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Annual Conference of Industrial Electronics Society

作者： Ruimin Yang Jeroen van Schieveen Stoyan Nihtianov Jo Spronck Faculty of Electrical Engineering Department of Microelectronics Electronic Instrumentation Laboratory Delft University of Technnology Delft Netherlands Faculty of 3mE Department of Precision and Microsystem Engineering Mechatronic System Design Delft University of Technnology Delft Netherlands

This article presents an optimized thermal stepper system which is used to align the critical parts of high-performance smart sensors, such as capacitive sensors. In order to apply the system in harsh and inaccessible industrial environments, improvements have been made, such as reduced power consumption (1mW during measurement mode), increased alignment accuracy (within 0.1um), increased interconnect reliability, and better immunity to external interferences. Experimental results are shown which demonstrate the improved performance of the thermal stepper system.

关键词： Temperature measurement Capacitive sensors Accuracy Power demand Heating

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Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

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Science Bulletin 2021年第16期66卷 1624-1633,M0003页

作者： Xumeng Zhang Jian Lu Zhongrui Wang Rui Wang Jinsong Wei Tuo Shi Chunmeng Dou Zuheng Wu Jiaxue Zhu Dashan Shang Guozhong Xing Mansun Chan Qi Liu Ming Liu Frontier Institute of Chip and System Fudan UniversityShanghai 200433China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical and Electronic Engineering the University of Hong KongHong KongChina Zhejiang Laboratory Hangzhou 311122China Department of Electronic and Computer Engineering the Hong Kong University of Science and TechnologyHong KongChina

Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic ***,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological ***,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very ***,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron *** hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic ***,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this *** work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.

关键词： Memristor Hybrid neuron In-situ learning Fully hardware Spiking neural network

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FAVER: Blind Quality Prediction of Variable Frame Rate Videos

arXiv

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arXiv 2022年

作者： Zheng, Qi Tu, Zhengzhong Madhusudana, Pavan C. Zeng, Xiaoyang Bovik, Alan C. Fan, Yibo State Key Laboratory of ASIC & System College of Microelectronics Fudan University Shanghai200000 China Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States

Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales. Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed videos can enable the delivery of more enjoyable content and perceptually optimized rate control. Accordingly, there is a pressing need to develop VQA models that can be deployed at enormous scales. While some recent effects have been applied to full-reference (FR) analysis of variable frame rate and HFR video quality, the development of no-reference (NR) VQA algorithms targeting frame rate variations has been little studied. Here, we propose a first-of-a-kind blind VQA model for evaluating HFR videos, which we dub the Framerate-Aware Video Evaluator w/o Reference (FAVER). FAVER uses extended models of spatial natural scene statistics that encompass space-time wavelet-decomposed video signals, to conduct efficient frame rate sensitive quality prediction. Our extensive experiments on several HFR video quality datasets show that FAVER outperforms other blind VQA algorithms at a reasonable computational cost. To facilitate reproducible research and public evaluation, an implementation of FAVER is being made freely available online: https://***/uniqzheng/ HFR-BVQA. © 2022, CC BY-NC-ND.

关键词： Bandpass filters

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Doppler diversity for OFDM wireless mobile communications. Part I: frequency domain approach

Doppler diversity for OFDM wireless mobile communications. P...

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IEEE Conference on Vehicular Technology (VTC)

作者： Byung-Chul Kim I-Tai Lu System Architecture Design Laboratory TN Business Samsung Electronics Company Limited Suwon Gyeonggi South Korea Department of Electrical and Computer Engineering Polytechnic University Melville NY USA

ISBN: (纸本)0780377575

A novel frequency domain Doppler diversity technique for OFDM systems is proposed. The received OFDM signal is first frequency shifted to generate various diversity branches. After demodulation, branch outputs are individually weighted and added together. For Doppler spread signal, it alters fading statistics and improves the bit error rate. Extensive simulations show that combining three diversity branches with proper frequency shifts reduces BER by an order when the normalized Doppler spread is larger than 0.3.

关键词： OFDM Wireless communication Mobile communication Frequency domain analysis Diversity reception Frequency diversity Bit error rate Signal generators Demodulation Fading

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Analog signal-selector circuit for quick gas detection from gas-sensor arrays

Analog signal-selector circuit for quick gas detection from ...

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： H.S. Abdel-Aty-Zohdy F.A. El-Licy Dept. of Electr. & Syst. Eng. Oakland Univ. Rochester MI USA Microelectronics System Design Laboratory Department of Electrical and Systems Engineering Oakland University Rochester MI USA

A Sixteen Input Maximum/Minimum Analog signal-selector Circuit (SIMMAC) is designed, simulated, and implemented using 2 /spl mu/m p-well (Orbit) CMOS technology. The circuit is useful in recognition of gas types from measurements of thin-film gas-sensor arrays. The output signal of each thin-film gas detector is typically a function of bias voltages, temperature and pressure, film type, frequency, and gas type. Gas sensor arrays provide profiles of resistivity vs. temperature, for a particular PPM, and/or profiles of resistivity vs. pressure at a particular temperature. Since different chemical gas types have peak resistivity values at a certain temperature and a certain pressure, a selector circuit is needed to identify chemical gas components. The SIMMAC design is based on a multiple-input current comparator median circuit. Simulation results gave an accuracy of better than 94% with analog input signal values from 1.6 to 3.6 V. The SIMMAC is suitable for gas detection from a sensor-array of up to 16-elements with known models and negligible signal distortion. Further, it may be used as a post processor to select the winning synapse in an unsupervised neural network for pattern recognition with distorted and unidentified models of gas mixtures.

关键词： Temperature sensors Conductivity Circuit simulation CMOS technology Thin film circuits Gas detectors Sensor arrays Chemicals Signal design CMOS analog integrated circuits

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Advances in Multimedia Information Processing - PCM 2010 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Guoping Qiu Kin Man Lam Hitoshi Kiya Xiang-Yang Xue C.-C. Jay Kuo Michael S. Lew

ISBN: (数字)9783642157028

ISBN: (纸本)9783642157011

The 2010 Pacific-Rim Conference on Multimedia (PCM 2010) was held in Shanghai at Fudan University, during September 21–24, 2010. Since its inauguration in 2000, PCM has been held in various places around the Pacific Rim, namely Sydney (PCM 2000), Beijing (PCM 2001), Hsinchu (PCM 2002), Singapore (PCM 2003), Tokyo (PCM 2004), Jeju (PCM 2005), Zhejiang (PCM 2006), Hong Kong (PCM 2007), Tainan (PCM 2008), and Bangkok (PCM 2009). PCM is a major annual international conference organized as a forum for the dissemination of state-of-the-art technological advances and research results in the fields of theoretical, experimental, and applied multimedia analysis and processing. PCM 2010 featured a comprehensive technical program which included 75 oral and 56 poster presentations selected from 261 submissions from Australia, Canada, China, France, Germany, Hong Kong, India, Iran, Italy, Japan, Korea, Myanmar, Norway, Singapore, Taiwan, Thailand, the UK, and the USA. Three distinguished researchers, Prof. Zhi-Hua Zhou from Nanjing University, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft Research Asia delivered three keynote talks to the conference. We are very grateful to the many people who helped to make this conference a s- cess. We would like to especially thank Hong Lu for local organization, Qi Zhang for handling the publication of the proceedings, and Cheng Jin for looking after the c- ference website and publicity. We thank Fei Wu for organizing the special session on large-scale multimedia search in the social network settings.

关键词： computer Applications Multimedia Information systems computer Communication Networks Information systems Applications (incl. Internet) Information Storage and Retrieval User Interfaces and Human computer Interaction

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Advances in Multimedia Information Processing - PCM 2010 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Guoping Qiu Kin Man Lam Hitoshi Kiya Xiang-Yang Xue C.-C. Jay Kuo Michael S. Lew

ISBN: (数字)9783642156960

ISBN: (纸本)9783642156953

关键词： Multimedia Information systems computer Communication Networks Information systems Applications (incl. Internet) Information Storage and Retrieval User Interfaces and Human computer Interaction Software engineering

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Numerical analysis of high-efficiency lead-free perovskite solar cell with NiO as hole transport material and PCBM as electron transport material

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CSI Transactions on ICT 2020年第2期8卷 111-116页

作者： T. R. Lenka A. C. Soibam K. Dey T. Maung F. Lin Microelectronics and VLSI Design Group Department of Electronics and Communication Engineering National Institute of Technology Silchar Silchar India Cavendish Laboratory Department of Physics University of Cambridge Cambridge UK Solar Energy Research Institute of Singapore National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In this work a lead free perovskite solar cell structure is proposed with NiO as the hole transport material (HTM), CH3NH3SnI3 as the perovskite absorber material and PCBM (phenyl C61 butyric acid methyl ester) as the electron transport material (ETM). Numerical analysis of the designed solar cell is performed using Solar Cell Capacitance Simulator (SCAPS-1D) program. The power conversion efficiency (PCE) of the optimized device stack is found to be above 29% with Voc = 0.98 V, Jsc = 34.86 mA/cm2, FF = 85.64%. The lead free perovskite solar cell with different HTM and ETM may be investigated for high PCE.

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Self-supervised On-device Federated Learning from Unlabeled Streams

arXiv

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arXiv 2022年

作者： Shi, Jiahe Wu, Yawen Zeng, Dewen Tao, Jun Hu, Jingtong Shi, Yiyu The State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China The Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

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A Compact Correlation Filter For On-Chip Learning in a Spiking Neural Network

A Compact Correlation Filter For On-Chip Learning in a Spiki...

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： Jacob N. Allen Hoda S. Abdel-Aty-Zohdy Robert L. Ewing Microelectronics System Design Laboratory Department of Elect. and Computer Engineering Oakland University Rochester MI USA Member IEEE Information Directorate Air Force Research Labs Wright-Patterson Air Force Base Ohio 45433

A Hebbian learning algorithm based on proportion sampling is presented that can be used to implement on-chip learning for a binary spiking neural network. A correlation filter estimates when statistical independence has been obtained between subsequent samples. Simulation shows that the correlation filter reduces falsely learned connections in environments were inputs are randomly activated an average of 83% of the total time. A correlation filter for 255 binary samples is implemented using 21 gates and a surface area of .0008cm2 for a .5μ fabrication process. Compared to traditional neural networks, the spiking neural network learned an odor in a single epoch resulting in only a 7% error, while classical learning algorithms required multiple epochs and typically resulted in 30% error.

关键词： Filters Network-on-a-chip Neural networks Neurons Hebbian theory Working environment noise microelectronics Sampling methods Active noise reduction Jacobian matrices

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