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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Asad Waqar Malik Samee U. Khan Department of Electrical and Computer Engineering National Strategic Planning and Analysis Research Center Mississippi State University USA Department of Electrical and Computer Engineering Mississippi State University USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

The vehicular edge computing model provides computational support to nearby vehicles requiring low-latency computation. However, existing algorithms typically only consider immediate neighbor nodes as potential vehicles for computation offloading. In this study, we address this limitation by modeling the task offloading problem using a Graph Neural Network approach. This approach incorporates dynamic properties of vehicles and edges, facilitating a more effective selection of vehicles for task offloading. By leveraging message passing and aggregate feature techniques, our proposed approach expands the search space for source vehicles, thereby distributing the workload across a wider spectrum, particularly benefiting vehicles that frequently generate computational tasks. Evaluation of our proposed system demonstrates significant improvements in the utilization factor of available vehicles, a reduction in queuing states, and an overall system efficiency of 92%.

关键词： Space vehicles Computational modeling Message passing Spread spectrum communication Search problems Graph neural networks Robustness Resource management Vehicle dynamics Low latency communication

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Leveraging ICTs for Effective COVID-19 Pandemics Management: Insights from a Health Information System Implementation in Cyprus

Leveraging ICTs for Effective COVID-19 Pandemics Management:...

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Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM International Symposium on

作者： Maria N. Anastasiadou Philippos Isaia Panayiotis Kolios Demetrios G. Eliades Christos Laoudias Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Department of Computer Science KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

The COVID-19 pandemic has imposed significant challenges on public health authorities, requiring them to handle a large influx of patients while also managing the vast amount of health information data generated by the crisis. Governments worldwide have made substantial investments in upgrading existing health information systems or developing new ones to effectively address these challenges. In this paper, we present the Health Information System (HIS) implemented in Cyprus for the management of the pandemic. We provide an overview of the systems and platforms that form the foundation of the HIS, enabling its functionalities. Additionally, we present statistical data reflecting the daily operations of the HIS, aligning with the evolution of the pandemic and the various interventions undertaken by local health authorities. Through this work, we aim to share our experiences and insights, serving as a valuable reference for other countries and states seeking to establish robust information systems for managing pandemics.

关键词： COVID-19 Pandemics Government Machine learning Market research Vaccines Public healthcare Monitoring Information systems Investment

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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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Many-Objective Multi-Verse Optimizer (MaOMVO): A Novel Algorithm for Solving Complex Many-Objective engineering Problems

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Journal of The Institution of Engineers (India): Series C 2024年第6期105卷 1467-1502页

作者： Kalita, Kanak Jangir, Pradeep Pandya, Sundaram B. Shanmugasundar, G. Chohan, Jasgurpreet Singh Abualigah, Laith Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Avadi600062 India Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Ostrava70800 Czech Republic Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Jadara University Research Center Jadara University Irbid Jordan Department of Electrical Engineering Shri K.J. Polytechnic Bharuch392001 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Centre for Research and Development Chandigarh University Mohali140413 India Computer Science Department Al-Bayt University Mafraq25113 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

research has demonstrated the effectiveness of metaheuristic algorithms in addressing optimization challenges involving multi-objectives. These algorithms excel in generating a range of solutions that not only approach the Pareto front but are also evenly distributed. Multi-objective algorithms are effective for problems with two or three objectives. However, their efficiency diminishes in scenarios with many objectives, as the diversity selection and convergence pressure become less effective. Balancing convergence and diversity in multi-objective optimization pose a significant challenge. In response, this study introduces a novel many-objective multi-verse optimizer algorithm named MaOMVO for addressing many-objective problems. It integrates reference point and niche preserve to improve convergence and diversity and employs an innovative information feedback mechanism technique for population renewal. The superiority of the MaOMVO algorithm is evident in tests with MaF problems having 5, 8 and 15 objectives, as well as five real-world problems (RWMaOP1—RWMaOP5). It outperforms four leading algorithms Many-objective moth flame optimization, many-objective particle swarm optimization, non-dominated sorting genetic algorithm-III and reference vector guided evolutionary algorithm in terms of generational distance by 70%, inverted generational distance by 52%, spacing by 46.66%, spread by 55.55%, hypervolume by 52% and running time by 52% with concave, convex and mixed pareto fronts, confirming its robustness in diverse optimization scenarios. © The Institution of Engineers (India) 2024.

关键词： Genetic algorithms

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A Joint Rogue Drone Detection and Tracking Fusing DOA and Passive Radar Measurements

A Joint Rogue Drone Detection and Tracking Fusing DOA and Pa...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Nicolas Souli Panagiotis Kardaras Panayiotis Kolios Georgios Ellinas Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Department of Computer Science KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Advancements in unmanned aircraft systems (UASs) have led to a considerable increase in unlawful operations of these systems over critical infrastructures and public spaces. In this work, a system that is implemented utilizing software-defined radio and signals of opportunity (SOPs) and is based on the fusion of direction of arrival and passive radar sensor measurements is proposed to counter unlawful UAS/drone activities. Specifically, passive radar methodology applied on SOPs (data collection, disturbance cancellation, cross-ambiguity, and constant-false alarm rate detection functions), along with direction of arrival (DOA) measurements obtained via the multiple signal classification algorithm, are combined to detect and track the rogue UAS/target in an area under observation. A prototype implementation of the detection-and-tracking system, with the use of small embedded processing units and the robot operating system is developed and examined in numerous outdoor experiments to thoroughly evaluate its performance.

关键词： Passive radar Target tracking Direction-of-arrival estimation Area measurement Prototypes Radar tracking Trajectory

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Creating a Robust and Expandable Framework for Cooperative Aerial Robots

Creating a Robust and Expandable Framework for Cooperative A...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Christos Georgiades Nicolas Souli Panayiotis Kolios Georgios Ellinas Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Department of Computer Science KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This work focuses on the design and development of a microservice-based software framework for multi-drone autonomous systems, that is fault-tolerant, expandable, and easily monitored. The custom framework utilizes the Robot Operating System (ROS) and is applied on onboard embedded devices to command and control multiple unmanned aerial vehicles (UAV) in various scenarios, including autonomous flight missions, inter-process communication, as well as measurement fusion for mapping and localization purposes. Further, the proposed framework is implemented and tested in both hardware-in-the-loop simulations and real-world field tests to demonstrate and evaluate its performance. Specifically, the developed prototype is evaluated in scenarios where a swarm of UAVs is tasked to map a predefined area for search-and-rescue purposes and a scenario incorporating faults where one UAV loses its GPS signal and obtains relative positioning with the help of the rest of the UAVs in the swarm.

关键词： Location awareness Fault tolerance Fault tolerant systems Microservice architectures Prototypes Autonomous aerial vehicles Robot sensing systems

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Investigation of Strategies for Expanding Capacity to Address Voltage Stability Challenges in Power Systems 4

Investigation of Strategies for Expanding Capacity to Addres...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Fesli, Ugur Ozdemir, M. Bahadir Akin, Murat Ertas, Halil Gazi University Technical Sciences Vocational School Electronic and Automation Department Ankara Turkey Gazi University Renewable Energy and Smart Grid Application and Research Center Ankara Turkey Gazi University Department of Energy Systems Engineering Ankara Turkey Gazi University Faculty of Engineering Ankara Turkey Gazi University Department of Electrical and Electronic Engineering Ankara Turkey

ISBN: (纸本)9798350306262

In this study, the stability problems caused by load variations in the IEEE 14 bus system are eliminated by determining the generation level. When new consumption areas (new loads) are added to power systems, the generation-consumption balance is not provided, and it causes instability problems. Therefore, the effect of new loads on the power system should be investigated in the planning stage. The maximum load levels are determined by increasing the load's power step by step. Line loadings, bus voltages and generator powers are considered as the stability criterion. The load value that causes any of these parameters to be outside the specified limits is accepted as the starting point of instability. In order to determine the generation capacity that eliminates the instabilities, the generator powers were increased step by step. The required generation capacity and its location are determined according to the generator power value, where all instabilities are eliminated. Simulation studies are performed in DIgSILENT PowerFactory platform to investigate the effectiveness of the proposed method. Also, Python scripts that interactively operate with DIgSILENT PowerFactory software are used to change the load and generator powers. The obtained results show that the proposed solution scenarios successfully eliminate the voltage stability problems. © 2024 IEEE.

关键词： Stability criteria

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A 1.65 to 1.84 GHz Filtering SPDT Switch With Fully Tunable Insertion Phase Using NRN and Mixed Electromagnetic Coupling Techniques

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IEEE Transactions on Circuits and Systems II: Express Briefs 2023年第9期70卷 3353-3357页

作者： Wei, Zhihua Chi, Pei-Ling Xu, Ruimin Yang, Tao School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan Research Center of Advanced RF Chips and Systems Nanhu Laboratory Jiaxing Zhejiang314000 China

This brief presents a novel filtering single-pole-double-throw (SPDT) switch with continuously tunable center frequency and insertion phase. It is comprised of six varactor-loaded microstrip resonators and three tunable non-resonating nodes (NRNs) at source and loads. The controllable mixed electromagnetic couplings are introduced on the two main paths of each channel of the switch. At ON-state, the designed filtering switch can generate a 4th-order bandpass filtering performance with high selectivity, and the insertion phase can be continuously tuned with a full 360° tuning range by changing the reactances of NRNs and switching the dominated modes of the mixed couplings. At OFF-state, the mixed electromagnetic couplings are set to zero, and hence high isolation can be realized. To validate the proposed technique, a circuit prototype is designed and fabricated. The measured results suggest that the designed filtering SPDT switch can operate with a tunable center frequency ranging from 1.65 GHz to 1.84 GHz, and its insertion phase at ON-state channel can be continuously tuned from -180° to 180°. During the whole tuning process, the measured in-band isolation for OFF-state channel keeps better than 40 dB. © 2023 IEEE.

关键词： Bandpass filters

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Work-in-Progress: A Hybrid Collaborative Multi Agent Decision Making Algorithm With Factored-Value Max-Plus

Work-in-Progress: A Hybrid Collaborative Multi Agent Decisio...

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2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023

作者： Cotae, Paul Reindorf, Nii Emil Alexander Kang, Myong Velazquez, Alexander The University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC United States Center for High Assurance Computer Systems U. S. Naval Research Laboratory WashingtonDC United States

ISBN: (纸本)9798350337822

We focus on a real-time multi agent decision-making algorithm that combines a centralized algorithm and a distributed algorithm. A network segmentation is unavoidable in a dynamic environment. In such cases, it is necessary for each agent to continually make the most urgent real-time decisions in both centralized and decentralized ways. In this paper, we present a Hybrid Factored-Value Max-Plus algorithm with cost which has online, anytime, and scalable properties despite network segmentation. We also study the performance of centralized and distributed algorithms to understand the performance characteristics of a hybrid algorithm. © 2023 IEEE.

关键词： Decision making

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Machine Learning-Assisted Multi-User Frequency Selective Beam Steering with a Reconfigurable Intelligent Surface in the Ka-Band

Machine Learning-Assisted Multi-User Frequency Selective Bea...

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International Conference on Infrared and Millimeter Waves

作者： Alexander Wolff Lukas Mueller Steffen Klingel Janis Krieger Lars Franke Ralf Stemler Marco Rahm Department of Electrical and Computer Engineering RPTU Kaiserslautern-Landau and Research Center OPTIMAS Kaiserslautern Germany

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

The phase shift and attenuation of a wave reflected from a reconfigurable intelligent surface (RIS) is dependent on the frequency of the wave. We exploit this dispersion in a wireless communication scenario to deflect a normally incident wave to multiple clients across different locations, each served at its unique carrier frequency. A Machine Learning model has been implemented to optimize the signal strength for all users simultaneously. The approach is experimentally verified for the case of two spatially separated users receiving signals at 27 GHz and 31 GHz. Here, we show that the electric field magnitude of the signal at each location can be increased by at least 5.5 dB and up to 28.4 dB (on average 17.15 dB) compared to signals originating from reflections of a flat aluminum plate of similar size as the RIS. The effect could be observed for deflection angles from 10° to 45°.

关键词： Wireless communication Beam steering Aluminum Machine learning Reconfigurable intelligent surfaces Attenuation Reflection Electric fields Dispersion

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