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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-si Gyeonggi-do17104 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. © 2025 IEEE.

关键词： Convex optimization

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Byzantine Distributed Quickest Change Detection Based on Bounded-Distance-Decoding

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IEEE Signal Processing Letters 2023年 30卷 1532-1536页

作者： Saputra, Bagus Aris Lin, Shih-Chun National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan National Taiwan University Department of Electrical Engineering and Graduate Institute of Communication Engineering Taipei106 Taiwan

Byzantine distributed quickest change detection (BDQCD) is a crucial problem in cyber-physical security. The challenge of this problem is that an AI plus IoT (AIoT) network needs to detect the change as quickly as possible, subject to a false alarm rate, to prevent device damage. In the BDQCD problem, dealing with compromised meters also becomes a challenge since these meters can collaboratively form an attack to lengthen the detection delay in an IoT network. Here we consider the network where a fusion center monitors the occurrence of an abrupt event through a bunch of distributed meters that may be compromised. To solve the challenge, a new coded framework for BDQCD utilizing bounded distance decoding at the fusion center is proposed. First, under sufficient meter-to-fusion-center link capacity, we achieve a theoretical result that the detection delay of our scheme can be asymptotically optimal in special cases. Next, new codebooks under insufficient link capacities are designed based on the theoretical result. Through the simulation results, our coded BDQCDs outperform the state-of-the-art works by achieving significantly shorter detection delay under various attacks. © 1994-2012 IEEE.

关键词： Probability density function

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Digital Signature Techniques for IoT Privacy and Trust Management 1

Digital Signature Techniques for IoT Privacy and Trust Manag...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Mohammed, Qais Abd Al-Salam Zaiter, Mohammed Joudah Farhan, Hamid Mohammed Department of Computer and Communication Engineering Electrical Engineering Technical College Middle Technical University Baghdad Iraq

ISBN: (纸本)9798331533557

In this article, a live solution for data acquisition from sensors and its representation on a web interface is discussed with the help of a Raspberry Pi in which MQTT is used for communication the Flask as the framework. Particularly emphasized is the improvement of the efficiency and the time needed for generating Digital signature using the ECDSA algorithm which is traditionally slow and requires much resources to be applied for real time IoT applications. For this, the authors try the approach of parallel processing on the Raspberry Pi which becomes many times shorter in generation of the signatures without any loss of the data integrity. It involves input data including temperature, humidity, motion, distance, and light intensity, processes them and then sends the input data over a secure MQTT connection. They also showed that the FCMP algorithm incurs less computation time, and the speed attained is almost constant and stable when parallel processing is incorporated in the generation of some of the ECDSA algorithm’s signatures. As presented in the experimental results, the processing time is reduced in comparison with sequential processing, which emphasizes the possibility of parallelization. Altogether we can state that the usage of Raspberry Pi and Flask as the core of the system along with MQTT as a communication protocol provides the necessary level of security and reliability for monitoring the sensor data in the IoT system. They show that parallel processing can indeed help enhance the cryptographic operations making this approach beneficial for performance optimization across multiple IoT applications. © 2024 IEEE.

关键词： Data privacy

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An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions

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Digital communications and Networks 2023年第1期9卷 79-89页

作者： Varun Kohli Sombuddha Chakravarty Vinay Chamola Kuldip Singh Sangwan Sherali Zeadally Department of Electrical and Computer Engineering Engineering National University of SingaporeSingapore Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Mechanical Engineering BITS-PilaniPilani Campus333031India College of Communication and Information University of KentuckyLexingtonKY40506-0224USA

There is an urgent need to control global warming caused by humans to achieve a sustainable ***_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global *** increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy *** energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and *** latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and *** review compiles estimates made by various studies from 2018 to *** compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as *** identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon ***,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.

关键词： Blockchain Carbon footprint Climate change Cryptocurrency Sustainability

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Comparative Analysis of Compact Modeled of Low-Voltage OTFTs on Flexible and Silicon Substrates for the Implementation of Logic Circuits

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第7期3卷 341-347页

作者： Singh Mehrolia, Mukuljeet Verma, Ankit Kumar Singh, Abhishek RGIPT Jais Department of Electrical and Electronics Engineering Uttar Pradesh Amethi229304 India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Ghaziabad201204 India

This article discusses the compact modeling of organic thin-film transistors (OTFTs) fabricated on both flexible and silicon substrates. These compact models are used to implement inverters, 2-input NAND gate, and half-adder circuits. For the compact modeling and circuit design, Silvaco TechModeler and Silvaco Gateway tools are utilized. Both the flexible and silicon substrate OTFTs operate at -2 V, with saturated currents of -2 and -3.9μA, respectively. Comparative analysis using dc and transient behavior reveals that the OTFT on the flexible substrate has a delay of 3.7 ns and a gain of 4.7, while the OTFT on the silicon substrate has a delay of 5.5 ns and a gain of 3.2. The OTFT on the flexible substrate is approximately 49% faster and exhibits a gain 1.47 times higher than the OTFT on the silicon substrate. Furthermore, the OTFT on the flexible substrate successfully realizes half-adder outputs for all four input cases (00, 01, 10, and 11), whereas the OTFT on the silicon substrate fails to do so for all cases. These results demonstrate that the OTFT on the flexible substrate significantly outperforms the OTFT on the silicon substrate in terms of delay, gain, and output consistency for the given inputs. In the future, the OTFT on the flexible substrate's quick response, high gain, and reliable performance in NAND and half-adder circuits could offer advantages in the development of complex memory circuits, analog circuits, and other applications. © 2022 IEEE.

关键词： SPICE

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Applications of Polymer Nanocomposites in High Voltage engineering: A Concise Review on Possible Applications and Questions Regarding Some Limitations

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Journal of engineering Science and Technology Review 2023年第5期16卷 156-163页

作者： Tsianakas, Vassilios Danikas, Michael G. Sarathi, Ramanujam Falekas, Georgios Democritus University of Thrace School of Engineering Department of Electrical and Computer Engineering Xanthi67100 Greece Indian Institute of Technology Madras Department of Electrical Engineering Chennai India

Polymer nanocomposites have been a topic of intensive research regarding High Voltage engineering since the nineties of the last century. They present an alternative to conventional polymers since the latter were diagnosed with certain drawbacks. In this paper, a short review is given as to the basic characteristics of the polymer nanocomposites and some comments are offered as to the possible applications of the said materials in High Voltage engineering. © 2023 School of Science, IHU. All rights reserved

关键词： Partial discharges

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in electrical Networks

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computer Modeling in engineering & Sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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A multi-mode real-time system verification model using efficient event-driven dataset

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1187-1200页

作者： Islam, Nayreet Azim, Akramul Department of Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa Canada

Real-time systems experience many safety and performance issues at run time due to different uncertainties in the environment. Systems are now becoming highly interactive and must be able to execute in a changing environment without experiencing any failure. A real-time system can have multiple modes of operation such as safety and performance. The system can satisfy its safety and performance requirements by switching between the modes at run time. It is essential for the designers to ensure that a multi-mode real-time system operates in the expected mode at run time. In this paper, we present a verification model that identifies the expected mode at run time and checks whether the multi-mode real-time system is operating in the correct mode or not. To determine the expected mode, we present a monitoring module that checks the environment of the system, identifies different real-world occurrences as events, determines their properties and creates an event-driven dataset for failure analysis. The dataset consumes less memory in comparison to the raw input data obtained from the monitored environment. The event-driven dataset also facilitates onboard decision-making because the dataset allows the system to perform a safety analysis by determining the probability of failure in each environmental situations. We use the probability of failure of the system to determine the safety mode in different environmental situations. To demonstrate the applicability of our proposed scheme, we design and implement a real-time traffic monitoring system that has two modes: safety, and performance. The experimental analysis of our work shows that the verification model can identify the expected operating mode at run time based on the safety (probability of failure) and performance (usage) requirements of the system as well as allows the system to operate in performance mode (in 3295 out of 3421 time intervals) and safety mode (in 126 out of 3421 time intervals). The experimental resul

关键词： Real time systems

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Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning

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Research on Biomedical engineering 2023年第3期39卷 705-718页

作者： Verma, Karan Kumar, Ashok Swaraj, Aman Sagar, Aditi Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Purpose: Coronavirus disease 2019 (COVID-19) has infected about 418 million people across the globe. So, the analysis of biomedical imaging accompanied with artificial intelligence (AI) approaches has transpired a vital role in diagnosing COVID-19. Until now, numerous classification approaches have been demonstrated for the detection of COVID-19. The assessment of COVID-19 patients according to severity level is not so far investigated. For this motivation, the classification of COVID-19 chest X-ray (CXR) images according to severity of the infection is presented in this work. Methods: Primarily, the 1527 CXR images are pre-processed to reshape images into unique size, denoised, and enhanced images through median filter and histogram equalization (HE) techniques, respectively. Afterward, reshaped, denoised, and enhanced CXR images are augmented using synthetic minority oversampling technique (SMOTE) to achieve the balanced dataset of 1752 CXR images. After augmentation, a pre-trained VGG16 and residual network 50 (Resnet50) deep transfer learning models with random forest (RF) and support vector machine (SVM) classifiers are utilized for feature extraction and classification of 1752 CXR images into diverse class labels such as normal, severe COVID-19, and non-severe COVID-19. Results: Our proposed ResNet50 model with SVM classifier provides the highest accuracy of about 95% for severity assessment and classification of COVID-19 CXR images as compared to other permutations. For the ResNet50 model with SVM classifier model, the average value of precision, recall, and F1-score are 91%, 94%, and 92%, respectively. Conclusion: The multi-class classification deep transfer learning models are presented to determine the severity assessment and classification of COVID-19 by using CXR images. Out of these proposed models, the ResNet50 model with SVM classifier will be highly favorable for doctors to classify patients according to their severity assessment and detection of COV

关键词： COVID-19

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Intelligent Crop Recommender System for Yield Prediction Using Machine Learning Strategy

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 979-987页

作者： Maheswary, Atchukatla Nagendram, Sanam Kiran, Kasi Uday Ahammad, Shaik Hasane Priya, Putcha Poorna Hossain, Md. Amzad Rashed, Ahmed Nabih Zaki Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering & ampTechnology Thirupatchur India Department of Artificial Intelligence KKR & ampKSR Institute of Technology and Sciences Guntur India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur522502 India Department of Electronics and Communication Engineering Dadi Institute of Engineering and Technology College Anakapalle India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Saveetha University Tamilnadu Chennai India

For most developed nations, agriculture is a significant economic force. The realm of contemporary agriculture is consistently growing with evolving farming techniques and agricultural innovations. Farmers face challenges in keeping pace with the evolving demands of the planet and meeting the requirements of profitable initiatives, characters, and various other stakeholders. Climate change brought on by industry emissions and soil erosion, soil's nutrient deficiency due to mineral's absence, which results in reduced crop growth, and the cultivation of the same crops repeatedly without trying out new varieties are a few of the difficulties farmers face. Without considering the lower quality or quantity, they arbitrarily infuse fertilizers. Using two separate metrics, entropy and Gini indexes, the study analyzes well-known procedures with K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classifier practices. Moreover, the precision under the agriculture paradigm, particularly "crop recommender systems," includes these methods. Based on the outcomes, the random forest strategy outperforms the other approaches to model accuracy and reliability. © The Institution of Engineers (India) 2024.

关键词： Fertilizers

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