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arXiv 2024年

作者： Horyna, Jiří Krátký, Vít Pritzl, Václav Báča, Tomáš Ferrante, Eliseo Saska, Martin Multi-Robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Technicka 2 Prague Czech Republic Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is designed to decrease the inter-agent oscillations common in standard reactive swarm models employed in fast collective motion. The novel swarming methodology is supplemented with an enhanced Multi-Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MRSE and the neighborhood model may rely on information exchange between agents, we introduce a communication-less version of the swarming framework based on estimating communicated states to decrease dependence on the often unreliable communication networks of large swarms. The proposed solution has been verified by a set of complex real-world experiments to demonstrate its overall capability in different conditions, including a UAV interception-motivated task with a group velocity reaching the physical limits of the individual hardware platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Quantization in Distributed Learning for Privacy Preserving: A Systematic Literature Review

Quantization in Distributed Learning for Privacy Preserving:...

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Lamees M. Al Qassem Maurizio Colombo Ernesto Damiani Rasool Asal Al Anoud Almemari Yousof Alhammadi EBTIC Khalifa University Abu Dhabi UAE Center for Cyber Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Department of Computer Science Khalifa University Abu Dhabi UAE Computer Science Department Universita degli Studi di Milano Milan Italy College of Engineering and IT University of Dubai Dubai UAE

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

A large literature is available on quantization for communication efficiency in distributed learning. However, these studies often overlook the enhancement of privacy through quantization. This paper aims to fill this research gap by undertaking a systematic literature review on the use of quantization in distributed learning for privacy enhancement. We explore peer-reviewed literature that utilizes quantization for privacy-preserving purposes. Our analysis identifies the limitations and challenges of current approaches. It also highlights the need to integrate quantization techniques for dual objectives (privacy and communication efficiency) in distributed learning frameworks.

关键词： computers Privacy computer aided instruction Cloud computing Quantization (signal) Distance learning Systematic literature review

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Accelerating temporal action proposal generation via high performance computing

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Frontiers of computer science 2022年第4期16卷 59-68页

作者： Tian Wang Shiye Lei Youyou Jiang Choi Chang Hichem Snoussi Guangcun Shan Yao Fu Institute of Artificial Intelligence Beihang UniversityBeijing100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191China School of Software Tsinghua UniversityBeijing100084China Department of Computer Engineering Gachon UniversitySeongnam13120South Korea Institute Charles Delaunay-LM2S FRE CNRS 2019 University of Technology of TroyesTroyes10010France School of Instrumentation Science and Opto-electronics Engineering Beihang UniversityBeijing100191China Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchun130033China

Temporal action proposal generation aims to output the starting and ending times of each potential action for long videos and often suffers from high computation *** address the issue,we propose a new temporal convolution network called Multipath Temporal ConvNet(MTCN).In our work,one novel high performance ring parallel architecture based is further introduced into temporal action proposal generation in order to respond to the requirements of large memory occupation and a large number of ***,the total data transmission is reduced by adding a connection between multiple-computing load in the newly developed *** to the traditional Parameter Server architecture,our parallel architecture has higher efficiency on temporal action detection tasks with multiple *** conduct experiments on ActivityNet-1.3 and THUMOS14,where our method outperforms-other state-of-art temporal action detection methods with high recall and high temporal *** addition,a time metric is further proposed here to evaluate the speed performancein the distributed training process.

关键词： temporal convolution temporal action proposal generation deep learning

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Secure Wireless Communications via Frequency Diverse Arrays

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IEEE Signal Processing Letters 2025年 32卷 2319-2323页

作者： Cheng, Zhenqiao Ouyang, Chongjun Zhang, Xingqi China Telecom Beijing Research Institute 6G Research Centre Beijing102209 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

A novel frequency diverse array (FDA)-assisted secure transmission framework is proposed, which leverages additional frequency offsets to enhance physical layer security. Specifically, an FDA-assisted wiretap channel is considered, where the transmit beamforming and frequency offsets at each antenna are jointly optimized. A novel alternating optimization-based method is introduced to address the non-convex problem of secure transmission, focusing on minimizing transmit power and maximizing the secrecy rate. Numerical results are provided to demonstrate the superiority of the FDA-based framework compared to systems employing traditional phased array antennas in secure transmission. © 1994-2012 IEEE.

关键词： Beamforming

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Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives

arXiv

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arXiv 2023年

作者： Ruan, Gan Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin CERCIA School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom The Honda Research Institute Europe GmbH Offenbach63073 Germany The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer algorithm for DMOPs with a changing number of objectives lacks sufficient diversity when the fitness landscape and Pareto front shape present nonseparability, deceptiveness or other challenging features. Therefore, we propose a knowledge transfer dynamic multi-objective evolutionary algorithm (KTDMOEA) to enhance population diversity after changes by expanding/contracting the Pareto set in response to an increase/decrease in the number of objectives. This enables a solution set with good convergence and diversity to be obtained after optimization. Comprehensive studies using 13 DMOP benchmarks with a changing number of objectives demonstrate that our proposed KTDMOEA is successful in enhancing population diversity compared to state-of-the-art algorithms, improving optimization especially in fast changing environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Remote sensing image scene classification based on multiscale features fusion

Remote sensing image scene classification based on multiscal...

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2022 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2022

作者： He, Jingyuan Yang, Bailong Ruhan, A. Tian, Luogeng Su, Yang Department of Operational Support PLA Rocket Force University of Engineering 2 Tongxin Road Shaanxi Xi’an710025 China School of Mathematics and Computer Science Yan’an University 580 Holy Land Road Shaanxi Yan’an716000 China

ISBN: (纸本)9781510655072

In order to solve the difficulties of multi-scale feature extraction and weak representation in remote sensing image scene classification, a classification method based on multi-scale feature fusion (MFF) was proposed. The convolutional representation and fully connected features generated by the feature fusion of the MFF are used as high-level features to generate discriminative scene representations, which are then input into the softmax classifier to obtain the semantic labels of scenes. The existing convolutional neural network-based methods and MFF methods are tested on three widely-used datasets. The results show that the MFF method has higher overall accuracy than the existing convolutional neural network-based methods and can better meet the current demand for remote sensing image scene classification. © 2022 SPIE.

关键词： Convolution

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Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

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computers, Materials & Continua 2022年第4期71卷 159-170页

作者： Dezheng Zhang Qi Jia Shibing Yang Xinliang Han Cong Xu Xin Liu Yonghong Xie School of Computer&Communication Engineering University of Science&Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Inspur Electronic Information Industry Co. Ltd.&State Key Laboratory of High-End Server&Storage TechnologyJinan250101China Surgical Simulation Research Lab Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1AlbertaCanada

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in *** analyze the reasoning path patterns from symptom to syndromes on the knowledge *** are two kinds of path patterns in the knowledge graph:one-hop and *** one-hop path pattern maps the symptom to syndromes *** two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic *** the different support strengths for the knowledge paths in reasoning,we design a dynamic weight *** utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score *** proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path *** evaluate the method with clinical records and clinical practice in *** preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in ***,the method is robust and explainable under the guide of the knowledge *** could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.

关键词： Traditional Chinese medicine automated diagnosis knowledge graph Naïve Bayes syndrome differentiation

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Traffic Classification based on Incremental Learning Algorithms for the Software-Defined Networks

Traffic Classification based on Incremental Learning Algorit...

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Eldhai, Arwa M. Hamdan, Mosab Khan, Suleman Hamzah, Muzaffar Marsono, M.N. University of Science and Technology Faculty of Engineering Khartoum11115 Sudan Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia University of São Paulo Department of Computer Science São Paulo05508-090 Brazil University of Central Lancashire School of Psychology and Computer Science PrestonPR1 2HE United Kingdom Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor81310 Malaysia

ISBN: (纸本)9798350345933

A new era of network administration has been ushered in by recent developments in software-defined networks (SDN) and traffic classification (TC) using machine learning (ML) techniques. All network devices are centrally managed and accessible through the SDN, which can simplify the TC process. The traditional data mining/ML approach that uses TC assumes that all of the task's data is always accessible and can be viewed simultaneously without processing time and memory restrictions. Therefore, these approaches are not effective in the case of stream learning since, in more realistic settings, the data is not available all at once and has a distinct distribution. Consequently, incremental learning algorithms (ILAs) can handle online data mining. This study's primary goal is to contrast various ILA approaches to enhance SDN's TC performance. In this study, we propose four ILAs: the self- adjusting memory coupled with the k Nearest Neighbor (kNN) classifier (SAMKNNC), the very fast decision rules classifier (VFDRC), the extremely fast decision tree classifier (EFDTC), and the streaming random patches ensemble classifier (SRPC). Both real and synthetic datasets are used for validation. Experimental findings reveal that the proposed techniques perform better in SDN traffic classification since they can effectively identify drift and use less memory and time. © 2022 IEEE.

关键词： Learning algorithms

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Deep Bispectral Analysis of Conversational Speech Towards Emotional Climate Recognition 5

Deep Bispectral Analysis of Conversational Speech Towards Em...

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5th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2023

作者： Alhussein, Ghada Alkhodari, Mohanad Khandoker, Ahsan H. Hadjileontiadis, Leontios J. Khalifa University of Science and Technology Department of Biomedical Engineering P.O. Box 127788 Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Healthcare Engineering Innovation Center P.O. Box 127788 Abu Dhabi United Arab Emirates University of Oxford Cardiovascular Clinical Research Facility Radcliffe Department of Medicine OxfordOX1 2JD United Kingdom Aristotle University of Thessaloniki Department of Electrical & Computer Engineering ThessalonikiGR 54124 Greece

ISBN: (纸本)9798350304152

Peers' conversational speech plays a significant role in shaping the emotional climate (EC) during interactions. Machine-based recognition of EC provides insights into the emotional perception of conversations by both peers and external observers. In this paper, we propose DeepBispec, a novel approach for EC recognition using deep bispectral analysis. DeepBispec applies windowed bispectral analysis to the 1D conversational speech signal. By capturing higher-order spectral correlations, the bispectrum magnifies the nonlinear characteristics present in speech signals. The estimated 2D -bispectrum magnitude contours, representing these interactions, are transformed into colored images and fed into a convolutional neural network (CNN). The CNN learns deep features from the bispectrum magnitude contours, enabling it to predict the valence (V) and arousal (A) labels associated with the EC. Evaluating DeepBispec on the K- EmoCon dataset using 10-fold cross-validation, we achieve an accuracy of 0.789 (A)/0.771 (V), an F1 score of 0.850 (A)/0.836 (V), and an area under the curve (AUC) of 0.812 (A)/0.788 (V). These results surpass existing benchmarks, demonstrating the effectiveness of bispectrum in capturing nonlinear characteristics and improving EC recognition. DeepBispec introduces an innovative approach to analyzing conversational speech for enhanced EC recognition. By leveraging deep bispectral analysis and CNN, it uncovers the higher-order spectral correlations and nonlinear dynamics of speech signals. This contributes to a deeper understanding of emotional dynamics in conversations and provides valuable insights into EC perception. © 2023 IEEE.

关键词： Emotion Recognition

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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