Publications

Lam, HK, Ling, SH & Nguyen, HT 2012, Computational intelligence and its applications: Evolutionary computation, fuzzy logic, neural network and support vector machine techniques.
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© 2012 by Imperial College Press. All rights reserved. This book focuses on computational intelligence techniques and their applications — fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

Dehestani, D, Guo, Y, Ling, SS, Su, SW & Nguyen, H 2012, 'Intelligent fault detection and isolation of HVAC system based on online support vector machine' in Lam, HK, Ling, SW & Nguyen, H (eds), Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, World Scientific Publishing, USA, pp. 287-304.
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Heating, Ventilation and Air Conditioning (HVAC) systems are often one of the largest energy consuming parts in modern buildings. Two focused issues of HVAC systems are energy saving and its safety. Regular checking and maintenance are usually the keys to tackle these problems. Due to the high cost of maintenance, preventive maintenance plays an important role. One cost-effective strategy is the development of analytic fault detection and isolation (FDI) modules by online monitoring of the key variables of HVAC systems. This chapter investigates real-time FDI for HVAC systems by using online support vector machine (SVM), by which we are able to train an FDI system with manageable complexity under real-time working conditions. It also proposes a new approach which allows us to detect unknown faults and update the classifier by using these previously unknown faults. Based on the proposed approach, a semi-unsupervised fault detection methodology has been developed for HVAC systems. This chapter also identifies the variables which are the indications of the particular faults we are interested in. Simulation studies are given to show the effectiveness of the proposed online FDI approach.

Lam, HK, Ling, SH & Nguyen, HT 2012, 'Preface', pp. v-viii.
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Ling, SS, San, PP & Nguyen, HT 2012, 'Hypoglycaemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach' in Lam, HK, Ling, SH & Nguyen, HT (eds), Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, World Scientific Publishing, USA, pp. 61-85.
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Insulin-dependent diabetes mellitus is classified as Type 1 diabetes and it can be further classified as immune-mediated or idiopathic. Hypoglycemia is a common and serious side effect of insulin therapy in patients with Type 1 diabetes. In this chapter, we measure physiological parameters continuously to provide a non-invasive hypoglycemia detection for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters of the electrocardiogram (ECG) signal, such as heart rate, corrected QT interval, change of heart rate and change of corrected QT interval, an evolved fuzzy inference model is developed for classification of hypoglycemia. To optimize the rules and membership functions of the fuzzy system, a hybrid particle swarm optimization with wavelet mutation (HPSOWM) is introduced. For the clinical study, 15 children with Type 1 diabetes are volunteered overnight. All the real data sets are collected from the Department of Health, Government of Western Australia and are randomly organized into a training set (10 patients) and testing set (5 patients). The results show that the evolved fuzzy inference system approach performs well in terms of sensitivity and specificity.

Maali, Y & Al-Jumaily, A 2012, 'Signal Selection for Sleep Apnea Classification' in Lecture Notes in Computer Science, Springer Berlin Heidelberg, pp. 661-671.
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Su, SW, Chen, WD, Zhang, Y, Guo, Y, Nguyen, N, Celler, B & Nguyen, HT 2012, 'Nonlinear Modeling Using Support Vector Machine for Heart Rate Response to Exercise' in Lam, HK, Ling, SH & Nguyen, HT (eds), Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, World Scientific Publishing, USA, pp. 255-270.
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In order to accurately regulate cardiovascular response to exercise for an individual exerciser, this study proposed a control oriented modeling approach to depict nonlinear behavior of heart rate response at both the onset and offset of treadmill exercise. With the aim of capturing nonlinear dynamic behaviors, a well designed exercise protocol has been applied for a healthy male subject. Non-invasively measured variables, such as ECG,, body movements and oxygen saturation (SpO2), have been reliably monitored and recorded. Based on several sets of experimental data, both steady state gain and time constant of heart rate response are identified. The nonlinear models relating to steady state gain and time constant (vs. walking speed) were built up based on support vector machine regression (SVR). The nonlinear behaviors at both onset and offset of exercise have been well described by using the established SVR models. The model provides the fundamentals for the optimization of exercise efforts by using model-based optimal control approaches, which is the following step of this study.

Su, SW, Zhang, Y, Celler, BG & Nguyen, HT 2012, 'Machine Learning-based Nonlinear Model Predictive Control for Heart Rate Response to Exercise' in Lam, HK, Ling, S & Nguyen, H (eds), Computational Intelligence and Its Applications Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, World Scientific Publishing, USA, pp. 271-286.
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This study explores control methodologies to handle time variant behavior for heart rate dynamics at onset and offset of exercise. To achieve this goal, a novel switching model predictive control (MPC) algorithm is presented to optimize the exercise effects at both onset and offset of exercise. Specifically, dynamic matrix control (DMC), one of the most popular MPC control algorithms, has been employed as the essential of the optimization of process regulation while switching strategy has been adopted during the transfer between onset and offset of exercise. The parameters of the DMC/MPC controller have been well tuned based on a previously established SVM-based regression model relating to both onset and offset of treadmill walking exercises. The effectiveness of the proposed modeling and control approach has been shown from the regulation of dynamical heart rate response to exercise through simulation using Matlab.

Abedin, MJ & Sanagavarapu, AM 2012, 'A Subspace-Based Compensation Method For The Mutual Coupling In Concentric Circular Ring Arrays For Near-Field Source Localisation', International Journal of Antennas and Propagation, vol. 2012, pp. 1-13.
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We propose a technique for compensating the effect of mutual coupling on parameter estimation that is suitable with any subspace-based super-resolution algorithms. A Concentric circular ring array (CCRA) formed using thin dipole antennas in the receiving

Ahmad, MY & Sanagavarapu, AM 2012, 'Multiple-bridge-loop Reader Antenna For Improved Positioning And Localisation', Electronics Letters, vol. 48, no. 16, pp. 979-980.
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Introduced is a novel HF RFID bridge reader antenna suitable for accurate positioning and localisation. Conventional HF RFID reader antennas can only indicate the presence or absence of a tag(s). The proposed reader antenna uses multiple loops to form mu

Al-Jumaily, A & Olivares, R 2012, 'Bio-Driven System-Based Virtual Reality For Prosthetic And Rehabilitation Systems', Signal, Image and Video Processing, vol. 6, no. 1, pp. 71-84.
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Current prosthetic and rehabilitation devices, used for those who are limbless or born with congenital defects or required rehabilitation, are difficult to use. The users have problems to adapt to their new hosts or receiving any bio-feedback despite reh

Anam, K & Al-Jumaily, A 2012, 'Active Exoskeleton Control Systems: state of the art', Procedia Engineering, vol. 41, no. 2012, pp. 988-994.
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To get a compliant active exoskeleton controller, the force interaction controllers are mostly used in form of either the impedance or admittance controllers. The impedance or admittance controllers can only work if they are followed by either the force or the position controller respectively. These combinations place the impedance or admittance controller as high-level controller while the force or position controller as low-level controller. From the application point of view, the exoskeleton controllers are equipped by task controllers that can be formed in several ways depend on the aims. This paper presents the review of the control systems in the existing active exoskeleton in the last decade. The exoskeleton control system can be categorized according to the model system, the physical parameters, the hierarchy and the usage. These considerations give different control schemes. The main consideration of exoskeleton control design is how to achieve the best control performances. However, stability and safety are other important issues that have to be considered

Aung, Y & Al-Jumaily, A 2012, 'sEMG based ANN for Shoulder Angle Prediction', Procedia Engineering, vol. 41, pp. 1009-1015.
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Many kinds of upper limb rehabilitation systems have been developing for physically weak and/or injured patients to assist their daily life activities and promote their quality of life. Among those systems, EMG controlled rehabilitation systems provide the most effective and fastest ways to restore the lost functions due to such weakness and injuries. This paper presents the prediction of shoulder angle based on acquisition of surface electromyogram (sEMG) signals. Backpropagation neural network (BPNN) controller is developed to predict the angle of shoulder flexion/extension and abduction/adduction movements. Virtual Reality (VR) human model is developed to simulate the predicted shoulder angle which results from BPNN controller. Four sEMG signals are collected from user arm muscles and processed to extract their feature with root mean square (RMS) method. Then, the signals features directed to the neural network as input and the network predicts the angle of the shoulder joint as an output. The angles from BPNN drive the shoulder joint of the VR human model in virtual environment. Experiments were carried out to evaluate the effectiveness of the developed system and it was found that the constructed BPNN model and VR model can well represent the relationship between sEMG and shoulder joint angles and rotation. These positive results elaborate a move to design and develop the EMG controlled upper limb rehabilitation robot system to rehabilitate the physically weak person and paralysed patients.

Beck, D, Brandl, MB, Boelen, L, Unnikrishnan, A, Pimanda, JE & Wong, JWH 2012, 'Signal analysis for genome-wide maps of histone modifications measured by ChIP-seq', BIOINFORMATICS, vol. 28, no. 8, pp. 1062-1069.
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Carroll, AP, Tran, NT, Tooney, PA & Cairns, MJ 2012, 'Alternative MRNA Fates Identified In MicroRNA-associated Transcriptome Analysis', BMC Genomics, vol. 13, pp. 1-19.
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Background: MicroRNA (miRNA) are small non-coding RNA molecules which function as nucleic acid-based specificity factors in the universal RNA binding complex known as the RNA induced silencing complex (RISC). In the canonical gene-silencing pathway, these activated RISC particles are associated with RNA decay and gene suppression, however, there is evidence to suggest that in some circumstances they may also stabilise their target RNA and even enhance translation. To further explore the role of miRNA in this context, we performed a genome-wide expression analysis to investigate the molecular consequences of bidirectional modulation of the disease-associated miRNAs miR-181b and miR-107 in multiple human cell lines.Results: This data was subjected to pathways analysis and correlated against miRNA targets predicted through seed region homology. This revealed a large number of both conserved and non-conserved miRNA target genes, a selection of which were functionally validated through reporter gene assays. Contrary to expectation we also identified a significant proportion of predicted target genes with both conserved and non-conserved recognition elements that were positively correlated with the modulated miRNA. Finally, a large proportion of miR-181b associated genes devoid of the corresponding miRNA recognition element, were enriched with binding motifs for the E2F1 transcription factor, which is encoded by a miR-181b target gene.Conclusions: These findings suggest that miRNA regulate target genes directly through interactions with both conserved and non-conserved target recognition elements, and can lead to both a decrease and increase in transcript abundance. They also multiply their influence through interaction with transcription factor genes exemplified by the observed miR-181b/E2F1 relationship.

Chan, KY, Yiu, C, Dillon, TS, Nordholmand, S & Ling, SS 2012, 'Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization', IEEE Transaction on Industrial Informatics, vol. 8, no. 4, pp. 869-879.
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For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a signi?cant improvement regarding speech recognition accuracy

Chan, M, Nguyen, N, Center, J, Eisman, J & Nguyen, T 2012, 'Absolute Fracture-Risk Prediction By A Combination Of Calcaneal Quantitative Ultrasound And Bone Mineral Density', Calcified Tissue International, vol. 90, no. 2, pp. 128-136.
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Abstract Quantitative ultrasound measurement (QUS) and bone mineral density (BMD) have each been shown to predict fracture risk in women. The present study examined whether a combination of QUS and BMD could improve the predictive accuracy of fracture risk. This is a population- based prospective study which involved 454 women and 445 men aged 6289 years. Femoral neck BMD (FNBMD) was measured by DXA and calcaneal QUS was measured as broadband ultrasound attenuation (BUA) by a CUBA sonometer. Fragility fracture was ascertained by X-ray reports during the follow-up period, which took place between mid-1989 and 2009. During the follow-up period (median 13 years, range 1115), 75 men and 154 women sustained a fragility fracture. In women, the model with FNBMD and BUA had a higher AUC compared to that without BUA (0.73 vs. 0.71 for any fracture, 0.81 vs. 0.77 for hip fracture, and 0.72 vs. 0.70 for vertebral fracture). Reclassification analysis yielded a total net reclassification improvement of 7.3%, 11.1%, and 5.2% for any, hip, and vertebral fractures, respectively. For men, the addition of BUA to FNBMD did not improve the predictive power for any, hip, or vertebral fracture. These results suggest that calcaneal QUS is an independent predictor of fracture risk and that a combination of QUS and BMD measurement could improve the predictive accuracy of fracture risk in elderly women.

Ellison, P, Tipper, JL, Jennings, LM & Fisher, J 2012, 'Biological activity of polyethylene wear debris produced in the patellofemoral joint', PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE, vol. 226, no. H5, pp. 377-383.
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Engels, B, Jannot, G, Remenyi, J, Simard, MJ & Hutvagner, GJ 2012, 'Polypyrimidine tract binding protein (hnRNP I) is possibly a conserved modulator of miRNA-mediated gene regulation.', PLoS One, vol. 7, no. 3, pp. 1-11.
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MiRNAs can regulate gene expression through versatile mechanisms that result in increased or decreased expression of the targeted mRNA and it could effect the expression of thousands of protein in a particular cell. An increasing body of evidence suggest that miRNAs action can be modulated by proteins that bind to the same 39UTRs that are targeted by miRNAs, suggesting that other factors apart from miRNAs and their target sites determine miRNA-modulation of gene expression. We applied an affinity purification protocol using biotinylated let-7 miRNA inhibitor to isolate proteins that are involved in let-7 mediated gene regulation that resulted in an affinity purification of Polypyrimidine Tract Binding protein (PTB). Here we show that PTB interacts with miRNAs and human Argonaute 2 (hAgo2) through RNA as well as identified potential mammalian cellular targets that are co-regulated by PTB and hAgo2. In addition, using genetic approach, we have demonstrated that PTB genetically interacts with Caenorhabditis elegans let-7 indicating a conserved role for PTB in miRNAmediated gene regulation.

Estrada, K, Styrkarsdottir, U, Evangelou, E, Hsu, Y, Duncan, E, Ntzani, E, Oei, L, Albagha, O, Amin, N, Kemp, J, Koller, D, Li, G, Liu, C, Minster, R, Moayyeri, A, Vandenput, L, Willner, D, Xiao, S, Yerges-armstrong, L, Zheng, H, Alonso, N, Eriksson, J, Kammerer, CM, Kaptoge, SK, Leo, PJ, Thorleifsson, G, Wilson, SG, Wilson, JF, Aalto, V, Alen, M, Aragaki, AK, Aspelund, T, Center, J, Dailiana, Z, Duggan, DJ, Garcia, M, Garcia-Giralt, N, Giroux, S, Hallmans, G, Hocking, LJ, Husted, LB, Jameson, KA, Khusainova, R, Su Kim, G, Kooperberg, C, Koromila, T, Kruk, M, Laaksonen, M, LaCroix, A, Lee, S, Leung, PC, Lewis, JR, Masi, L, Mencej-Bedrac, S, Nguyen, T, Nogues, X, Patel, MS, Prezelj, J, Rose, LM, Scollen, S, Siggeirsdottir, K, Smith, AV, Svensson, O, Trompet, S, Trummer, O, van Schoor, NM, Woo, J, Zhu, K, Balcells, S, Brandi, M, Buckley, BM, Cheng, S, Christiansen, C, Cooper, C, Dedoussis, G, Ford, I, Frost, M, Goltzman, D, González-Macías, J, Kähönen, M, Karlsson, M, Khusnutdinova, E, Koh, J, Kollia, P, Langdahl, B, Leslie, WD, Lips, P, Ljunggren, Ö, Lorenc, RS, Marc, J, Mellström, D, Obermayer-Pietsch, B, Olmos, JM, Pettersson-Kymmer, U, Reid, DM, Riancho, JA, Ridker, PM, Rousseau, F, Slagboom, PE, Tang, NL, Urreizti, R, Van Hul, W, Viikari, J, Zarrabeitia, MT, Aulchenko, YS, Castano-Betancourt, M, Grundberg, E, Herrera, L, Ingvarsson, T, Johannsdottir, H, Kwan, T, Li, R, Luben, R, Medina-Gómez, C, Palsson, ST, Reppe, S, Rotter, JI, Sigurdsson, G, van Meurs, JB, Verlaan, D, Williams, FM, Wood, AR, Zhou, Y, Gautvik, KM, Pastinen, T, Raychaudhuri, S, Cauley, JA, Chasman, DI, Clark, GR, Cummings, SR, Danoy, P, Dennison, EM, Eastell, R, Eisman, JA, Gudnason, V, Hofman, A, Jackson, RD, Jones, G, Wouter Jukema, J, Khaw, K, Lehtimäki, T, Liu, Y, Lorentzon, M, McCloskey, E, Mitchell, BD, Nandakumar, K, Nicholson, GC, Oostra, BA, Peacock, M, Pols, HA, Prince, RL, Raitakari, O, Reid, IR, Robbins, J, Sambrook, P, Sham, P, Shuldiner, AR, Tylavsky, FA, van Duijn, CM, Wareham, NJ, Cupples, A, Econs, MJ, Evans, DM, Harris, TB, Kung, A, Psaty, BM, Reeve, J, Spector, TD, Streeten, EA, Carola Zillikens, M, Thorsteinsdottir, U, Ohlsson, C, Karasik, D, Richards, JB, Brown, MA, Stefansson, K, Uitterlinden, A, Ralston, SH, Ioannidis, JP, Kiel, DP & Rivadeneira, F 2012, 'Genome-Wide Meta-Analysis Identifies 56 Bone Mineral Density Loci And Reveals 14 Loci Associated With Risk Of Fracture', Nature Genetics, vol. 44, no. 5, pp. 491-503.
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Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 x 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 x 10(-4), Bonferroni corrected), of which six reached P < 5 x 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

Fisher, J, Al Hajjar, M, Williams, S, Tipper, J, Ingham, E & Jennings, L 2012, '(v) Simulation and measurement of wear in metal-on-metal bearings in vitro- understanding the reasons for increased wear', Orthopaedics and Trauma, vol. 26, no. 4, pp. 253-258.
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A new Stratified Approach For Enhanced Reliability (SAFER) pre-clinical simulation testing of joint prostheses has been described in a preceding paper in this volume. The application of SAFER in vitro simulation and testing to metal-on-metal bearings is described in this review paper. The review aims to provide further understanding of the reasons for, and causes of, increased wear in metal-on-metal hips in a proportion of patients. Variation in positioning (mal-positioning) of the head and cup in hip prostheses results in the head contacting the rim of the cup and producing increased wear. Variation in both translational and rotational positioning has been investigated. Variation in translational positioning of the centres of the head and cup, which is not detected on radiographs, is a frequent occurrence clinically and can result in a substantial increase in wear rate. The variation in translational positioning acts synergistically with variation in rotational positioning to produce substantial increases in wear. These recent findings are consistent with the wear mechanisms and formation of stripe wear reported for ceramic-on-ceramic bearings over a decade ago, and provide insight into the reasons for the variation and increases in the wear rate found clinically in metal-on-metal hips in specific patients, which may cause premature failure. © 2012 Elsevier Ltd.

Gargiulo, GD, Cohen, G, McEwan, AL, Oh, TI, Mohamed, A, Tapson, J, Nguyen, DT, van Schaik, A & Wabnitz, A 2012, 'Active electrode design suitable for simultaneous EIT and EEG', ELECTRONICS LETTERS, vol. 48, no. 25.
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Ho-pham, L, Mai, L, Pham, H, Nguyen, ND & Nguyen, T 2012, 'Reference ranges for vertebral heights and prevalence of asymptomatic (undiagnosed) vertebral fracture in Vietnamese men and women', Archives of Osteoporosis, vol. 7, pp. 257-266.
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Summary Based on quantitative measurements of vertebral heights, the prevalence of undiagnosed vertebral fracture in Vietnamese men and women aged 50 years and older was 23 and 26 %, respectively Background The present study sought to develop reference ranges for vertebral heights and to determine the prevalence of asymptomatic vertebral fracture in Vietnamese men and women. Methods The study included 312 men and 657 women aged over 18 who were randomly selected from the community. The ImageJ software program was used to measure anterior height (Ha), middle height (Hm), and posterior height (Hp) for each vertebra (T4 to T12 and L1 to L5). Four vertebral ratios were determined: Ha/Hp, Hm/Hp, Hp/Hp+1, and Hp/Hp -1. Reference ranges for the ratios were then developed by the method of Winsorized mean. Vertebral fracture was diagnosed as a ratio lower than three standard deviations from the normal mean. Results For any given vertebra, Ha, Hm, and Hp in men were higher than in women. In both sexes, Ha and Hm increased in a stepwise fashion from T4 to L3 and then gradually reduced in L4L5. Vertebral heights for T4T9 tended to decrease, while vertebral height for T10L5 tended to increase with advancing age. Among those aged over 50 years, the prevalence of vertebral fracture in men was 23.3 % (95 % confidence interval (CI) 16.831.3 %) which was lower than that in women (26.5 %; 95 % CI 22.431.1 %). The prevalence increased with advancing age, such that from the age of over 70, 41 % of men and 42 % women had at least one vertebral fracture. Conclusion One fourth of Vietnamese men and women aged 50 years and older have a symptomatic vertebral fracture. This prevalence is equivalent to that in Caucasian populations.

Ho-Pham, LT, Vu, BQ, Lai, TQ, Nguyen, ND & Nguyen, TV 2012, 'Vegetarianism, bone loss, fracture and vitamin D: a longitudinal study in Asian vegans and non-vegans', EUROPEAN JOURNAL OF CLINICAL NUTRITION, vol. 66, no. 1, pp. 75-82.
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Jennings, LM, Al-Hajjar, M, Brockett, CL, Williams, S, Tipper, JL, Ingham, E & Fisher, J 2012, '(iv) Enhancing the safety and reliability of joint replacement implants', Orthopaedics and Trauma, vol. 26, no. 4, pp. 246-252.
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A new Stratified Approach For Enhanced Reliability (SAFER) pre-clinical simulation testing of joint prostheses is presented in this article. The aim of this approach is preclinical systematic testing of wear performance in the much wider envelope of conditions found clinically rather than relying only on the standard testing conditions that are currently used. The approach includes variations in surgical delivery, variations in kinematics, variations in the patient population and degradation of the biomaterial properties. Clinical experience of existing prostheses has been used to validate the new in vitro methods. © 2012 Elsevier Ltd.

Lai, JC, Leung, FH, Ling, SS & Shi, EC 2012, 'An Improved differential evolution and its industrial application', Journal of Intelligence Learning Systems and Application, vol. 4, no. 2, pp. 81-97.
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In this paper, an improved Differential Evolution (DE) that incorporates double wavelet-based operations is proposed to solve the Economic Load Dispatch (ELD) problem. The double wavelet mutations are applied in order to enhance DE in exploring the solution space more effectively for better solution quality and stability. The first stage of wavelet operation is embedded in the DE mutation operation, in which the scaling factor is governed by a wavelet function. In the second stage, a wavelet-based mutation operation is embedded in the DE crossover operation. The trial population vectors are modified by the wavelet function. A suite of benchmark test functions is employed to evaluate the performance of the proposed DE in different problems. The result shows empirically that the proposed method out-performs signifycantly the conventional methods in terms of convergence speed, solution quality and solution stability. Then the proposed method is applied to the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) problem, which is a process to share the power demand among the online generators in a power system for minimum fuel cost. Two different conditions of the ELD problem have been tested in this paper. It is observed that the proposed method gives satisfactory optimal costs when compared with the other techniques in the literature

Ling, SH, San, PP, Nguyen, HT & Leung, FHF 2012, 'Non-invasive nocturnal hypoglycemia detection for insulin-dependent diabetes mellitus using genetic fuzzy logic method', International Journal of Computational Intelligence and Applications, vol. 11, no. 4, pp. 1-17.
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Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection. © 2012 Imperial College Press.

Ling, SS & Nguyen, HT 2012, 'Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model', Artificial Intelligence in Medicine, vol. 55, no. 3, pp. 177-184.
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Introduction: Low blood glucose (hypoglycemia) is a common and serious side effect of insulin therapy in patients with diabetes. This paper will make a contribution to knowledge in the modeling and design of a non-invasive hypoglycemia monitor for patients with type 1 diabetes mellitus (T1DM) using a fuzzy-reasoning system. Methods: Based on the heart rate and the corrected QT interval of the electrocardiogram (ECG) signal, we have developed a hybrid particle-swarm-optimization-based fuzzy-reasoning model to recognize the presence of hypoglycemic episodes. To optimize the fuzzy rules and the fuzzy-membership functions, a hybrid particle-swarm-optimization with wavelet mutation operation is investigated. Conclusion: We have investigated the detection for the natural occurrence of nocturnal hypoglycemic episodes in T1DM using a hybrid particle-swarm-optimization-based fuzzy-reasoning model with physiological parameters. In this study, no restricted environment (e.g. patient's dietary requirements) is required. Furthermore, the sampling time is between 5 and 10 min. To conclude, we have shown that the testing performances of the proposed algorithm for detection of advanced hypoglycemic and hypoglycemic episodes for T1DM patients are satisfactory.

Ling, SSH, Nguyen, H & Lam, HK 2012, 'Computational intelligence in health technologies.', International journal of bioinformatics research and applications, vol. 8, no. 5-6, pp. 323-324.

Maali, Y & Al-Jumaily, A 2012, 'Automated Detecting and Classifying of Sleep Apnea Syndrome Based on Genetic- SVM', International Journal of Hybrid Intelligent Systems, vol. 9, no. 4, pp. 203-210.
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Sleep apnea (SA) is one of the common sleep disorders. It has several consequences that can affect daily life activities. The common diagnose procedure is carried out through an overnight sleep test. The test usually includes of several bio-signals recordings that are used to detect this syndrome. The conventional approach of detecting the sleep apnea uses a manual analysis of most bio-signals to achieve reasonable accuracy. The manual process of this test, is highly cost and time consuming. This paper presents a novel automatic system for detecting and classifying apnea events by using just a few of bio-signals that are related to breathe defect. This method uses only the air flow, thoracic and abdominal respiratory movement as inputs for the system. The proposed technique consists of four main parts which are; signal segmentation, feature generation, feature selection and data reduction based on genetic SVM, and classification. Statistical analyzes on the attained results show efficiency of this system and its superiority versus previous methods even with more bio-signals as input.

Maali, Y & Al-Jumaily, A 2012, 'Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection', International Journal of Machine Learning and Computing, vol. 2, no. 5, pp. 685-688.
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Sleep Apnea (SA) is one of the most common andimportant part of sleep disorders. Unfortunately, sleep apneamay be going undiagnosed for years, because of the personsunawareness. The common diagnose procedure usuallyrequired an overnight sleep test. During the test, a recording ofmany biosignals, which related to breath, are obtained bypolysomnography machine to detect this syndrome. Themanual process for detecting the sleep Apnea by analysis therecording data is highly cost and time consuming. So, severalworks tried to develop systems that achieve this automatically.This paper proposes a genetic fuzzy approach for detectingApnea/Hypopnea events by using Air flow, thoracic andabdominal respiratory movement signals and Oxygen desaturation as the inputs. Results show efficiently of this approach.

Martiniello-Wilks, R, Suurbach, JH, Tran, N, Mcgowan, EM, Simpson, A, Larsen, SR & Russell, PJ 2012, 'Cellular and genetic medicines advancing the treatment of prostate cancer', JOURNAL OF TISSUE ENGINEERING AND REGENERATIVE MEDICINE, vol. 6, pp. 289-289.

McGowan, EM, Tran, NT, Alling, N, Yagoub, D, Sedger, LM & Martiniello-Wilks, R 2012, 'P14ARF Post-transcriptional Regulation Of Nuclear Cyclin D1 In Mcf-7 Breast Cancer Cells: Discrimination Between A Good And Bad Prognosis?', Plos One, vol. 7, no. 7, pp. 1-16.
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As part of a cell's inherent protection against carcinogenesis, p14ARF is upregulated in response to hyperproliferative signalling to induce cell cycle arrest. This property makes p14ARF a leading candidate for cancer therapy. This study explores the con

Montavon, C, Gloss, B, Warton, K, Barton, C, Statham, A, Scurry, J, Tabor, B, Nguyen, T, Qu, W, Samimi, G, Hacker, N, Sutherland, R, Clark, S & O'brien, P 2012, 'Prognostic And Diagnostic Significance Of DNA Methylation Patterns In High Grade Serous Ovarian Cancer', Gynecologic Oncology, vol. 124, no. 3, pp. 582-588.
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Objective. Altered DNA methylation patterns hold promise as cancer biomarkers. In this study we selected a panel of genes which are commonly methylated in a variety of cancers to evaluate their potential application as biomarkers for prognosis and diagnosis in high grade serous ovarian carcinoma (HGSOC); the most common and lethal subtype of ovarian cancer. Methods. The methylation patterns of 10 genes (BRCA1, EN1, DLEC1, HOXA9, RASSF1A, GATA4, GATA5, HSULF1, CDH1, SFN) were examined and compared in a cohort of 80 primary HGSOC and 12 benign ovarian surface epithelium (USE) samples using methylation-specific headloop suppression PCR. Results. The genes were variably methylated in primary HGSOC, with HOXA9 methylation observed in 95% of cases. Most genes were rarely methylated in benign USE, with the exception of SFN which was methylated in all HGSOC and benign USE samples examined. Methylation of DLEC1 was associated with disease recurrence, independent of tumor stage and suboptimal surgical debulking (HR 3.5 (95% CI:1.10-11.07), p=0.033). A combination of the methylation status of HOXA9 and EN1 could discriminate HGSOC from benign USE with a sensitivity of 98.8% and a specificity of 91.7%, which increased to 100% sensitivity with no loss of specificity when pre-operative CA125 levels were also incorporated. Conclusions. This study provides further evidence to support the feasibility of detecting altered DNA methylation patterns as a potential diagnostic and prognostic approach for HGSOC.

Moradi, A, Zhand, S, Ghaemi, A, Javid, N & Tabarraei, A 2012, 'Mutations in the S gene region of hepatitis B virus genotype D in Golestan Province-Iran', VIRUS GENES, vol. 44, no. 3, pp. 382-387.
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Nguyen, DT, Jin, C, Thiagalingam, A & McEwan, AL 2012, 'A review on electrical impedance tomography for pulmonary perfusion imaging', PHYSIOLOGICAL MEASUREMENT, vol. 33, no. 5, pp. 695-706.
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Nguyen, H, Pourian, M, Bystrom, B, Dahlin, I, Duc, P, Nguyen, T, Von Schoultz, B & Hirschberg, A 2012, 'Low Aglycone Content In Commercial Soy Drink Products', Asia Pacific Journal Of Clinical Nutrition, vol. 21, no. 1, pp. 52-56.
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The effectiveness of soy isoflavones to prevent bone loss in postmenopausal women is controversial. While consumption of soy in Vietnam is very high, we recently reported a prevalence of osteoporosis comparable to that of many Western populations. In the present study, we analyzed the isoflavone content of soy drink products commercially available in Vietnam and Sweden, and we also compared these products to "home-made" soy drink from beans of different origin. The amounts of the bioactive aglycones (daidzein, glycitein and genistein) and their glycoside isomers were quantified by high-pressure liquid chromatography. We found that the total isoflavone content was low in all preparations, around 70-100 mg/L and of this only 10% were bioactive aglycones. Of these, the Vietnamese products contained significantly lower levels of glycitein than the products from Sweden and "home-made" soy drink preparations. The results show that consumption of several liters of soy drink per day would be needed to achieve threshold levels for a protective effect on bone. There was no significant association between total protein and isoflavone content in different products. Accurate labeling of soy drink and other products eg of aglycone and glycoside content would allow health professionals and researchers to better explore the possible benefits of soy in dietary intervention studies.

Nguyen, H, Von Schoultz, B, Nguyen, T, Dzung, D, Duc, P, Thuy, V & Hirschberg, A 2012, 'Vitamin D Deficiency In Northern Vietnam: Prevalence, Risk Factors And Associations With Bone Mineral Density', Bone, vol. 51, no. 6, pp. 1029-1034.
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Purpose: Vitamin D deficiency has been linked to osteoporosis and also to the risk of cancer, autoimmune disorders and cardiovascular diseases. This study sought to determine the prevalence of, and risk factors for, vitamin D deficiency and its relationship with bone mineral density (BMD) in a Vietnamese population. Methods: This cross-sectional study involved 269 women and 222 men aged 13-83 years, who were randomly selected from urban and rural areas in northern Vietnam. Serum concentrations of 25-hydroxy-vitamin D [25(OH)D] and parathyroid hormone (PTH) were measured by electrochemiluminescence immunoassay. Vitamin D deficiency was defined as serum 25(OH)D levels below 20 ng/mL. BMD was measured by dual X-ray absorptiometry. Results: The prevalence of vitamin D deficiency in women was 30%, almost two-fold higher than in men (16%). Significant predictors of vitamin D deficiency in women were urban residency (p<0.01) and age less than 30 years (p<0.01), whereas use of contraceptive pills was protective (p<0.01). In men, winter season was the only significant predictor of vitamin D deficiency (p<0.01). In multiple linear regression analysis, serum levels of 25(OH)D were positively associated with BMD in both women (p<0.001) and men (p<0.001). Conclusions: These data suggest that the prevalence of vitamin D deficiency is high in the Vietnamese population, and that part of this prevalence could be explained by low exposure to sunlight (urban residency and winter season). The high prevalence of vitamin D deficiency should raise the awareness of potentially important health issues such as osteoporosis within the Vietnamese society

Nguyen, TV & Eisman, JA 2012, 'Genetics and the individualized prediction of fracture.', Current Osteoporosis Reports, vol. 10, no. 3, pp. 236-244.
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Recent genome-wide association studies have identified many genetic variants associated with fracture risk. These genetic variants are common in the general population but have very modest effect sizes. A remaining challenge is to translate these genetic variant discoveries to better predict the risk of fracture based on an individual's genetic profile (ie, individualized risk assessment). Empirical and simulation studies have shown that 1) the utility of a single genetic variant for fracture risk assessment is very limited; but 2) a profile of 50 genetic variants, each with odds ratio ranging from 1.02 to 1.15, can improve the accuracy of fracture prediction and classification beyond that obtained by conventional clinical risk factors. These results are consistent with the view that genetic profiling, when integrated in existing risk assessment models, can inform a more accurate prediction of fracture risk in an individual.

Nguyen, TV, Eisman, JA, Center, JR, Pocock, NA, Jones, G, March, L, Clifton-Bligh, R, Naganathan, V & Seibel, MJ 2012, 'In Memoriam: Philip Neil Sambrook OBITUARY', OSTEOPOROSIS INTERNATIONAL, vol. 23, no. 7, pp. 1835-1836.
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Nuryani, N, Ling, SS & Nguyen, HT 2012, 'Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection', Annals Of Biomedical Engineering, vol. 40, no. 4, pp. 934-945.
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Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Oldfield, CC, McHenry, CR, Clausen, PD, Chamoli, U, Parr, WCH, Stynder, DD & Wroe, S 2012, 'Finite element analysis of ursid cranial mechanics and the prediction of feeding behaviour in the extinct giant Agriotherium africanum', JOURNAL OF ZOOLOGY, vol. 286, no. 2, pp. 163-170.
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Parr, WCH, Wroe, S, Chamoli, U, Richards, HS, McCurry, MR, Clausen, PD & McHenry, C 2012, 'Toward integration of geometric morphometrics and computational biomechanics: New methods for 3D virtual reconstruction and quantitative analysis of Finite Element Models', JOURNAL OF THEORETICAL BIOLOGY, vol. 301, pp. 1-14.
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Pickering, H, Lee, M, Moseley, GL, Minei, P & Lin, CY 2012, 'Sensory disturbances evoked by immobilization of an experimentally inflamed limb', Clinical Neurophysiology, vol. 137, no. 7, pp. e71-e72.
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Rahman, MA & Al-Jumaily, A 2012, 'Design and Development of a Hand Exoskeleton for Rehabilitation Following Stroke', Procedia Engineering, vol. 41, pp. 1028-1034.
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In Australia, a major cause of disability is the stroke and it is the second highest cause of death after coronary heart disease. Studies have predicted that form 2008 to 2017 more than 0.5 million people is likely to suffer from stroke in Australia. In addition, after stroke 88% of the patients suffer from disability and stays at home. In this paper, a post stroke therapeutic device has been designed for hand motor function rehabilitation that a stroke survivor can use for bilateral movement practice. Out of twenty-one degrees of freedom of hand fingers, the prototype of the hand exoskeleton allowed fifteen degrees of freedom. The device is designed to be portable so that the user can engage in other activities while using the device. A prototype of the device is fabricated to provide complete flexion and extension motion of individual fingers of the left hand (impaired hand) based on the movements of the right hand (healthy hand) fingers. In addition, testing of the device on a healthy subject was conducted to validate if the design met the requirements.

Rana, M & Sanagavarapu, AM 2012, 'Segmented-Locally-One-Dimensional-FDTD Method for EM Propagation Inside Large Complex Tunnel Environments', IEEE Transactions On Magnetics, vol. 48, no. 2, pp. 223-226.
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We propose a novel segmented locally one dimensional finite difference time domain (S-LOD-FDTD) method for modeling the electromagnetic wave propagation inside electrically large tunnels. The proposed S-LOD-FDTD method reduces the computational resources by dividing the problem space into segments. To validate this method, we simulate the propagation in real tunnels and compare the results with the published measured data. The comparisons reveal that the proposed method can predict the fields accurately in real, large tunnels at longer ranges with significant savings in execution time and memory.

Roohani-Esfahani, SI, Lu, ZF, Li, JJ, Ellis-Behnke, R, Kaplan, DL & Zreiqat, H 2012, 'Effect of self-assembled nanofibrous silk/polycaprolactone layer on the osteoconductivity and mechanical properties of biphasic calcium phosphate scaffolds', ACTA BIOMATERIALIA, vol. 8, no. 1, pp. 302-312.
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Su, SW, Anderson, B, Chen, W & Nguyen, HT 2012, 'Multi-realization Of Nonlinear Systems', Automatica, vol. 48, no. 7, pp. 1455-1461.
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The system multi-realization problem is to find a state-variable realization for a set of systems, sharing as many parameters as possible. A multi-realization can be used to efficiently implement a multi-controller architecture for multiple model adaptive control. We extend the linear multi-realization problem to nonlinear systems. The problem of minimal multi-realization of a set of MIMO systems is introduced and solved for static feedback linearizable systems.

Suñer, S, Tipper, JL & Emami, N 2012, 'Biological effects of wear particles generated in total joint replacements: Trends and future prospects', Tribology - Materials, Surfaces and Interfaces, vol. 6, no. 2, pp. 39-52.
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Joint replacements have considerably improved the quality of life of patients with joints damaged by disease or trauma. However, problems associated with wear particles generated due to the relative motion between the components of the bearing are still present and can lead to the eventual failure of the implant. The biological response to wear debris affects directly the longevity of the prosthesis. The identification of the mechanisms by which cells respond to wear debris and how particles distribute into the human body may provide valuable information for the long term success of artificial joints. During the last few decades, orthopaedic research has been focused on predicting the in vivo performance of joint replacements. However, the exact relationship between material physicochemical properties and inflammatory response has not been fully understood. Laboratory wear simulators provide an accurate prediction of implant wear performance. Though, particles generated from such wear simulators require validation to compare them with particles extracted from peri-implant tissues. This review focuses initially on the current status of total joint replacements (hard on soft and hard on hard bearings) as well as on the tribological behaviour of the potential materials currently under investigation. Then, the correspondence between particles observed in vivo and those generated in vitro to predict the cellular response to wear debris is discussed. Finally, the biological effects of the degradation products generated by wear and corrosion are described. © 2012 W. S. Maney & Son Ltd.

Suurbach, JH, McGowan, EM, Simpson, A, Tran, N & Martiniello-Wilks, R 2012, 'A unique bioluminescent prostate cancer mouse model for the evaluation of stem-cell based gene therapy', JOURNAL OF TISSUE ENGINEERING AND REGENERATIVE MEDICINE, vol. 6, pp. 288-288.

Tavakoli, J, Miar, S, Zadehzare, MM & Akbari, H 2012, 'Evaluation of effectiveness of herbal medication in cancer care: A review study', Iranian Journal of Cancer Prevention, vol. 5, no. 3, pp. 144-156.
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Based on a common belief, herbal medicine with the least possible side effects should be the center of attention in cancer care; however, in many cases they have not been properly studied with reliable clinical trials in human subjects. In this review, it was attempted to identify the available evidence on the use and clinical effects of herbs in cancer care. The research consists of two major parts including immunomodulator and chemopreventive herbal compounds whose mechanism, biological response, anticancer element of extract and related benefits were completely studied. Also, the safety of herbal anticancer compounds was discussed. Although the use of herbal medicines in treating cancer shows less chemotherapy-induced, toxicity, more researches are required to reach their full therapeutic potentials.

Vicars, R, Prokopovich, P, Brown, TD, Tipper, JL, Ingham, E, Fisher, J & Hall, RM 2012, 'The Effect of Anterior-Posterior Shear on the Wear of CHARITE Total Disc Replacement', SPINE, vol. 37, no. 9, pp. E528-E534.
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Warkiani, ME, Lou, C-P, Liu, H-B & Gong, H-Q 2012, 'A high-flux isopore micro-fabricated membrane for effective concentration and recovering of waterborne pathogens', BIOMEDICAL MICRODEVICES, vol. 14, no. 4, pp. 669-677.
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Waterloo, S, Ahmed, LA, Center, JR, Eisman, JA, Morseth, B, Nguyen, ND, Nguyen, T, Sogaard, AJ & Emaus, N 2012, 'Prevalence of vertebral fractures in women and men in the population-based Tromso Study.', BMC Musculoskeletal Disorders, vol. 13, pp. 1-9.
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BACKGROUND: Osteoporotic vertebral fractures are, as the hip fractures, associated with increased morbidity and mortality. Norway has one of the highest reported incidences of hip fractures in the world. Because of methodological challenges, vertebral fractures are not extensively studied. The aim of this population based study was to describe, for the first time, the age- and sex specific occurrence of osteoporotic vertebral fractures in Norway. METHODS: Data was collected in the Tromso Study, 2007/8 survey. By the use of dual x-ray absorptiometry (GE Lunar Prodigy) vertebral fracture assessments were performed in 2887 women and men aged from 38 to 87 years, in addition to measurements of bone mineral density at the femoral sites. Information on lifestyle was collected through questionnaires. Comparisons between fractures and non-fractures were done sex stratified, by univariate analyses, adjusting for age when relevant. RESULTS: The prevalence of vertebral fractures varied from about 3% in the age group below 60 to about 19% in the 70+ group in women, and from 7.5% to about 20% in men, with an overall prevalence of 11.8% in women and 13.8% in men (p = 0.07). Among those with fractures, only one fracture was the most common; two and more fractures were present in approximately 30% of the cases. Fractures were seen from the fourth lumbar to the fifth thoracic vertebrae, most common between first lumbar and sixth thoracic vertebrae. The most common type of fracture was the wedge type in both sexes. Bone mineral density at the hip differed significantly according to type of fracture, being highest in those with wedge fractures and lowest in those with compression fractures. CONCLUSIONS: The prevalence of vertebral fractures increased by age in women and men, but the overall prevalence was lower than expected, considering the high prevalence of hip and forearm fractures in Norway. In both sexes, the wedge type was the fracture type most frequently observed and most ...

Waterloo, S, Nguyen, T, Ahmed, LA, Center, J, Morseth, B, Nguyen, ND, Eisman, J, Søgaard, AJ & Emaus, N 2012, 'Important risk factors and attributable risk of vertebral fractures in the population-based Tromsø study', Bmc Musculoskeletal Disorders, vol. 13.
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Background: Vertebral fractures, the most common type of osteoporotic fractures, are associated with increased risk of subsequent fracture, morbidity, and mortality. The aim of this study was to examine the contribution of important risk factors to the variability in vertebral fracture risk. Methods. Vertebral fracture was ascertained by VFA method (DXA, GE Lunar Prodigy) in 2887 men and women, aged between 38 and 87 years, in the population-based Tromsø Study 2007/2008. Bone mineral density (BMD; g/cm2) at the hip was measured by DXA. Lifestyle information was collected by questionnaires. Multivariable logistic regression model, with anthropometric and lifestyle factors included, was used to assess the association between each or combined risk factors and vertebral fracture risk. Population attributable risk was estimated for combined risk factors in the final multivariable model. Results: In both sexes, age (odds ratio [OR] per 5 year increase: 1.32; 95% CI 1.19-1.45 in women and 1.21; 95% CI 1.10-1.33 in men) and BMD (OR per SD decrease: 1.60; 95% CI 1.34-1.90 in women and1.40; 95% CI 1.18-1.67 in men) were independent risk factors for vertebral fracture. At BMD levels higher than 0.85 g/cm2, men had a greater risk of fracture than women (OR 1.52; 95% CI 1.14-2.04), after adjusting for age. In women and men, respectively, approximately 46% and 33% of vertebral fracture risk was attributable to advancing age (more than 70 years) and low BMD (less than 0.85 g/cm 2), with the latter having a greater effect than the former. Conclusions: These data confirm that age and BMD are major risk factors for vertebral fracture risk. However, in both sexes the two factors accounted for less than half of fracture risk. The identification of individuals with vertebral fracture is still a challenge

Yang, S, Nguyen, N, Eisman, J & Nguyen, T 2012, 'Association Between Beta-Blockers And Fracture Risk: A Bayesian Meta-Analysis', Bone, vol. 51, no. 5, pp. 969-974.
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Background: The association between beta-blockers (BB) and fracture risk is controversial, due largely to conflicting findings from previous studies. The present study sought to evaluate the effect of BB on fracture risk by using a Bayesian meta-analysis approach. Methods and results: We systematically retrieved 13 observational studies on the association between BB use and fracture risk. This meta-analysis involved more than 907.000 men and women with mean/median age of individual studies ranging from 43 to 81 years. We used a hierarchical Bayesian random effects model to synthesize the results. BB use was associated with an average 17% reduction in the risk of any fracture (risk ratio [RR] 0.83: 95% credible interval [Crl]: 0.71-0.93), hip fracture (RR 0.83: 95% Crl: 0.70-0.92) and vertebral fracture (RR 0.81:95% Crl: 0.61-0.99). The probability that BB use reduces fracture risk by at least 10% was 0.91. Conclusions: Beta-blockers are associated with reduced risk of fracture in older adults, but the effect size is likely to be modest. (C) 2012 Elsevier Inc. All rights reserved.

Yuwono, M, Moulton, BD, Su, SW, Celler, BG & Nguyen, HT 2012, 'Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems', Biomedical Engineering Online, vol. 11, pp. 1-11.
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Background: Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. Method: We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. Results: Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion: The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall detection systems.

Zhang, Y, Chen, W, Su, SW & Celler, BG 2012, 'Nonlinear modelling and control for heart rate response to exercise', International Journal of Bioinformatics Research and Applications, vol. 8, no. 5-6, pp. 397-416.
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In order to accurately regulate cardiovascular response to exercise for individual exerciser, this study proposes a modelling and control integrated approach based on ε-insensitive Support Vector Regression (SVR) and switching control strategy. Firstly, a control oriented modelling approach is proposed to depict nonlinear behaviours of cardiovascular response at both onset and offset of treadmill exercises by using support vector machine regression. Then, based on the established nonlinear time-variant model, a novel switching Model Predictive Control (MPC) algorithm has been proposed for the optimisation of exercise efforts. The designed controller can take into account both coefficient drifting and parameter jump by embedding the identified model coefficient into the optimiser and adopting switching strategy during the transfer between onset and offset of exercises. The effectiveness of the proposed modelling and control approach was shown from the regulation of dynamical heart rate response to exercise through simulation using MATLAB.

Zhou, J, Su, SW, Guo, A & Chen, W 2012, 'Abnormalities Detection of IMU based on PCA in motion monitoring', Applied Mechanics and Materials, vol. 224, no. 1, pp. 533-538.
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Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking peoples daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotellings T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible.

Ahmad, MY & Mohan, AS 2012, 'Multi-loop-bridge antenna for improved positioning using HF-RFID', 2012 IEEE Antennas and Propagation Society International Symposium (APSURSI), IEEE Antennas-and-Propagation-Society International Symposium (APSURSI), IEEE, Chicago, IL, USA.
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This paper describes a novel HF RFID multi loop bridge reader antenna for improved positioning and localization. Bridge configuration is employed to split reader recognition area and to obtain error signals for positioning. The proposed approach reduces uncertainties for HF-RFID reader based positioning even under sparse floor tag infrastructure. Simulation results highlight the usefulness of the proposed technique. © 2012 IEEE.

Ahmed, LA, Nguyen, ND, Bjornerem, A, Joakimsen, RM, Jorgensen, L, Bliuc, D, Center, JR, Eisman, JA, Nguyen, TV & Emaus, N 2012, 'EXTERNAL VALIDATION OF THE GARVAN NOMOGRAMS FOR PREDICTING ABSOLUTE FRACTURE RISK: THE TROMSO STUDY', OSTEOPOROSIS INTERNATIONAL, IOF-ECCEO European Congress on Osteoporosis and Osteoarthritis / 2nd IOF-ESCEO Pre-Clinical Symposium, SPRINGER LONDON LTD, Bordeaux, FRANCE, pp. S251-S252.

Amirsadri, A, Bishop, AN, Kim, J, Trumpf, J & Petersson, L 2012, 'A computationally efficient low-bandwidth method for very-large-scale mapping of road signs with multiple vehicles', Information Fusion (FUSION), 2012 15th International Conference on, IEEE, pp. 1351-1358.

Aung, Y & Al-Jumaily, A 2012, 'AR based upper limb rehabilitation system', 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2012, International Conference on Biomedical Robotics and Biomechatronics, IEEE, Rome, Italy, pp. 213-218.
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More than 62,000 Australians were reported suffered from Traumatic Brain Injury (TBI), Spinal Cord Injury (SCI) and Cerebrovascular Accident (CVA) or stroke in 2011. These injuries and accidents lead to physical disability that yields in limitation for performing a personâs daily life activities. To overcome such limitations, physical rehabilitation is conducted which requires one-to-one attention that creates a shortage in therapists and lead to high cost. In this paper, a development of an effective augmented reality (AR) based upper limb rehabilitation system with low cost is presented. Our development aims to close the gap in shortage of therapists, high health care cost of TBI, SCI and stroke. The proposed system can be used at home as well as in rehabilitation centers, units, and hospitals with minimum therapist supervision. It consists of two modules: AR based rehabilitation exercises module and real-time active muscle module. The first module aims to increase the upper limb range of motion via reaching exercises, and strengthen the associated muscles. In second module, the patientâs EMG signals were used as an input to monitor the muscle performance in real time during training. Our development had tested with 10 healthy subjects and had demonstrated in Port Kembla Rehabilitation Hospital.

Aung, Y & Al-Jumaily, A 2012, 'Shoulder rehabilitation with biofeedback simulation', International Conference on Mechatronics and Automation (ICMA), International Conference on Mechatronics and Automation, IEEE, Chengdu, pp. 974-979.
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More than 62,000 Australians were suffered from Traumatic Brain Injury, Spinal Cord Injury and stroke in 2011. The results of such injuries lead to physical disabilities that yield to prohibiting from performing a personâs daily life activities and reduce the quality of life. Moreover, the cost of such affects is almost AUD 13 billion per annum in health care sector. To overcome such situation, rehabilitation is essential for recovery to return to normal life. This paper presents the development of an effective shoulder rehabilitation system with motivational approach. Our development aims to reduce the one-to-one patient-therapist treatment relation contact, make it less regular consultation, and reduce high health care cost. The system is made up of two modules: rehabilitation exercises module where rehabilitation exercises are developed and real-time biofeedback simulation module that detect the active muscle in real time based on sEMG threshold which is predefined by therapist. Four rehabilitation exercises are developed and integrated with biofeedback system. The effectiveness of proposed system has been evaluated through testing with ten subjects and got high performance. The system has demonstrated in Port Kembla Rehabilitation Hospital

Bishop, AN 2012, 'Stochastic model validation and estimation for linear discrete-time systems with partial prior information', IFAC Proceedings Volumes (IFAC-PapersOnline), IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Elsevier, Mexico City, Mexico, pp. 427-431.
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The problem of recursive estimation and model validation for linear discrete-time systems with partial prior information is examined. More specifically, an underlying linear discrete-time system is considered where the statistics of the driving noise is assumed to be known only partially; i.e. a class of noise inputs is given from which the underlying actual noise is assumed to be chosen. A set-valued estimator is then derived and the conditional expectation is shown to belong to an ellipsoidal set consistent with the measurements and the underlying noise description. When the underlying noise is consistent with the underlying partial model and a sequence of realized measurements is given then the ellipsoidal, set-valued, estimate is computable using a Kalman filter-type algorithm. The estimator inherently solves a stochastic model validation problem whereby it is possible to estimate the consistency between the assumed model, knowledge on the partial prior noise statistics and the measured data. © 2012 IFAC.

Bishop, AN, Shames, I & Anderson, BDO 2011, 'Stabilization of rigid formations with direction-only constraints', Proceedings of the IEEE Conference on Decision and Control, IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC), IEEE, Orlando, FL, USA, pp. 746-752.
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Direction-based formation shape control for a collection of autonomous agents involves the design of distributed control laws that ensure the formation moves so that certain relative bearing constraints achieve, and maintain, some desired value. This paper looks at the design of a distributed control scheme to solve the direction-based formation shape control problem. A gradient control law is proposed based on the notion of bearing-only constrained graph rigidity and parallel drawings. This work provides an interesting and novel contrast to much of the existing work in formation control where distance-only constraints are typically maintained. A stability analysis is sketched and a number of illustrative examples are also given. © 2011 IEEE.

Bishop, AN, Summers, TH & Anderson, BDO 2012, 'Control of triangle formations with a mix of angle and distance constraints', Proceedings of the IEEE International Conference on Control Applications, IEEE International Conference on Control Applications, IEEE, Dubrovnik, Croatia, pp. 825-830.
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A distributed control law for triangular formation control with a mixture of bearing and range measurements and relative pair-wise inter-agent angle constraints and a single range constraint is introduced. The control law is weak in the sense that two agents are free to choose their own heading within a relatively large range of values. Indeed, the agents can determine their heading independently at run-time given any criteria they desire as long as certain relaxed conditions are met. A convergence result is established that ensures the desired formation configuration is asymptotically stable. Illustrative examples are provided to demonstrate the claims. © 2012 IEEE.

Chai, R, Hunter, G, Ling, SS & Nguyen, HT 2012, 'Real-Time Microcontroller based Brain Computer Interface for Mental Task Classifications using Wireless EEG Signals from Two Channels', Proceedings of the Ninth IASTED International Conference on Biomedical Engineering, IASTED International Conference on Biomedical Engineering, ACTA Press, Innsbruck, Austria, pp. 336-342.
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A brain computer interface (BCI) using electroencephalography (EEG) to measure brain activities could provide severely disabled people with alternative means of control and communication. In a practical system, portability, low power and real-time operation are the keys requirements. This could be accomplished by using an embedded microcontroller based system. The main contribution of this paper shows the development of a real-time BCI prototype system to classify groups of mental tasks based on such a system. The relevant mental tasks used are mental arithmetic, figure rotation, letter composing, visual counting and eyes closed action. Moreover, the system uses a separate two channels only wireless EEG measurement module with the active positions at parietal and occipital lobes. The result shows the wireless EEG module has a good performance with a CMRR of more than 95dB. In addition, the size of the module is small (36x36 mm2) and current consumption is low enough to operate off a 3V coin cell battery. The mental tasks were classified using a feed-forward back-propagation artificial neural network (ANN) trained with the Levenberg-Marquardt algorithm. An accuracy of around 70% was achieved with bit rate at around 0.4 bits/trial for six subjects tested to select between three separate mental tasks.

Chai, R, Ling, SH, Hunter, GP & Nguyen, HT 2012, 'Mental task classifications using prefrontal cortex electroencephalograph signals', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 1831-1834.
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For an electroencephalograph (EEG)-based brain computer interface (BCI) application, the use of gel on the hair area of the scalp is needed for low impedance electrical contact. This causes the set up procedure to be time consuming and inconvenient for a practical BCI system. Moreover, studies of other cortical areas are useful for BCI development. As a more convenient alternative, this paper presents the EEG based-BCI using the prefrontal cortex non-hair area to classify mental tasks at three electrodes position: Fp1, Fpz and Fp2. The relevant mental tasks used are mental arithmetic, ringtone, finger tapping and words composition with additional tasks which are baseline and eyes closed. The feature extraction is based on the Hilbert Huang Transform (HHT) energy method and the classification algorithm is based on an artificial neural network (ANN) with genetic algorithm (GA) optimization. The results show that the dominant alpha wave during eyes closed can still clearly be detected in the prefrontal cortex. The classification accuracy for five subjects, mental tasks vs. baseline task resulted in average accuracy is 73% and the average accuracy for pairs of mental task combinations is 72%. © 2012 IEEE.

Chai, R, Ling, SH, Hunter, GP & Nguyen, HT 2012, 'Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 5266-5269.
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This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels. © 2012 IEEE.

Chai, R, Ling, SS, Hunter, G & Nguyen, HT 2012, 'Mental non-motor imagery tasks classifications of brain computer interface for wheelchair commands using genetic algorithm-based neural network', Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN 2012), IEEE International Joint Conference on Neural Networks, IEEE, Brisbane, Australia, pp. 978-984.
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A genetic algorithm (GA)-based neural network classification in the application of brain computer interface (BCI) for controlling a wheelchair is presented in this paper. This study uses an electroencephalography (EEG) as a non-invasive BCI approach to discriminate three non-motor imagery mental tasks for disabled individuals who may have difficulty in using BCI based motor imagery tasks. The three tasks classification is mapped into three wheelchair movements: left, right and forward and the relevant combination mental tasks used in this study are mental arithmetic, letter composing, Rubik's cube rolling, visual counting, ringtone imagery and spatial navigation. The results show the proposed system provides good classification performance after selecting the most effective of three discriminative tasks across combination of the different non-motor imagery mental tasks for the five subjects tested. The average classification accuracy is between 76% and 85 %, with information transfer rates varies from 0.5 to 0.8 bits per trial.

Chan, KY, Ling, SS, Nguyen, HT & Jiang, F 2012, 'A hypoglycemic episodes diagnosis system based on neural networks for Type 1 diabetes mellitus', IEEE Congress on Evolutionary Computation, IEEE Congress on Evolutionary Computation, IEEE, Brisbane, QLD, Australia, pp. 2046-2051.
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Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients blood glucose levels based on these patients physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated.

Dehestani, D, Su, S, Nguyen, H, Guo, Y, Wall, J & Eftekhari, F 2012, 'Comprehensive sensitivity analysis of Heat Ventilating and Air Conditioning (HVAC) system based on neural network model', 10th International Conference on Healthy Buildings 2012, pp. 1555-1560.
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Finding healthy HVAC model as the health reference for monitoring and fault tolerant system is the main aim in this area. To dispel this concern a comprehensive transient model of Heat Ventilation and Air Conditioning (HVAC) systems is developed by a fast Artificial Neural Network (ANN) in this study. The model is based on experimental data that are taken from our HVAC laboratory scale. The neural network is developed by using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system by minimum error. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish highquality health monitoring and result in energy saving. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems.

Dehestani, D, Su, S, Nguyen, H, Vakiloroaya, V, Wall, J & Guo, Y 2012, 'Intelligent model based fault detection for heat ventilating and air conditioning (HVAC) system based on ANN model and SVM classifier', 10th International Conference on Healthy Buildings 2012, pp. 1253-1258.
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Due to a growing demand in improving energy efficiency in the built environment, reducing the energy consumption and operating costs of heating, ventilating and air-conditioning (HVAC) systems while still maintaining occupant comfort has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection and isolation (FDI) system plays a crucial role for identifying equipment and other system failures. Applying these techniques to HVAC system fault detection makes it possible to improve total cost effectiveness of maintenance and thus increase the capacity utilization rates of equipment. Reduction of energy wasting in the system by on time fault detection is another important goal of applying these techniques. Therefore, this work proposes a new model based fault detection technique for HVAC systems based on Neural Network (NN) model and online support vector machines (SVM) classifier which integrates a dimension reduction scheme to analyze the failure of system. The NN generate a high accurate model which is based reference for SVM classifier. Finally, a series of experimental fault data are applied to evaluate the effectiveness of the proposed technique. Final results show that online SVM can accurately detect faults in a HVAC cooling tower with minimum usage data. The technique is also shown to outperform an offline SVM on such energy systems for classification.

Dinevan, A, Aung, Y & Al-Jumaily, A 2011, 'Human Computer Interactive System for Fast Recovery based Stroke Rehabilitation', 11th International Conference on Hybrid Intelligent Systems, International Conference on Hybrid Intelligent Systems, IEEE, Malacca, Malaysia, pp. 647-652.
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In Australia, about 88% of stroke survivors live at home with disabilities affecting their daily life activities and quality of their lives. Therefore, there is a need to improve their lost functions and promote their lives via rehabilitation process. One way to improve the stroke rehabilitation process is through human interactive system, which can be achieved by augmented reality technology. This development draws from the work currently being pursued in the gaming industry to make the augmented reality technology more accessible to the medical industry for the improvement of stroke rehabilitation. In this paper, two augmented reality games: Pong Game and Goal Keeper Game were developed. These games have been designed for rehabilitation with consideration to human interactive systems and have features such as on-screen feedbacks and high immersive value to keep stroke victims motivated in the rehabilitation process. The developed games were aimed to replace boring and repetitive traditional rehabilitation exercises. This paper details the success of implementing augmented reality into the rehabilitation process, which will in turn contribute to society by minimizing the number of people living at home with stroke related disabilities and the requirement for direct supervision from therapist.

Guo, Y, Dehestani, D, Li, J, Wall, J, West, S & Su, S 2012, 'Intelligent outlier detection for HVAC system fault detection', 10th International Conference on Healthy Buildings 2012, Healthy Buildings, Queensland University of Technology, Brisbane, Queensland, Australia, pp. 1553-1554.

Haddad, A, Su, SW, Celler, BG & Nguyen, HT 2012, 'Enhancement interval training exercise based on the analysis of dynamic cardio-respiratory response', Proceedings of the IASTED International Conference Biomedical Engineering (BioMed 2012), IASTED International Conference on Biomedical Engineering, Acta Press, Innsbruck, Austria, pp. 458-464.
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Interval training is an effective method of improving aerobic function and cardiovascular fitness. Heart rate (HR) and oxygen uptake (VO2) are major indicators of human cardiovascular response to exercises. This study investigates human heart rate as well as oxygen uptake response dynamics to running exercises. Eight healthy male subjects were asked to run on a motor-controlled treadmill under a predefined running protocol. Heart rate and oxygen consumption were monitored and recorded using a COSMED portable gas analyzer (K4b2, Cosmed). The running protocol was repeated twice by each subject and averaged values were taken of each data set to reduce the influence of various internal and environmental factors on the measurements. Experimental results showed that the time constant of offset exercise for both heart rate and oxygen uptake is longer than that of onset exercise; they also showed that VO2 reached the steady state faster than heart rate for both onset and offset cases. These experimental results will also be used to build an interval training protocol. This study also showed how onset and offset time constants, as well as onset and offset steady state gains of an average VO2 profile can be used to simulate an interval training protocol.

Hossain, MD & Sanagavarapu, AM 2012, 'Breast Cancer Localization in Three Dimensions using Time Reversal DORT Method', Proceedings of ISAP2012, Nagoya, Japan, IEEE International Symposium on Antennas and Propagation, IEEE, Nagoya, Japan, pp. 471-474.
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Breast cancer is a common type of cancer among women all over the world. Successful treatment and prevention of death greatly depends on early detection. Mammography is so far the gold standard for screening breast cancer. But mammogram has high false positive rates for early stage detection and especially not highly reliable for younger women since their breasts are composed of dense breast tissues. Despite continued efforts and some progress, efficient early stage breast cancer detection is still an unsolved challenge. Radar based microwave imaging can be a promising alternative to mammography as it is a non-ionising as well as a non-invasive technique. In the microwave frequency range of interest the attenuation due to breast tissue is low enough to obtain image of the entire breast. All these facts have motivated the researchers to develop microwave imaging techniques for the early detection of breast cancer [1-3].

Hossain, MD, Abedin, MJ & Mohan, AS 2014, 'Beamspace based time reversal processing for breast cancer detection', 2012 IEEE Antennas and Propagation Society International Symposium (APSURSI), IEEE Antennas-and-Propagation-Society International Symposium (APSURSI), IEEE, Chicago, USA.
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Jiang, F, Ling, SS, Chan, KY, Chaczko, ZC, Leung, FH & Frater, MR 2012, 'An immunology-inspired multi-engine anomaly detection system with hybrid particle swarm optimisations', 2012 IEEE International on Fuzzy Systems (FUZZ-IEEE),, IEEE International Conference on Fuzzy Systems, IEEE WCCI, Brisbane, Australia, pp. 1279-1286.
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In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks.

Ling, SS, Nguyen, HT, Leung, FH, Chan, KY & Jiang, F 2012, 'Intelligent fuzzy particle swarm optimization with cross-mutated operation', IEEE Congress on Evolutionary Computation, IEEE Congress on Evolutionary Computation, IEEE, Australia, pp. 3009-3016.
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This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCM method performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods

Ling, SS, Nuryani, N & Nguyen, HT 2012, 'Hybrid Particle Swarm - based Fuzzy Support Vector Machine for Hypoglycaemia Detection', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Australia, pp. 450-455.
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Severe hypoglycemia is potentially life-threatening. This article introduces a novel hypoglycemia detection strategy using a hybrid particle swarm - based fuzzy support vector machine (SFisSvm) technique. The inputs of this system are six electrocardiographic (ECG) parameters. The system parameters of SFisSvm are optimized using a particle swarm optimization method. The proposed hypoglycemia detector system is a combination of two subsystems, namely, fuzzy inference system (FIS) and support vector machine (SVM). Two most significant inputs, heart rate and RTpc are fed to FIS, and its output is used for input of the SVM. The other ECG parameters and the output of FIS are fed to SVM and, then, are classified to indicate the presence of hypoglycemia. In this study, three and five membership functions are investigated for FIS. Furthermore, radial basis function (RBF), sigmoid and linear kernel functions are employed for mapping the inputs to high dimensional space in SVM. Performances of SFisSvm with different kernel functions are compared. As conclusion, the performance of SFisSvm is found with 75.19%, 83.71% and 79.33% in terms of sensitivity, specificity and geometric mean.

Maali, Y & Al-Jumaily, A 2012, 'A Novel Partially Connected Cooperative Parallel PSO-SVM Algorithm: Study Based on Sleep Apnea Detection', IEEE Congress on Evolutionary Computation (CEC), IEEE Congress on Evolutionary Computation, IEEE, Brisbane, Australia, pp. 267-274.
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Sleep disorders are common in a general population. It effect one in 5 adults and has several short term and long term bad side effects on health. Sleep apnea (SA) is the most important and common component of sleep disorders. This paper presents an automatic approach for detecting apnea events by using few bio-singles that are related to breathe defect. This work uses only air flow, thoracic and abdominal respiratory movement as input. The proposed algorithm consists of three main parts which are signal segmentation, feature generation and classification. A new proposed segmentation method intelligently segments the input signals for further classification, then features are generated for each segment by wavelet packet coefficients and also original signals. In classification phase a unique parallel PSO-SVM algorithm is investigated. PSO used to tune SVM parameters, and also data reduction. Proposed parallel structure used to help PSO to search space more efficiently, also avoiding fast convergence and local optimal results that are common problem in similar parallel algorithms. Obtained results demonstrate that the proposed method is effective and robust in sleep apnea detection and statistical tests on the results shown superiority of it versus previous methods even with more input signals, and also versus single PSO-SVM. Using fewer signals means more comfortable to subject and also, reduction of cost during recording the data.

Maali, Y & Al-Jumaily, A 2012, 'Hierarchical Parallel PSO-SVM Based Subject-Independent Sleep Apnea Classification', Lecture Notes in Computer Science, International Conference on Neural Information Processing, Springer, Doha, Qatar, pp. 500-507.
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This paper presents a method for subject independent classification of sleep apnea by a parallel PSO-SVM algorithm. In the proposed structure, swarms are separated into masters and slaves and accessing to the global information is restricted according to their types. Biosignal records that used as the input of the system are air flow, thoracic and abdominal respiratory movement signals. The classification method consists of the three main parts; feature generation, feature selection and data reduction based on parallel PSO-SVM, and the final classification. Statistical analyses on the achieved results show efficiency of the proposed system.

Maali, Y & Al-Jumaily, A 2012, 'Signal Selection for Sleep Apnea Classification', Lecture Notes in Computer Science, Mediterranean Conference on Medical and Biological Engineering and Computing, Springer, Sydney, Australia, pp. 661-671.
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This paper presents a method for signals and features selection when classifying sleep apnea. This study uses a novel hierarchical parallel particle swarm optimization structure as proposed by the authors previously. In this structure, the swarms are separated into `masters and `slaves and access to global information is restricted according to their types. This method is used to classify sleep apneic events into apnea or hypopnea. In this study, ten different biosignals are used as the inputs for the system albeit with different features. The most important signals are subsequently determined based on their contribution to classification of the sleep apneic events. The classification method consists of three main parts which are: feature generation, signal selection, and data reduction based on PSO-SVM, and the final classifier. This study can be useful for selecting the best subset of input signals for classification of sleep apneic events, by attention to the trade of between more accuracy of higher number of input signals and more comfortable of less signals for the patient

Maali, Y, Al-Jumaily, A & Laks, L 2012, 'Self-Advising SVM for Sleep Apnea Classification', Workshop on New trends of computational intelligence in health applications, Australasian Joint Conference on Artificial Intelligence, CEUR, Sydney, Australia, pp. 24-33.
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In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to transfer more information from training phase to the test phase in compare to the traditional SVM. In this paper Sleep apnea events are classified to central, obstructive or mixed, by using just three signals, airflow, abdominal and thoracic movement, as inputs. Statistical tests show that self-advising SVM performs better than traditional SVM in sleep apnea classification

Nguyen, AV, Nguyen, LB, Su, S & Nguyen, HT 2012, 'Development of a Bayesian neural network to perform obstacle avoidance for an intelligent wheelchair', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 1884-1887.
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This paper presents an extension of a real-time obstacle avoidance algorithm for our laser-based intelligent wheelchair, to provide independent mobility for people with physical, cognitive, and/or perceptual impairments. The laser range finder URG-04LX mounted on the front of the wheelchair collects immediate environment information, and then the raw laser data are directly used to control the wheelchair in real-time without any modification. The central control role is an obstacle avoidance algorithm which is a neural network trained under supervision of Bayesian framework, to optimize its structure and weight values. The experiment results demonstrated that this new approach provides safety, smoothness for autonomous tasks and significantly improves the performance of the system in difficult tasks such as door passing. © 2012 IEEE.

Nguyen, JS, Nguyen, TN, Tran, Y, Su, SW, Craig, A & Nguyen, HT 2012, 'Real-time performance of a hands-free semi-autonomous wheelchair system using a combination of stereoscopic and spherical vision', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 3069-3072.
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This paper is concerned with the operational performance of a semi-autonomous wheelchair system named TIM (Thought-controlled Intelligent Machine), which uses cameras in a system configuration modeled on the vision system of a horse. This new camera configuration utilizes stereoscopic vision for 3-Dimensional (3D) depth perception and mapping ahead of the wheelchair, combined with a spherical camera system for 360-degrees of monocular vision. The unique combination allows for static components of an unknown environment to be mapped and any surrounding dynamic obstacles to be detected, during real-time autonomous navigation, minimizing blind-spots and preventing accidental collisions with people or obstacles. Combining this vision system with a shared control strategy provides intelligent assistive guidance during wheelchair navigation, and can accompany any hands-free wheelchair control technology for people with severe physical disability. Testing of this system in crowded dynamic environments has displayed the feasibility and real-time performance of this system when assisting hands-free control technologies, in this case being a proof-of-concept brain-computer interface (BCI). © 2012 IEEE.

Nguyen, L, Nguyen, V, Ling, SS & Nguyen, HT 2012, 'A particle swarm optimization-based neural network for detecting nocturnal hypoglycemia using electroencephalograph (EEG) signals', 2012 IEEE World Congress on Computational Intelligence, IEEE World Congress on Computational Intelligence, IEEE, Brisbane, Australia, pp. 2730-2735.
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For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia or the state of low blood glucose level is a very common but dangerous complication. Hypoglycemia episodes can lead to a large number of serious symptoms and effects, including unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. By analyzing electroencephalography (EEG) signals from five T1DM patients during an overnight study, we find that under hypoglycemia, both centroid theta frequency and centroid alpha frequency change significantly against non-hypoglycemia conditions. Furthermore, a neural network is developed to detect hypoglycemia using the mentioned two EEG features. A standard particle swarm optimization strategy is applied to optimize the parameters of this neural network. By using the proposed method, we obtain the classification performance of 82% sensitivity and 63% specificity. The results demonstrate that hypoglycemia episodes can be detected non-invasively and effectively from EEG signals.

Nguyen, L, Nguyen, V, Ling, SS & Nguyen, HT 2012, 'An adaptive strategy of classification for detecting hypoglycemia using only two EEG channels', Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, San Diego, California, USA, pp. 3515-3518.
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Hypoglycemia is the most common but highly feared side effect of the insulin therapy for patients with Type 1 Diabetes Mellitus (T1DM). Severe episodes of hypoglycemia can lead to unconsciousness, coma, and even death. The variety of hypoglycemic symptoms arises from the activation of the autonomous central nervous system and from reduced cerebral glucose consumption. In this study, electroencephalography (EEG) signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected non-invasively using EEG signals from only two channels. This paper demonstrates that a significant advantage can be achieved by implementing adaptive training. By adapting the classifier to a previously unseen person, the classification results can be improved from 60% sensitivity and 54% specificity to 75% sensitivity and 67% specificity.

Nguyen, LL, Su, S & Nguyen, HT 2012, 'Identification of Hypoglycemia and Hyperglycemia in Type 1 Diabetic patients using ECG parameters', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 2716-2719.
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Hypoglycemia and Hyperglycemia are both serious diseases related to diabetes mellitus. Among Type 1 Diabetic patients, there are who experience both hypoglycemic and hyperglycemic events. The aim of this study was to identify of hypoglycemia and hyperglycemia based on ECG changes in this population. An ECG Acquisition and Analysis System based on LabVIEW software has been developed for collecting ECG signals and extracting features with abnormal changes. ECG parameters included Heart rate (HR), corrected QT interval (QTeC), PR interval, corrected RT interval (RTC) and corrected TpTe interval (TpTe C ). Blood glucose levels were used to classify glycemic states in subjects as hypoglycemic state ( 60 mml/l, Hypo), as normoglycemic state (80 to 110 mmol/l, Normo), and as hyperglycemic state 150 mml/l, Hyper). The results indicated that hypoglycemic and hyperglycemic states produce significant inverse changes on those ECG parameters. © 2012 IEEE.

Nguyen, T, Tran, T, Allen, NA, Chan, KT & Lee, H 2012, 'BODYWEIGHT, LIFESTYLE BEHAVIORS, TYPE 2 DIABETES AND HEART DISEASE AMONG FOREIGN BORN OLDER ADULT VIETNAMESE AMERICANS', GERONTOLOGIST, OXFORD UNIV PRESS INC, pp. 399-399.

Rana, M & Sanagavarapu, AM 2012, 'Rotationally symmetric (RS)-LOD-FDTD with CPML for analysing resonant structures', Antennas and Propagation (ISAP), 2012 International Symposium on, IEEE International Symposium on Antennas and Propagation, IEEE, Nagoys, Japan, pp. 930-933.
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In this paper, we present a RS-LOD-FDTD formulation for rotationally symmetric resonant cavities and derive the convolutional perfectly matched layer (CPML) absorbing boundary condition for this method. We also extend digital signal processing based spectral estimation technique to efficiently analyse the resonant frequencies of cylindrical resonant structures. The numerical results show that the RS-LOD-FDTD-CPML method with improved spectral estimation provides accurate results while reducing the computational time.

San, P, Ling, SS & Nguyen, HT 2012, 'Hybrid Particle Swarm Optimization Based Normalized Radial Basis Function Neural Network For Hypoglycemia Detection', International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Brisbane, Australia, pp. 2718-2723.
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In this study, a normalized radial basis function neural network (NRBFNN) is presented for detection of hypoglycemia episodes by using physiological parameters of electrocardiogram (ECG) signal. ypoglycemia is a common and serious side effect of insulin therapy in patients with Type 1 diabetes. Based on heart rate (HR) and corrected QT interval (QTc) of electrocardiogram (ECG) signal, a hybrid particle swarm optimization based normalized RBFNN is developed for recognization of hypoglycemia episodes. A global learning algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used to optimize the parameters of NRBFNN. From a clinical study of 15 children with Type 1 diabetes, natural occurrence of nocturnal hypoglycemic episodes associated with increased heart rates and corrected QT interval are studied. The overall data are organized into a training set (5 patients), validation set (5 patients) and testing set (5 patients) randomly selected. Using the optimized NRBFNN, the testing performance for detection of hypoglycemic episodes are satisfactory with 76.74% of sensitivity and 51.82% of specificity.

San, P, Ling, SS & Nguyen, HT 2012, 'Optimized variable translation wavelet neural network and its application on hypoglycemia detection system', 7th IEEE Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications, IEEE, Singapore, pp. 547-551.
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An hybrid particle swarm optimization based optimized variable translation wavelet neural network (VTWNN) is proposed for detection of hypoglycemic episodes in patients with Type 1 diabetes mellitus (T1DM). Due to excellent performance in capturing nonstationary signal and nonlinear function modeling of VTWNN, it is used as a suitable classifier in hypoglycemia detection system. A global training algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) operation is investigated for parameters optimization of proposed VTWNN detection system. In this clinical study, 15 children with Type 1 diabetes were observed overnight. All the real data sets collected from Department of Heath, Government of Western Australia. Several experiments are performed over a randomly selected training set 5 patients (184 data points), validation set 5 patients (192 data points) and testing set 5 patients (153 data points) respectively. Using variable translation wavelet neural network (VTWNN), the value of testing sensitivity and specificity are 79.07 % and 50.00 %. The results show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity.

San, PP, Ling, SH & Nguyen, HT 2012, 'Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 6325-6328.
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Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. © 2012 IEEE.

Warkiani, ME, Gong, H-Q & Fane, A 2011, 'Surface modification of micro/nano-fabricated filters', MATERIALS INTEGRATION, 2nd International Symposium on Advanced Synthesis and Processing Technology for Materials (ASPT2011)/8th Materials Science School for Young Scientists (KINKEN-WAKATE2011), TRANS TECH PUBLICATIONS LTD, Sendai, PEOPLES R CHINA, pp. 87-+.
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Wong, G, Leung, FH & Ling, SS 2012, 'Predicting protein-ligand binding site with differential and support vector machine', International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Australia, pp. 2724-2729.
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Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. When the scores are calculated from these methods, the method of classification is very important and can affect the prediction results greatly. A developed support vector machine (SVM) is used to classify the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential, offset from protein, conservation score and the information around the pockets. Since SVM is sensitive to the input parameters and the positive samples are more relevant than negative samples, differential evolution (DE) is applied to find out the suitable parameters for SVM. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.

Wroe, S, Chamoli, U, Parr, W, Ridgely, RC & Witmer, LM 2012, '3D BIOMECHANICAL MODELLING OF MARSUPIAL AND PLACENTAL SABRE-TOOTHS: A DIFFERENT KIND OF BITE FOR AN EXTREME POUCHED PREDATOR', JOURNAL OF VERTEBRATE PALEONTOLOGY, 72nd Annual Meeting of the Society-of-Vertebrate-Paleontology, TAYLOR & FRANCIS INC, N Carolina Museum Nat Sci, Raleigh, NC, pp. 196-196.

Yang, F & Sanagavarapu, AM 2012, 'Complex natural resonances of dielectric objects embedded in inhomogeneous breast models', 2012 IEEE Antennas and Propagation Society International Symposium (APSURSI), IEEE Antennas-and-Propagation-Society International Symposium (APSURSI), IEEE, Chicago, USA, pp. 1-2.
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This paper investigates techniques of calibration of complex natural resonances (CNR) of dielectric targets embedded in inhomogeneous breast models used for early stage breast cancer detection. We apply two step procedure viz., calibration of CNRs in free space and half space dielectric medium, and compare with corresponding embedded conducting objects. We demonstrate that the CNRs related to the object's shape can be detected even in inhomogeneous lossy dielectric media

Yang, F & Sanagavarapu, AM 2012, 'Microwave imaging for breast cancer detection using Vivaldi antenna array', Antennas and Propagation (ISAP), 2012 International Symposium on, IEEE International Symposium on Antennas and Propagation, IEEE, Nagoya, Japan, pp. 479-482.
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The radar based UWB microwave imaging techniques have shown great potential for the localization of embedded dielectric targets in breast phantoms [1], [2]. In practical UWB microwave imaging systems, antennas play a key role in determining the characteristics of the received signals. The antennas behave as spatial-temporal filters as they have to radiate narrow pulses and thus the radiated signal may be distorted. Besides, the multipath effects and dispersion can also affect the reconstruction process. With this aim, in this paper, we investigate the effects of UWB antenna array on the image reconstruction for early stage breast cancer detection. A full-scale radar based microwave imaging system was reported in the literature that employed a small slot antenna with copper cage that was positioned into a hemispherical array to illuminate the breast [1]. Different types of horn antennas were also investigated for breast imaging [2], [3]. In this paper, we investigate the problem of breast tumor detection in an inhomogeneous breast phantom using a Vivaldi antenna array. We first investigate the experimental UWB characterization of the prototype- Vivaldi antenna element. Later, we perform FDTD simulations with a 7-element Vivaldi antenna array that is placed in a semi-circle arc around the numerical 3-D breast phantom. We employ the time-of-arrival calibration [6] and use the signal processing methods to reconstruct suspicious regions to localize the presence of tumor. We present results on image reconstruction using FDTD simulations.

Yuwono, M, Su, SW, Moulton, BD & Nguyen, HT 2012, 'Fast unsupervised learning method for rapid estimation of cluster centroids', 2012 IEEE Congress of Evolutionary Computation, IEEE Congress on Evolutionary Computation, IEEE, Brisbane, QLD, Australia, pp. 889-896.
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Data clustering is a process where a set of data points is divided into groups of similar points. Recent approaches for data clustering have seen the development of unsupervised learning algorithms based on Particle Swarm Optimization (PSO) techniques. These include Particle Swarm Clustering (PSC) and Modified PSC (mPSC) algorithms for solving clustering problems. However, the PSC and mPSC algorithms tend to be computationally expensive when applied to datasets that have higher levels of dimensionality and large volumes. This paper presents a novel and more efficient swarm clustering strategy we call Rapid Centroid Estimation (RCE). We compare the performance of RCE with the performance of PSC and mPSC in several ways including complexity analyses and particle behavior analyses. Our benchmark testing suggests that RCE can reach a solution 274 times quicker than PSC and 270 times quicker than mPSC for a clustering task where the dataset has a dimension of 80 and a volume of 500. We also investigated particle behaviors on two-class two-dimensional datasets with volume of 500, presenting 250 data for each well-separated class with known Gaussian centers. We found that RCE converged to the appropriate centers at 70 updates on average, compared to 19802 updates for PSC and 23006 updates for mPSC. An ANOVA indicates RCE is significantly faster than both PSC and mPSC.

Yuwono, M, Su, SW, Moulton, BD & Nguyen, HT 2012, 'Gait cycle spectrogram analysis using a torso-attached inertial sensor', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 6539-6542.
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Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We describe a new way to detect walking, and measure gait cadence, by using time-frequency signal processing together with spectrogram analysis of signals from a chest-worn inertial measurement unit (IMU). A pilot study of 11 participants suggests that this method is able to distinguish between walk and non-walk activities with up to 88.70% sensitivity and 97.70% specificity. Limitations of the method include instability associated with manual fine-tuning of local and global threshold levels. © 2012 IEEE.

Yuwono, M, Su, SW, Moulton, BD & Nguyen, HT 2012, 'Gait episode identification based on wavelet feature clustering of spectrogram images', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 2949-2952.
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Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We propose a new approach to detect gait episodes using Neural Network and and clustering of wavelet-decomposed spectrogram images. Signals from a chest-worn inertial measurement unit (IMU) is processed using Explicit Complementary Filter (ECF) to estimate and track torso angle. Using the feature obtained from wavelet decomposition of spectrogram images, we use an Augmented Radial Basis Neural Network (ARBF) to classify walking episodes. Cluster centroids of ARBF are optimized using Rapid Cluster Estimation (RCE). A pilot study of 11 participants suggests that our approach is able to distinguish between walk and non-walk activities with up to 85.71% sensitivity and 91.34% specificity. © 2012 IEEE.

Yuwono, M, Su, SW, Moulton, BD & Nguyen, HT 2012, 'Method for increasing the computation speed of an unsupervised learning approach for data clustering', IEEE Congress of Evolutionary Computation, IEEE Congress on Evolutionary Computation, IEEE, Brisbane, pp. 2957-2964.
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Clustering can be especially effective where the data is irregular, noisy and/or not differentiable. A major obstacle for many clustering techniques is that they are computationally expensive, hence limited to smaller data volume and dimension. We propose a lightweight swarm clustering solution called Rapid Centroid Estimation (RCE). Based on our experiments, RCE has significantly quickened optimization time of its predecessors, Particle Swarm Clustering (PSC) and Modified Particle Swarm Clustering (mPSC). Our experimental results show that on benchmark datasets, RCE produces generally better clusters compared to PSC, mPSC, K-means and Fuzzy C-means. Compared with K-means and Fuzzy C-means which produces clusters with 62% and 55% purities on average respectively, thyroid dataset has successfully clustered on average 71% purity in 14.3 seconds.

Yuwono, M, Su, SW, Moulton, BD & Nguyen, HT 2012, 'Optimization strategies for rapid centroid estimation', Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, San Diego, California, USA, pp. 6212-6215.
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Particle swarm algorithm has been extensively utilized as a tool to solve optimization problems. Recently proposed particle swarm±based clustering algorithm called the Rapid Centroid Estimation (RCE) is a lightweight alteration to Particle Swarm Clustering (PSC). The RCE in its standard form is shown to be superior to conventional PSC algorithm. We have observed some limitations in RCE including the possibility to stagnate at a local minimum combination and the restriction in swarm size. We propose strategies to optimize RCE further by introducing RCE+ and swarm RCE+. Five benchmark datasets from UCI machine learning database are used to test the performance of these new strategies. In Glass dataset swarm RCE+ is able to achieve highest purity centroid combinations with less iteration (90.3%±1.1% in 9±5 iterations) followed by RCE+ (89%±3.5% in 65±62 iterations) and RCE (87%±5.9% in 54±44). Similar quality is also reflected in other benchmark datasets including Iris, Wine, Breast Cancer, and Diabetes.

Zhang, Y, Haddad, A, Su, SW, Celler, BG & Nguyen, HT 2012, 'Onset and offset exercise response model in electronic terms', Proceedings of the IASTED International Conference Biomedical Engineering (BioMed 2012), IASTED International Conference on Biomedical Engineering, Acta Press, Innsbruck, Austria, pp. 122-128.
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This paper investigated human heart rate (HR) and oxygen uptake (VO2) dynamics to running exercise and developed an electronic circuitry based mathematical model to quantitatively depict the metabolic and energy-generating process at both onset and offset of exercises. In order to investigate transient responses of both HR and VO2, eight healthy male subjects were asked to run on a motor-controlled treadmill exercise under a predefined running protocol. Heart rate and oxygen consumption were monitored and recorded using a COSMED portable gas analyzer (K4b2, Cosmed). The observed experimental results verified the belief that cardio-respiratory responses dynamics at onset and offset of moderate or high intense exercises are significantly different. In order to find a rational explanation for this phenomenon, a novel idea was inspired by applying a simple switchable resistance-capacitor (RC) circuit to unify the complex dynamics at onset and offset of exercises. The proposed physical system can not only analytically explain body's energy-generating process, metabolism and cardio-respiratory responses, but also mathematically account for cardio-respiratory dynamics at both onset and offset of exercises, in which the continuity of the output and states during switching is guaranteed.

Zhang, Y, Su, S, Savkin, A, Celler, B & Nguyen, H 2012, 'Multi-loop integral controllability analysis for nonlinear multiple-input single-output processes', 2012 2nd Australian Control Conference, AUCC 2012, Australian Control Conference, IEEE, Institute of Electrical and Electronics Engineers, Sydney, NSW, Australia, pp. 81-85.
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Multi-loop integral control is still one of the most popular control strategies in industry due to its simplicity, efficiency, offset free tracking, and capability for fault tolerance. Skogestad and Morari introduced Decentralized Integral Controllability (DIC) to investigate the decentralized unconditional stability under multi-loop integral control for square systems. However, in engineering practice, some multivariable processes may not be square, which often utilize multiple redundant control inputs for the regulation of only one single output. This study extends the concept of Decentralized Integral Controllability to non-square systems, and presents sufficient conditions for Multiple-Input Single-Output nonlinear processes based on singular perturbation analysis. The proposed controllability analysis method is applied in the control of a real time temperature control system and achieves desired temperature tracking results. © 2012 Institute of Engineers.

Zhou, J, Su, SW & Guo, A 2012, 'Fault Detection and Identification of COSMED K4b2 based on PCA and Neural Network', WASET: International Conference on Information, Communications and Signal Processing, International Conference on Information, Communications and Signal Processing, WASET, Penang, Malaysia, pp. 729-734.
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COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.

Ajuyah, PA & Tran, NT 2012, 'Regulation of a tumour suppressor gene by multiple miRNAs'.
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miRNA are small non-coding RNAs which are potent regulators of gene expression. Given the numerous miRNA binding sites on the 3’UTR of target genes, it is likely that regulation is mediated by multiple miRNAs. The tumour suppressor gene Programmed Cell Death 4 (PDCD4) has been shown to have diagnostic and prognostic significance in various cancers, but despite this, not much is known about the regulatory mechanisms of PDCD4. We propose that PDCD4 is co-regulated by two different miRNAs and provide a mechanistic insight to this unique co-regulation.

Ajuyah, PA, Hutvagner, GH & Tran, NT 2012, 'Co-regulation of PDCD4 by miR-21 and miR-499'.

Khoury, SK & Tran, NT 2012, 'Microrna expression across tumour and sera of HNSCC patients'.
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Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is the 5th common malignancy worldwide with a high morbidity rate. We propose that HNSCC cells release small RNAs into the circulation which can be potential biomarkers. Our comprehensive data set of miRNA expression from solid tumours and sera of the same patient provides an opportunity to identify biomarkers for early detection of HNSCC. This approach will assist in improving the prognostic outlook of HNSCC patients. Methods: MiRNA expression across tonsil tumours and serum was compared using Partek Genomics Suite. Results: We identified approximately 225 dysregulated miRNAs common to tumour and sera. A selection of these miRNAs was then validated using TaqMan qPCR. Conclusions: Our study is the first to compare expression levels of miRNAs across tumour and sera of the same patient with QPCR analysis supporting the findings. This is a promising foundation for further analysis of miRNA expression across tumour and sera in 3 remaining HNSCC subtypes.