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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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A Novel Deep Multi-Instance Convolutional Neural Network for Disaster Classification From High-Resolution Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 2098-2114页

作者： Li, Chengfan Zhang, Zixuan Liu, Lan Kim, Jung Yoon Sangaiah, Arun Kumar the School of Computer Engineering and Science Shanghai University Shanghai200444 China the Key Laboratory of National Geographic Census and Monitoring Ministry of Natural Resources Wuhan University Wuhan430079 China the Key Laboratory for Digital Land and Resources of Jiangxi Province East China University of Technology Nanchang330013 China the School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China the College of Future Industry Gachon University Seongnam-Si Gyeonggi-do13120 Korea Republic of the International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu64002 Taiwan the Department of Electrical and Computer Engineering Lebanese American University Byblos11020 Lebanon

—The fully supervised deep convolutional neural network (CNN) cannot detect the discriminant local information that is responsible for spatial transformations in high-resolution remote sensing images. To address the various types and missing labels of natural disasters, a new deep multi-instance convolutional neural network (DMCNN) model for disaster classification in high-resolution remote sensing image is presented in this article. Specifically, based on sample enhancement and atrous spatial pyramid pooling, we first extract and integrate the features via the CNN structure to obtain the instance feature of bags in the image. Besides, introducing a prototype learning layer with distance measure, the instance features extracted from pretrained CNN are mapped into a series of prototype instance features with bag-level. Subsequently, all instance features from prototype and bag take part in disaster detection and image classification. Finally, we conduct extensive experiments on xBD dataset and discussions from qualitative and quantitative aspects. Experimental results show that the proposed DMCNN model achieves better classification accuracy of natural disaster from high-resolution remote sensing images compared to traditional CNNs, and effectively improves the disaster classification performance with weakly supervised from high-resolution remote sensing images. © 2023 The Authors.

关键词： Remote sensing

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A Framework for Cyber-Secure Monitoring and Safe Operation of Solar PV Microgrids

A Framework for Cyber-Secure Monitoring and Safe Operation o...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Kim, Jaewon Ilic, Marija Xie, Le Massachusetts Institute of Technology Laboratory for Information & Decision Systems CambridgeMA02139 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Texas A&m University Department of Electrical & Computer Engineering College StationTX77843 United States

ISBN: (纸本)9798350376067

In this paper, we introduce a systematic design of an end-to-end cyber-secure monitoring and safe operation framework for solar photovoltaic (PV)-rich microgrids, by focusing on the use of on-line attack detector for defending against potential cyber-physical attacks and sensor measurement data processing for operational safety. The proposed approach is based on: 1) identifying component models using their input and output measurements;2) dynamic watermarking (DW) methodology for on-line cyber-attack detection and its timing;and 3) an adaptive event-driven safe shutdown procedure after attack detection. It is shown how these three components interactive steps jointly to ensure safe operation under both normal operational scenarios and compromised operational scenarios with some malicious sensors caused by cyber-attacks on solar PV microgrids. Through this on-line integration, the proposed framework dynamically adapts to varying solar irradiance, load demands, operating conditions, and potential cyber-physical attacks. This forms a cyber-physical system which can pro-actively detect and mitigate potential cyber-physical attacks on solar PV microgrids. We describe its proof-of-concept implementation using real-world industrial solar farm data provided by the CenterPoint Energy. This study contributes to enhancing the cyber-security and safe operation of solar PVrich power systems, offering a scalable solution to other types of emerging renewable-rich energy distribution systems. © 2024 IEEE.

关键词： Cyber attacks

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Resilience of gas interchangeability in hydrogen-blended integrated electricity and gas systems:A transient approach with dynamic gas composition tracking

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iEnergy 2023年第2期2卷 143-154页

作者： Sheng Wang Hongxun Hui Pierluigi Siano State Key Laboratory of Internet of Things for Smart City University of MacaoTaipaMacao SARChina Department of Electrical and Computer Engineering University of MacaoTaipaMacao SARChina Department of Management&Innovation Systems University of SalernoFisciano 84084Italy Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg 2006South Africa

Green hydrogen can be produced by consuming surplus renewable *** can be injected into the natural gas networks,accelerating the decarbonization of energy ***,with the fluctuation of renewable energies,the gas composition in the gas network may change dramatically as the hydrogen injection *** gas interchangeability may be adversely *** investigate the ability to defend the fluctuated hydrogen injection,this paper proposes a gas interchangeability resilience evaluation method for hydrogen-blended integrated electricity and gas systems(H-IEGS).First,gas interchangeability resilience is defined by proposing several novel ***,A two-stage gas interchangeability management scheme is proposed to accommodate the hydrogen *** steady-state optimal electricity and hydrogen-gas energy flow technique is performed first to obtain the desired operating state of the ***,the dynamic gas composition tracking is implemented to calculate the real-time traveling of hydrogen contents in the gas network,and evaluate the time-varying gas interchangeability ***,to improve the computation efficiency,a self-adaptive linearization technique is proposed and embedded in the solution process of discretized partial derivative ***,an IEEE 24 bus reliability test system and Belgium natural gas system are used to validate the proposed method.

关键词： Integrated electricity and gas systems gas composition hydrogen resilience renewable energy

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Recent Developments in Event-Triggered Control of Nonlinear systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

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3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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A 1.65 to 1.84 GHz Filtering SPDT Switch With Fully Tunable Insertion Phase Using NRN and Mixed Electromagnetic Coupling Techniques

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IEEE Transactions on Circuits and systems II: Express Briefs 2023年第9期70卷 3353-3357页

作者： Wei, Zhihua Chi, Pei-Ling Xu, Ruimin Yang, Tao School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan Research Center of Advanced RF Chips and Systems Nanhu Laboratory Jiaxing Zhejiang314000 China

This brief presents a novel filtering single-pole-double-throw (SPDT) switch with continuously tunable center frequency and insertion phase. It is comprised of six varactor-loaded microstrip resonators and three tunable non-resonating nodes (NRNs) at source and loads. The controllable mixed electromagnetic couplings are introduced on the two main paths of each channel of the switch. At ON-state, the designed filtering switch can generate a 4th-order bandpass filtering performance with high selectivity, and the insertion phase can be continuously tuned with a full 360° tuning range by changing the reactances of NRNs and switching the dominated modes of the mixed couplings. At OFF-state, the mixed electromagnetic couplings are set to zero, and hence high isolation can be realized. To validate the proposed technique, a circuit prototype is designed and fabricated. The measured results suggest that the designed filtering SPDT switch can operate with a tunable center frequency ranging from 1.65 GHz to 1.84 GHz, and its insertion phase at ON-state channel can be continuously tuned from -180° to 180°. During the whole tuning process, the measured in-band isolation for OFF-state channel keeps better than 40 dB. © 2023 IEEE.

关键词： Bandpass filters

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Intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter 13

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Benaissa, Rabie Mansouri, Smail Ouledali, Omar University of Adrar Faculty of Sciences and Technology Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science 01000 Algeria University of Adrar Faculty of Sciences and Technology Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems 01000 Algeria

ISBN: (纸本)9798350356199

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper's primary objective is to enhance DSIPMSM's performance by the use of strong control using the proposed approach. Given its benefits - including high torque density, high efficiency, high power density, and minimal maintenance - DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control's performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method's worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple. © 2024 IEEE.

关键词： Disturbance rejection

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