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arXiv 2020年

作者： Aman, Waqas Mahboob Ur Rahman, M. Abbas, Hassan T. Khalid, Muhammad Arslan Imran, Muhammad A. Alomainy, Akram Abbasi, Qammer H. Electrical Engineering Department Information Technology University Lahore54000 Pakistan Department of Electronics and Nano Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Division of Biomedical Engineering School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom School of Electronic Engineering and Computer science Queen Mary University of London LondonE1 4NS United Kingdom

Nanoscale communication systems operating in Terahertz (THz) band are anticipated to revolutionise the healthcare systems of the future. Global wireless data traffic is undergoing a rapid growth. However, wireless systems, due to their broadcasting nature, are vulnerable to malicious security breaches. In addition, advances in quantum computing poses a risk to existing crypto-based information security. It is of the utmost importance to make the THz systems resilient to potential active and passive attacks which may lead to devastating consequences, especially when handling sensitive patient data in healthcare systems. New strategies are needed to analyse these malicious attacks and to propose viable countermeasures. In this manuscript, we present a new authentication mechanism for nanoscale communication systems operating in THz band at the physical layer. We assessed an impersonation attack on a THz system. We propose using path loss as a fingerprint to conduct authentication via two-step hypothesis testing for a transmission device. We used hidden Markov Model (HMM) viterbi algorithm to enhance the output of hypothesis testing. We also conducted transmitter identification using maximum likelihood and Gaussian mixture model (GMM) expectation maximization algorithms. Our simulations showed that the error probabilities are a decreasing functions of SNR. At 10 dB with 0.2 false alarm, the detection probability was almost one. We further observed that HMM out-performs hypothesis testing at low SNR regime (10% increase in accuracy is recorded at SNR = 5 dB) whereas the GMM is useful when ground truths are noisy. Our work addresses major security gaps faced by communication system either through malicious breaches or quantum computing, enabling new applications of nanoscale systems for Industry 4.0. Copyright © 2020, The Authors. All rights reserved.

关键词： Maximum likelihood

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Towards expedited impedance tuning of a robotic prosthesis for personalized gait assistance by reinforcement learning control

arXiv

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arXiv 2020年

作者： Li, Minhan Wen, Yue Gao, Xiang Si, Jennie Huang, Helen NCSU/UNC Department of Biomedical Engineering NC State University RaleighNC27695-7115 United States University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning has been demonstrated in a principled way, the next critical issue is to improve tuning efficiency and speed it up for the human user, in clinic settings, while maintaining human safety. We therefore propose a Policy Iteration with Constraint Embedded (PICE) method as an innovative solution to the problem under the framework of reinforcement learning. Central to PICE is the use of a projected Bellman equation with a constraint of assuring positive semi-definiteness of performance values during policy evaluation. Additionally, we developed both online and offline PICE implementations that provide additional flexibility for the designer to fully utilize measurement data, either from on-policy or off-policy, to further improve PICE tuning efficiency. Our human subject testing showed that the PICE provided effective policies with significantly reduced tuning time. For the first time, we also experimentally evaluated and demonstrated the robustness of the deployed policies by applying them to different tasks and users. Putting it together, our new way of problem solving has been effective as PICE has demonstrated its potential towards truly automating the process of control parameter tuning for robotic knee prosthesis users. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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Temperature dependent dielectric spectroscopy of muscle tissue phantom

Temperature dependent dielectric spectroscopy of muscle tiss...

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2019 European Microwave Conference in Central Europe, EuMCE 2019

作者： Fiser, Ondrej Ley, Sebastian Helbig, Marko Sachs, Jurgen Kantova, Michaela Vrba, Jan Department of Biomedical Technology CTU Prague Czech Republic Faculty of Computer Science and Automation TU Ilmenau Germany Department of Electrical Engineering and Information Technology TU Ilmenau Germany Department of Electromagnetic Field CTU Prague Czech Republic

ISBN: (纸本)9782874870675

The temperature dependence of the dielectric parameters of tissues and tissue mimicking phantoms is very important for the non-invasive temperature measurement in medical applications using microwaves. We investigated and proposed a procedure of the measurement of this dependence in the temperature range 25 - 50 °C. Our proposed procedure was verified using distilled water. The results were compared to the literature model in the frequency range 150 - 3000 MHz. Using this method, the temperature dependence of dielectric parameters of new tissue mimicking phantom based on agar, PE powder and TX-151 was measured in the temperature range 25 -50 °C. © 2019 EuMCE.

关键词： Temperature measurement

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Correction: Experimental evaluation of the impact of sEMG interfaces in enhancing embodiment of virtual myoelectric prostheses

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Journal of neuroengineering and rehabilitation 2024年第1期21卷 64页

作者： Theophil Spiegeler Castañeda Mathilde Connan Patricia Capsi-Morales Philipp Beckerle Claudio Castellini Cristina Piazza Department of Computer Engineering Technical University of Munich (TUM) Garching bei Munich Germany. Institute of Robotics and Mechatronics German Aerospace Center (DLR) Oberpfaffenhofen Germany. Department of Computer Engineering Technical University of Munich (TUM) Garching bei Munich Germany. patricia.capsi-morales@tum.de. Munich Institute of Robotics and Machine Intelligence Technical University of Munich (TUM) Munich Germany. patricia.capsi-morales@tum.de. Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen- Nürnberg (FAU) Erlangen Germany. Department of Electrical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany. Munich Institute of Robotics and Machine Intelligence Technical University of Munich (TUM) Munich Germany.

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Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

arXiv

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arXiv 2020年

作者： Khodatars, Marjane Shoeibi, Afshin Sadeghi, Delaram Ghassemi, Navid Jafari, Mahboobeh Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Zare, Assef Kong, Yinan Khosravi, Abbas Nahavandi, Saeid Hussain, Sadiq Acharya, U. Rajendra Berk, Michael The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran The School of Engineering Macquarie University Sydney2109 Australia The Dibrugarh University Assam786004 India The Dept. of Electronics and Computer Engineering Ngee Ann Polytechnic 599489 Singapore The Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan The Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore The Deakin University IMPACT - the Institute for Mental and Physical Health and Clinical Translation School of Medicine Barwon Health Geelong Australia The Orygen The National Centre of Excellence in Youth Mental Health Centre for Youth Mental Health Florey Institute for Neuroscience and Mental Health The Department of Psychiatry The University of Melbourne Melbourne Australia

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works. © 2020, CC BY-NC-ND.

关键词： Neuroimaging

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Iterative temporal differencing with random synaptic feedback weights support error backpropagation for deep learning

arXiv

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arXiv 2019年

作者： Dargazany, Aras R. Department of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI02881 United States

This work shows that a differentiable activation function is not necessary any more for error backpropagation. The derivative of the activation function can be replaced by an iterative temporal differencing using fixed random feedback alignment. Using fixed random synaptic feedback alignment with an iterative temporal differencing is transforming the traditional error backpropagation into a more biologically plausible approach for learning deep neural network architectures. This can be a big step toward the integration of STDP-based error backpropagation in deep learning. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

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Optimal Blending of Regenerative and Friction Braking at Low Speeds for Maximizing Energy Extraction in Electric Vehicles

Optimal Blending of Regenerative and Friction Braking at Low...

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IEEE Energy Conversion Congress and Exposition

作者： Shoeib Heydari Poria Fajri Reza Sabzehgar Arash Asrari Electrical and Biomedical Engineering Department University of Nevada Reno Reno NV USA Electrical and Computer Engineering Department San Diego State University San Diego CA USA Electrical and Computer Engineering Department Southern Illinois University Carbondale IL USA

This paper proposes a new approach to maximize the harvested energy through regenerative braking process in electric vehicles (EVs) by optimal blending of regenerative and friction brakes. In the proposed approach, the low-speed boundary, under which regenerative braking capability of an EV is ineffective, is determined using the traction motor (TM) regenerative braking performance map. During braking at low speeds, if the TM operating point falls below the defined low-speed boundary, it is shifted to an optimal point on the map such that motor regenerative braking capability is utilized more effectively. A MATLAB/SIMULINK model that represents the vehicle TM is used to validate the proposed approach. The results show an increase in the extracted energy compared to a case where regenerative braking is completely disabled below the low-speed boundary.

关键词： Brakes Torque Friction Batteries Axles Transportation Traction motors

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Least Mean Squares Adaptive Algorithms Optimization for Fetal Phonocardiogram Extraction

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IFAC-PapersOnLine 2018年第6期51卷 60-65页

作者： Kahankova, Radana Martinek, Radek Jaros, Rene Nedoma, J. Fajkus, M. Vanus, J. Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic

The paper deals with extraction of fetal phonocardiogram (fPCG) from the abdominal signal mixture by using adaptive filters based on Least Mean Squares (LMS) algorithms. Optimal setting of the filter parameters is a vital condition to ensure efficiency of adaptive filters. In this paper, we suggest ideal parameters setting for this purpose. The filtration quality was assessed by means of objective parameters, namely Signal to Noise Ratio (SNR) and Positive Predictive Value (PPV). The results showed that if optimized, LMS-based adaptive algorithms are efficient in suppressing maternal component in the composed abdominal signal. © 2018

关键词： Signal to noise ratio Adaptive algorithms Adaptive filtering Adaptive filters Extraction Fetal monitoring Optimization Phonocardiography Abdominal signals Algorithms optimizations Least mean square(LMS) Least mean squares algorithm Objective parameters Parameters setting Phonocardiograms Positive predictive values

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Energy Management of Multi-Energy Storage Systems Using Energy Path Decomposition

Energy Management of Multi-Energy Storage Systems Using Ener...

引用

IEEE Energy Conversion Congress and Exposition

作者： Sima Aznavi Poria Fajri Arash Asrari Reza Sabzehgar Department of Electrical and Biomedical Engineering University of Nevada Reno Reno NV USA Department of Electrical and Computer Engineering Southern Illinois University Carbondale IL USA Department of Electrical and Computer Engineering San Diego State University San Diego Ca USA

In this paper, a novel energy management strategy based on the decomposition of energy paths is proposed. The proposed approach allows energy management systems to accurately recognize the energy exchange flows within complex systems by determining a pricing policy for the energy of storage systems in terms of average cumulative cost. The proposed approach is implemented to fulfill the cost-effective energy management of a typical residential unit with several energy storage systems and energy resources. Simulation studies are conducted to investigate the performance of the proposed energy management strategy and to verify its effectiveness compared to a commonly available energy management strategy from technical and economic viewpoints.

关键词： Energy management Batteries State of charge Complex systems Linear programming Pricing

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Drug-disease graph: predicting adverse drug reaction signals via graph neural network with clinical data

arXiv

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arXiv 2020年

作者： Kwak, Heeyoung Lee, Minwoo Yoon, Seunghyun Chang, Jooyoung Park, Sangmin Jung, Kyomin Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Biomedical Sciences Seoul National University Seoul Korea Republic of Department of Family Medicine Seoul National University Hospital Seoul Korea Republic of

Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than pre-marketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develop a novel graph-based framework for ADR signal detection using healthcare claims data. We construct a Drug-disease graph with nodes representing the medical codes. The edges are given as the relationships between two codes, computed using the data. We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database. The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships. Furthermore, our model predicts ADR pairs that do not exist in the established ADR database, showing its capability to supplement the ADR database. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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