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arXiv 2024年

作者： Chen, Yixuan Chen, Mingzhe Xu, Hao Yang, Zhaohui Wong, Kai-Kit Zhang, Zhaoyang Hangzhou310027 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Yonsei Frontier Laboratory Yonsei University Seoul03722 Korea Republic of

In this letter, we study the energy efficiency maximization problem for a fluid antenna system (FAS) in near field communications. Specifically, we consider a point-to-point near-field system where the base station (BS) transmitter has multiple fixed-position antennas and the user receives the signals with multiple fluid antennas. Our objective is to jointly optimize the transmit beamforming of the BS and the fluid antenna positions at the user for maximizing the energy efficiency. Our scheme is based on an alternating optimization algorithm that iteratively solves the beamforming and antenna position subproblems. Our simulation results validate the performance improvement of the proposed algorithm and confirm the effectiveness of FAS. Copyright © 2024, The Authors. All rights reserved.

关键词： Beam forming networks

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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

Wi-Wheat+: Contact-free wheat moisture sensing with commodit...

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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Exploring Data Geometry for Continual Learning

arXiv

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arXiv 2023年

作者： Gao, Zhi Xu, Chen Li, Feng Jia, Yunde Harandi, Mehrtash Wu, Yuwei Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to inferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed curvature space, we propose an incremental search scheme, through which the growing geometric structures are encoded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capable of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Submilliwatt, widely tunable coherent microcomb generation with feedback-free operation

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Advanced Photonics 2023年第3期5卷 144-154页

作者： Haowen Shu Lin Chang Chenghao Lao Bitao Shen Weiqiang Xie Xuguang Zhang Ming Jin Yuansheng Tao Ruixuan Chen Zihan Tao Huajin Chang Shaohua Yu Qifan Yang Xingjun Wang John E.Bowers Peking University School of ElectronicsState Key Laboratory of Advanced Optical Communication Systems and NetworksBeijingChina University of California Santa BarbaraDepartment of Electrical and Computer EngineeringSanta BarbaraCaliforniaUnited States Peking University School of PhysicsState Key Laboratory for Artificial Microstructure and Mesoscopic PhysicsBeijingChina Peng Cheng Laboratory ShenzhenChina Peking University Frontiers Science Center for Nano-OptoelectronicsBeijingChina

Microcombs are revolutionizing optoelectronics by providing parallel, mutually coherent wavelengthchannels for time-frequency metrology and information processing. To implement this essential function inintegrated photonic systems, it is desirable to drive microcombs directly with an on-chip laser in a simpleand flexible way. However, two major difficulties have prevented this goal: (1) generating mode-lockedcomb states usually requires a significant amount of pump power and (2) the requirement to align laser andresonator frequency significantly complicates operation and limits the tunability of the comb lines. Here, weaddress these problems by using microresonators on an AlGaAs on-insulator platform to generate dark-pulsemicrocombs. This highly nonlinear platform dramatically relaxes fabrication requirements and leads to arecord-low pump power of <1 mW for coherent comb generation. Dark-pulse microcombs facilitated bythermally controlled avoided mode crossings are accessed by direct distributed feedback laser *** any feedback or control circuitries, the comb shows good coherence and stability. With around150 mW on-chip power, this approach also leads to an unprecedentedly wide tuning range of over one freespectral range (97.5 GHz). Our work provides a route to realize power-efficient, simple, and reconfigurablemicrocombs that can be seamlessly integrated with a wide range of photonic systems.

关键词： dark-pulse microcomb direct pumping scheme widely tunable source

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Exploring Data Geometry for Continual Learning

Exploring Data Geometry for Continual Learning

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhi Gao Chen Xu Feng Li Yunde Jia Mehrtash Harandi Yuwei Wu Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University and Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to in-ferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and pre-vents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed cur-vature space, we propose an incremental search scheme, through which the growing geometric structures are en-coded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capa-ble of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.

关键词：

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Symbiotic Control of Uncertain Dynamical systems*

Symbiotic Control of Uncertain Dynamical Systems*

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IEEE Conference on Decision and Control

作者： Tansel Yucelen Selahattin Burak Sarsılmaz Emre Yildirim Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems (LACIS http://lacis.eng.usf.edu/) University of South Florida Tampa Florida United States of America Department of Electrical and Computer Engineering Utah State University Logan Utah United States of America

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider both the fixed-gain control and adaptive learning architectures to suppress the effects of uncertainties. We note that fixed-gain control provides more predictable closed-loop system behavior, but it comes at the cost of knowing uncertainty bounds. On the other hand, adaptive learning removes the requirement of this knowledge at the expense of less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that integrates the advantages of both fixed-gain control and adaptive learning architectures. In particular, the proposed framework utilizes both control architectures to suppress the negative effects of uncertainties with more predictable closed-loop system behavior and without the knowledge of uncertainty bounds. Both parametric and nonparametric uncertainties are considered, where we use neural networks to approximate the unknown uncertainty basis for the latter case. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.

关键词： Symbiosis Hands Adaptive learning Uncertainty Upper bound Costs Neurons Closed loop systems Dynamical systems Biological neural networks

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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Prototype Design and Experimental Test for A Hydraulic-Driven Soft Robotic Arm

Prototype Design and Experimental Test for A Hydraulic-Drive...

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International Conference on Biomimetic Intelligence and Robotics & Medical Robotics Forum, ICBIR+MRF 2024

作者： Lei, Yu Du, Pengkun Xie, Minghao Chen, Zheng Gu, Jason Ocean College Zhejiang University Zhoushan310058 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China Hainan Institute Zhejiang University Sanya572025 China Leicester International Institute Dalian University of Technology Panjin124221 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

Soft robotics has gained considerable attention in recent years for its structural flexibility and inherent safety in environmental interactions. To address the pitfalls of pneumatic actuation systems, namely sluggish response and inefficiencies, this paper introduces the design and fabrication of a Hydraulic-Driven Soft Robotic Arm (HDSRA). The study establishes a comprehensive platform that integrates actuation, sensing, and control software to provide an experimental prototype for validating control algorithms. A novel fabrication technique utilizing water-soluble PVA for single-step mold creation enhances the structural reliability of soft actuators. Through closed-loop control experiments, the HDSRA demonstrates rapid and precise tracking of 1Hz signals, encompassing sine, square, and ramp waves, thus confirming the platform's reliability. This foundational work lays a robust foundation for future research and the verification of control algorithms in HDSRAs. © 2024 The Authors.

关键词： Robotic arms

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