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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Zehua Dou Yuezhen Xu Zhiying Wei Hannes Emmerich Juergen Czarske David Weik Faculty of Electrical and Computer Engineering Laboratory of Measurement and Sensor System Technique Technische Universität Dresden Dresden Germany Institute of Fluid Dynamics Helmholtz-Zentrum Dresden-Rossendorf Dresden Germany

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Low temperature fuel cells (LTFCs) present promising technical solutions for enabling broad applications of green Hydrogen energy. However, the efficiencies and durability of LTFCs are limited by the dynamic water evolution and transport, especially when they are operated at high current densities. Developing water monitoring techniques therefore presents a crucial strategy for optimizing the designs and operating schemes of LTFCs, and eventually overcoming such water management issues. This work explores the possibilities of using ultrasonic techniques to investigate the water contents in the gas diffusion layers (GDL), as one of the key components of the membrane electrode assembly in LTFCs. For this purpose, we numerically and experimentally investigated the propagation of longitudinal mode ultrasonic waves in GDLs at different water saturation levels. The numerical simulations and experimental investigations both reveal the changes in the speed of sound and acoustic attenuation coefficient with respect to the water saturation level of the GDL. These physical effects open new horizons for developing simplified and cost-effective ultrasound based water monitoring systems towards applications in operating LTFCs.

关键词： Temperature measurement Electrodes Ultrasonic imaging Ultrasonic variables measurement Fuel cells Green hydrogen Acoustics Temperature control Water monitoring Assembly

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A Lightweight Attention Network for Camouflaged Object Fixation Prediction

A Lightweight Attention Network for Camouflaged Object Fixat...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Qingbo Wu Guanxing Wu Shengyong Chen Key Laboratory of Computer Vision and Systems (Ministry of Education) Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper proposes a novel encoder-decoder architecture for camouflaged object fixation prediction (COFP), which builds on a large-kernel decomposition technique and depth-wise separable convolution embedded attention. Specifically, the encoder with the large-kernel decomposition adopts a Visual Attention Network (VAN) as the backbone to extract more accurate features, and the decoder predicts fixation maps from concealed objects using the attention module incorporating depth-wise separable convolution. Both designs aim to reduce the number of learnable parameters, making the model lighter and simultaneously maintaining the performance of COFP. In addition, we propose using the reverse Kullback-Leibler Divergence (KLDiv) as the loss function. Extensive experiments demonstrate that the proposed method performs well against SOTA approaches in quality and quantity.

关键词： Industrial electronics Visualization Accuracy Convolution Feature extraction Decoding

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Adapting the Simulated Annealing Algorithm to Enhance the Performance of the LEACH Protocol

Adapting the Simulated Annealing Algorithm to Enhance the Pe...

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International Conference on computer and Applications (ICCA)

作者： Mohammed Benhadji Kaddi Mohammed Omari Mohammed Material Sciences Department Laboratory of Energy Environment and Information System (LEESI) University of Adrar Algeria Mathematics and Computer Science Department LDDI Laboratory University of Adrar Algeria Computer Science and Engineering Department American University of Ras Al Khaimah Ras Al Khaimah United Arab Emirates

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

In parallel with the proliferation and extension of wireless sensor networks (WSNs), as well as the diversity of their applications, such networks continue to fail to operate for lengthy periods of time due to node failure and the inability to replace sensor batteries in most circumstances. This hinders the networks' ability to remain operational over extended periods. That necessitates making the best and most efficient use of the nodes' energy. Hierarchical clustering methods augmented with metaphysical algorithms have produced amazing benefits in terms of network longevity. One of the most significant of these algorithms that draws inspiration from nature and yields excellent results is the simulated annealing technique. In this research, we report on our analysis of this method and how it performs best when used to optimize routing in WSN.

关键词： Wireless sensor networks Protocols Heuristic algorithms Metaheuristics Energy conservation Clustering algorithms Simulated annealing Spread spectrum communication Routing Testing

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Graphene-based dual-function acoustic transducers for machine learning-assisted human-robot interfaces

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InfoMat 2023年第2期5卷 66-79页

作者： Hao Sun Xin Gao Liang-Yan Guo Lu-Qi Tao Zi Hao Guo Yangshi Shao Tianrui Cui Yi Yang Xiong Pu Tian-Ling Ren Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijingPeople's Republic of China CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijingPeople's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingPeople's Republic of China School of Computer Science&Technology Beijing Institute of TechnologyBeijingPeople's Republic of China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijingPeople's Republic of China

Human–robot interface(HRI)electronics are critical for realizing robotic ***,we report graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces(GHRI).The GHRI functions both an artificial ear through the triboelectric acoustic sensing mechanism and an artificial mouth through the thermoacoustic sound emission *** success of the integrated device is also attributed to the multifunctional laser-induced graphene,as either triboelectric materials,electrodes,or thermoacoustic *** systematically optimizing the structure parameters,the GHRI achieves high sensitivity(4500 mV Pa^(–1))and operating durability(1000000 cycles and 60 days),capable of recognizing speech identities,emotions,content,and other information in the human *** the assistance of machine learning,30 speech categories are trained by a convolutional neural network,and the accuracy reaches 99.66%and 96.63%in training datasets and test ***,GHRI is used for artificial intelligence communication based on recognized speech *** work shows broad prospects for the development of robotic intelligence.

关键词： human-robot interface laser-induced graphene machine learning thermoacoustic triboelectric nanogenerator

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Clustering With Adaptive Graph Learning and Spectral Rotation 7

Clustering With Adaptive Graph Learning and Spectral Rotatio...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of engineering & Technology 2023.

关键词： Fuzzy clustering

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Quantum noise of dark pulse microcombs

Quantum noise of dark pulse microcombs

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Lao, Chenghao Jin, Xing Chang, Lin Xie, Weiqiang Shu, Haowen Wang, Xingjun Bowers, John E. Yang, Qi-Fan State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontiers Science Center for Nano-Optoelectronics School of Physics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China

We investigate the fundamental timing jitter of dark pulse microcombs generated in an AlGaAs microresonator, where the quantum-limited frequency noise of the 91-GHz repetition rate is measured to be 0.5 Hz2/Hz. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Aluminum gallium arsenide

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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Enhancing Security in OpenID Connect Single Sign-On (SSO) systems: A Comprehensive Vulnerability Analysis and Mitigation Approach 4

Enhancing Security in OpenID Connect Single Sign-On (SSO) Sy...

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Al-Shabi, Mohamed Bennour, Akram Al-Sarem, Mahamed Khalaf, Osama Ibrahim Sikder, Rajesh Algburi, Sameer Department of management Information system Taibah University Medina Saudi Arabia "LAMIS" Laboratory Echahid Chikh Larbi Tebessi University Tebessa Algeria College of Computer Science and Engineering Taibah University Medina Saudi Arabia Al-Nahrain University Jadriya Baghdad Iraq Information Technology University of the Cumberlands United States Al-Kitab University College of Engineering Techniques Kirkuk Iraq

ISBN: (纸本)9781839539954

Single Sign-On (SSO) systems have become popular for simplifying the login process by allowing users to authenticate with a single identity provider (IDP). However, the widespread use of these systems raises concerns about user privacy, as IDPs like Google or Facebook can gather extensive information about user behavior on the web. This poses a significant challenge for privacy-conscious users who wish to limit the disclosure of their web access behavior to third-party organizations. In this paper, we present a comprehensive and large-scale practical study focused on the OpenID Connect protocol, a widely used SSO protocol. Our analysis delves into the various stages of the protocol's operation, highlighting its security flaws and vulnerabilities. Additionally, we conduct a systematic examination of the privacy implications associated with user access to SSO systems. We provide a detailed account of how easily user information can be accessed, shedding light on the potential risks. © The Institution of engineering & Technology 2023.

关键词： Differential privacy

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MISP: An Efficient Quantum-resistant Misbehavior Preventing Scheme with Self-enforcement for Vehicle-to-Everything

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Ying He, Debiao Bao, Zijian Wang, Huaqun Luo, Min Wuhan University School of Cyber Science and Engineering Wuhan430072 China Key Laboratory of Computing Power Network & Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Nanjing University of Posts and Telecommunications College of Computer Nanjing210023 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network & Information System Security Jinan250014 China

In the Vehicle-to-Everything (V2X) communication system, the presence of ambiguous warnings significantly increases the risk of severe accidents, posing a substantial threat to the safety of autonomous driving. It is crucial to detect such confusing warnings to avoid danger. Existing solutions to address this issue heavily rely on trust entities or are constructed based on number theory assumptions, leading to low efficiency and vulnerability to quantum attacks. In this paper, we leverage the double authentication-preventing signature scheme (DAPS) to present a revocable identity-based double-authentication preventing signature scheme (RIDAPS) and provides an instantiation from lattice. Furthermore, we propose a post-quantum secure misbehavior preventing scheme (Misp) based on our RIDAPS scheme. We give a detailed proof in the random oracle model (ROM) to demonstrate that our contribution achieves security requirements. Additionally, the efficiency evaluation results demonstrate that our scheme is suitable to be applied in V2X. © 2004-2012 IEEE.

关键词： Authentication

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Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading

arXiv

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arXiv 2024年

作者： Hajighasemlou, Mostafa Sheikhaei, Samad Soltanian-Zadeh, Hamid School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14399 Iran Advancom Lab. School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14395 Iran Medical Image Analysis Laboratory Departments of Radiology and Research Administration Henry Ford Health System DetroitMI48202 United States

Artificial intelligence algorithms have demonstrated their image classification and segmentation ability in the past decade. However, artificial intelligence algorithms perform less for actual clinical data than those used for simulations. This research aims to present a novel hybrid learning model using self-supervised learning and knowledge distillation, which can achieve sufficient generalization and robustness. The self-attention mechanism and tokens employed in ViT, besides the local-to-global learning approach used in the hybrid model, enable the proposed algorithm to extract a high-dimensional and high-quality feature space from images. To demonstrate the proposed neural network's capability in classifying and extracting feature spaces from medical images, we use it on a dataset of Diabetic Retinopathy images, specifically the EyePACS dataset. This dataset is more complex structurally and challenging regarding damaged areas than other medical images. For the first time in this study, self-supervised learning and knowledge distillation are used to classify this dataset. In our algorithm, for the first time among all self-supervised learning and knowledge distillation models, the test dataset is 50% larger than the training dataset. Unlike many studies, we have not removed any images from the dataset. Finally, our algorithm achieved an accuracy of 79.1±1% in the linear classifier and 74.36% in the k-NN algorithm for multiclass classification. Compared to a similar state-of-the-art model, our results achieved higher accuracy and more effective representation spaces. © 2024, CC BY.

关键词： Self-supervised learning

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