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arXiv 2019年

作者： Palmieri, Nunzia Yang, Xin-She de Rango, Floriano Santamaria, Amilcare Francesco Department of Computer Engineering Modeling Electronics and System Science University of Calabria Italy Design Engineering and Mathematics School of Science and Technology Middlesex University London United Kingdom

This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behaviour of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental *** Codes 65D19, 68T40, 78M32, 90C26 Copyright © 2019, The Authors. All rights reserved.

关键词： Industrial robots

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Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)

arXiv

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arXiv 2020年

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Hussain, Sadiq Khosravi, Abbas Koohestani, Afsaneh Zangooei, Mohammad Hossein Abdar, Moloud Beykikhoshk, Adham Shoeibi, Afshin Zare, Assef Panahiazar, Maryam Nahavandi, Saeid Srinivasan, Dipti Atiya, Amir F. Acharya, U. Rajendra Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran System Administrator Dibrugarh University Assam786004 India University of Texas Dallas United States Applied Artificial Intelligence Institute Deakin University Geelong Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Computational Health Sciences University of California San Francisco United States Dept. of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased. Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

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High Conductance Margin for Efficient Neuromorphic Computing Enabled by Stacking Nonvolatile van der Waals Transistors

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Physical Review Applied 2021年第4期16卷 044049-044049页

作者： Liping Xu (徐丽萍) Hao Xiong (熊浩) Zhichao Fu (付志超) Menghan Deng (邓梦晗) Shuiyuan Wang (王水源) Jinzhong Zhang (张金中) Liyan Shang (商丽燕) Kai Jiang (姜凯) Yawei Li (李亚巍) Liangqing Zhu (朱亮清) Liang He Zhigao Hu (胡志高) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education) Department of Materials School of Physics and Electronic Science East China Normal University Shanghai 200241 China Center for Advanced Electronic Materials and Devices School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science and Technology East China Normal University Shanghai 200062 China ASIC & System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai 200433 China

High-performance artificial synaptic devices are key building blocks for developing efficient neuromorphic computing systems. However, the nonlinear and asymmetric weight update of existing devices has restricted their practical applications. Herein, floating gate nonvolatile memory (FG NVM) devices based on two-dimensional (2D) HfS2/h-BN/FG-graphene heterostructures have been designed and investigated as multifunctional NVM and artificial optoelectronic synapses. Benefiting from the FG architecture, the HfS2-based NVM device exhibits competitive performances, such as a high on:off ratio (>105), large memory window (approximately 100 V), excellent charge retention ability (>104s), and robust durability (>103 cycles). Notably, the artificial optoelectronic synapses based on HfS2 FG NVM show an impressive large conductance margin and good linearity, owing to the ultrahigh photoresponsivity and photogain of HfS2. The energy consumption of per spike in our artificial synapse is as low as 0.2 pJ. Therefore, a high recognition accuracy up to 91.5% of the artificial neural network on the basis of our HfS2-based optoelectronic synapse at the system level has been achieved, which is superior to other reported 2D artificial optoelectronic synapses. This work paves the way forward for all 2D material-based memory for developing efficient optogenetics-inspired neuromorphic computing in brain-inspired intelligent systems.

关键词： Electrical conductivity Optoelectronics Synapses 2-dimensional systems Artificial neural networks Transistors Transition metal dichalcogenides Lithography

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Preface

引用

International Journal of computer Applications in Technology 2021年第2期66卷 87-88页

作者： Kumar, Dinesh Jain, Shruti Manocha, Amit Kumar Singh, Ram Sewak Department of Electrical and Computer System Engineering RMIT University Melbourne Australia Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Solan India Department of Electrical Engineering Maharaja Ranjit Singh Punjab Technical University Punjab India Adama Science and Technology University Adama Ethiopia

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Fabrication of highly reliable joint based on Cu/Ni/Sn double-layer powder for high temperature application

Fabrication of highly reliable joint based on Cu/Ni/Sn doubl...

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作者： Xu, Hongyan Shen, Yaochun Hu, Yihua Li, Jianqiang Xu, Ju Beijing Engineering Laboratory of Electrical Drive System and Power Electronic Device Packaging Technology Micro-Nano Fabrication Technology Department Institute of Electronic Engineering Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering Electronics and Computer Science University of Liverpool Brownlow Hill LiverpoolL69 3GJ United Kingdom State Key Laboratory of Multiphase Complex System Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China

A highly reliable three-dimensional network structure joint was fabricated based on Cu/Ni/Sn powder with double-layer coatings and transient liquid phase bonding (TLPB) technology for high temperature application. The Cu/Ni/Sn joint is characterized by Cu metal particles embedded in the matrix of (Cu,Ni)6Sn5/Ni3Sn4 intermetallic compounds (IMCs), with a low void ratio, and can be reflowed at low temperatures (6Sn5/ Ni3Sn4 IMCs, and the Cu/Ni/Sn joint had a lower void ratio and a higher shear strength than those of Cu/Sn. The mechanism of the Ni-coating layer inhibiting phase transformation was studied. The high reliable three-dimensional network structure joint based on Cu/Ni/Sn double-layer powder was fabricated for high temperature application. Copyright © International Microelectronics Assembly and Packaging Society

关键词： High temperature applications

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A multi-functional reconfigurable metasurface: Electromagnetic design accounting for fabrication aspects

arXiv

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arXiv 2020年

作者： Pitilakis, Alexandros Tsilipakos, Odysseas Liu, Fu Kossifos, Kypros M. Tasolamprou, Anna C. Kwon, Do-Hoon Mirmoosa, Mohammad Sajjad Manessis, Dionysios Kantartzis, Nikolaos V. Liaskos, Christos Antoniades, Marco A. Georgiou, Julius Soukoulis, Costas M. Kafesaki, Maria Tretyakov, Sergei A. The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece The Institute of Electronic Structure and Laser Foundation for Research and Technology Hellas Heraklion71110 Greece The Institute of Electronic Structure and Laser Foundation for Research and Technology Hellas Heraklion Crete71110 Greece The Ames Laboratory-U.S. DOE and Department of Physics and Astronomy Iowa State University AmesIA50011 United States The Department of Electronics and Nanoengineering Aalto University P.O. Box 15500 AaltoFI-00076 Finland The Department of Electrical and Computer Engineering University of Cyprus Cyprus The Department of Electrical Computer & Biomedical Engineering Ryerson University ONM5B 2K3 Canada The Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States System Integration and Interconnection Technologies Fraunhofer IZM Berlin Germany The Institute of Computer Science Foundation for Research and Technology Hellas Heraklion Greece The University of Ioannina Computer Science Engineering Department Greece

In this paper we present the theoretical considerations and the design evolution of a proof-of-concept reconfigurable metasurface, primarily used as a tunable microwave absorber, but also as a wavefront manipulation and polarization conversion device in reflection. We outline the design evolution and all considerations taken into account, from the selection of patch shape, unit cell size, and substrate, to the topology of the structure that realizes the desired tunability. The presented design conforms to fabrication restrictions and is co-designed to work with an integrated circuit chip for providing tunable complex loads to the metasurface, using a commercially available semiconductor process. The proposed structure can perform multiple tunable functionalities by appropriately biasing the integrated circuit: Perfect absorption for a wide range of incidence angles of both linear polarization states, accommodating a spectral range in the vicinity of 5 GHz, with potential also for wavefront control, exemplified via anomalous reflection and polarization conversion. The end vision is for such a design to be scalable and deployable as a practical HyperSurface, i.e., an intelligent multi-functional metasurface capable of concurrent reconfigurable functionalities: absorption, beam steering, polarization conversion, wavefront shaping, holography, and sensing. Copyright © 2020, The Authors. All rights reserved.

关键词： Wavefronts

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Epileptic seizures detection using deep learning techniques: A review

arXiv

引用

arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Moridian, Parisa Alizadehsani, Roohallah Panahiazar, Maryam Khozeimeh, Fahime Zare, Assef Hosseini-Nejad, Hossein Khosravi, Abbas Atiya, Amir F. Aminshahidi, Diba Hussain, Sadiq Rouhani, Modjtaba Nahavandi, Saeid Acharya, Udyavara Rajendra K. N. Toosi University of Technology Tehran*** Iran The Computer Engineering Department Ferdowsi University of Mashhad Mashhad*** Iran The Mashhad Branch Islamic Azad University Mashhad91735413 Iran Electrical and Computer Engineering Faculty Semnan University Semnan*** Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran*** Iran Deakin University Waurn PondsVIC3217 Australia The Institute for Computational Health Sciences School of Medicine University of California San FranciscoCA94143 United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad*** Iran The Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran*** Iran The Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt System Administrator at Dibrugarh University Assam 786004 India The Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore599494 Singapore The Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore The Department of Bioinformatics and Medical Engineering Taichung City41354 Taiwan

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated. © 2020, CC BY-NC-ND.

关键词： Feature extraction

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Texture evaluation of transparent acrylic resin using self-organizing maps

Texture evaluation of transparent acrylic resin using self-o...

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2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in computer Science, computer engineering and Applied Computing, CSCE 2018

作者： Wakabayashi, Y. Chanthasen, K. Yoshida, S. Kawabata, Y. Ogawa, T. Sugibayashi, T. Department of Mechanical System Engineering Takushoku University Tokyo Japan Mechanical CAD Unit Polytechnic University of Japan Tokyo Japan Department of Electronics and Computer System Takushoku University Tokyo Japan

ISBN: (纸本)1601324804

This paper presents the evaluation method of transparent acrylic resin using Self-organizing maps. Surface texture of products needs the sensitive information for adding high-value to the products. It needs the technology method to convey sensitive information for designing and producing products. For this purpose, the method using a self-organizing map (an unsupervised learning neural network) was presented in this report. A transparent acrylic resin, shot blasted, was used for a specimen and its surface texture parameters were evaluated by self-organizing map. The surface texture parameters: arithmetic mean height;arithmetic mean roughness;transmittance;haze are measured and the relation of these parameters was shown on the self-organizing map. This map showed clearly the relation of these parameters and it indicates that the self-organizing map can be used for the evaluation of acrylic resin texture shot blasted. © 2018 CSREA Press. All rights reserved.

关键词： Surface roughness

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Erratum to “Modeling Avalanche Induced Degradation for 4H-SiC Power MOSFETs”

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IEEE Transactions on Power electronics 2021年第2期36卷 2447-2447页

作者： Jiaxing Wei Siyang Liu Xiaobing Zhang Weifeng Sun Alex Q. Huang National ASIC System Engineering Research Center Southeast University Nanjing China Semiconductor Power Electronics Center Electrical and Computer Engineering Department The University of Texas at Austin USA Display R&D Center Southeast University Nanjing China

Presents corrections to author affiliation information in the above named paper.

关键词： Wide band gap semiconductors Silicon compounds Degradation MOSFET Semiconductor device reliability

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