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Improved Joint Transmit and Receive Port Selection for Capacity Maximization in Fluid-MIMO Systems

IEEE Wireless Communications Letters 2025年第6期14卷 1693-1697页

作者： Chen, Jung-Chieh Cheng, Tzu-Lu Wong, Kai-Kit Shin, Hyundong National Cheng Kung University Institute of Computer and Communication Engineering Department of Electrical Engineering Tainan701401 Taiwan University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of

One unique capability of fluid antenna system (FAS) is its position reconfigurability for enhancing wireless communication systems. This reconfigurability allows FASs to fully exploit spatial diversity, leading to significant improvements in metrics such as outage probability and capacity. Nevertheless, optimizing port selection remains a significant challenge, particularly in fluid multiple-input multiple-output (MIMO) systems where multiple fluid antennas are deployed at both ends. Existing joint convex relaxation-based methods, despite their performance benefits, are prohibitively complex, and scales badly with the number of ports. To overcome this, we propose a novel probability learning-based scheme within the cross-entropy optimization (CEO) framework to tackle the joint transmit and receive port selection problem. Unlike existing approaches, our proposed CEO-based approach avoids approximations to the original problem, hence mitigating performance degradation. Simulation results show that the proposed algorithm not only surpasses state-of-the-art algorithms but also significantly reduces computational complexity, providing an efficient solution for modern FAS applications. © 2012 IEEE.

关键词： Convex optimization

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Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 315-325页

作者： Ancheng Xue He Kong Feiyang Xu Junbo Zhao Naichao Chang Joe H.Chow Haiyan Hong State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering University of ConnecticutStorrs06268USA National Electric Power Dispatching and Control Center Beijing 100031China Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNY 12180USA

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart ***,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on *** address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are *** general formulations for PAD estimation are then *** simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of *** latter is used by second-order Taylor series expansion to estimate PADs *** reduce the impact of possible abnormal amplitude data in field data,the IGG(institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is *** using both simulated and field data verify the effectiveness and robustness of the proposed methods.

关键词： Correction line parameters parameter identification phasor measurement time-varying phase angle difference deviation

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Privacy-preserving multi-keyword hybrid search over encrypted data in cloud

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 261-274页

作者： Singh, Niharika Kumar, Jitendra Singh, Ashutosh Kumar Mohan, Anand School of Computer Sciences Bennett University Uttar Pradesh Noida India CISIR Department of Electrical and Electronics Engineering University Teknologi Petronas Perak Seri Iskandar Malaysia Department of Computer Applications National Institute of Technology Tiruchirappalli Tamilnadu Tiruchirappalli India Department of Computer Applications National Institute of Technology Kurukshetra Haryana Kurukshetra India Department of Electronics Engineering Indian Institute of Technology-BHU Uttar Pradesh Varanasi India

Data outsourcing to the cloud is gaining popularity day by day in cloud computing. It requires a dynamic architecture that avoids loopholes and stops intruders from entering the cloud environment. To retrieve the enciphered documents from the cloud, the latest technique that is enrolled effectively is multi-keyword fuzzy search. The proposed research work uses this technique along with the n-gram corpus algorithm that helps to achieve relevancy score and sub-linear search time. This algorithmic combination is able to predict the privacy ranking measure of the retrieved data. To validate the opposed approach, simulation analysis has been performed to preserve user-oriented privacy in a cloud environment. The ranking precision of the proposed method is compared with (Fu et al. in IEEE Trans Inf Forensics Secur 12(8):1874–1884. https://***/10.1109/TIFS.2017.2692728, 2017) which uses a stemming algorithm and a matching algorithm based on the inner product. The results express the efficiency of the proposed approach by showing a relative improvement in security precision up to 55%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Privacy-preserving techniques

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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computer Systems Science & engineering 2023年第7期46卷 255-271页

作者： T.Satyanarayana Murthy P.Udayakumar Fayadh Alenezi E.Laxmi Lydia Mohamad Khairi Ishak Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science and Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)*** security issues directly result from inappropriate security management protocols and their implementation by IoT gadget ***-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)*** this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their *** recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying *** current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT *** presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT *** accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square *** detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this ***,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition *** proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct *** comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

关键词： False data injection attack security internet of things deep learning coot optimization algorithm

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Adaptive Control Design for Robust Quality Control of an Online Checkweighing and Rejection System 12

Adaptive Control Design for Robust Quality Control of an Onl...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Aamir, Narmeen Masood, Syed Muhammad Sabih Abid, Rasheed Illinois Institute of Technology Department of Electrical & Computer Engineering ChicagoIL United States NED University of Engineering & Technology Department of Electrical Engineering Karachi Pakistan Illinois Institute of Technology Department of Biomedical Engineering ChicagoIL United States

ISBN: (纸本)9798350391398

In modern process industries, maintaining precise weight measurements is critical for ensuring product quality and operational efficiency. Accurate weight measurement systems not only aid in meeting regulatory standards but also play a crucial role in minimizing waste and optimizing production processes. This paper provides an overview of an online checkweighing system installed in a process industry and introduces an adaptive control algorithm that calculates the running average of box weights using the Simple Moving Average (SMA) algorithm. This average is applied recursively to account for the impact of process parameter variations, such as temperature or density, on the accuracy of the checkweighing and rejection system. The proposed algorithm was successfully implemented to verify product count and detect underweight and overweight conditions in packed products. The results show that the rejection accuracy of the proposed algorithm is unaffected by weight variations caused by process variables, unlike the existing control algorithm, which could only accommodate weight variations within the limits of a single soap bar weight. © 2024 IEEE.

关键词： Adaptive control systems

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Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

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Complex System Modeling and Simulation 2025年第1期5卷 68-85页

作者： Xuewu Wang Jianing Zhang Yi Hua Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and *** paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion *** address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is *** NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution *** dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two *** addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is *** local search operators based on ideal point are proposed to find a better local *** improve the global exploration ability of the algorithm,a dual population restart mechanism is *** tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.

关键词： limited automatic guided vehicle(AGV)scheduling problem nondominated sorting genetic algorithm II(NSGA-II) dual population co-evolution matrix manufacturing workshop

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Tunable VO_(2) cavity enables multispectral manipulation from visible to microwave frequencies

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Light(Science & Applications) 2024年第3期13卷 510-518页

作者： Hang Wei Jinxin Gu Tao Zhao Zhiyuan Yan He-Xiu Xu Shuliang Dou Cheng-Wei Qiu Yao Li Center for Composite Materials and Structure Harbin Institute of TechnologyHarbin 150001China National University of Singapore Department of Electrical&Computer EngineeringSingapore 117583Singapore School of Chemical Engineering and Technology Harbin Institute of TechnologyHarbin 150001China Suzhou Laboratory Suzhou 215123China

Optical materials capable of dynamically manipulating electromagnetic waves are an emerging field in memories,optical modulators,and thermal ***,their multispectral design preliminarily attracts much attention,aiming to enhance their efficiency and integration of ***,the multispectral manipulation based on these materials is challenging due to their ubiquitous wavelength dependence restricting their capacity to narrow *** this article,we cascade multiple tunable optical cavities with selective-transparent layers,enabling a universal approach to overcoming wavelength dependence and establishing a multispectral platform with highly integrated *** on it,we demonstrate the multispectral(ranging from 400 nm to 3 cm),fast response speed(0.9 s),and reversible manipulation based on a typical phase change material,vanadium *** platform involves tandem VO_(2)-based Fabry–Pérot(F-P)cavities enabling the customization of optical responses at target bands *** can achieve broadband color-changing capacity in the visible region(a shift of~60 nm in resonant wavelength)and is capable of freely switching between three typical optical models(transmittance,reflectance,and absorptance)in the infrared to microwave regions with drastic amplitude tunability exceeding *** work represents a state-of-art advance in multispectral optics and material science,providing a critical approach for expanding the multispectral manipulation ability of optical systems.

关键词： spectral microwave visible

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Watch You under Low-Resolution and Low-Illumination: Face Enhancement via Bi-Factor Degradation Decoupling

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第5期34卷 3481-3495页

作者： Ding, Xin Wang, Zheng Fang, Jing Shu, Zhenyu Hu, Ruimin Lin, Chia-Wen NingboTech University School of Computer and Data Engineering Ningbo315100 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Wuhan430072 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300044 Taiwan

Face enhancement aims to improve low-quality face images to a higher-quality level. However, in real-world nighttime scenes, complex degradation factors often affect these images, making it challenging to preserve important facial details. Existing image enhancement algorithms typically focus on independently conducting image super-resolution and brightness enhancement, assuming a fixed degradation level based on simulated training datasets. Nonetheless, real nighttime scenes involve complex degradation processes, where degradation factors dynamically and variably manifest. Therefore, achieving effective face enhancement in such scenarios is particularly daunting. This work analyzes and unveils the multiple factors of low resolution and low illumination during degradation. Based on this analysis, we propose a Bi-factor Degradation Decoupling network. Our method leverages a decoupling network to generate qualitative and quantitative features corresponding to each factor's degradation degree in the low-quality environment. These features are then combined with robust facial feature constraints to recover the details of low-quality faces. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in both enhancement and face super-resolution. © 1991-2012 IEEE.

关键词： Image reconstruction

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Short-Term Electricity Price Forecasting of Ontario Market by LASSO-LSTM Model of Deep Learning

Short-Term Electricity Price Forecasting of Ontario Market b...

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International Conference on Computational Intelligence and Smart Technologies in electrical engineering, CISTEE 2023

作者： Prajesh, Ashish Jain, Prerna Nayak, Deepak Ranjan Sharma, Navneet Kumar, Amit Harit, Abhishek Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Department of Computer Science Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9789819799152

The deregulation of the electricity market led to an increase in competition, which has compelled many strategies makers to opt for forecasting electricity prices because the more accuracy of prediction better the strategy they can make. Various models are present for electricity price forecasting but in this study, our main concern is how we can minimize their forecasting error and maximize their forecasting accuracy. One way of doing that is before applying any model to the dataset, we can make that dataset is understandable enough to the model that can give us possibly better results, this can be achieved by employing feature selection techniques on a dataset, and after this, those all fetched optimal features dimension were reduced using the Principle Component Analysis (PCA), then this final dataset with optimal features and reduced dimensions are fed to the Deep Neural Network (DNN) model for forecasting purposes. This paper has considered Least Absolute Shrinkage and Selection Operator (LASSO) feature selection technique and its results have been compared with other benchmark models on the scale of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2 score metrics to evaluate the effectiveness of the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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