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arXiv 2023年

作者： Nguyen, Nhan Thanh Nguyen, Van-Dinh Nguyen, Hieu Van Wu, Qingqing Tölli, Antti Chatzinotas, Symeon Juntti, Markku Wireless Communications University of Oulu P.O.Box 4500 FI-90014 Finland The College of Engineering and Computer Science The Center for Environmental Intelligence VinUniversity Vinhomes Ocean Park Hanoi100000 Viet Nam The Faculty of Electronic and Telecommunication Engineering The University of Danang University of Science and Technology Da Nang50000 Viet Nam The Department of Electronic Engineering Shanghai Jiao Tong University 200240 China University of Luxembourg L-1855 Luxembourg

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% − 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements. © 2023, CC BY.

关键词： Beamforming

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Modeling of Multiple Cantilevers System for Broadband Vibration Energy Harvester

Modeling of Multiple Cantilevers System for Broadband Vibrat...

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International Seminar on Research of Information technology and Intelligent Systems (ISRITI)

作者： Li Wah Thong Swee Leong Kok Roszaidi Ramlan Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia Faculty of Mechanical Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9781665401524

Piezoelectric energy harvester has the capability in powering small scale semiconductor devices particularly in the low power sensors applications in Internet of Things (IoT) environment. It is known that the bandwidth and power of these energy harvesters can be improved by increasing the number of cantilevers in the system. This research presents the electromechanical model of multimode piezoelectric energy harvesters with different polarity connections between the cantilever beams to improve the broadband performance of frequency response in the system. The theoretical model and experimental results of the proposed multi-mode system exhibited a significant escalation of output voltage at the gap between two resonance frequencies when the polarity configuration in the cantilever connection is reversed accordingly. The outcomes designate that by interchanging the polarity of the electrical connection between the cantilever beams, the output voltage between the resonance frequency of the multi-mode system can be increase significantly in comparison with the conventional series interconnection.

关键词： Vibrations Seminars Resonant frequency Voltage Bandwidth Broadband communication Structural beams

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Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach

arXiv

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arXiv 2023年

作者： Du, Zhen Liu, Fan Xiong, Yifeng Han, Tony Xiao Eldar, Yonina C. Jin, Shi The School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China The School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Co. Ltd Shenzhen China The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Integrated sensing and communications is regarded as a key enabling technology in the sixth generation networks, where a unified waveform, such as orthogonal frequency division multiplexing (OFDM) signal, is adopted to facilitate both sensing and communications (S&C). However, the random communication data embedded in the OFDM signal results in severe variability in the sidelobes of its ambiguity function (AF), which leads to missed detection of weak targets and false detection of ghost targets, thereby impairing the sensing performance. Therefore, balancing between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes. Subsequently, we propose an optimal probabilistic constellation shaping (PCS) approach by maximizing the achievable information rate (AIR) under the fourth-moment, power and probability constraints, where the optimal input distribution may be numerically specified through a modified Blahut-Arimoto algorithm. To reduce the computational overheads, we further propose a heuristic PCS approach by actively controlling the value of the fourth-moment, without involving the communication metric in the optimization model, despite that the AIR is passively scaled with the variation of the input distribution. Numerical results show that both approaches strike a scalable performance tradeoff between S&C, where the superiority of the PCS-enabled constellations over conventional uniform constellations is also verified. Notably, the heuristic approach achieves very close performance to the optimal counterpart, at a much lower computational complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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The Iterative Dichotomiser 3 Method Improves Soft Skills Training Decisions Using Survey Data

The Iterative Dichotomiser 3 Method Improves Soft Skills Tra...

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Machine Learning and Applied Network Technologies (ICMLANT), IEEE International Conference on

作者： Subir Gupta Quang-Trung Hoang Bhaswati Roy Subrata Chowdhury Sandip Mukherjee Aditya Kumar Pathak Diep Can Duc Dept. of CSE(AIML) Haldia Institute of Technology West Bengal India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi Vietnam Dept. of FMS Dr.B.C.Roy Engineering College Durgapur WB India Dept. of Computer Science and Machine Learning Sreenivasa Inst. of Technology and Management Studies Chittoor Andhra Pradesh India Dept. of IT Haldia Institute of Technology West Bengal India Electric Power University Hanoi Vietnam

ISBN: (数字)9798350389180

ISBN: (纸本)9798350389197

This investigation explores student expectations and perceptions regarding higher education institutions, focusing on aligning professional programs with student career aspirations. The study evaluates how effectively academic institutions understand and meet training needs essential for student success in professional fields. Students were categorized using the Iterative Dichotomiser 3 methodology to forecast their training requirements. Analytical tools such as the confusion matrix and the ROC curve assessed the precision of these predictions. The results show a specificity rate of 74.1 %, a sensitivity of 78.6%, and an overall accuracy of 76.7 % . The combined sensitivity and specificity score is 3.527, and the empirical area under the ROC curve is 0.76, suggesting the model's reasonable efficacy. The findings indicate a significant student demand for enhanced instruction in soft skills, underscoring the need for educational institutions to adapt their curricula to better prepare students for professional challenges.

关键词： Training Surveys Technological innovation Sensitivity Accuracy Focusing Machine learning Sensitivity and specificity Iterative methods Decision trees

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A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

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computers, Materials & Continua 2022年第5期71卷 3533-3556页

作者： Siti Julia Rosli Hasliza A Rahim Khairul Najmy Abdul Rani Ruzelita Ngadiran Wan Azani Mustafa Muzammil Jusoh Mohd Najib Mohd Yasin Thennarasan Sabapathy Mohamedfareq Abdulmalek Wan Suryani Firuz Wan Ariffin Ahmed Alkhayyat Advanced Communication Engineering Centre of Excellence(ACE)Universiti Malaysia Perlis(UniMAP)01000 KangarPerlisMalaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis(UniMAP)02600 ArauPerlisMalaysia Advanced Computing(AdvCOMP) Centre of ExcellenceUniversiti Malaysia Perlis(UniMAP)02600 ArauPerlisMalaysia Faculty of Electrical Engineering Technology Universiti Malaysia Perlis(UniMAP)02600 ArauPerlisMalaysia Faculty of Engineering and Information Science University of Wollongong in DubaiDubai 20183United Arab Emirates Department of Computer Technical Engineering College of Technical Engineeringthe Islamic University54001 NajafIraq

The essential purpose of radar is to detect a target of interest and provide information concerning the target’s location,motion,size,and other *** knowledge about the pulse trains’properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance.A low autocorrelation binary sequence(LABS)is a complex combinatorial *** main problems of LABS are low Merit Factor(MF)and shorter length ***,the maximum possible MF equals 12.3248 as infinity length is unable to be ***,this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm(HMSCACSA)using Inverse Filtering(IF)and clipping method to achieve better *** proposed algorithms,LABS-IF and HMSCACSA-IF,achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237,respectively,where the optimal solutions belong to the skew-symmetric *** MF outperformed up to 24.335%and 2.708%against the state-of-the-art LABS heuristic algorithm,xLastovka,and Golay,*** results indicated that the proposed algorithm’s simulation had quality solutions in terms of fast convergence curve with better optimal means,and standard deviation.

关键词： Merit factor autocorrelation skew-symmetric sequences combinatorial optimization sine cosine algorithm cuckoo search algorithm radar system wearable antenna antenna and propagation

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Building a Better Metaverse: How Federated Learning is Revolutionizing Virtual Worlds

Building a Better Metaverse: How Federated Learning is Revol...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Ahmad Chaddad Yihang Wu Reem Kateb School of Artificial Intelligence Guilin University of Electronic Technology Guilin China The Laboratory for Imagery Vision and Artificial Intelligence Ecole de Technologie Supérieure Montreal QC Canada Faculty of Computer Sciences and Engineering Jeddah University Jeddah Saudi Arabia Taibah University Saudi Arabia

The rapid development of artificial intelligence (AI) has been made possible by the swift advancement of technology. Recently, there has been a significant increase in interest in the concept of the Metaverse, which is viewed as the ultimate manifestation of the internet. However, building a vast Metaverse requires a substantial allocation of resources to maintain it. Such circumstances pose several challenges, including the disclosure of sensitive data and the problem of non-independent and homogeneous data distribution. In this study, we will focus on explaining federated learning (FL), a methodology that addresses these issues by training localized models and then consolidating them on a global scale to create the ultimate model without sharing the original data. Additionally, we will also discuss the current challenges and potential developments that should be considered in the upcoming years.

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Explainable Deep Learning Model for Cardiac Arrhythmia Classification

Explainable Deep Learning Model for Cardiac Arrhythmia Class...

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Future Trends in Smart Communities (ICFTSC), International Conference on

作者： Talal A.A. Abdullah Mohd Soperi Bin Mohd Zahid Tong Boon Tang Waleed Ali Maged Nasser Computer & Information Sciences Universiti Teknologi PETRONAS Perak Malaysia Electrical & Electronic Engineering Universiti Teknologi PETRONAS Perak Malaysia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Perak Malaysia School of Computer Sciences Universiti Sains Malaysia Penang Malaysia

ISBN: (纸本)9798350334555

In this work, we proposed a hybrid deep learning model that (CNN-GRU) combines a One-Dimensional Neural Network (1D CNN) and a Gated Recurrent Unit (GRU) to classify four types of cardiac arrhythmia and applied LIME to provide explanations for its predictions. LIME is a well-known local explanation method that can explain any machine learning model by simulating its behaviours to generate explanations. However, LIME can only explain tabular, text, and image datasets. Therefore, we proposed a visual presentation of LIME on signal dataset by applying a heatmap to highlight important areas on the heartbeat signals. Moreover, we propose an effective method to segment heartbeats from ECG records, ensuring that all key features are extracted correctly, such as QRS Complex, P Wave, and T Wave. The proposed hybrid model was trained using ECG lead II from the MIT-BIH dataset and evaluated based on accuracy, precision, recall, f1 score, and AUC-ROC performance matrix. To highlight the proposed model’s validity, we compare it against the standalone CNN and GRU models and prove its superiority in terms of accuracy and ROC.

关键词： Heating systems Deep learning Training Visualization Heart beat Arrhythmia Predictive models

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TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework

arXiv

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arXiv 2021年

作者： Lyu, Bo Wen, Shiping Yan, Z. School of Computer Science and Engineering University of Electronic Science and Technology of China China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Ultimo2007 Australia

Differentiable architecture search has gradually become the mainstream research topic in the field of Neural Architecture Search (NAS) for its high efficiency compared with the early NAS (EA-based, RL-based) methods. Recent differentiable NAS also aims at further improving the search performance and reducing the GPU-memory consumption. However, these methods are no longer naturally capable of tackling the non-differentiable objectives, e.g., energy, resource-constrained efficiency, and other metrics, let alone the multi-objective search demands. Researches in the multi-objective NAS field target this but requires vast computational resources cause of the sole optimization of each candidate architecture. In light of this discrepancy, we propose the TND-NAS, which is with the merits of the high efficiency in differentiable NAS framework and the compatibility among non-differentiable metrics in Multi-objective NAS. Under the differentiable NAS framework, with the continuous relaxation of the search space, TND-NAS has the architecture parameters (α) been optimized in discrete space, while resorting to the progressive search space shrinking by α. Our representative experiment takes two objectives (Parameters, Accuracy) as an example, we achieve a series of high-performance compact architectures on CIFAR10 (1.09M/3.3%, 2.4M/2.95%, 9.57M/2.54%) and CIFAR100 (2.46M/18.3%, 5.46/16.73%, 12.88/15.20%) datasets. Favorably, compared with other multi-objective NAS methods, TND-NAS is less time-consuming (1.3 GPU-days on NVIDIA 1080Ti, 1/6 of that in NSGA-Net). Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Perceptual-based features for blind image quality assessment using extreme learning machine for biodiversity monitoring

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AIP Conference Proceedings 2024年第1期2898卷

作者： Nutveesa Verak P. Ehkan R. Ngadiran S. Jungjit F. F. Zakaria M. N. M. Warip M. Z. Ilyas Faculty of Electronic Engineering Technology University Malaysia Perlis (UniMAP) Arau Perlis Malaysia Department of Computer and Information Technology Faculty of Science Thaksin University Phatthalung Thailand

Blind Image Quality Assessment (BIQA) is crucial for various image processing applications, including image denoising, transmission evaluation, optimization, watermarking, and situations where a reference image is unavailable. However, existing state-of-the-art Image Quality Assessment (IQA) metrics are often specific to certain types of distortion and fail to align with human perception. To address this issue, our research study proposes a novel approach that incorporates perceptual-based features and utilizes a pooling algorithm based on Extreme Learning Machine (ELM). By considering human visual characteristics and the impact on image content, we aim to mimic human perception, which can detect noticeable differences at specific frequency ranges, akin to neurons. The work is divided into three phases. In the first phase, we derive perceptual features using lifting wavelet, focusing on texture, edge, and contrast components. Subsequently, in the second phase, these features are trained to generate output for pooling using Extreme Learning Machine (ELM). The pooling strategy of ELM is chosen due to its ability to overcome limitations found in previous pooling techniques like Neural Networks (NNs) and Support Vector Regression (SVR). This approach enables us to evaluate the quality score of images accurately, benefiting from ELM's superior learning accuracy and faster learning speed. The third phase involves performance evaluation, including statistical analysis for algorithm validation and a comparison with existing BIQA methods using MATLAB software. We verify our proposed approach on diverse image databases containing various distortion types, aiming to create a general-purpose BIQA solution. The outcomes of this work will have significant implications in several image processing applications, such as optimizing image enhancement for medical purposes like tumor or cancer detection, image watermarking for security applications, image coding and compression, and ima

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