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2023 International Technical Conference on Circuits/Systems, Computers, and Communications, ITC-CSCC 2023

作者： Gyaang, Raymond Abdul-Rahman, Ahmed Gookyi, Dennis Agyemanh Nana Jang, Sung-Joon Lee, Sang-Seol Bolgatanga Technical University Department of Electrical and Electronics Engineering Bolgatanga Ghana Kwame Nkrumah University of Science and Technology Department of Telecommunication Engineering Kumasi Ghana Institute for Scientific and Technological Information Council for Scientific and Industrial Research Accra Ghana Korea Electronics Technology Institute Intelligent Image Processing Research Center Seongnam-si Korea Republic of

ISBN: (纸本)9798350326413

This paper presents an accurate analysis and design of a Schiffman phase shifter with optimally positioned compensation capacitances for improved performances. The approach synthesized the microstrip coupler's even and odd mode characteristic impedance and its coupling effect to enhance the phase shift with minimal phase deviation. This was achieved by positioning artificial capacitances along the microstrip directional coupler. The new design method is valid for all substrate thicknesses and permittivity thereby overcoming the bandwidth limitation presented by the minimum spacing between the coupled lines in a conventional microstrip directional coupler. The simulation result demonstrated a maximum phase deviation of ± 3° and a maximum return loss of -0.613 dB. The source and load reflections were well below - 10 dB. The obtained dataset is highly accurate and can be used to train a deep neural network for improved phase error and return loss of a Schiffman phase shifter. © 2023 IEEE.

关键词： Deep neural networks

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On the Secrecy Performance of Reconfigurable Intelligent Surfaces-Assisted Satellite Networks Under Shadow-Rician Channels

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6794-6808页

作者： Le, Si-Phu Nguyen, Tan N. Le-Tien, Thuong Duy, Tran Trung Nguyen, Tien-Tung Ng, Derrick Wing Kwan Tu, Lam-Thanh Voznak, Miroslav Ton Duc Thang University Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ho Chi Minh72900 Viet Nam VSB-Technical University of Ostrava Department of Telecommunications Faculty of Electrical Engineering and Computer Science Ostrava70800 Czech Republic University of Technology Vietnam National University Ho Chi Minh72900 Viet Nam Posts and Telecommunications Institute of Technology Faculty of Telecommunications 2 Ho Chi Minh72900 Viet Nam Industrial University of Ho Chi Minh City Faculty of Electronics Technology Ho Chi Minh70000 Viet Nam University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this study, we investigate the secrecy performance of reconfigurable intelligent surfaces-assisted satellite (RISAS) networks. Specifically, we analytically derive closed-form expressions of the secrecy outage probability (SOP) and the tight lower bound secrecy rate (SR) exploiting the moment matching method (MMM). Furthermore, we explore the asymptotic behavior of the SOP as the satellite transmit power is sufficiently large, offering valuable insights into network performance. Numerical simulations are conducted to validate the established framework and elucidate the influence of critical parameters such as transmission distance, transmit power, and the number of reconfigurable intelligent surfaces (RIS) elements on SOP and SR. Our findings highlight the efficacy of increasing the number of phase shifters of the RIS to mitigate the adverse effects of fading, shadowing, and transmission environments. Finally, we unveil the adaptability of the proposed networks, illustrating their suitability for various satellite systems, including low earth orbit (LEO) and geostationary earth orbit (GEO). Among these, LEO offers superior performance due to its shorter transmission distance compared to medium earth orbit (MEO) and GEO. © 1965-2011 IEEE.

关键词： Satellite communication systems

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Rule-Based Controller Design for Frequency Control in Islanded Microgrids 11

Rule-Based Controller Design for Frequency Control in Island...

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11th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2024

作者： Singh, Sugandh Pratap Singh, V.P. Benedetto, Francesco Indian Institute of Technology Electrical Engineering Department UP Kanpur India Malaviya National Institute of Technology Electrical Engineering Department Rajasthan Jaipur India University of Roma Tre Signal Processing for Telecommunications and Economics Laboratory Economic Department Italy

This paper presents a Cohen-Coon rule-based controller designed for frequency regulation in islanded microgrids, crucial for maintaining stability during grid disconnections. The microgrid integrates diverse renewable and conventional energy sources, including PV panels, wind turbines, and biodiesel generators, alongside energy storage systems. The study focuses on transforming the microgrid's higher-order model into a simplified first-order plus dead time model and optimizing controller tuning using the Cohen-Coon method. Real-time simulations on Opal-RT validate the controller's performance under varying load conditions and intermittent renewable energy inputs. Results demonstrate the controller's effectiveness in minimizing frequency deviations, affirming its robustness for ensuring stable power supply in dynamic microgrid environments. © 2024 IEEE.

关键词： Windmill

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Rule-Based Controller Design for Frequency Control in Islanded Microgrids

Rule-Based Controller Design for Frequency Control in Island...

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International Conference on Power Electronic Drives

作者： Sugandh Pratap Singh V. P. Singh Francesco Benedetto Electrical Engineering Department Indian Institute of Technology Kanpur UP India Electrical Engineering Department Malaviya National Institute of Technology Jaipur Rajasthan India Economic Department Signal Processing for Telecommunications and Economics Laboratory University of ROMA TRE Italy

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

关键词： Renewable energy sources Biological system modeling Microgrids Power system stability Real-time systems Stability analysis Robustness Wind turbines Tuning Load modeling

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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AI-Based Security Surveillance and Hazard Detection for Train Platform Safety

AI-Based Security Surveillance and Hazard Detection for Trai...

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IEEE International Conference on Electro-Information Technology

作者： Alvaro Aparicio Serna Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Safety in train platforms is a common concern in railway systems around the world. There are grave risks associated with the tracks and the crowds that often end up in injuries or deaths. Traditional approaches for ensuring safety in train transport networks rely on passive closed-circuit television (CCTV) monitorization that needs constant human attention. This paper proposes an AI-based alternative for detecting anomalies in surveillance videos, that is more efficient and cost-effective than traditional methods. Through Video Instance Segmentation (VIS), this work detects common risks such as overcrowding on the platform, people or objects standing on the edge of the platform, individuals or objects falling onto the tracks, the presence of firearms, and the presence of unattended baggage. The proposed algorithm combines state-of-the-art models like YOLOv8, ByteTrack, and Segment Anything Model (SAM) to classify, track, and segment object detections respectively. Additionally, this paper presents a custom-trained YOLOv8 model for gun detection. The results show that the system can successfully analyze video, create surveillance annotations, detect hazardous situations to alert authorities, and help prevent accidents and incidents on train platforms.

关键词： Instance segmentation Annotations Surveillance image edge detection Rail transportation Hazards Classification algorithms

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Smart Multi-Building Energy Monitoring System

Smart Multi-Building Energy Monitoring System

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IEEE International Conference on Electro-Information Technology

作者： Saurabh Saluja Colin Prochnow Grant Couper Filip Zivko Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents an intelligent system for monitoring energy usage in smart buildings. It functions as a bridge between the smart grid and smart buildings. A web-based platform is designed to monitor sensor data and track power consumption on the grid. AI technology provides tips for energy conservation based on factors such as power usage patterns and weather conditions. The adaptability of our approach allows for the tuning of automated protocols based on evolving power usage patterns and grid conditions. Data is transmitted between the client devices and the central server via a Zigbee network organized in a star formation, allowing for mutual communication. Our implementation results in the improvement of the overall energy efficiency of supported infrastructure while ensuring continuous and optimized operation in case of a bulk grid blackout.

关键词： Smart buildings Energy conservation Zigbee Stars Smart homes Energy efficiency Smart grids

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Basketball Video Analysis for Automated Game Data Acquisition Deep Learning

Basketball Video Analysis for Automated Game Data Acquisitio...

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IEEE International Conference on Electro-Information Technology

作者： Diego Rodriguez Garcia Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

The utilization of data analytics to gain insights into the game of basketball has seen a remarkable surge in the past decade. Leagues such as the National Basketball Association are continuously exploring innovative methods to analyze game data, an approach that has significantly influenced the dynamics of the game. But to perform these analyses, a growing amount of data is needed, which is traditionally annotated by humans. This work proposes a 3-stage system able to automatically acquire relevant basketball game data from a broadcast video. The first stage is an object detector combined with a tracking algorithm to extract the main elements present in a basketball game video. Then, the players' visual information is analyzed to identify the players based on pixel color analysis and number recognition. Finally, a statistics generation algorithm assigns the game events to the corresponding player and team, so that the system can be used as an aid for box score annotation in major leagues, low-cost annotation in amateur games, or in-depth game video analysis.

关键词： Visualization Annotations image color analysis Data acquisition Optical character recognition Games Detectors

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Advanced Robotic Surveillance for Urban Air Quality Safety

Advanced Robotic Surveillance for Urban Air Quality Safety

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IEEE International Conference on Electro-Information Technology

作者： Guillaume Hansen James R. Benson Muhammad Rafay Danish Khoa Truong Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents a multifunctional quadruped robot, specifically engineered for comprehensive air quality monitoring and emergency assistance. Designed to navigate through urban environments, the robot autonomously collects and transmits real-time air quality data to a centralized database. This interdisciplinary paper integrates robotics, environmental science, and emergency response protocols to propose novel solutions for smart city infrastructure, public health, and disaster management. Specifically, this research highlights the critical role that autonomous systems play in monitoring environmental conditions and advancing safety protocols. This research leverages a comprehensive array of sensors including CO2 (MH-Z19), particle (SDS 011), GPS (ADA 746), DHT 22, MQ 9, and ADS 1115 converter, each meticulously implemented to ensure precise environmental data collection and analysis. Furthermore, the integration of cutting-edge technologies such as Robot Operating System (ROS), .NET MAUI, Python, and MariaDB establishes a robust framework for seamless operation, data processing, and secure storage within the quadruped robot system.

关键词： Temperature sensors Protocols Robot sensing systems Dynamic scheduling Sensors Quadrupedal robots Servers

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Noise and performance analysis on fundus images with CNN and transformer models

Noise and performance analysis on fundus images with CNN and...

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Conference on Information and Communication Technology (CICT)

作者： Niranjana Vannadil Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and cataract. However, the quality of fundus images can be significantly affected by noise, mainly additive white Gaussian noise (AWGN), which is inherent in many imaging systems. The presence of noise in real-world data poses significant challenges for computer vision tasks. In the field of medical image classification, a wrong diagnoisis has heavy consequences. Understanding the impact of AWGN on fundus images is crucial for developing practical denoising algorithms and improving diagnostic accuracy. This work presents an analysis of AWGN noise in fundus images aims to characterize its effects on image quality and assess its impact on diagnostic tasks. The work also analyzes the performance of six models (3 each) of two popular deep learning architectures, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in the presence of AWGN. AWGN is first introduced to the clean image datasets to conduct the analysis. The CNN and ViT models are trained on the noisy datasets to evaluate the performance of the image classification task. The work also involves six denoising algorithms and a popular image enhancement algorithm- Contrast Limited Adaptive Histogram Equalization (CLAHE).

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