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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Zhang, Yimin D. Ding, Yanwu Pham, Khanh Shen, Dan Chen, Genshe Temple University Department of Electrical and Computer Engineering PhiladelphiaPA19122 United States Wichita State University Department of Electrical and Computer Engineering WichitaKS67260 United States Air Force Research Laboratory Kirtland Air Force BaseNM87117 United States Intelligent Fusion Technology Inc. GermantownMD20876 United States

ISBN: (纸本)9798350354058

We consider the single-satellite, single-pass localization of ground electromagnetic interference (EMI) sources to protect the quality of service in satellite communications. For a stationary EMI source, the analysis of the time-varying Doppler signature caused by satellite motion provides valuable information for its localization. Due to the high-speed motion of the satellite, the instantaneous Doppler frequency exhibits a high dynamic range and significant nonlinearity. To address this, we previously developed a knowledge-aided signal stationarization approach using the concept of virtual ground references (VGR), thereby enabling a reduced sampling rate, extended coherent processing time, and improved EMI detectability and accuracy. In this paper, we propose several extended approaches, including simplified multi-VGR data fusion as well as iterative and sequential EMI localization methods, to further enhance EMI localization performance while reducing processing complexity. © 2024 IEEE.

关键词： Data fusion

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Latest Innovations in Intelligent Network-on-Chip Architectures: A Systematic Review

Latest Innovations in Intelligent Network-on-Chip Architectu...

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International Workshop on Network on Chip Architectures, NoCArc

作者： Elena Kakoulli Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798331506421

ISBN: (纸本)9798331506438

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures.

关键词： Technological innovation Scalability Power system management computer architecture Network-on-chip Predictive models Routing Energy efficiency Complexity theory Security

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Parasitic Parameters Effect on the Operation of High Frequency Electric Drive System

Parasitic Parameters Effect on the Operation of High Frequen...

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2024 International Conference on electrical Machines, ICEM 2024

作者： Abdo, Fawzy A. Tawfiq, Kotb B. Sergeant, Peter Systems and Metal Engineering Ghent University Department of Electromechanical Ghent9000 Belgium Flanders Make@UGent Core Lab MIRO Leuven3001 Belgium Menoufia University Department of Electrical Engineering Menoufia32511 Egypt Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350370607

The rapid switching of semiconductor power modules in electric drive systems generates high frequency (HF) common mode voltage at the motor terminals which causes bearing failure and winding degradation. Therefore, accurate characterization of the inverter and motor parameters - including parasitic effects - is vitally important to understand the high frequency performance as well as to model and mitigate the switching oscillations of the electric drive system. In this paper, the high frequency operation of the inverter in the electric drive system, as well as the direct effect of the parasitic components on the complete system are thoroughly investigated. Moreover, a detailed extraction process of the high frequency parasitic parameters is introduced. An equivalent circuit model of the complete drive system based on the impedance characteristics is introduced. Additionally, a HF model of a 750W three-phase induction motor is developed with parameter values determined from measurements on this motor. © 2024 IEEE.

关键词： Terminals (electric)

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Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI

Self-supervised Contrastive Learning for 6G UM-MIMO THz Comm...

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IEEE International Conference on Communications Workshops, ICC

作者： Rafid Umayer Murshed Md Saheed Ullah Mohammad Saquib Moe Z. Win Department of Electrical and Computer Engineering The University of Texas Dallas Texas USA Department of Electrical and Computer Engineering University of Delaware Delaware USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Massachusetts USA

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods.

关键词： 6G mobile communication Wireless communication Array signal processing Heuristic algorithms Conferences System performance Contrastive learning

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Energy-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

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computers, Materials & Continua 2022年第4期71卷 871-888页

作者： Samira Mujkic Suad Kasapovic Mohammed Abuibaid Department of Telecommunications Faculty of Electrical EngineeringUniversity of TuzlaTuzla75000Bosnia Herzegovina 2Department of Systems and Computer Engineering Faculty of Engineering and DesignCarleton UniversityOttawaK1S 5B6Canada

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base station(BS)adapts the number of antennas to the daily load profile(DLP).This paper takes into consideration user location distribution(ULD)variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO *** variation is modeled by dividing the cell into two coverage areas with different user densities:boundary focused(BF)and center focused(CF)*** cells are assumed identical in terms of BS configurations,cell loading,and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak *** with energy efficiency(EE)we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size,available bandwidth,output power level of the BS,and maximum output power of the power amplifier(PA)at different cell *** also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business,residential,street,and highway areas.

关键词： Massive MIMO traffic load energy efficiency user location distribution optimization

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Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance

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Autonomous Intelligent systems 2023年第1期3卷 84-95页

作者： Panpan Zhou Shupeng Lai Jinqiang Cui Ben M.Chen Department of Mechanical and Automation Engineering The Chinese University of Hong KongShatinN.T.Hong Kong. Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore117583Singapore. Peng Cheng Laboratory Shenzhen518055China.

We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems,be it single-rotor helicopters or multi-copters,with physical constraints and with inter-agent collision avoidance,in cluttered *** proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision *** specifically,we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity,acceleration and jerk,and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free ***,a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion ***,both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.

关键词： Multi-agent system Unmanned systems Rotorcraft systems Formation control Consensus control

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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MaGNIFIES: Manageable GAN Image Augmentation Framework for Inspection of Electronic systems

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Journal of Hardware and systems Security 2024年第1期8卷 44-59页

作者： Ghosh, Pallabi Lee, Gijung Zhu, Mengdi Dizon-Paradis, Olivia P. Botero, Ulbert J. Woodard, Damon L. Forte, Domenic Department of Electrical and Computer Engineering University of Florida Gainesville USA MIT Lincoln Laboratory Lexington USA

Electronic counterfeiting is a long-lasting problem that continues to cost original manufacturers billions, fund organized crime, and jeopardize national security and mission-critical infrastructures. Manual inspection is a popular and standardized way to detect counterfeit electronic components, but it is time-consuming and requires subject matter experts for classification. State-of-the-art machine learning, deep learning, and computer vision-based physical inspection methods are promising to alleviate these issues. However, the main bottleneck for doing so is a lack of high-quality, publicly available counterfeit image data for training. Producing such datasets is also time-consuming and often requires expensive equipment. In addition, most test labs are not allowed to freely publish images taken from their customer’s chips. One solution to this data shortage bottleneck can be addressed by augmenting synthetic data. In this paper, (i) data multiplication using Progressive GAN, StyleGAN, and classical methods in counterfeit data domain is explored; (ii) a novel framework, named MaGNIFIES, is proposed; and (iii) an efficient Convolutional Neural Network architecture is proposed, which can detect defective parts by training only on the synthetic dataset generated using (i) or (ii). For proof of concept, we have used low-quality images of resistors and capacitors with and without scratch defects as counterfeit and golden components respectively. We have also illustrated how our approach using MaGNIFIES addresses the shortcomings of the existing augmentation methods. Separate data augmentation detection models are trained with each type of augmented data generated using MaGNIFIES, as well as existing techniques, and tested on a test set of real data.

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Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

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computers, Materials & Continua 2022年第7期72卷 2141-2158页

作者： Muhammad Irfan Nasir Ayub Faisal Althobiani Zain Ali Muhammad Idrees Saeed Ullah Saifur Rahman Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Fahad Salem Alkahtani Samar Alqhtani Piotr Gas Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science Federal Urdu University of Science and TechnologyIslamabad44000Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical and Power Engineering AGH University of Science and Technology Mickiewicza 30 AvenueKrakow30-059Poland

One of the major concerns for the utilities in the Smart Grid(SG)is electricity *** the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously *** this purpose,we have taken historical data of energy thieves and normal *** avoid imbalance observation,biased estimates,we applied the interpolation ***,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further *** proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time *** minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark *** proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,***,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

关键词： Smart grids and meters electricity theft detection machine learning AdaBoost optimization techniques

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Exploring the Integration of Educational Robotics and the Internet of Things in Learning Environments

Exploring the Integration of Educational Robotics and the In...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Elena Kakoulli Salomi Evripidou Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This study explores the integration of Educational Robotics (ER) and the Internet of Things (IoT) in learning environments, highlighting their collective impact on educational practices. It assesses ER and IoT’s application, challenges, and opportunities, offering insights into their role in enhancing pedagogy and learning outcomes. Specifically, our exploration uncovers the transformative potential of ER and IoT in fostering critical thinking, problem-solving skills, and digital literacy among students and reveals the pivotal role these technologies play in preparing learners for the demands of the digital age and Industry 4.0, while also highlighting the need for strategic implementation and teacher support. Our findings elucidate the potential of ER and IoT to innovate teaching strategies and curriculum design, serving as a guide for educational stakeholders, such as curriculum developers, educators, researchers, and policymakers, in leveraging these technologies to revolutionize instructional practices.

关键词： Training Information age Fourth Industrial Revolution Internet of Things Stakeholders Problem-solving Distributed computing

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