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Innovative Competency-Based Electrical engineering Curriculum 54

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Innovative Competency-Based Electrical Engineering Curriculu...

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Rehder, Gustavo P. Serrano, Ariana L. C. Grimoni, José B. Brandão, Anarosa A. F. Silva, Magno M. T. Moriya, Henrique T. Panazio, Cristiano M. Barbero, Edson Alayo, Marco I. Gianesi, Irineu G. N. Seabra, Antonio C. Universidade de São Paulo Electronic Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Electric Automation and Energy Dept. The Escola Politécnica São Paulo Brazil Universidade de São Paulo Computer Engineering and Digital Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Telecommunications and Control Dept. The Escola Politécnica São Paulo Brazil Á lvares Penteado School of Commerce Foundation São Paulo Brazil Instituto Mauá de Tecnologia São Paulo Brazil

ISBN: (纸本)9798350351507

The Electrical engineering undergraduate program of the Escola Politécnica of the Universidade de São Paulo has created an alternative innovative curriculum path for its freshmen students in 2024. The Competency-based Path emphasizes the development of competencies and skills for the unprecedented technological advancements that necessitate a holistic education for future electrical engineers, preparing them with practical skills for real-world challenges. The program was designed using an innovative methodology and centers around semester-long or year-long integrative projects. These projects bridge the gap between theory and practice, as students deal with real-world challenges while fostering social responsibility. © 2024 IEEE.

关键词： Students

One-to-Many Semantic Communication systems: Design, Implementation, Performance Evaluation

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arXiv 2022年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics 210000 China The College of Communication and Information Engineering Nanjing University of Posts and Telecommunications 210003 China The Department of Electrical and Computer Engineering Utah State University LoganUT United States

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Research on Geomembrane Leakage Detection Method Based on Quadruped Robot

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Research on Geomembrane Leakage Detection Method Based on Qu...

Chinese Automation Congress (CAC)

作者： Pengcheng Zhang Ningning Hu Xiao Wang Qingbiao Xi Haohao Zhao Xingchen Wu Linfeng Qin Dongli Yuan Investigation & Testing Research Institute Shanghai Investigation Design & Research Institute Co. Ltd. Shanghai China System Department 2 Xi'an ASN Technology Group Co. Ltd. Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the problem of leakage detection of seepage control geomembranes in landfills, a fully autonomous dipole method detection system based on a quadruped robot equipped with a dipole sensor is designed. The feasibility and superiority of using a quadruped robot are analyzed on the basis of the detection principle of the dipole method. Due to the problems existing in the process of upgrading the artificial static detection to autonomous dynamic detection, a dynamic detection method based on a quadruped robot is designed while the structure construction of the dipole method on the quadruped robot is completed. The experimental results show that the efficiency of geomembrane leakage detection can be improved with detection system method employed in this paper.

关键词： Legged locomotion Costs Automation Landfills Dogs Robot sensing systems Software Quadrupedal robots Intelligent control

Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization with Variance Reduction

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arXiv 2023年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen School of Automation Central South University Hunan Changsha410083 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Department of Mechanical Engineering City University of Hong Kong Kowloon Tong Hong Kong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This paper aims to handle a class of general composite optimization problems over multi-agent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient model-aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed ProxDBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into ProxDBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sub-linearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A trade-off between convergence accuracy and Byzantine resilience in both linear and sub-linear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine-learning problem under various Byzantine attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous agents

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

Secure Communication of Active RIS Assisted NOMA Networks

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arXiv 2024年

作者： Li, Xuehua Pei, Yingjie Yue, Xinwei Liu, Yuanwei Ding, Zhiguo The Key Laboratory of Information and Communication Systems Ministry of Information Industry The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

As a revolutionary technology, reconfigurable intelligent surface (RIS) has been deemed as an indispensable part of the 6th generation communications due to its inherent ability to regulate the wireless channels. However, passive RIS (PRIS) still suffers from some pressing issues, one of which is that the fading of the entire reflection link is proportional to the product of the distances from the base station to the PRIS and from the PRIS to the users, i.e., the productive attenuation. To tackle this problem, active RIS (ARIS) has been proposed to reconfigure the wireless propagation condition and alleviate the productive attenuation. In this paper, we investigate the physical layer security of the ARIS assisted non-orthogonal multiple access (NOMA) networks with the attendance of external and internal eavesdroppers. To be specific, the closed-form expressions of secrecy outage probability (SOP) and secrecy system throughput are derived by invoking both imperfect successive interference cancellation (ipSIC) and perfect SIC. The secrecy diversity orders of legitimate users are obtained at high signal-to-noise ratios. Numerical results are presented to verify the accuracy of the theoretical expressions and indicate that: i) The SOP of ARIS assisted NOMA networks exceeds that of PRIS-NOMA, ARIS/PRIS-assisted orthogonal multiple access (OMA);ii) Due to the balance between the thermal noise and residual interference, introducing excess reconfigurable elements at ARIS is not helpful to reduce the SOP;and iii) The secrecy throughput performance of ARIS-NOMA networks outperforms that of PRIS-NOMA and ARIS/PRIS-OMA networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

OFDM Achieves the Lowest Ranging Sidelobe Under Random ISAC Signaling

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arXiv 2024年

作者： Liu, Fan Zhang, Ying Xiong, Yifeng Li, Shuangyang Yuan, Weijie Gao, Feifei Jin, Shi Caire, Giuseppe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China The School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China Technical University of Berlin Berlin10623 Germany The Department of Automation Tsinghua University Beijing100084 China

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC): What is the optimal communication-centric ISAC waveform for ranging? Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic autocorrelation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadrature amplitude modulation

Zero-Shot Audio Captioning Using Soft and Hard Prompts

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arXiv 2024年

作者： Zhang, Yiming Xu, Xuenan Du, Ruoyi Liu, Haohe Dong, Yuan Tan, Zheng-Hua Wang, Wenwu Ma, Zhanyu The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Electronic Systems Aalborg University Aalborg9220 Denmark The Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space. In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP. We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method. © 2024, CC BY.

关键词： Semantics

Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors 13

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Cogging Force Identification Based on Self-Adaptive Hybrid S...

13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Fu, Xuewei Ding, Chenyang Zanchetta, Pericle Yang, Xiaofeng Tang, Mi Liu, Yang State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical University of Pavia Pavia Italy Power Electronics Machine and Control Group University of Nottingham Nottingham United Kingdom Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728172101

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method. © 2021 IEEE

关键词： Radial basis function networks