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IEEE Southeastcon

作者： Md Junayed Hossain Shoptorshi Rahman Md Fahad Monir Tarem Ahmed Department of Computer Science & Engineering Independent University Bangladesh (IUB) Bangladesh Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Real-time object detection is pivotal in various computer vision and robotics applications, such as security surveillance, autonomous navigation, and interactive systems. This study evaluates several object detection algorithms under low illumination conditions, comparing traditional methods including SIFT, SURF, ORB, BRISK, KAZE, and AKAZE with the deep learning model YOLOv5. To simulate these conditions, we utilized the COCO dataset and implemented a sequence of preprocessing procedures, such as adjusting the gamma correction throughout the dataset, introducing Gaussian noise, and equalizing the histogram to improve image contrast. Here, YOLOv5 exhibited superior performance, achieving a detection accuracy of 92.47% and a processing time of 45 milliseconds per image. Traditional algorithms like SURF and KAZE, although slower, demonstrated precision rates of 78.54% and 76.86%, respectively. YOLOv5 also identified the highest number of key points and matches. Our findings indicate that while YOLOv5 excels in real-time detection under low-light conditions, traditional methods are effective for specific tasks in resource-constrained environments. This study benchmarks these algorithms and proposes a hybrid approach to enhance real-time object detection systems under low illumination conditions.

关键词： YOLO Deep learning Histograms computer vision Surveillance Interactive systems Gaussian noise Lighting Real-time systems Security

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An Intelligent Traffic Management of Vehicles using Deep Learning Approach in Smart Cities 3

An Intelligent Traffic Management of Vehicles using Deep Lea...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Elov, Botir Dauletov, Adilbek Sucharitha, Yadala Khalilova, Feruza Latipova, Mukhayyo Abdullayeva, Mukhtasarkhon Alisher Navo'i Tashkent State University of Uzbek Language and Literature Tashkent Uzbekistan Alfraganus University Department of Digital Technologies Tashkent Uzbekistan Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Fergana Polytechnic Institute Department of Electrical Power Engineering Fergana Uzbekistan

ISBN: (纸本)9798331508456

Smart traffic management of automobiles has been one of the utmost sought-after issues in the academic community due to the ever-increasing pace of urbanization and civilization over the last several centuries. Vehicle dissection, traffic density appraisal, and vehicle tracing are the three main components of smart traffic management. This problem becomes more difficult to resolve when there are occlusions, background clutter, and fluctuations in traffic density. In light of this need, we explore a deeper learning approach based on R-CNN s for faster vehicle segmentation in this study. There is adaptive backdrop modeling in the computational framework. It solves problems with lighting and shadows as well. The use of topological active net deformable models allows for segmentation that is more precise. The deformations mentioned can be accomplished with the aid of topological and stretched topological active nets. Minimizing energy allows for mesh deformation. Using a tweaked version of the stretched topological active net improves the segmentation accuracy to 98.3%. The investigational findings show that this framework is better than other methods. © 2025 IEEE.

关键词： Highway administration

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Receiver Side Regulation of LCC Wireless Power Transfer System with Variable Notch Filter

Receiver Side Regulation of LCC Wireless Power Transfer Syst...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Hsin-Che Hsieh Jih-Sheng Lai The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

The robustness and relative insensitivity to misalignments and mismatches make the LCC topology popular in wireless power transfer (WPT) applications, including more challenging dynamic or multi-receiver WPT systems. The output of LCC topology based WPT systems is typically regulated by an additional dc-dc converter stage, either before or after the WPT stage. The additional dc-dc stage increases the cost and complexity of the system and would require wireless communication for output regulation if placed on the transmitter side, making the system susceptible to electromagnetic interference. In this paper, a receiver side modulation scheme based on a variable inductor controlled notch filter is proposed. By modulating the variable inductor, the output can be independently adjustable in a wide-range. The variable inductor is controlled with simple pulse width modulation (PWM). The proposed control scheme is especially suitable for multi-receiver WPT systems; where transmitter based regulation schemes cannot simultaneously accommodate multiple receivers. The proposed system is verified experimentally with a 48-V o(max) , 72-W prototype WPT system with multi-receiver capability.

关键词： Wireless communication Prototypes Receivers Wireless power transfer Pulse width modulation Regulation Topology Inductors Voltage control Notch filters

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Optimized ROI Extraction and Pixel Reduction Methods for Kidney Abnormality Detection

Optimized ROI Extraction and Pixel Reduction Methods for Kid...

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IEEE Southeastcon

作者： Md Junayed Hossain Md Fahad Monir Tarem Ahmed Department of Computer Science & Engineering Independent University Bangladesh (IUB) Bangladesh Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

A global public health concern, Chronic Kidney Disease causes renal failure, cardiovascular disease, and premature death. Given the frequency of kidney diseases and their significant impact on public health, detection of any abnormalities in the kidney is crucial. Effective patient treatment and medical care planning depend on the early and precise diagnosis of a variety of kidney abnormalities. Early detection enable treatments that enhance patient outcomes, slow the progression of disease, and lessen the strain on healthcare systems. In order to detect anomalies in kidney, we utilized the Region of Interest (ROI) and Pixel Reduction techniques separately in this work. We have applied the Bicubic Interpolation method to reduce the size of individual pixels in an image while preserving a significant data of the original image data. A novel hybrid strategy that aims to preserve diagnostic data while reducing computing complexity uses the original images for ROI extraction. The deep learning method is then independently trained and tested using the pixel-reduced and localized dataset. In order to categorize the images, we use Inception V3 and EfficientNet B7 as Deep Learning models. Using the ROI-extracted data, we were able to achieve approximately 99.75% accuracy with the EfficientNet B7 model. Moreover, the same algorithm and dataset were used to obtain 98.45%, 99.02%, and 98.78% Precision, Recall, and F1-score, respectively. This framework provides medical professionals with a helpful prognostic tool to early detect identify various forms of kidney abnormalities.

关键词： Deep learning Interpolation Medical treatment Data models Planning Data mining Public healthcare Kidney Medical diagnostic imaging Strain

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Path Planning and Optimization for Cuspidal 6R Manipulators

arXiv

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arXiv 2025年

作者： Elias, Alexander J. Wen, John T. Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

A cuspidal robot can move from one inverse kinematics (IK) solution to another without crossing a singularity. Multiple industrial robots are cuspidal. They tend to have a beautiful mechanical design, but they pose path planning challenges. A task-space path may have a valid IK solution for each point along the path, but a continuous joint-space path may depend on the choice of the IK solution or even be infeasible. This paper presents new analysis, path planning, and optimization methods to enhance the utility of cuspidal robots. We first demonstrate an efficient method to identify cuspidal robots and show, for the first time, that the ABB GoFa and certain robots with three parallel joint axes are cuspidal. We then propose a new path planning method for cuspidal robots by finding all IK solutions for each point along a task-space path and constructing a graph to connect each vertex corresponding to an IK solution. Graph edges are weighted based on the optimization metric, such as minimizing joint velocity. The optimal feasible path is the shortest path in the graph. This method can find non-singular paths as well as smooth paths which pass through singularities. Finally, this path planning method is incorporated into a path optimization algorithm. Given a fixed workspace toolpath, we optimize the offset of the toolpath in the robot base frame while ensuring continuous joint motion. Code examples are available in a publicly accessible repository. © 2025, CC BY.

关键词： Inverse kinematics

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Terahertz Channel Modeling Considering Near-to-Far Field Transition: Benefits & Challenges

Terahertz Channel Modeling Considering Near-to-Far Field Tra...

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European Conference on Antennas and Propagation, EuCAP

作者： Priyangshu Sen Sherif Badran Josep M. Jornet Arjun Singh Department of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Terahertz band (0.1-10 THz) communication is set to play a pivotal role in the advancement of subsequent generations of wireless technology by facilitating high data rates, ultra-secure transmission, high-resolution sensing, and low-latency links, among others. Nevertheless, the issues of low transmission power and high path loss necessitate large-aperture high-gain antennas, which can result in a considerably expanded near-field operational region. The distinctive mechanism of wave propagation within this region, as well as its transition to the far-field area, necessitates a comprehensive reevaluation of channel modeling. In this paper, we discuss the effects of antennas on path loss models and channel metrics while operating in both the near- and far-field regions. We present a detailed analysis of near-field propagation mechanisms, illustrating that an antenna-agnostic channel model is not feasible. Furthermore, we discuss the potential benefits and challenges associated with these regions.

关键词： Wireless communication Measurement Wireless sensor networks Propagation Transmitting antennas Propagation losses Transceivers Sensors Channel models Terahertz communications

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A Novel Approach to Smart Grid Energy Management Using the Osprey Optimization Algorithm 4

A Novel Approach to Smart Grid Energy Management Using the O...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rudrapogu, Nissisolomon Hemalatha, N. Warkad, Sanjay B Jayanthi, A. Vinoth, K. Jayaram, B. Lecturer in Eee Nissisolomon Rudrapogu Mbts Government Polytechnic College Department of Electrical and Electronics Engineering Section Andhra Pradesh Guntur522005 India Bharath Institute of Higher Education and Research Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India P. R. Pote Patil College of Engineering & Management Department of Electrical Engineering Maharashtra Amravati444602 India Jeppiaar Institute of Technology Department of Physics Tamil Nadu Sriperumbudur631502 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798331523923

Smart Grid Energy Management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization. © 2025 IEEE.

关键词： Clean energy

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Investigation of a New Stator Permanent Magnet with the effects of Flux-Reversal and Flux-Switching

Investigation of a New Stator Permanent Magnet with the effe...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mohammad Reza Sarshar Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Seyed Hamid Montazeri Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications.

关键词： Magnetic flux Torque Switches Stators Motors Permanent magnets Power electronics Topology Torque measurement Saturation magnetization

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A Hybrid Deep Learning Framework for IoT Security Enhancement and Anomaly Detection 3

A Hybrid Deep Learning Framework for IoT Security Enhancemen...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Baswaraj, D. Rahman, Abdur Pandey, Deependra Bhargavi, T. Ismoilov, Muslimbek Deepthi, Natha Vasavi College of Engineering Department of Computer Science and Engineering Hyderabad India School of Business & Technology Emporia State University EmporiaKS United States Amity University Department of Electronics and Communication Engineering Lucknow India St. Martins Engineering College Department of Information Technology Hyderabad India Fergana Polytechnic Institute Department of Electrical Engineering Fergana Uzbekistan Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798331508456

Cyber threats targeting Internet-of-Things(IoT) devices have grown in frequency and severity alongside the IoT's fast uptake and usage in the past few years. Because of this, creating a model that can detect hostile attacks and lessen security risks in IoT-devices is becoming a very important subject. Botnets pose a significant risk because they disrupt the networks that Internet of Things (IoT) devices rely on to function. An method called GA-HDLAD, which stands for genetic-algorithm with hybrid deep learning(DL), was created in this study to advance security in the IoT environment by detecting botnets. Botnet detection makes use of hybrid deep learning, which combines attention concepts, feature-extraction-techniques(FETs), and recurrent-neural-networks(RNNs). The hybrid-DL approach can identify botnet attacks, which frequently use complicated patterns. In addition, the model's RNNs capture temporal dependencies, while FETs guarantee efficient feature extraction from geographical data. To choose the hyperparameters required by the HDL method, simulated annealing (SA) is employed. This paper compares the GA-HDLAD system to other detection approaches and finds that it outperforms them experimentally using a benchmark botnet dataset. © 2025 IEEE.

关键词： Feature Selection

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Optimal Location of Renewable Energy Generators in Transmission and Distribution System of Deregulated Power Sector:A Review

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Energy engineering 2025年第3期122卷 823-859页

作者： Digambar Singh Najat Elgeberi Mohammad Aljaidi Ramesh Kumar Rabia Emhamed Al Mamlook Manish Kumar Singla Department Electrical Engineering Government Polytechnic CollegeBTE LucknowEtah207120India University of Nevada RenoNV 89557USA Department of Computer Science Zarqa UniversityZarqa13110Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering&TechnologyChitkara UniversityChandigarh160002India Jadara University Research Center Jadara UniversityIrbid00962Jordan Department of Industrial Engineering Al Zawiya UniversityAl-ZawiyaP.O.Box 16418Libya Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Biosciences Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India

The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in *** study focuses on the most relevant publications directly addressing the research problem at ***,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission ***,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively *** demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution *** presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and ***,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy *** paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.

关键词： Optimization of RESs distributed generation modeling and selection of RESs hybrid systems standalone systems optimal location

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