduces multi-layer signal processing to avoid over-slicing and over-labelling problem.
Ma, B, Lin, X, Wang, X, Liu, B, He, Y, Ni, W & Liu, RP 1970, 'New Cloaking Region Obfuscation for Road Network-Indistinguishability and Location Privacy', Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2022: 25th International Symposium on Research in Attacks, Intrusions and Defenses, ACM, Cyprus.
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Mei, G, Huang, X, Liu, J, Zhang, J & Wu, Q 1970, 'Unsupervised Point Cloud Pre-Training Via Contrasting and Clustering', 2022 IEEE International Conference on Image Processing (ICIP), 2022 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 66-70.
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The annotation for large-scale point clouds is still time-consuming and unavailable for many complex real-world tasks. Point cloud pre-training is a promising direction to auto-extract features without labeled data. Therefore, this paper proposes a general unsupervised approach, named ConClu for point cloud pre-training by jointly performing contrasting and clustering. Specifically, the contrasting is formulated by maximizing the similarity feature vectors produced by encoders fed with two augmentations of the same point cloud. The clustering simultaneously clusters the data while enforcing consistency between cluster assignments produced different augmentations. Experimental evaluations on downstream applications outperform state-of-the-art techniques, which demonstrates the effectiveness of our framework.
Mei, G, Huang, X, Zhang, J & Wu, Q 1970, 'Overlap-Guided Coarse-to-Fine Correspondence Prediction for Point Cloud Registration', 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Taipei, Taiwan, pp. 1-6.
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Establishing reliable correspondences between a pair of point clouds is essential for registration with partial overlaps. However, existing correspondence estimation works usually struggle to distinguish the points in overlap and non-overlap regions. This paper thus proposes an Overlap-guided Coarse-to-Fine Network, named OCFNet, which first establishes correspondences at a coarse level and then refines them at a point level. Specifically, at the coarse level, our model first aggregates two point clouds into smaller sets of super-points with associated features and overlap scores, followed by establishing coarse-level correspondences between the two sets of super-points under the guidance of overlap scores. On the fine stage, a decoder recovers the raw points while jointly learning the associated features and overlap scores. Coarse-level proposals are then expanded to patches, and point-level correspondences are sequentially refined from the corresponding patches. We conducted comprehensive experiments on 3DMatch, 3DLoMatch, and KITTI benchmarks to show the effectiveness of the proposed method. [code]
Mei, G, Huang, X, Zhang, J & Wu, Q 1970, 'Partial Point Cloud Registration Via Soft Segmentation', 2022 IEEE International Conference on Image Processing (ICIP), 2022 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 681-685.
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Most existing correspondence-free registration methods suffer from performance degradation in partial overlapped point clouds. To solve the partial overlapped point cloud registration, this paper proposes, SegReg, a soft Segmentationbased correspondence-free Registration approach. Specifically, we first softly segment both source and target point clouds into a discrete number of geometric partitions, respectively. Then registration is achieved through iteratively using the IC-LK algorithm to minimize the distance between the feature descriptors of the corresponded partitions. Extensive experiments on synthetic synthetic dataset ModelNet40 and real dataset 7Scene show that the proposed method achieves state-of-the-art performance.
Mei, G, Saltori, C, Poiesi, F, Zhang, J, Ricci, E, Sebe, N & Wu, Q 1970, 'Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding', BMVC 2022 - 33rd British Machine Vision Conference Proceedings, British Machine Vision Conference, London, UK.
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Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods. However, data augmentation is not ideal as it requires a careful selection of the type of augmentations to perform, which in turn can affect the geometric and semantic information learned by the network during self-training. To overcome this issue, we propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. SoftClu assumes that the points belonging to a cluster should be close to each other in both geometric and feature spaces. This differs from typical contrastive learning, which builds similar representations for a whole point cloud and its augmented versions. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task. Under the constraint that these pseudo-labels induce the equipartition of the point cloud, we cast SoftClu as an optimal transport problem. We formulate an unsupervised loss to minimize the standard cross-entropy between pseudo-labels and predicted labels. Experiments on downstream applications, such as 3D object classification, part segmentation, and semantic segmentation, show the effectiveness of our framework in outperforming state-of-the-art techniques [code].
Nguyen, CT, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'MetaChain: A Novel Blockchain-based Framework for Metaverse Applications', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland.
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Metaverse has recently attracted paramount attention due to its potential for future Internet. However, to fully realize such potential, Metaverse applications have to overcome various challenges such as massive resource demands, interoperability among applications, and security and privacy concerns. In this paper, we propose MetaChain, a novel blockchain-based framework to address emerging challenges for the development of Metaverse applications. In particular, by utilizing the smart contract mechanism, MetaChain can effectively manage and automate complex interactions among the Metaverse Service Provider (MSP) and the Metaverse users (MUs). In addition, to allow the MSP to efficiently allocate its resources for Metaverse applications and MUs’ demands, we design a novel sharding scheme to improve the underlying blockchain’s scalability. Moreover, to leverage MUs’ resources as well as to attract more MUs to support Metaverse operations, we develop an incentive mechanism using the Stackelberg game theory that rewards MUs’ contributions to the Metaverse. Through numerical experiments, we clearly show the impacts of the MUs’ behaviors and how the incentive mechanism can attract more MUs and resources to the Metaverse.
Nguyen, CT, Nguyen, DN, Hoang, DT, Pham, H-A & Dutkiewicz, E 1970, 'Optimize Coding and Node Selection for Coded Distributed Computing over Wireless Edge Networks', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 1248-1253.
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This paper aims to develop a highly-effective framework to significantly enhance the efficiency in using coded computing techniques for distributed computing tasks over heterogeneous wireless edge networks. In particular, we first formulate a joint coding and node selection optimization problem to minimize the expected total processing time for computing tasks, taking into account the heterogeneity in the nodes' computing resources and communication links. The problem is shown to be NP-hard. To circumvent it, we leverage the unique characteristic of the problem to develop a linearization approach and a hybrid algorithm based on binary search and branch-and-bound (BB) algorithms. This hybrid algorithm can not only guarantee to find the optimal solution, but also significantly reduce the computational complexity of the BB algorithm. Simulations based on real-world datasets show that the proposed approach can reduce the total processing time up to 2.4 times compared with that of state-of-the-art approach, even without perfect knowledge regarding the node's performance and their straggling parameters.
Nguyen, N-T, Yu, H, Tuan, HD, Nguyen, DN & Dutkiewicz, E 1970, 'Maximization of Geometric Mean of Secrecy Rates in RIS-aided Communications Networks', 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE.
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Nguyen, TV & Nguyen, DN 1970, 'Secondary Reflections Amongst Multiple IRSs: Friends or Foes?', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE.
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Ni, Z, Zhang, JA, Huang, X & Yang, K 1970, 'Asynchronous Uplink Sensors Fused in Perceptive Mobile Networks', 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Seoul, SOUTH KOREA, pp. 824-829.
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This paper proposes a scheme that solves two challenging problems in parameter estimation using communication signals: (1) asynchronous transmitter and receiver; and (2) sensing receiver with a small number of antennas. These problems exist in parameter estimation for perceptive mobile networks and WiFi. The geometrically-separated transmitter and receiver in communications are typically asynchronous at clock level. For a small base-station or WiFi, the number of antenna elements in an array is usually limited, which limits the resolution of estimating the angle-of-arrivals (AOAs) of multipath signals. In this paper, we employ cross-antenna cross-correlation (CACC) operation to resolve the asynchronous issue and use the CACC outputs to generate a multi-domain signal block that combines three-domain receive samples to efficiently increase the resolution of AOAs. The proposed scheme enables the direct use of uplink communication signals for radio sensing, without requiring any modifications on infrastructure or advanced hardware, such as a full-duplex transceiver. It also enables the estimation of more number of paths than the number of antennas, hence sensing in a small base-station or WiFi becomes possible.
Ni, Z, Zhang, JA, Yang, K & Liu, R 1970, 'Frequency-Hopping Based Joint Automotive Radar-Communication Systems Using A Single Device', 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Seoul, SOUTH KOREA, pp. 480-485.
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Dual-functional radar-communication (DFRC), integrating the two functions into one system and sharing one transmitted signal, shows its great potential in self-driving networks. In this paper, we develop a single-device based multi-input single-output (MISO) DFRC vehicular system. Modulations of un-slotted ALOHA frequency-hopping (UA-FH) and fast FH, commonly used in automotive radar, are adopted to transmit the DFRC waveforms and to address severe interferences caused by an interfering vehicle that serves as a communication transmitter. Due to the asynchrony between vehicles, the FH sequences of the interfering vehicle are chosen from a fixed codebook. All channel parameters are then extracted via FH decoding from radar backscattered channels and communication channels, respectively. To further increase the accuracy, we proceed to propose an iterative algorithm that divides the signals into short segments and jointly obtains all parameters with high resolution. Finally, simulation results are provided and validate the proposed DFRC vehicular system.
Nikkhah, N, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 1970, 'Efficient Dual-Band Single-Port Rectifier for RF Energy Harvesting at FM and GSM Bands', 2022 Wireless Power Week (WPW), 2022 Wireless Power Week (WPW), IEEE, Bordeaux, France, pp. 141-145.
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This paper presents an efficient dual-band rectifier for radiofrequency energy harvesting (RFEH) applications at FM and GSM bands. The single-port rectifier circuit, which comprises a 3-port network, optimized T-matching circuits and voltage doubler, is designed, simulated and fabricated to obtain a high RF-to-DC power conversion efficiency (PCE). Measurement results show PCE of26% and 22% at -20dBm, and also 58% and 51% at -10dBm with a maximum amount of 69% and 65% at -2.5dBm and -5dBm, with single tone at 95 and 925 MHz, respectively. Besides, the fractional bandwidth of 21% at FM and 11% at GSM band is achieved. The measurement and simulation results are in good agreement. Consequently, the proposed rectifier can be a potential candidate for ambient RF energy harvesting and wireless power transfer (WPT). It should be noted that a 3-port network as a duplexer is designed to be integrated with single-port antennas which cover both FM and GSM bands as a low-cost solution. Moreover, based on simulation results, PCE has small variations when the load resistor varies from 10 to 18 k$\Omega$. Therefore, this rectifier can be utilized for any desired resistance within the range, such as sensors and IoT devices.
Parnell, J, Jauregi Unanue, I & Piccardi, M 1970, 'A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization', Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, IRELAND, pp. 5112-5128.
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Pearce, A, Zhang, JA & Xu, R 1970, 'Regional Trajectory Analysis through Multi-Person Tracking with mmWave Radar', 2022 IEEE Radar Conference (RadarConf22), 2022 IEEE Radar Conference (RadarConf22), IEEE, New York, NY.
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Saputra, YM, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'In-Network Caching and Learning Optimization for Federated Learning in Mobile Edge Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, Seoul, Korea, Republic of, pp. 1653-1658.
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In this paper, we develop a novel privacy-aware framework to address straggling problem in a federated learning (FL)-based mobile edge network through maximizing profit for the mobile service provider (MSP). In particular, unlike the conventional FL process when participating mobile users (MUs) have to train their all data locally, we propose a highly-effective solution that allows MUs to encrypt parts of local data and upload/cache the encrypted data to nearby mobile edge nodes (MENs) and/or a cloud server (CS) to perform additional training processes. In this way, we can not only mitigate the straggling problem caused by limited computing/communications resources at MUs but also enhance the usage efficiency of learning data from all MUs in the FL process. To optimize portions of encrypted data cached and trained at MENs/CS given constraints from MUs and the MSP while considering data privacy and training costs, we first formulate the profit maximization problem for the MSP as an optimal in-network encrypted data caching and learning optimization. We then prove that the objective function is concave, and thus an interior-point method algorithm can be effectively adopted to quickly find the optimal solution. The numerical results demonstrate that our proposed framework can enhance the profit of the MSP up to 5.39 times compared with other FL methods.
Song, L-Z, Qin, P-Y & Du, J 1970, 'E-Band Multibeam Conformal Transmitarrays for Beyond 5G Wireless Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Tan, Y, Long, Y, Zhao, S, Gong, S, Hoang, DT & Niyato, D 1970, 'Energy Minimization for Wireless Powered Data Offloading in IRS-assisted MEC for Vehicular Networks', 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022 International Wireless Communications and Mobile Computing (IWCMC), IEEE, pp. 731-736.
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In this paper, we consider an IRS-assisted and wireless-powered mobile edge computing (MEC) system that allows both edge users and the IRS to harvest energy from the hybrid access point (HAP), co-located with the MEC server. Each edge user uses the harvested energy to offload its data to the MEC server. The IRS not only assists downlink energy transfer to the edge users, but also improves the users' uplink offloading rates. To minimize the overall energy consumption, we jointly optimize the users' offloading decisions, the HAP's active beamforming, as well as the IRS's energy harvesting and passive beamforming strategies. The energy minimization problem is intractable due to complicated couplings in both the objective function and constraints. We decompose this problem into the downlink energy transfer and the uplink data offloading phases. The uplink phase can be efficiently optimized by the conventional semi-definite relaxation (SDR) method, while the downlink phase depends on the alternating optimization between the users' offloading decisions and the joint active and passive beamforming strategies. Numerical results demonstrate that the proposed offloading scheme can significantly reduce the HAP's energy consumption compared with typical benchmarks.
Tong, M, Huang, X & Zhang, JA 1970, 'Frame-based Decision Directed Successive Interference Cancellation for FTN Signaling', 2022 IEEE Globecom Workshops (GC Wkshps), 2022 IEEE Globecom Workshops (GC Wkshps), IEEE, pp. 1670-1674.
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In this paper, we propose a frame-based decision directed successive interference cancellation to improve the detection performance of Faster-than-Nyquist (FTN) signaling. The main idea of this method is to directly decide all data symbols in a complete transmission frame after minimum-mean-square-error (MMSE) equalization and regenerate the noise-free signal with the decided symbols. The difference between the equalized and regenerated signals represents the residual inter-symbol interference (ISI) which depends on the bit-error-rate (BER) of the decision. After adding the normalized residual ISI to the decided symbols, the date symbols in the transmission frame are decided recursively, leading to a decision directed successive interference cancellation (DDSIC) scheme. The simulation results in both Gaussian and multipath fading channels demonstrate that our proposed method enables lower complexity and better performance FTN systems compared with existing symbol-by-symbol interference cancellation methods.
Vu, TT, Hoang, DT, Phan, KT, Nguyen, DN & Dutkiewicz, E 1970, 'Energy-based Proportional Fairness for Task Offloading and Resource Allocation in Edge Computing', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, Seoul, Korea, Republic of, pp. 1912-1917.
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By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs’ computing/communications resources to given favorable sets of users may block other devices from their service. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare (e.g., minimizing the total energy consumption) but not consider the computing/battery status of each mobile device. This work develops a proportional fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipment (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it NP-hard. To tackle it, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branchand-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decision and subproblems (SPs) for resource allocation. The SPs can either find their closed-form solutions or be solved in parallel at ENs, thus help reduce the complexity. The numerical results show that the DBBD returns the optimal solution of the problem maximizing the fairness between UEs. The DBBD has higher fairness indexes, i.e., Jain’s index and min-max ratio, in comparing with the existing ones that minimize the total consumed energy.
Wen, Y & Qin, P-Y 1970, 'Yagi-Uda Monopoles with Elevated-Angle Suppression for Endfire Radiation', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Wu, K, Qin, P & Chen, S-L 1970, 'A High-Efficiency 3D-Printed E-Band Dielectric Transmitarray For Integrated Space and Terrestrial Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 1970, 'Removing False Targets For Cyclic Prefixed OFDM Sensing With Extended Ranging', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland.
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Employing cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework is developed recently, greatly extending the ranging capability of CP-OFDM sensing. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveform. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to removing false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution.
Xia, J, Qu, W, Huang, W, Zhang, J, Wang, X & Xu, M 1970, 'Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
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Xiao, D, Ni, W, Zhang, JA, Liu, R, Chen, S & Qu, Y 1970, 'AI-Enabled Automated and Closed-Loop Optimization Algorithms for Delay-Aware Network', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 806-811.
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Network slicing is one of the core techniques of the current 5G networks. To accommodate as many network slices as possible with limited hardware resources, service providers need to avoid over-provisioning of resources. In this paper, we first propose a Deep Q-Network (DQN) based network slicing algorithm to maximize the acceptance ratio and ensure prior placement of higher-priority requests for Ultra-Reliable Low-Latency Communication (URLLC) services. Specifically, we model the network slicing as a Markov Decision Process (MDP), where we consider Virtual Network Function (VNF) placements to be the actions of the MDP, and define a reward function based on service priority. For every service request, we use the DQN to choose an MDP action for performing the VNF placement. The placement results in an MDP reward that we can use to train the DQN. Once trained, the DQN approximates the optimal solution of the MDP. Considering the over-provisioning of resources, we then propose a Binary Search Assisted Transfer Learning algorithm (BSATL), in which the available hardware resources are scaled down/up and the knowledge learned from the source task is transferred to the target task in each iteration, to achieve automated and closed-loop optimization for the ever changing infrastructure, a scenario of 6G Event Defined uRLLC (EDuRLLC). Numerical evaluations show that our proposed scheme can significantly improve cost-utility while maintaining the optimal acceptance ratio.
Yang, S, Sun, P, Jiang, Y, Xia, X, Zhang, R, Yuan, Z, Wang, C, Luo, P & Xu, M 1970, 'OBJECTS IN SEMANTIC TOPOLOGY', ICLR 2022 - 10th International Conference on Learning Representations.
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A more realistic object detection paradigm, Open-World Object Detection, has arised increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their annotations progressively arrive. Previous works rely on independent modules to recognize unknown categories and perform incremental learning, respectively. In this paper, we provide a unified perspective: Semantic Topology. During the life-long learning of an open-world object detector, all object instances from the same category are assigned to their corresponding pre-defined node in the semantic topology, including the 'unknown' category. This constraint builds up discriminative feature representations and consistent relationships among objects, thus enabling the detector to distinguish unknown objects out of the known categories, as well as making learned features of known objects undistorted when learning new categories incrementally. Extensive experiments demonstrate that semantic topology, either randomly-generated or derived from a well-trained language model, could outperform the current state-of-the-art open-world object detectors by a large margin, e.g., the absolute open-set error (the number of unknown instances that are wrongly labeled as known) is reduced from 7832 to 2546, exhibiting the inherent superiority of semantic topology on open-world object detection.
Yang, S, Yang, E, Han, B, Liu, Y, Xu, M, Niu, G & Liu, T 1970, 'Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network', Proceedings of Machine Learning Research, pp. 25302-25312.
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In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean labels to noisy labels (i.e., clean-label transition matrix (CLTM)) has been widely exploited to learn a clean label classifier by employing the noisy data. Motivated by that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal labels to noisy labels (i.e., Bayes-label transition matrix (BLTM)) and learn a classifier to predict Bayes optimal labels. Note that given only noisy data, it is ill-posed to estimate either the CLTM or the BLTM. But favorably, Bayes optimal labels have less uncertainty compared with the clean labels, i.e., the class posteriors of Bayes optimal labels are one-hot vectors while those of clean labels are not. This enables two advantages to estimate the BLTM, i.e., (a) a set of examples with theoretically guaranteed Bayes optimal labels can be collected out of noisy data; (b) the feasible solution space is much smaller. By exploiting the advantages, we estimate the BLTM parametrically by employing a deep neural network, leading to better generalization and superior classification performance.
Yin, Q, Wang, Z, Song, Y, Xu, Y, Niu, S, Bai, L, Guo, Y & Yang, X 1970, 'Improving Deep Embedded Clustering via Learning Cluster-level Representations', Proceedings - International Conference on Computational Linguistics, COLING, pp. 2226-2236.
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Driven by recent advances in neural networks, various Deep Embedding Clustering (DEC) based short text clustering models are being developed. In these works, latent representation learning and text clustering are performed simultaneously. Although these methods are becoming increasingly popular, they use pure cluster-oriented objectives, which can produce meaningless representations. To alleviate this problem, several improvements have been developed to introduce additional learning objectives in the clustering process, such as models based on contrastive learning. However, existing efforts rely heavily on learning meaningful representations at the instance level. They have limited focus on learning global representations, which are necessary to capture the overall data structure at the cluster level. In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations. Here, we extend the embedded topic modelling approach to introduce reconstruction constraints to help learn cluster-level representations. Experimental results on real-world short text datasets demonstrate that our model produces meaningful clusters.
Zhang, Z, Wang, X, Yu, G, Ni, W, Liu, RP, Georgalas, N & Reeves, A 1970, 'A Community Detection-Based Blockchain Sharding Scheme', Springer Nature Switzerland, pp. 78-91.
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Zhou, C, Lyu, B, Hoang, DT & Gong, S 1970, 'Reconfigurable Intelligent Surface Assisted Secure Symbiotic Radio Multicast Communications', 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), IEEE, London, United Kingdom, pp. 1-6.
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In this paper we propose a reconfigurable intelligent surface RIS assisted secure transmission scheme for a symbiotic radio multicast system where the RIS not only assists the confidential information multicasting from a primary transmitter PT to multiple primary users PUs to against the information interception by eavesdroppers but also delivers its own signal to a secondary user SU by passive reflections We formulate a signal to noise ratio SNR maximization problem for the SU by jointly optimizing the active beamforming at the PT amplitude reflection coefficients and phase shifts of the RIS To address the non convexity of the formulated problem we propose to decompose the original problem into two sub problems and solve them independently in an iteratively alternating manner For the first sub problem we adopt the successive convex approximation SCA and semidefinite relaxation SDR techniques to design the active beamforming by proving the tightness of SDR For the second sub problem the sequential rank one constraint relaxation SROCR technique is adopted to handle the rank one constraint for reflection coefficients optimization Numerical results show that compared to the benchmark schemes the proposed scheme can achieve up to 68 3 performance gain in terms of SNR
Zhu, H & Guo, YJ 1970, 'Compact and Wideband Filtering Power Dividers with Arbitrary and Constant Output Phase Difference', 2022 Asia-Pacific Microwave Conference (APMC), 2022 Asia-Pacific Microwave Conference (APMC), IEEE.
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Zhu, H, Song, L-Z & Guo, YJ 1970, 'Wideband Hybrid Couplers and Their Applications to Multi-beam Antenna Feed Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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