Abidi, S, Piccardi, M, Tsang, WH & Williams, M-A 2019, 'Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence.
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Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach.
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Miniature tri-wideband Sierpinski–Minkowski fractals metamaterial perfect absorber', IET Microwaves, Antennas & Propagation.
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Awwad, S, Tarvade, S, Piccardi, M & Gattas, DJ 2019, 'The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene', International Journal for Quality in Health Care, vol. 31, no. 1, pp. 36-42.
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(i) To demonstrate the feasibility of automated, direct observation and collection of hand hygiene data, (ii) to develop computer visual methods capable of reporting compliance with moment 1 (the performance of hand hygiene before touching a patient) and (iii) to report the diagnostic accuracy of automated, direct observation of moment 1.Observation of simulated hand hygiene encounters between a healthcare worker and a patient.Computer laboratory in a university.Healthy volunteers.Sensitivity and specificity of automatic detection of the first moment of hand hygiene.We captured video and depth images using a Kinect camera and developed computer visual methods to automatically detect the use of alcohol-based hand rub (ABHR), rubbing together of hands and subsequent contact of the patient by the healthcare worker using depth imagery.We acquired images from 18 different simulated hand hygiene encounters where the healthcare worker complied with the first moment of hand hygiene, and 8 encounters where they did not. The diagnostic accuracy of determining that ABHR was dispensed and that the patient was touched was excellent (sensitivity 100%, specificity 100%). The diagnostic accuracy of determining that the hands were rubbed together after dispensing ABHR was good (sensitivity 83%, specificity 88%).We have demonstrated that it is possible to automate the direct observation of hand hygiene performance in a simulated clinical setting. We used cheap, widely available consumer technology and depth imagery which potentially increases clinical application and decreases privacy concerns.
Bah, AO, Qin, P-Y, Ziolkowski, RW, Guo, YJ & Bird, TS 2019, 'A Wideband Low-Profile Tightly Coupled Antenna Array With a Very High Figure of Merit', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 4, pp. 2332-2343.
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Ben, X, Gong, C, Zhang, P, Jia, X, Wu, Q & Meng, W 2019, 'Coupled Patch Alignment for Matching Cross-View Gaits', IEEE Transactions on Image Processing, vol. 28, no. 6, pp. 3142-3157.
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© 1992-2012 IEEE. Gait recognition has attracted growing attention in recent years, as the gait of humans has a strong discriminative ability even under low resolution at a distance. Unfortunately, the performance of gait recognition can be largely affected by view change. To address this problem, we propose a coupled patch alignment (CPA) algorithm that effectively matches a pair of gaits across different views. To realize CPA, we first build a certain amount of patches, and each of them is made up of a sample as well as its intra-class and inter-class nearest neighbors. Then, we design an objective function for each patch to balance the cross-view intra-class compactness and the cross-view inter-class separability. Finally, all the local-independent patches are combined to render a unified objective function. Theoretically, we show that the proposed CPA has a close relationship with canonical correlation analysis. Algorithmically, we extend CPA to 'multi-dimensional patch alignment' that can handle an arbitrary number of views. Comprehensive experiments on CASIA(B), USF, and OU-ISIR gait databases firmly demonstrate the effectiveness of our methods over other existing popular methods in terms of cross-view gait recognition.
Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2019, 'Coupled Bilinear Discriminant Projection for Cross-view Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods, because these methods can learn only one projection matrix, thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the Coupled bilinear discriminant projection (CBDP). Specifically, CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs’ spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, CBDP can learn the optimal matrix subspace where GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of CBDP are both monotonically increasing, so the convergence of CBDP is demonstrated. In terms of accuracy, the comparative experiments on the CASIA(B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, the encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.
Chen, X, Ni, W, Chen, T, Collings, I, Wang, X, Liu, RP & Giannakis, GB 2019, 'Multi-Timescale Online Optimization of Network Function Virtualization for Service Chaining', IEEE Transactions on Mobile Computing.
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IEEE Network Function Virtualization (NFV) can cost-efficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the real-time decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of [1], for any 0. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learn-and-adapt strategy is further designed to speed the placement up with an improved tradeoff. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 23% and reduce the queue length (or delay) by 74%, as compared to existing benchmarks.
Chen, Z, Wong, H & Liu, Y 2019, 'A Polarizer Integrated Dielectric Resonator Antenna for Polarization Reconfigurability', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 4, pp. 2723-2728.
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Cheng, H, Zhang, J, Wu, Q & An, P 2019, 'A computational model for stereoscopic visual saliency prediction', IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 678-689.
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© 2018 IEEE. Depth information plays an important role in human vision as it provides additional cues that distinguish objects from their backgrounds. This paper explores depth information for analyzing stereoscopic saliency and presents a computational model that predicts stereoscopic visual saliency based on three aspects of human vision: 1) the pop-out effect; 2) comfort zones; and 3) background effects. Through an analysis of these three phenomena, we find that most of the stereoscopic saliency region can be explained. Our model comprises three modules, each describing one aspect of saliency distribution, and a control function that can be used to adjust the three models independently. The relationship between the three models is not mutually exclusive. One, two, or three phenomena may appear in one image. Therefore, to accurately determine which phenomena the image conforms to, we have devised a selection strategy that chooses the appropriate combination of models based on the content of the image. Our approach is implemented within a framework based on the multifeature analysis. The framework considers surrounding regions, color/depth contrast, and points of interest. The selection strategy can improve the performance of the framework. A series of experiments on two recent eye-tracking datasets shows that our proposed method outperforms several state-of-the-art saliency models.
Chomsiri, T, He, XS, Nanda, P & Tan, Z 2019, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called 'tree rules'. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.
Chu, L, Shi, J & Braun, R 2019, 'The equivalent Young's modulus prediction for vacancy defected graphene under shear stress', PHYSICA E-LOW-DIMENSIONAL SYSTEMS & NANOSTRUCTURES, vol. 110, pp. 115-122.
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Dai, M, Cheng, S, He, X & Wang, D 2019, 'Object tracking in the presence of shaking motions', Neural Computing and Applications, pp. 1-18.
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© 2018 The Natural Computing Applications Forum Visual tracking can be particularly interpreted as a process of searching for targets and optimizing the searching. In this paper, we present a novel tracker framework for tracking shaking targets. We formulate the underlying geometrical relevance between a search scope and a target displacement. A uniform sampling among the search scopes is implemented by sliding windows. To alleviate any possible redundant matching, we propose a double-template structure comprising of initial and previous tracking results. The element-wise similarities between a template and its candidates are calculated by jointly using kernel functions which provide a better outlier rejection property. The STC algorithm is used to improve the tracking results by maximizing a confidence map incorporating temporal and spatial context cues about the tracked targets. For better adaptation to appearance variations, we employ a linear interpolation to update the context prior probability of the STC method. Both qualitative and quantitative evaluations are performed on all sequences that contain shaking motions and are selected from the OTB-50 challenging benchmark. The proposed approach is compared with and outperforms 12 state-of-the-art tracking methods on the selected sequences while running on MATLAB without code optimization. We have also performed further experiments on the whole OTB-50 and VOT 2015 datasets. Although the most of sequences in these two datasets do not contain motion blur that this paper is focusing on, the results of our method are still favorable compared with all of the state-of-the-art approaches.
Esmaili, N, Piccardi, M, Kruger, B & Girosi, F 2019, 'Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models (vol 13, e0206274, 2018)', PLOS ONE, vol. 14, no. 4.
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Gao, X, Du, J, Zhang, T & Guo, YJ 2019, 'High-T c Superconducting Fourth-Harmonic Mixer Using a Dual-Band Terahertz On-Chip Antenna of High Coupling Efficiency', IEEE Transactions on Terahertz Science and Technology, vol. 9, no. 1, pp. 55-62.
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© 2019 IEEE. This paper presents a dual-band on-chip antenna-coupled high-T c superconducting (HTS) Josephson-junction subterahertz (THz) fourth-harmonic mixer. The antenna utilizes a couple of different structured twin slots to enable the resonant radiations at two frequencies, and integrates a well-designed coplanar waveguide network for achieving good radiation coupling and signal isolation characteristics. The electromagnetic simulations show that coupling efficiencies as high as -4 and -3.5 dB are achieved for the 160- and 640-GHz operating frequency bands, respectively. Based on this dual-band antenna, a 640-GHz HTS fourth-harmonic mixer is developed and characterized in a range of operating temperatures. The mixer exhibits a measured conversion gain of around -18 dB at 20 K and -22 dB at 40 K, respectively. The achieved intermediate frequency bandwidth is larger than 23 GHz. These are the best results reported for HTS harmonic mixers at comparable sub-THz frequency bands to date.
Gill, AQ & Chew, E 2019, 'Configuration information system architecture: Insights from applied action design research', Information and Management.
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© 2018 Elsevier B.V. One of the critical information systems that enables service resilience is the service configuration information system (CiS). The fundamental challenge for organisations is the effective designing and implementation of the CiS architecture. This paper addresses this important research problem and reports insights from a completed applied action design research (ADR) project in an Australian financial services organisation. This paper aims to provide guidance to researchers and practitioners contemplating ADR, rooted in the organisational context, for practice-oriented academia-industry collaborative research. This research also contributes in terms of the CiS reference architecture design knowledge and demonstrates the applicability of the ADR method.
Gong, R-J, Ban, Y-L, Lian, J-W, Liu, Y & Nie, Z 2019, 'Circularly Polarized Multibeam Antenna Array of ME Dipole Fed by 5 x 6 Butler Matrix', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 18, no. 4, pp. 712-716.
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Guo, L, Zhu, H & Abbosh, A 2019, 'Phase reconfigurable microwave power divider', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 1, pp. 21-25.
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© 2004-2012 IEEE. A reconfigurable power divider (PD) that can operate either in an in-phase mode or out-of-phase mode is presented. To that end, the novel concept of using tunable phase shifter, which is a reflection-type loaded coupled lines, to emulate an effective variable length transmission line (TL) is utilized. The proposed PD uses a quarter-wavelength TL, a 100 Ω isolation resistor and two tunable phase shifters. The presented theoretical analysis shows that by properly selecting the parameters of the phase shifter, it can be used to approximate the performance of variable length TL. To validate the design, a prototype of dimensions 50 mm × 25 mm is built, using Rogers RO3010 substrate, and tested. The results indicate that the device can operate as an in-phase and out-of-phase by using suitable biasing voltages. Across the band 0.9-1.1 GHz, the device has more than 12 dB return loss at all the ports and more than 15 dB isolation between the two output ports with less than 5° phase deviation for both of the in-phase and out-of-phase states.
Han, F, Zhuo, J, Liu, N, Liu, Y, Liu, H & Liu, QH 2019, 'Fast solution of electromagnetic scattering for 3-D inhomogeneous anisotropic objects embedded in layered uniaxial media by the BCGS-FFT method', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1748-1759.
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© 1963-2012 IEEE. In this paper, the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method is used to solve the electromagnetic scattering problem for 3-D inhomogeneous uniaxial anisotropic objects embedded in a layered uniaxial medium. The optical axis of the background medium is perpendicular to the layer boundaries. However, the optical axis of the scatterer can be rotated through any angles. The electric field integral equation (EFIE) is formulated in the layered uniaxial anisotropic background medium. The volumetric roof-top function is used for the basis function as well as the testing function. The dyadic Green's functions (DGFs) for both the electric field and the magnetic vector potential are used to solve the EFIE, and the results as well as the performance are compared. Several numerical simulations are carried out to validate the accuracy and efficiency of the proposed method. The major new contribution of this paper is to extend the BCGS-FFT method to accommodate the uniaxial anisotropy of the layered background medium. Therefore, the DGFs for the layered uniaxial medium are evaluated before solving the EFIE. In addition, the DGF for the electric field is also used to solve the 3-D scattering problem through BCGS-FFT iterations, which is different from the previous work.
Han, L, Zhou, M, Han, S, Jia, W, Sun, C & Fu, C 2019, 'Targeting malware discrimination based on reversed association task', Concurrency Computation.
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©2018 John Wiley & Sons, Ltd. Regarding the current situation that the recognition rate of malware is decreasing, the article points out that the reason for this dilemma is that more and more targeting malware have emerged, which share little or no common feature with traditional malware. The premise of malware recognition judging whether a software is malicious or benign is actually a decision problem. We propose that malware discrimination should resort to the corresponding task or purpose. We first present a formal definition of a task and then provide further classifications of malicious tasks. Based on the decidable theory, we prove that task performed by any software is recursive and determinable. By establishing a mapping from software to task, we prove that software is many-to-one reducible to corresponding tasks. Thus, we demonstrate that software, including malware, is also recursive and can be determined by the corresponding tasks. Finally, we present the discrimination process of our method. Nine real malwares are presented, which were firstly discriminated by our method but at that time could not be identified by Kaspersky, McAfee, Symantec Norton, or Kingsoft Antivirus.
Han, L, Zhou, M, Jia, W, Dalil, Z & Xu, X 2019, 'Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model', Information Sciences, vol. 476, pp. 491-504.
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© 2018 Elsevier Inc. An effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system's detection efficiency and energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.
Huang, L, Zhe, T, Wu, J, Wu, Q, Pei, C & Chen, D 2019, 'Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision', IEEE Access, vol. 7, pp. 46059-46070.
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© 2013 IEEE. Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).
Huang, X, An, P, Cao, F, Liu, D & Wu, Q 2019, 'Light-field compression using a pair of steps and depth estimation', Optics Express, vol. 27, no. 3, pp. 3557-3573.
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Huang, Y, Xu, J, Wu, Q, Zheng, Z, Zhang, Z & Zhang, J 2019, 'Multi-pseudo Regularized Label for Generated Data in Person Re-Identification.', IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1391-1403.
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Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images (i.e., annotation), the amount of available training data (i.e., real data) is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to generate artificial sample data (i.e., generated data). However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to predefined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets respectively, and outperforms state-of-the-art methods.
Huang, Y, Zhong, Y, Wu, Q, Dutkiewicz, E & Jiang, T 2019, 'Cost-Effective Foliage Penetration Human Detection under Severe Weather Conditions based on Auto-Encoder/Decoder Neural Network', IEEE Internet of Things Journal.
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IEEE Military surveillance events and rescue activities are vital missions for the Internet-of-things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot performs promisingly under severe weather conditions such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an Auto-Encoder/Decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing (DFS) techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally-strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding Additive White Gaussian Noise (AWGN) to the original target signals with different Signal to Noise Ratios (SNRs). Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.
Jan, MA, Usman, M, He, X & Rehman, AU 2019, 'SAMS: A Seamless and Authorized Multimedia Streaming framework for WMSN-based IoMT', IEEE Internet of Things Journal.
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IEEE An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for long-term usage. These nodes are capable of capturing both multimedia and non-multimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, the underlying routing protocols need to provide an acceptable level of Quality-of-Service (QoS) support for the multimedia traffic. In this paper, we propose a seamless and authorized multimedia streaming framework (SAMS) for a cluster-based hierarchical WMSN. SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their cluster heads. Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its cluster head. This waiting may result in an excessive packet loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for inter-cluster communication. In case of buffer overflow, a member node in one cluster switches to a neighboring cluster head provided that the latter has an available channel for allocation. The experimental results show that SAMS provides an acceptable level of QoS and enhances the security of the underlying network.
Jiang, S, Li, K & Xu, YDR 2019, 'Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications. Most existing variations of NMF only consider how each row/column vector of factorised matrices should be shaped, and ignore the relationship among pairwise rows or columns. In many cases, such pairwise relationship enables better factorisation, for example, image clustering and recommender systems. In this paper, we propose an algorithm named, Relative Pairwise Relationship constrained Non-negative Matrix Factorisation (RPR-NMF), which places constraints over relative pairwise distances amongst features by imposing penalties in a triplet form. Two distance measures, squared Euclidean distance and Symmetric divergence, are used, and exponential and hinge loss penalties are adopted for the two measures respectively. It is well known that the so-called "multiplicative update rules" result in a much faster convergence than gradient descend for matrix factorisation. However, applying such update rules to RPR-NMF and also proving its convergence is not straightforward. Thus, we use reasonable approximations to relax the complexity brought by the penalties, which are practically verified. Experiments on both synthetic datasets and real datasets demonstrate that our algorithms have advantages on gaining close approximation, satisfying a high proportion of expected constraints, and achieving superior performance compared with other algorithms.
Ju, M, Ding, C, Guo, YJ & Zhang, D 2019, 'Remote Sensing Image Haze Removal Using Gamma-Correction-Based Dehazing Model', IEEE Access, vol. 7, pp. 5250-5261.
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© 2013 IEEE. Haze is evident in most remote sensing (RS) images, particularly for the RS scenes captured in inclement weather, which severely hinders image interpretation. In this paper, two simple yet effective visibility restoration formulas are proposed for RGB-channel RS (RRS) images and multi-spectral RS (MSRS) images, respectively. More specifically, a robust gamma-correction-based dehazing model (RGDM) is first defined, which can better address the non-uniform illumination problem in hazy images. Then, the scene albedo restoration formula (SARF) used for the RRS images is obtained by imposing the existing prior knowledge on this RGDM, which enables us to simultaneously eliminate the interferences of haze and non-uniform illumination. In subsequence, according to Rayleigh's law, an expanded restoration formula (E-SARF) is further developed for MSRS data. Using the proposed E-SARF, the spatially varying haze in each band can be thoroughly removed without using any extra information. The experiments are conducted on the challenging RRS and MSRS images, including images with non-uniform illumination, non-uniform haze distribution, and heavy haze. The results reveal that the SARF and the E-SARF are superior to most other state-of-the-art techniques in terms of both the recover quality and the implementation efficiency.
Ju, M, Ding, C, Zhang, D & Guo, YJ 2019, 'BDPK: Bayesian Dehazing Using Prior Knowledge', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model’s assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable atmospheric scattering model (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability (MAP) one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique (AMT) is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.
Karmokar, DK, Chen, SL, Bird, TS & Guo, YJ 2019, 'Single-Layer Multi-Via Loaded CRLH Leaky-Wave Antennas for Wide-Angle Beam Scanning With Consistent Gain', IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 2, pp. 313-317.
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© 2018 IEEE. Achieving continuous backward-to-forward wide-angle beam scanning with consistent gain by employing composite right/left-handed (CRLH) leaky-wave antennas (LWAs) is reported. For structural and design simplicity, a single-layer one-dimensional structure is considered in which each unit cell consists of a patch shorted centrally to the ground plane. It was found that, when using only one via at the center of a unit cell, a continuous beam scan requires a large-diameter via when all other parameters remain unchanged. To eliminate this limitation while maintaining a single-layer structure, novel unit cells are proposed using multiple vias in each unit cell. An LWA design for continuous beam scan with three vias in each unit cell is investigated, and the results show good performance and design flexibility. A prototype antenna has been realized, and the measured results show that the antenna can scan the radiation beam continuously in a wide range, from -60° to +66° with a consistent gain. The measured gain variation within the scan range is only 2.9 dB, and the 3 dB gain bandwidth is 58.6%.
Lan, T, Liu, N, Liu, Y, Han, F & Liu, QH 2019, '2-D Electromagnetic Scattering and Inverse Scattering from Magnetodielectric Objects Based on Integral Equation Method', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 2, pp. 1346-1351.
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Le, AT, Tran, LC, Huang, X, Jay Guo, Y & Vardaxoglou, JYC 2019, 'Frequency-domain characterization and performance bounds of ALMS loop for RF self-interference cancellation', IEEE Transactions on Communications, vol. 67, no. 1, pp. 682-692.
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© 1972-2012 IEEE. Analog least mean square (ALMS) loop is a promising method to cancel self-interference (SI) in in-band full-duplex (IBFD) systems. In this paper, the steady state analyses of the residual SI powers in both analog and digital domains are firstly derived. The eigenvalue decomposition is then utilized to investigate the frequency domain characteristics of the ALMS loop. Our frequency domain analyses prove that the ALMS loop has an effect of amplifying the frequency components of the residual SI at the edges of the signal spectrum in the analog domain. However, the matched filter in the receiver chain will reduce this effect, resulting in a significant improvement of the interference suppression ratio (ISR). It means that the SI will be significantly suppressed in the digital domain before information data detection. This paper also derives the lower bounds of ISRs given by the ALMS loop in both analog and digital domains. These lower bounds are joint effects of the loop gain, tap delay, number of taps, and transmitted signal properties. The discovered relationship among these parameters allows the flexibility in choosing appropriate parameters when designing the IBFD systems under given constraints.
Li, H, Wang, TQ, Huang, X & Guo, YJ 2019, 'Adaptive AoA and Polarization Estimation for Receiving Polarized mmWave Signals', IEEE Wireless Communications Letters.
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IEEE This paper proposes a novel hybrid dual-polarized antenna array which exploits two orthogonally-collocated dipoles to capture the full power of a polarized millimeter wave signal. To maximize the received signal-to-noise ratio (SNR), we study the adaptive angle-of-arrival and polarization state estimation, and develop a differential beam tracking algorithm and a cross-correlation-to-power ratio polarization tracking algorithm for interleaved hybrid dual-polarized arrays. Simulation results verify the superior performance of the proposed algorithms, and confirm the significant improvement of SNR obtained by using the proposed array and algorithms.
Li, H, Wang, TQ, Huang, X, Zhang, JA & Guo, YJ 2019, 'Low-Complexity Multiuser Receiver for Massive Hybrid Array mmWave Communications', IEEE TRANSACTIONS ON COMMUNICATIONS, vol. 67, no. 5, pp. 3512-3524.
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Liberal, I, Ederra, I & Ziolkowski, RW 2019, 'Grating Lobes in Higher-Order Correlation Functions of Arrays of Quantum Emitters: Directional Photon Bunching Versus Correlated Directions', PHOTONICS, vol. 6, no. 1.
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Lin, JY, Wong, SW, Wu, YM, Yang, Y, Zhu, L & He, Y 2019, 'Three-way multiple-mode cavity filtering crossover for narrowband and broadband applications', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 3, pp. 896-905.
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© 1963-2012 IEEE. In this paper, the design of a cavity crossover with three intersecting channels is presented. The three fundamental modes of a cavity resonator, namely, TE011, TE101, and TM110 modes, are adopted to resonate at each of three channels, respectively. Due to the modal orthogonality of these fundamental modes, high isolation among three channels can be achieved. Two kinds of crossovers, for narrowband and broadband applications, are presented. For the narrowband case, the proposed crossover resonates at 2.91 GHz with the fractional bandwidth of 1.4%. For the broadband case, the proposed crossover resonates at 3 GHz with the fractional bandwidth of 24%. The isolations of both designs reach more than 50 dB. For a proof of concept, the broadband example of the cavity crossover structures is fabricated and measured. A good agreement between the simulated and the measured results verifies the accuracy of the proposed design methodology.
Lin, L, Xu, L, Huang, Y, Xiang, Y & He, X 2019, 'On exploiting priority relation graph for reliable multi-path communication in mobile social networks', Information Sciences, vol. 477, pp. 490-507.
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© 2018 Elsevier Inc. A mobile social network (MSN) consists of certain amount of mobile users with social characteristics, and it provides data delivery concerning social relationships between mobile users. In MSN, ordinary people contact each other more frequently if they have more social features in common. In this paper, we apply a new topology structure–priority relation graph (PRG) to evaluate the data delivery routing in MSN on the system-level. By using the natural order of nodes’ representation, the diameter, the regular degree and the multi-path technology, we determine the priority relation graph-based social feature routing (PRG-SFR) algorithm to find disjointed multi-paths in MSN. Here, the multi-path technology can be exploited for ensuring that, between each pair of sender and receiver, the important information can be delivered through a highly reliable path. Then we calculate the tolerant ability of ‘faults’ and estimate the availability of MSN on the theoretical level. Finally, we analyze the efficiency of PRG-SFR algorithm from the numerical standpoint in terms of fault tolerance, forwarding number, transmission time and delivery rate. Moreover, we make comparisons between PRG-SFR algorithm and certain state-of-the-art technologies.
Lin, W & Ziolkowski, RW 2019, 'Electrically Small Huygens Antenna-Based Fully-Integrated Wireless Power Transfer and Communication System', IEEE ACCESS, vol. 7, pp. 39762-39769.
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Lin, W & Ziolkowski, RW 2019, 'Wirelessly Powered Light and Temperature Sensors Facilitated by Electrically Small Omnidirectional and Huygens Dipole Antennas', Sensors (Basel, Switzerland), vol. 19, no. 9.
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Wirelessly powered, very compact sensors are highly attractive for many emerging Internet-of-things (IoT) applications; they eliminate the need for on-board short-life and bulky batteries. In this study, two electrically small rectenna-based wirelessly powered light and temperature sensors were developed that operate at 915 MHz in the 902-928-MHz industrial, scientific, and medical (ISM) bands. First, a metamaterial-inspired near-field resonant parasitic (NFRP) Egyptian axe dipole (EAD) antenna was seamlessly integrated with a highly efficient sensor-augmented rectifier without any matching network. It was electrically small and very thin, and its omnidirectional property was ideal for capturing incident AC wireless power from any azimuthal direction and converting it into DC power. Both a photocell as the light sensor and a thermistor as the temperature sensor were demonstrated. The resistive properties of the photocell and thermistor changed the rectifier's output voltage level; an acoustic alarm was activated once a threshold value was attained. Second, an electrically small, low-profile NFRP Huygens antenna was similarly integrated with the same light- and temperature-sensor-augmented rectifiers. Their unidirectional nature was very suitable for surface-mounted wireless power transfer (WPT) applications (i.e., on-body and on-wall sensors). Measurements of the prototypes of both the light- and temperature-sensor-augmented omni- and unidirectional rectenna systems confirmed their predicted performance characteristics.
Liu, C, Zhu, L, He, X & Chen, J 2019, 'Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data', IEEE Transactions on Dependable and Secure Computing.
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IEEE When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too time-consuming for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.
Liu, M, Xue, Z, He, X & Chen, J 2019, 'Cyberthreat-Intelligence Information Sharing', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 8, no. 3, pp. 17-22.
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Lu, Y, Fang, J, Guo, Z & Zhang, J 2019, 'Distributed Transmit Beamforming for UAV to Base Communications', China Communications, vol. 16, no. 1, pp. 15-25.
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Lyu, B, Dinh, TH & Yang, Z 2019, 'User Cooperation in Wireless-Powered Backscatter Communication Networks', IEEE WIRELESS COMMUNICATIONS LETTERS, vol. 8, no. 2, pp. 632-635.
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Makhdoom, I, Abolhasan, M, Abbas, H & Ni, W 2019, 'Blockchain's adoption in IoT: The challenges, and a way forward', Journal of Network and Computer Applications.
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Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to The Internet of Things', Communications Surveys and Tutorials, IEEE Communications Society.
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Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2019, 'A hybrid encryption technique for Secure-GLOR: The adaptive secure routing protocol for dynamic wireless mesh networks', Future Generation Computer Systems.
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As we progress in into a digital era where most aspects of our life depend upon a network of computers,it is essential to focus on digital security. Each component of a network, be it a physical network, virtualnetwork or social network requires security when transmitting data. Hence the dynamic wireless meshnetwork must also deploy high levels of security as found in current legacy networks. This paper presentsa secure Geo-Location Oriented Routing (Secure-GLOR) protocol for wireless mesh networks, whichincorporates a hybrid encryption scheme for its multilevel security framework. The hybrid encryptiontechnique improves the network’s overall performance compared to the basic encryption by using acombination of symmetric key as well as asymmetric key encryption. Using the combination of the twoencryption schemes, the performance of the network can be improved by reducing the transmitted datasize, reduced computational overhead and faster encryption–decryption cycles. In this paper discussedmultiple encryption schemes for both symmetric and asymmetric encryption, compare their performancein various experimental scenarios. Proposed security scheme achieves better performance based on theresults obtained with most viable options for our network model.
Nguyen, H, Nguyen, D, Dinh, H & Dutkiewicz, E 2019, 'Jam Me If You Can: Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS.
Qin, C, Ni, W, Tian, H, Lu, L & Liu, RP 2019, 'Radio over Cloud (RoC): Cloud-Assisted Distributed Beamforming for Multi-class Traffic', IEEE Transactions on Mobile Computing.
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IEEE Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), l1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with l1-norm approximation and reducing the feasible solution regions at later stages.
Rao, T, Li, X, Zhang, H & Xu, M 2019, 'Multi-level region-based Convolutional Neural Network for image emotion classification', NEUROCOMPUTING, vol. 333, pp. 429-439.
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Ren, C, Lyu, X, Ni, W, Tian, H & Liu, RP 2019, 'Distributed Online Learning of Fog Computing under Nonuniform Device Cardinality', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 1147-1159.
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© 2014 IEEE. Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.
Roselin, AG, Nanda, P, Nepal, S & He, X 2019, 'Testbed Evaluation of Lightweight Authentication Protocol(LAUP) fo r6LoWPAN wireless sensor networks', Concurrency and Computation: Practice and Experience.
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Rouzbehi, K, Miranian, A, Escaño, JM, Rakhshani, E, Shariati, N & Pouresmaeil, E 2019, 'A Data-Driven Based Voltage Control Strategy for DC-DC Converters: Application to DC Microgrid', Electronics, vol. 8, no. 5, pp. 493-493.
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This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.
Sang, L, Xu, M, Qian, S & Wu, X 2019, 'Multi-modal multi-view Bayesian semantic embedding for community question answering', NEUROCOMPUTING, vol. 334, pp. 44-58.
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Saputra, YM, Hoang, DT, Nguyen, DN, Dutkiewicz, E, Niyato, D & Kim, DI 2019, 'Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks', IEEE Wireless Communications Letters, pp. 1-1.
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Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 2018, pp. 1-19.
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Maximum‐margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing‐based algorithm that is able to mitigate the issue of local minima in the maximum‐margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k‐means++ and SVM at each step of the annealing process. More precisely, k‐means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
Shen, W, Wu, Y, Yuan, J, Duan, L, Zhang, J & Jia, Y 2019, 'Robust Distracter-Resistive Tracker via Learning a Multi-Component Discriminative Dictionary', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE Discriminative dictionary learning (DDL) provides an appealing paradigm for appearance modeling in visual tracking. However, most existing DDL based trackers cannot handle drastic appearance changes, especially for scenarios with background cluster and/or similar object interference. One reason is that they often suffer from the loss of subtle visual information which is critical to distinguish an object from distracters. In this paper, we explore the use of deep features extracted from the Convolutional Neural Networks (CNNs) to improve the object representation and propose a robust distracter-resistive tracker via learning a multi-component discriminative dictionary. The proposed method exploits both the intra-class and the interclass visual information to learn shared atoms and the classspecific atoms. By imposing several constraints into the objective function, the learned dictionary is reconstructive, compressive and discriminative, thus can better distinguish an object from the background. In addition, our convolutional features (deep features extracted from CNNs) have structural information for object localization and balance the discriminative power and semantic information of the object. Tracking is carried out within a Bayesian inference framework where a joint decision measure is used to construct the observation model. To alleviate the drift problem, the reliable tracking results obtained online are accumulated to update the dictionary. Both the qualitative and quantitative results on the CVPR2013 benchmark, the VOT2015 dataset and the SPOT dataset demonstrate that our tracker achieves better performance over the state-of-the-art approaches.
Shi, J, Chu, L & Braun, R 2019, 'A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method', International journal of molecular sciences, vol. 20, pp. 2355-2355.
Shi, T, Tang, M-C, Wu, Z, Xu, H-X & Ziolkowski, RW 2019, 'Improved Signal-to-Noise Ratio, Bandwidth-Enhanced Electrically Small Antenna Augmented With Internal Non-Foster Elements', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 4, pp. 2763-2768.
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Siyari, P, Krunz, MM & Nguyen, D 2019, 'Distributed Power Control in Single-stream MIMO Wiretap Interference Networks with Full-duplex Jamming Receivers', IEEE Transactions on Signal Processing.
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Sun, F, Zhu, H, Zhu, X, Yang, Y, Sun, Y & Zhang, X 2019, 'Design of millimeter-wave bandpass filters with broad bandwidth in Si-based technology', IEEE Transactions on Electron Devices, vol. 66, no. 3, pp. 1174-1181.
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© 2019 IEEE. In this paper, a novel design approach is proposed for on-chip bandpass filter (BPF) design with improved passband flatness and stopband suppression. The proposed approach simply uses a combination of meander-line structures with metal-insulator-metal (MIM) capacitors. To demonstrate the insight of this approach, a simplified equivalent LC-circuit model is used for theoretical analysis. Using the analyzed results as a guideline along with a full-wave electromagnetic (EM) simulator, two BPFs are designed and implemented in a standard 0.13-μm (Bi)-CMOS technology. The measured results show that good agreements between EM simulated and measured results are achieved. For the first BPF, the return loss is better than 10 dB from 13.5 to 32 GHz, which indicates a fractional bandwidth (FBW) of more than 78%. In addition, the minimum insertion loss of 2.3 dB is achieved within the frequency range from 17 to 27 GHz and the in-band magnitude ripple is less than 0.1 dB. The chip size of this design, excluding the pads, is 0.148 mm 2 . To demonstrate a miniaturized design, a second design example is given. The return loss is better than 10 dB from 17.3 to 35.9 GHz, which indicates an FBW of more than 70%. In addition, the minimum insertion loss of 2.6 dB is achieved within the frequency range from 21.4 to 27.7 GHz and the in-band magnitude ripple is less than 0.1 dB. The chip size of the second design, excluding the pads, is only 0.066 mm 2 .
Sun, HH, Ding, C, Zhu, H, Jones, B & Guo, YJ 2019, 'Suppression of Cross-Band Scattering in Multiband Antenna Arrays', IEEE Transactions on Antennas and Propagation, vol. 67, no. 4, pp. 2379-2389.
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© 1963-2012 IEEE. This paper presents a novel method of suppressing cross-band scattering in dual-band dual-polarized antenna arrays. The method involves introducing chokes into low-band (LB) elements to suppress high-band (HB) scattering currents. The experimental results show that by inserting LB-pass HB-stop chokes into LB radiators, suppression of induced HB currents on the LB elements is achieved. This greatly reduces the pattern distortion of the HB array caused by the presence of LB elements. The array considered is configured as two columns of HB antennas operating from 1.71 to 2.28 GHz interleaved with a single column of LB antennas operating from 0.82 to 1.0 GHz. The realized array with choked LB element has stable and symmetrical radiation in both HB and LB.
Sun, X, Gui, G, Li, Y, Liu, RP & An, Y 2019, 'ResInNet: A Novel Deep Neural Network with Feature Reuse for Internet of Things', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 679-691.
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© 2014 IEEE. Deep neural networks (DNNs) have widely used in various Internet-of-Things (IoT) applications. Pursuing superior performance is always a hot spot in the field of DNN modeling. Recently, feature reuse provides an effective means of achieving favorable nonlinear approximation performance in deep learning. Existing implementations utilizes a multilayer perception (MLP) to act as a functional unit for feature reuse. However, determining connection weight and bias of MLP is a rather intractable problem, since the conventional back-propagation learning approach encounters the limitations of slow convergence and local optimum. To address this issue, this paper develops a novel DNN considering a well-behaved alternative called reservoir computing, i.e., reservoir in network (ResInNet). In this structure, the built-in reservoir has two notable functions. First, it behaves as a bridge between any two restricted Boltzmann machines in the feature learning part of ResInNet, performing a feature abstraction once again. Such reservoir-based feature translation provides excellent starting points for the following nonlinear regression. Second, it serves as a nonlinear approximation, trained by a simple linear regression using the most representative (learned) features. Experimental results over various benchmark datasets show that ResInNet can achieve the superior nonlinear approximation performance in comparison to the baseline models, and produce the excellent dynamic characteristics and memory capacity. Meanwhile, the merits of our approach is further demonstrated in the network traffic prediction related to real-world IoT application.
Sutton, G, Liu, RP & Guo, J 2019, 'Coexistence Performance and Limits of Frame-Based Listen-Before-Talk', IEEE Transactions on Mobile Computing, pp. 1-1.
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Sutton, GJ, Zeng, J, Liu, RP, Ni, W, Nguyen, DN, Jayawickrama, BA, Huang, X, Abolhasan, M, Zhang, Z, Dutkiewicz, E & Lv, T 2019, 'Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Tang, MC, Duan, Y, Wu, Z, Chen, X, Li, M & Ziolkowski, RW 2019, 'Pattern reconfigurable, vertically polarized, low-profile, compact, near-field resonant parasitic antenna', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1467-1475.
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© 1963-2012 IEEE. A vertically polarized, low-profile, compact, near-field resonant parasitic antenna with pattern reconfigurability is demonstrated. The antenna has three dynamic end-fire states facilitated with only three p-i-n diodes. The radiation pattern in each state covers more than 120° in its azimuth plane and, hence, it achieves beam scanning that covers the entire azimuth plane. The antenna height and transverse size are, respectively, only 0.048λ 0 and 0.1λ 02 . Measured results, in good agreement with their simulated values, demonstrate that the antenna exhibits a 11% fractional impedance bandwidth, and a 6.6 dBi peak realized gain in all three of its pattern-reconfigurable states. Stable and high peak realized gain values are realized over its entire operational band surrounding 2.22 GHz.
Tang, MC, Wu, Z, Shi, T & Ziolkowski, RW 2019, 'Dual-Band, Linearly Polarized, Electrically Small Huygens Dipole Antennas', IEEE Transactions on Antennas and Propagation, vol. 67, no. 1, pp. 37-47.
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© 1963-2012 IEEE. Two electrically small, dual-band Huygens dipole antennas are reported. In both designs, two pairs of magnetic and electric near-field resonant parasitic (NFRP) elements are combined organically within an electrically small, low profile package. The NFRP elements are excited effectively using only one coaxial-fed-driven element. One dual-band Huygens system produces parallel, linearly polarized (LP) fields at its two operating frequencies. The other dual-band system produces two orthogonal LP fields. Additional parasitic elements are introduced to mitigate the mutual coupling effects between the pairs of NFRP elements. The measured values for prototypes of both antennas in the L-band demonstrate their electrically small size (ka< 1) and low profile (∼ 0.03λ0). They also confirm their broadside radiation and polarization performance characteristics, as well as the isolation between each operating frequency. Their fractional bandwidths, peak realized gains, front-to-back ratios, and radiation efficiencies are, respectively, 0.6%, > 2 dBi, > 10 dB, and > 60% at both frequencies. These dual-band systems would provide multifunctional performance in a variety of portable, compact wireless devices.
Usman, M, He, X, Lam, KKM, Xu, M, Chen, J, Bokhari, SMM & Jan, MA 2019, 'Error Concealment for Cloud-based and Scalable Video Coding of HD Videos', IEEE Transactions on Cloud Computing.
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IEEE The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.
Usman, M, Jan, MA, He, X & Chen, J 2019, 'A Mobile Multimedia Data Collection Scheme for Secured Wireless Multimedia Sensor Networks', IEEE Transactions on Network Science and Engineering.
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IEEE Wireless Multimedia Sensor Networks (WMSNs) produce an enormous amount of big multimedia data. Due to large size, Multimedia Sensor Nodes (MSNs) cannot store generated multimedia data for a long time. In this scenario, mobile sinks can be utilized for data collection. However, due to vulnerable nature of wireless networks, there is a need for an efficient security scheme to authenticate both MSNs and mobile sinks. In this paper, we propose a scheme to protect an underlying WMSN during mobile multimedia data collection. The proposed scheme is a two-layer scheme. At the first layer, all MSNs are distributed into small clusters, where each cluster is represented by a single Cluster Head (CH). At the second layer, all CHs verify identities of mobile sinks before sharing multimedia data. Authentication at both layers ensures a secure data exchange. We evaluate the performance of proposed scheme through extensive simulation results. The simulation results prove that the proposed scheme performs better as compared to existing state-of-the-art approaches in terms of resilience and handshake duration. The proposed scheme is also analyzed in terms of authentication rate, data freshness, and packet delivery ratio, and has shown a better performance.
Usman, M, Jan, MA, He, X & Chen, J 2019, 'P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications', IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1222-1230.
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© 1983-2012 IEEE. The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.
Usman, M, Jan, MA, He, X & Nanda, P 2019, 'QASEC: A secured data communication scheme for mobile Ad-hoc networks', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Mobile Adhoc NETworks (MANETs) are valuable for various applications due to an efficient, flexible, low-cost and dynamic infrastructure. In these networks, proper utilization of network resources is desirable to maintain Quality of Service (QoS). In multi-hop end-to-end communication, intermediate nodes may eavesdrop on data in transit. As a result, a secured and reliable data delivery from source to destination is required. In this paper, we propose a novel scheme, known as QASEC, to achieve better throughput by securing end-to-end communication in MANETs. The QoS is maintained through an optimal link selection from a queue of available transmission links. The end-to-end communication is secured by authentication. A simple secret-key based symmetric encryption is deployed for interacting nodes. Our proposed QASEC scheme prevents the malicious nodes from data exchange with legitimate intermediate nodes on any established path between the source and the destination. Experimental results show that QASEC performs better in terms of packet-loss rate, jitter and end-to-end delay. Furthermore, QASEC is efficient against various attacks and has a much better performance in terms of associated costs, such as key generation, encryption, and storage and communication.
Van Huynh, N, Hoang, DT, Nguyen, DN, Dutkiewicz, E, Niyato, D & Wang, P 2019, 'Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning', IEEE Transactions on Communications, pp. 1-1.
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Van Huynh, N, Thai Hoang, D, Nguyen, DN & Dutkiewicz, E 2019, 'Optimal and Fast Real-Time Resource Slicing with Deep Dueling Neural Networks', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 37, no. 6, pp. 1455-1470.
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© 1983-2012 IEEE. Effective network slicing requires an infrastructure/network provider to deal with the uncertain demands and real-time dynamics of the network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This paper develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demands from tenants. Specifically, we first propose a novel system model that enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case, in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling, that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with the real-time resource requests and the dynamic demands of the users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with the state-of-the-art network slicing approaches.
Wang, TQ, Li, H & Huang, X 2019, 'Analysis and Mitigation of Clipping Noise in Layered ACO-OFDM Based Visible Light Communication Systems', IEEE Transactions on Communications.
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IEEE Due to the limited dynamic range of the off-theshelf electrical and optical components, deliberate digital clipping (DDC) is widely applied to optical orthogonal frequency division multiplexing (OFDM) based visible light communication systems. In this paper, we present a theoretical characterization of the layered asymmetrically clipped optical OFDM (ACO-OFDM) signals subject to peak clipping. We decouple a clipped L-layer ACO-OFDM symbol to L single-layer ACO-OFDM symbols, each corresponding to a layer, and show that these symbols are subject to symmetrical peak clippings at random levels. Using the Bussgang’s theorem, the resulting attenuation factors and variances of the additive noise associated with each layer are derived. It is shown that the clipping noise caused by the DDC mainly falls onto the first layer, and its impact is gradually reduced in the subsequent layers. In order to combat the clipping noise, a novel receiver based on decision aided reconstruction is proposed. Simulation results show that the proposed receiver can effectively mitigate the clipping noise, leading to significant improvement of bit error rates over the conventional receiver.
Wang, W, Hoang, DT, Hu, P, Xiong, Z, Niyato, D, Wang, P, Wen, Y & Kim, DI 2019, 'A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks', IEEE Access, vol. 7, pp. 22328-22370.
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© 2013 IEEE. The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.
Wang, X, Song, B, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks', SECURITY AND COMMUNICATION NETWORKS.
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Wang, X, Zha, X, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Game Theoretic Suppression of Forged Messages in Online Social Networks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-11.
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Wang, X, Zha, X, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Survey on blockchain for Internet of Things', COMPUTER COMMUNICATIONS, vol. 136, pp. 10-29.
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Wang, Y, Shuai, Y, Zhu, Y, Zhang, J & An, P 2019, 'Jointly learning perceptually heterogeneous features for blind 3D video quality assessment', Neurocomputing, vol. 332, pp. 298-304.
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© 2018 Elsevier B.V. 3D videos quality assessment (3D-VQA) is essential to various 3D video processing applications. However, it has not been well investigated on how to make use of perceptual multi-channel video information to improve 3D-VQA under different distortion categories and degrees, especially under asymmetrical distortions. In the paper, we propose a new blind 3D-VQA metric by jointly learning perceptually heterogeneous features. Firstly, a binocular spatio-temporal internal generative mechanism (BST-IGM) is proposed to decompose the views of 3D video into multi-channel videos. Then, we extract perceptually heterogeneous features by proposed multi-channel natural video statistics (MNVS) model, which are characterized 3D video information. Furthermore, a robust AdaBoosting Radial Basis Function (RBF) neural network is utilized to map the features to the overall quality of 3D video. On two benchmark databases, the extensive evaluations demonstrate that the proposed algorithm significantly outperforms several state-of-the-art quality metrics in term of prediction accuracy and robustness.
Wei, F, Yang, ZJ, Qin, PY, Guo, YJ, Li, B & Shi, XW 2019, 'A Balanced-to-Balanced In-Phase Filtering Power Divider with High Selectivity and Isolation', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 2, pp. 683-694.
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© 1963-2012 IEEE. In this paper, a balanced-to-balanced in-phase filtering power divider (FPD) is proposed, which can realize a two-way equal power division with high selectivity and isolation. The design of the proposed FPD is primarily based on microstrip/slotline transition structures and slotline T-junction. A U-type microstrip feed line integrated with a stepped-impedance slotline resonator is adopted at the input and output ports, which makes the differential-mode (DM) responses independent of the common-mode (CM) ones. Meanwhile, superior DM transmission and CM suppression are achieved intrinsically, thereby simplifying the design procedure significantly. By employing slotline resonators loaded with resistors, the isolation between the two output ports can be improved greatly. In addition, a DM passband with a sharp filtering performance is realized by introducing the microstrip stub-loaded resonators (SLRs). By changing the electrical length of the open stub of the SLR, the fractional bandwidth is controllable. In order to verify the feasibility of the proposed design method, two prototype circuits of the proposed FPDs with different bandwidths are fabricated and measured. Good agreement between the simulation and measurement results is observed.
Wu, K, Ni, W, Su, T, Liu, RP & Guo, YJ 2019, 'Efficient angle-of-arrival estimation of lens antenna arrays for wireless information and power transfer', IEEE Journal on Selected Areas in Communications, vol. 37, no. 1, pp. 116-130.
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© 1983-2012 IEEE. Antenna design and angle-of-arrival (AoA) estimation are critical to the efficiency of wireless information and power transfer. The AoA estimation is challenging for energy-efficient lens antenna arrays (LAAs), due to discrete sets of fixed discrete Fourier transform (DFT) beams. This paper presents a novel fast and accurate approach for the AoA estimation of LAAs. The key idea is that we prove the two differential outputs of three adjacent lens beams, referred to as 'DFT beam differences (DBDs),' that are the strongest at the two sides of an AoA. They are easy to identify and robust to noises, and their powers are proved to provide an accurate estimate of the AoA. Another important aspect is a new beam synthesis technique which produces different beam widths based on DFT beams and practical 1-bit phase shifts in real time. As a result, the angular region containing the AoA can exponentially narrow down, and the two strongest DBDs can be quickly identified. The proposed approach can operate in coupling with successive interference cancellation to estimate the AoAs of multiple paths. Simulations show that the proposed approach is able to outperform the state of the art by orders of magnitude in terms of accuracy. The power transfer efficiency can be dramatically improved.
Wu, K, Ni, W, Su, T, Liu, RP & Guo, YJ 2019, 'Expeditious Estimation of Angle-of-Arrival for Hybrid Butler Matrix Arrays', IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 18, no. 4, pp. 2170-2185.
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Xi, Y, Zheng, J, He, X, Jia, W, Li, H, Xie, Y, Feng, M & Li, X 2019, 'Beyond context: Exploring semantic similarity for small object detection in crowded scenes', Pattern Recognition Letters.
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© 2019 Small object detection in crowded scene aims to find those tiny targets with very limited resolution from crowded scenes. Due to very little information available on tiny objects, it is often not suitable to detect them merely based on the information presented inside their bounding boxes, resulting low accuracy. In this paper, we propose to exploit the semantic similarity among all predicted objects’ candidates to boost the performance of detectors when handling tiny objects. For this purpose, we construct a pairwise constraint to depict such semantic similarity and propose a new framework based on Discriminative Learning and Graph-Cut techniques. Experiments conducted on three widely used benchmark datasets demonstrate the improvement over the state-of-the-art approaches gained by applying this idea.
Xiao, C, Zeng, J, Ni, W, Liu, RP, Su, X & Wang, J 2019, 'Delay Guarantee and Effective Capacity of Downlink NOMA Fading Channels', IEEE Journal on Selected Topics in Signal Processing.
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IEEE Non-orthogonal multiple access (NOMA) is promising for increasing connectivity and capacity. But there has been little consideration on the quality of service (QoS) of NOMA; let alone that in generic fading channels. This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami-$m$ and Rician NOMA channels, by exploiting stochastic network calculus (SNC). The key challenge addressed is to derive the Mellin transforms of the service processes in the NOMA fading channels. The transforms are proved to be stable, and incorporated into the SNC to provide the closed-form upper bounds of the delay violation probability. The paper also applies the Mellin transforms to develop the closed-form expressions for the effective capacity of the NOMA fading channels, which measures the channel capacity under statistical delay guarantees. By further applying the min-max and max-min rules, two new power allocation algorithms are proposed to optimize the closed-form expressions, which can provide the NOMA users fairness in terms of delay violation probability and effective capacity. Simulation results substantiate the derived upper bounds of the delay violation probabilities and the effective capacity. The proposed power allocation algorithms are also numerically validated.
Xiao, C, Zeng, J, Ni, W, Su, X, Liu, RP, Lv, T & Wang, J 2019, 'Downlink MIMO-NOMA for Ultra-Reliable Low-Latency Communications', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 37, no. 4, pp. 780-794.
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Xu, J-X, Li, H-Y, Zhang, XY, Yang, Y, Xue, Q & Dutkiewicz, E 2019, 'Compact Dual-Channel Balanced Filter and Balun Filter Based on Quad-Mode Dielectric Resonator', IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, vol. 67, no. 2, pp. 494-504.
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Xu, K-D, Li, D & Liu, Y 2019, 'High-Selectivity Wideband Bandpass Filter Using Simple Coupled Lines With Multiple Transmission Poles and Zeros', IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS, vol. 29, no. 2, pp. 107-109.
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Xu, K-D, Xu, H & Liu, Y 2019, 'Low-Profile Filtering End-Fire Antenna Integrated With Compact Bandstop Filtering Element for High Selectivity', IEEE ACCESS, vol. 7, pp. 8398-8403.
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Xu, Z, Liu, Y, Li, M & Li, Y 2019, 'Linearly Polarized Shaped Power Pattern Synthesis With Dynamic Range Ratio Control for Arbitrary Antenna Arrays', IEEE ACCESS, vol. 7, pp. 53621-53628.
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Yang, T, Ding, C & Guo, Y 2019, 'A Highly Birefringent and Nonlinear AsSe2-As2S5 Photonic Crystal Fiber with Two Zero-Dispersion Wavelengths', IEEE Photonics Journal.
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OAPA A hybrid AsSe$_{2}$-As$_{2}$S$_{5}$ photonic crystal fiber (PCF) with a solid elliptical core is proposed and studied theoretically by full-vector finite element method (FEM). The core and cladding of the PCF are made of AsSe$_{2}$ and As$_{2}$S$_{5}$ glasses, respectively. Simulation results demonstrates that, at the operating wavelength of 1.55 μm, the proposed PCF not only exhibits a very high birefringence of 0.091 but also has large nonlinear coefficients of 147.8 and 78.2 W$^{-1}$m$^{-1}$ for the X- and Y-polarized (X- and Y-pol) modes, respectively. Moreover, it is able to achieve two zero-dispersion wavelengths (ZDWs) for both the X-pol (1.52 and 2.19 μm) and Y-pol (1.43 and 2.12 μm) modes. The proposed hybrid PCF exhibits excellent polarization maintaining and nonlinearity performance, thus suitable to be used in supercontinuum spectrum generation and polarization maintaining nonlinear signal processing.
Yang, Y, Liu, Y, Ma, X, Li, M, Xu, K-D & Guo, YJ 2019, 'Synthesizing Unequally Spaced Pattern-Reconfigurable Linear Arrays With Minimum Interspacing Control', IEEE ACCESS, vol. 7, pp. 58893-58900.
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Yao, L, Kusakunniran, W, Wu, Q, Zhang, J, Tang, Z & Yang, W 2019, 'Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image', Pattern Recognition Letters.
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© 2019 Gait features have been widely applied in human identification. The commonly-used representations for gait recognition can be roughly classified into two categories: model-free features and model-based features. However, due to the view variances and clothes changes, model-free features are sensitive to the appearance changes. For model-based features, there is great difficulty in extracting the underlying models from gait sequences. Based on the confidence maps and the part affinity fields produced by a two-branch multi-stage CNN network, a new model-based representation, Skeleton Gait Energy Image (SGEI), has been proposed in this paper. Another contribution is that a hybrid representation has been produced, which uses SGEI to remedy the deficiency of model-free features, Gait Energy Image (GEI) for instance. The experimental performances indicate that our proposed methods are more robust to the cloth changes, and contribute to increasing the robustness of gait recognition in the unconstrained environments with view variances and clothes changes.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Multiple Visual Senses for Web Learning', IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 184-196.
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© 1999-2012 IEEE. Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time consuming and labor intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this paper, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then, we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and reranking search results demonstrate the superiority of our proposed approach.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Privileged Information for Enhancing Classifier Learning', IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 436-450.
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© 1992-2012 IEEE. The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instance-level multi-instance learning model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark data sets demonstrate the superiority of our proposed approach.
Yuan, C, Tao, X, Li, N, Ni, W, Liu, RP & Zhang, P 2019, 'Analysis on Secrecy Capacity of Cooperative Non-Orthogonal Multiple Access With Proactive Jamming', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 68, no. 3, pp. 2682-2696.
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Zhang, A, Huang, X, Guo, YJ, Yuan, J & Heath, RW 2019, 'Multibeam for Joint Communication and Sensing Using Steerable Analog Antenna Arrays', IEEE Transactions on Vehicular Technology.
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IEEE Beamforming has great potential for joint communication and sensing (JCAS), which is becoming a demanding feature on many emerging platforms such as unmanned aerial vehicles and smart cars. Although beamforming has been extensively studied for communication and radar sensing respectively, its application in the joint system is not straightforward due to different beamforming requirements by communication and sensing. In this paper, we propose a novel multibeam framework using steerable analog antenna arrays, which allows seamless integration of communication and sensing. Different to conventional JCAS schemes that support JCAS using a single beam, our framework is based on the key innovation of multibeam technology: providing fixed subbeam for communication and packet-varying scanning subbeam for sensing, simultaneously from a single transmitting array. We provide a system architecture and protocols for the proposed framework, complying well with modern packet communication systems with multicarrier modulation. We also propose low-complexity and effective multibeam design and generation methods, which offer great flexibility in meeting different communication and sensing requirements. We further develop sensing parameter estimation algorithms using conventional digital Fourier transform and 1D compressive sensing techniques, matching well with the multibeam framework. Simulation results are provided and validate the effectiveness of our proposed framework, beamforming design methods and the sensing algorithms.
Zhang, J, Wu, Q, Zhang, J, Shen, C, Lu, J & Wu, Q 2019, 'Heritage image annotation via collective knowledge', Pattern Recognition, vol. 93, pp. 204-214.
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© 2019 Elsevier Ltd The automatic image annotation can provide semantic illustrations to understand image contents, and builds a foundation to develop algorithms that can search images within a large database. However, most current methods focus on solving the annotation problem by modeling the image visual content and tag semantic information, which overlooks the additional information, such as scene descriptions and locations. Moreover, the majority of current annotation datasets are visually consistent and only annotated by common visual objects and attributes, which makes the classic methods vulnerable to handle the more diverse image annotation. To address above issues, we propose to annotate images via collective knowledge, that is, we uncover relationships between the image and its neighbors by measuring similarities among metadata and conduct the metric learning to obtain the representations of image contents, we also generate semantic representations for images given collective semantic information from their neighbors. Two representations from different paradigms are embedded together to train an annotation model. We ground our model on the heritage image collection we collected from the library online open data. Annotations on the heritage image collection are not limited to common visual objects, and are highly relevant to historical events, and the diversity of the heritage image content is much larger than the current datasets, which makes it more suitable for this task. Comprehensive experimental results on the benchmark dataset indicate that the proposed model achieves the best performance compared to baselines and state-of-the-art methods.
Zhang, R, Chengpo, M, Min, X, Lixin, X & Xiaofeng, X 2019, 'Facial Component-Landmark Detection with Weakly-supervised LR-CNN', IEEE Access, no. 2169-3536.
Zhang, T, Bao, JF, Zeng, RZ, Yang, Y, Bao, LL, Bao, FH, Zhang, Y & Qin, F 2019, 'Long lifecycle MEMS double-clamped beam based on low stress graphene compound film', Sensors and Actuators, A: Physical, vol. 288, pp. 39-46.
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© 2019 Elsevier B.V. Graphene based three-layer compound film on the silicon substrate is formed by gold deposition of electron beam evaporation (EBE) and graphene transfer. Processed with different high temperature annealing in nitrogen, the film with residual tensile stress of 52.58 MPa at 500 ℃ can be achieved by using an X-ray diffraction (XRD) method. Based on this low stress film, a series of long lifecycle MEMS double-clamped beams (DCBs) are fabricated by the standard MEMS manufacturing technology. The achieved beam can be turned on/off for up to 100 million times at the pull-in voltage of 30 V, which is compatible with the conventional, complementary metal-oxide-semiconductor (CMOS) circuit requirements.
Zheng, D, Zhang, H, Andrew Zhang, J & Li, Y 2019, 'Consensus of the Second-order Multi-agent Systems under Asynchronous Switching with a Controller Fault', International Journal of Control, Automation and Systems, vol. 17, no. 1, pp. 136-144.
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© 2019, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature. Asynchronous switching differing from asynchronous consensus may hinder the system to reach a consensus. This receives very limited attention, especially when the multi-agent systems have a controller fault. In order to analyze the consensus in this situation, this paper studies the consensus of the second-order multi-agent systems under asynchronous switching with a controller fault. We convert the consensus problems under asynchronous switching into stability problems and obtain important results for consensus with the aid of linear matrix inequalities. An example is given to illustrate the effect of asynchronous switching on the consensus, and to validate the analytical results in this paper.
Zhu, H & Guo, YJ 2019, 'Wideband Filtering Phase Shifter Using Transversal Signal-Interference Techniques', IEEE Microwave and Wireless Components Letters, vol. 29, no. 4, pp. 252-254.
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© 2001-2012 IEEE. A wideband filtering phase shifter is presented in this letter using transversal signal-interference techniques. The proposed structure creates two signal-propagation paths by introducing extra coupling between two short-ended stubs. Cascaded coupled-line sections and parallel short-ended stubs can generate a constant passband and multiple transmission zeros (TZs) in the stopband and meanwhile provide the arbitrary value of phase shift within the filtering band range. The positions of TZs and phase shift range can be easily controlled. Explicit relations between the objective filtering band and the related parameters are given, and cases with different in-band phase shift values are studied. To validate the design, a prototype is built, simulated, and tested using microstrip lines. The experimental results agree with the predicted ones, demonstrating that the device can realize any given in-band phase shift with a wideband filtering response.
Zhu, H, Cheng, Z & Guo, YJ 2019, 'Design of wideband in-phase and out-of-phase power dividers using microstrip-to-slotline transitions and slotline resonators', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 4, pp. 1412-1424.
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© 2019 IEEE. A new class of in-phase and out-of-phase power dividers with constant equal-ripple frequency response and wide operating bandwidth is presented in this paper. The proposed design is based on microstrip-to-slotline transitions and slotline resonators. A slotted T-junction is adopted to split the power into two parts and obtain wideband isolation between the two output signals at the same time. The characteristic impedance of the transitions and resonators determines the operating bandwidth and in-band magnitude response. By reversing the placement direction of the slotline-to-microstrip transition, the electrical field is reversed, thus resulting in out-of-phase responses between output ports. A thorough analysis of the relations between the structure and the characteristic functions is provided to guide the selection of parameters of the structure in order to meet the design objectives. In the structure, simulation and measurement are conducted to verify the design method. For both in-phase and out-of-phase cases, more than 110% bandwidth has been achieved with excellent matching at all ports and isolation of output signals. Constant in-band ripple is obtained within the operating band of the power dividers, indicating that the proposed design can realise minimal power deviations, which is extremely desired in wireless systems.
Zuo, Y, Wu, Q, Fang, Y, An, P, Huang, L & Chen, Z 2019, 'Multi-scale Frequency Reconstruction for Guided Depth Map Super-resolution via Deep Residual Network', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Abdollahi, M, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2019, 'A Routing Protocol for SDN-based Multi-hop D2D Communications', 2019 16TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC), 16th IEEE Annual Consumer Communications and Networking Conference (CCNC), IEEE, Las Vegas, NV.
Anwar, M & Gill, A 2019, 'A Review of the Seven Modelling Approaches for Digital Ecosystem Architecture', 21st IEEE Conference on Business Informatics, Moscow, Russia.
Bożejko, W, Chaczko, Z, Nadybski, P & Wodecki, M 2019, 'Meta-heuristic task scheduling algorithm for computing cluster with 2D packing problem approach', Advances in Intelligent Systems and Computing, pp. 74-82.
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© Springer International Publishing AG, part of Springer Nature 2019. In this paper we present a mathematical model and an algorithm for solving a task scheduling problem in computing cluster. The problem is considered as a 2D packing problem. Each multi-node task is treated as a set of separate subtasks with common constrains. For optimization the tabu search metaheuristic algorithm is applied.
Braun, R, Brookes, W, Hadgraft, R & Chaczko, Z 2019, 'Assessment Design for Studio-Based Learning', Proceedings of the Twenty-First Australasian Computing Education Conference, Sydney, Australia, pp. 106-111.
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Chemalamarri, VD, Braun, R, Lipman, J & Abolhasan, M 2018, 'A Multi-agent Controller to enable Cognition in Software Defined Networks', 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), International Telecommunication Networks and Applications Conference, IEEE, Sydney, Australia, pp. 52-56.
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Current SDN controllers are not cognitive. We propose a new architecture for an SDN controller to enable intelligence. The proposed new architecture is based on Multi-agent systems. As a prototype, we have built a MAS-SDN controller using the GOAL agent programming language. We highlight the motivation behind the new architecture, describe the architecture and provide some initial results
Cui, P, Zhang, J, Lu, W, Guo, Y & Zhu, H 2018, 'Sparse Channel Modelling Using Multi-measurement Vector Compressive Sensing', IEEE Global Communications Conference (GLOBECOM), Abu Dahbi, UAE.
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Cui, P, Zhang, J, Lu, W-J, Guo, Y & Zhu, H 2019, 'Influence of Human Body on Massive MIMO Indoor Channels', Kuala Lumpur, Malaysia.
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Cui, P, Zhang, J, Lu, W-J, Guo, YJ & Zhu, H 2019, 'Influence of Human Body on Massive MIMO Indoor Channels', Kuala Lumpur, Malaysia.
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Dasgupta, A, Gill, A & Hussain, F 2019, 'A Review of General Data Protection Regulation for Supply Chain Ecosystem', The 13-th International Conference on Complex, Intelligent, and Software Intensive Systems, Sydney, Australia.
Dasgupta, A, Gill, A & Hussain, F 2019, 'Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems', 8th International Conference on Data Science, Technology and Applications, Prague, Czech Republic.
Ding, C, Wang, K & Guo, YJ 2018, 'Building Antennas on Perovskite Solar Cell (PSC) for Hybrid Solar/EM Wireless Energy Harvesting and Transfer', 2018 Asian Wireless Power Transfer Workshop (AWPT), Sendai, Japan.
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Karmokar, DK, Chen, SL & Guo, YJ 2019, 'Novel Continuous Beam Scanning Leaky-Wave Antennas Using 1-D Mushroom Structure', ISAP 2018 - 2018 International Symposium on Antennas and Propagation.
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© 2018 KIEES. Novel continuous backward-to-forward beam-scanning leaky-wave antennas (LWAs) are designed using a 1-D mushroom structure. An effective method is proposed to suppress the bandgap of a mushroom structure. A smooth transition between the backward and forward leaky modes is achievable by choosing a suitable value of the via inductance, and hence the antenna has design flexibility. The study starts from an equivalent circuit of a unit cell and is verified through simulation and measurement. The measured results confirm a continuous 126° beam scan, starting from -60°, with less than 3 dB gain variation. Moreover, the measured 3dB gain bandwidth is over 58%, which is better than most of the reported LWAs.
Kempegowda, SM & Chaczko, Z 2018, 'Essential Skill of Enterprise Architect Practitioners for Digital Era', 2018 26th International Conference on Systems Engineering (ICSEng), International Conference on Systems Engineering, IEEE, Sydney, Australia, pp. 1-5.
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The technology landscape has evolved from
Mainframe to Digital platform. In this paper, we are proposing
the skills that are essential for an Enterprise Architect to be
successful in the Digital Era.
Kempegowda, SM & Chaczko, Z 2018, 'The optimum number of Principles ideal for Enterprise Architecture practice', 2018 26th International Conference on Systems Engineering (ICSEng), International Conference on Systems Engineering, IEEE, Sydney, Australia, pp. 1-4.
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Every organization defines Principles for Enterprise Architecture (EA) practice. As there is no set standard, the principles identified exceeds the recommended number 20 by TOGAF. More the number of Principles defined it will be ignored by the Enterprise Architects instead referring for their decision making. In this paper, we identify the ideal number of principles that will motivate Architects to refer to perform their task
Le, AT, Tran, LC, Huang, X & Guo, YJ 2019, 'Beam-Based Analog Self-Interference Cancellation with Auxiliary Transmit Chains in Full-Duplex MIMO Systems', The IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019, IEEE, Cannes, France, pp. 1-5.
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Li, Q, Wu, Q, Zhu, C, Zhang, J & Zhao, W 2019, 'Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem'.
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Liao, Q, Xu, M, Wang, D & Holewa, H 2018, 'Fine-Grained Categorization by Deep Part-Collaboration Convolution Net', 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, Digital Image Computing: Techniques and Applications (DICTA), Australia.
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© 2018 IEEE. In part-based categorization context, the ability to learn representative feature from quantitative tiny object parts is of similar importance as to exactly localize the parts. We propose a new deep net structure for fine-grained categorization that follows the taxonomy workflow, which makes it interpretable and understandable for humans. By training customized sub-nets on each manually annotated parts, we increased the state-of-the-art part-based classification accuracy for general fine-grained CUB-200-2011 dataset by 2.1%. Our study shows the proposed method can produce more activation to discriminate detail part difference while maintaining high computing performance by applying a set of strategies to optimize the deep net structure.
Lin, W & Ziolkowski, RW 2018, 'Dual-Functional Electrically Small Huygens Antenna System', ISAP 2018 - 2018 International Symposium on Antennas and Propagation, International Symposium on Antennas and Propagation, IEEE, South Korea.
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© 2018 KIEES. A dual-functional electrically small Huygens antenna system is introduced for wireless power transfer (WPT) and communication applications at 915 MHz. This dual-functional system is facilitated by using two orthogonally-oriented electrically small Huygens linearly polarized (HLP) dipole subsystems. Each HLP antenna consists of two metamaterial-inspired near field resonant parasitic (NFRP) elements, i.e., the capacitive loaded loop (CLL) and the Egyptian axe dipole (EAD). A rectifier circuit is integrated with one of the HLP antennas to facilitate its function as a rectenna for WPT applications. The other HLP antenna serves the communication applications. Due to the large isolation (> 30 dB) between these two HLP subsystems, their functionalities are independent. A successfully fabricated and measured prototype demonstrates that this highly-integrated dual-functional antenna system has excellent performance characteristics. It is electrically-small (ka < 0.77) and produces unidirectional Huygens (cardioid) broadside realized gain patterns with broad beamwidths. It has a high AC to DC conversion efficiency. The antenna system is an excellent, practical candidate for wireless Internet-of-Things (IoT) applications.
Liu, F, Liu, Y, Guo, YJ & Liu, QH 2019, 'Synthesis of Rotated Sparse Linear Dipole Array with Shaped Power Pattern', 2018 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2018.
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© 2018 ACES. A new shaped pattern synthesis method is presented in which element rotations, positions and phases are co-optimized to produce a shaped beam pattern for a sparse dipole array. Compared with conventional shaped pattern synthesis using excitation amplitude and phase optimization, the proposed method can not only reduce the number of elements But also avoid the usage of unequal power dividers. A synthesis example is provided to verify the performance of the proposed method.
Mohammed, F, He, X, Chen, J & Lin, Y 2019, 'A Novel Model for Classification of Parkinson’s Disease: AccuratelyIdentifying Patients for Surgical Therapy', HICSS, Hawaii International Conference on System Sciences, USA, pp. 3741-3750.
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Parkinson’s disease (PD) is a neurodegenerative
disorder and a global health problem that has no
curative therapies. Surgery is a well-established
therapy for controlling symptoms of advanced PD
patients. This paper proposes a streamlined model
to classify PD and to identify appropriate patients for
surgical therapy. The data was gathered from the
Parkinson’s Progressive Markers Initiative consisting of
1080 subjects. Multilayer Perceptron (MLP), Decision
trees, Support Vector Machine and Na¨ıve Bayes are used
as classifiers. MLP achieves the highest accuracy as
compared to other three classifiers. The dataset used
in our experiments is from the Parkinson Progressive
Markers Initiative. With feature selection, it is observed
that the same classification accuracy is achieved with
60% of the attributes as that using all attributes. It
is demonstrated that our classification model for PD
patients produces the most accurate results and achieves
the highest accuracy of 98.13%
More, FJ & Chaczko, Z 2018, 'Non-invasive Methods in the Detection of Coronary Artery Disease', 2018 26th International Conference on Systems Engineering (ICSEng), International Conference on Systems Engineering, IEEE, Sydney, Australia, pp. 1-5.
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Coronary Artery Disease (CAD) is the prime causal factor in cardiovascular disease in the 21st century throughout the world. In Australia, CAD related diseases result in 12% morbidity and mortality rate. This paper summarizes the non-invasive methods of diagnosis of CAD. The association between medical science and biomedical engineering has led to the development of non-invasive methods of diagnosis of CAD. The use of new technology that exploits IoT and Body Area Networks using wearable sensor devices over the patient’s body and medical experts to diagnose CAD. Progression of clinical assessment, diagnosis, and evaluation of CAD have been achieved in the last decade. The current treatment plan for CAD focused on clinical prevention, surgical or a combination of both depending on the severity of disease. The analysis of coronary artery disease, chest pain, and various things involved in the assessment of patient’s history with relieving factors such as risk stratification and non-invasive tests used in diagnosis of CAD.
Shen, J, Wang, Y & Zhang, J 2018, 'Memory optimized Deep Dense Network for Image Super-resolution', Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.
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CNN methods for image super-resolution consume a large number of training-time memory, due to the feature size will not decrease as the network goes deeper. To reduce the memory consumption during training, we propose a memory optimized deep dense network for image super-resolution. We first reduce redundant features learning, by rationally designing the skip connection and dense connection in the network. Then we adopt share memory allocations to store concatenated features and Batch Normalization intermediate feature maps. The memory optimized network consumes less memory than normal dense network. We also evaluate our proposed architecture on highly competitive super-resolution benchmark datasets. Our deep dense network outperforms some existing methods, and requires relatively less computation.
Takalkar, MA, Zhang, H & Xu, M 2019, 'Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 652-664.
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© 2019, Springer Nature Switzerland AG. Micro-expressions are generated involuntarily on a person’s face and are usually a manifestation of repressed feelings of the person. Micro-expressions are characterised by short duration, involuntariness and low intensity. Because of these characteristics, micro-expressions are difficult to perceive and interpret correctly, and they are profoundly challenging to identify and categorise automatically. Previous work for micro-expression recognition has used hand-crafted features like LBP-TOP, Gabor filter, HOG and optical flow. Recent work also has demonstrated the possible use of deep learning for micro-expression recognition. This paper is the first work to explore the use of hand-craft feature descriptor and deep feature descriptor for micro-expression recognition task. The aim is to use the hand-craft and deep learning feature descriptor to extract features and integrate them together to construct a large feature vector to describe a video. Through experiments on CASME, CASME II and CASME+2 databases, we demonstrate our proposed method can achieve promising results for micro-expression recognition accuracy with larger training samples.
Vo, A & Eryk Dutkiewicz 2018, 'Optimal Length-Constrained Segmentation and Subject-Adaptive Learning for Real-time Arrhythmia Detection', IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, Washington, DC, USA.
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Wang, X, Zha, X, Yu, G, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2018, 'Attack and Defence of Ethereum Remote APIs', 2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings, IEEE Globecom Workshops, IEEE, Abu Dhabi, United Arab Emirates, United Arab Emirates.
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© 2018 IEEE. Ethereum, as the first Turing-complete blockchain platform, provides various application program interfaces for developers. Although blockchain has highly improved security, faulty configuration and usage can result in serious vulnerabilities. In this paper, we focus on the security vulnerabilities of the official Go-version Ethereum client (geth). The vulnerabilities are because of the insecure API design and the specific Ethereum wallet mechanism. We demonstrate attacks exploiting these vulnerabilities in an Ethereum testbed. The vulnerabilities are confirmed by the scanning results on the public Internet. Finally, corresponding countermeasures against attacks are provided to enhance the security of the Ethereum platform.
Webb, BA & Ziolkowski, RW 2018, 'Enabling Transmission Through Reentry Plasmas: Simulation of Plasma-SRR Layered Materials', 2018 6TH IEEE INTERNATIONAL CONFERENCE ON WIRELESS FOR SPACE AND EXTREME ENVIRONMENTS (WISEE), IEEE International Conference on Wireless for Space and Extreme Environments, IEEE, Huntsville, AL, pp. 101-103.
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Wilson, K, Alabd, R, Safavi-Naeini, M, Abolhasan, M & Franklin, D 2019, 'Localisation of the Lines of Response in a Continuous Cylindrical Shell PET Scanner', IEEE Engineering in Medicine and Biology Conference, Berlin, Germany.
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This work presents a technique for localising the endpoints of the lines of response in a PET scanner based on a continuous cylindrical shell scintillator. The technique is demonstrated by applying it to a simulation of a sensitivity-optimised continuous cylindrical shell PET system using two novel scintillator materials - a transparent ceramic garnet, GLuGAG:Ce, and a LuF$_3$:Ce-polystyrene nanocomposite. Error distributions for the endpoints of the lines of response in the axial, tangential and radial dimension as well as overall endpoint spatial error are calculated for three source positions; the resultant distribution of error in the placement of the lines of response is also estimated.
Yan, B, Zhao, Q, Zhang, JA, Li, Y & Wang, Z 2019, 'Convergence acceleration for multiobjective sparse reconstruction via knowledge transfer', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 475-487.
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© Springer Nature Switzerland AG 2019. Multiobjective sparse reconstruction (MOSR) methods can potentially obtain superior reconstruction performance. However, they suffer from high computational cost, especially in high-dimensional reconstruction. Furthermore, they are generally implemented independently without reusing prior knowledge from past experiences, leading to unnecessary computational consumption due to the re-exploration of similar search spaces. To address these problems, we propose a sparse-constraint knowledge transfer operator to accelerate the convergence of MOSR solvers by reusing the knowledge from past problem-solving experiences. Firstly, we introduce the deep nonlinear feature coding method to extract the feature mapping between the search of the current problem and a previously solved MOSR problem. Through this mapping, we learn a set of knowledge-induced solutions which contain the search experience of the past problem. Thereafter, we develop and apply a sparse-constraint strategy to refine these learned solutions to guarantee their sparse characteristics. Finally, we inject the refined solutions into the iteration of the current problem to facilitate the convergence. To validate the efficiency of the proposed operator, comprehensive studies on extensive simulated signal reconstruction are conducted.
Yao, L, Kusakunniran, W, Wu, Q, Zhang, J & Tang, Z 2018, 'Robust CNN-based Gait Verification and Identification using Skeleton Gait Energy Image', 2018 Digital Image Computing: Techniques and Applications (DICTA), Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.
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Ye, P, Wang, Y, Xia, Y, An, P & Zhang, J 2019, 'Enhanced saliency prediction via free energy principle', Communications in Computer and Information Science, pp. 31-44.
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© Springer Nature Singapore Pte Ltd 2019. Saliency prediction can be treated as the activity of human brain. Most saliency prediction methods employ features to determine the contrast of an image area relative to its surroundings. However, only few studies have investigated how human brain activities affect saliency prediction. In this paper, we propose an enhanced saliency prediction model via free energy principle. A new AR-RTV model, which combines the relative total variation (RTV) structure extractor with autoregressive (AR) operator, is firstly utilized to decompose an original image into the predictable component and the surprise component. Then, we adopt the local entropy of ‘surprise’ map and the gradient magnitude (GM) map to estimate the component saliency maps-sub-saliency respectively. Finally, inspired by visual error sensitivity, a saliency augment operator is designed to enhance the final saliency combined two sub-saliency maps. Experimental results on two benchmark databases demonstrate the superior performance of the proposed method compared to eleven state-of-the-art algorithms.
Zhang, Z, Wu, Q, Wang, Y & Chen, F 2018, 'Size-Invariant Attention Accuracy Metric for Image Captioning with High-Resolution Residual Attention', 2018 Digital Image Computing: Techniques and Applications (DICTA), Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.
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Zhu, H & Jay Guo, Y 2019, 'Modified wideband tandem couplers with arbitrary coupling coefficient and its implementation in beam-forming networks', Asia-Pacific Microwave Conference Proceedings, APMC, pp. 542-544.
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© 2018 IEICE This paper presents a wideband quadrature coupler using a modified Tandem structure with two stages of cascaded coupled-lines. The proposed design is built in the stripline configuration, which can achieve wide operating bandwidth and excellent matching as well as high isolation across the whole band range. The proposed design with coupling coefficient of 3-dB and 1.77-dB is applied in the design of a wideband beam-forming network for wideband applications. Experimental result has been carried out, verifying that the design approach is useful for wideband applications.
Zhu, H, Cao, Y, Ding, C, Wei, G & Guo, YJ 2018, 'Main Beam Manipulation of Patch Antenna Using Non-Uniform Meta-Surface', International Symposium on Antennas and Propagation (ISAP), 2018 International Symposium on Antennas and Propagation (ISAP), Busan, South Korea.
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Zhu, J, Chu, C, Deng, L, Yang, Y & Li, S 2019, 'Mm-wave high gain substrate integrated cavity excited patch antenna array', Asia-Pacific Microwave Conference Proceedings, APMC, pp. 591-593.
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© 2018 IEICE A wideband and high gain cavity-backed 4x4 patch antenna array is proposed in this paper. Each patch antenna element of the array is enclosed by a rectangular cavity and differentially-fed by the slot underneath. Taking advantages of the higher-order substrate integrated cavity excitation, the elements of the array are efficiently fed with the same amplitude and phase in a simplified feeding mechanism instead of the conventional bulky and lossy power-splitter-based feeding network. Measured results show the antenna bandwidth is from 56 to 63.1-GHz (16.1%) with the peak gain reaching 21.4 dBi. The radiation patterns of the array are very stable over the entire frequency band and the cross-polarizations are as low as -30 dB. These good characteristics demonstrate that the proposed array can be a good candidate for the future 60-GHz communication system applications.
Ziolkowski, RW 2018, 'Metamaterial-inspired Electrically Small Platforms: Enhanced Directivity Properties', 2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Proceedings, IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, IEEE, Boston, MA, USA, pp. 873-874.
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© 2018 IEEE. A variety of near-field resonant parasitic (NFRP) antennas have been developed as electrically small platforms to realize high directivity. These include compact arrays and Huygens dipole and multipole radiating systems. A brief review of these developments and their scattering equivalents will be presented.