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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Accelerating the cryo-EM structure determination in RELION on GPU cluster

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Frontiers of computer science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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Highly Efficient Three-Level AC-AC Converter with Identical In-Phase and Antiphase Buck-Boost Operations

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IEEE Transactions on Power Electronics 2024年第8期39卷 9929-9942页

作者： Ahmed, Hafiz Furqan Khan, Ashraf Ali Al Zaabi, Omar Mousavi, Seyyed Mohammad Javad Babaei, Ebrahim National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Memorial University of Newfoundland Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates University of Tabriz Faculty of Electrical and Computer Engineering Tabriz51666 Iran Near East University Engineering Faculty Nicosia99138 Turkey

In this article, a new coupled-inductors based three-level bipolar buck-boost ac-ac converter is proposed. The proposed converter can produce highly efficient and symmetric in-phase and antiphase buck and boost modes of operation with much lower component voltage and current stresses. This reduces the operation and design complexity, and helps to achieve a high-efficiency operation. The high-frequency modulating switches of the proposed converter are implemented with coupled-inductor-based dual-buck phase leg, producing three-level input and output voltages, and providing inherent protection from voltage-source short-circuit issue during switch transitions. The other salient features of the proposed converter are no-commutation issue, no need for RC snubbers or dedicated safe-commutation algorithms, provision of high-quality and continuous input/output currents, and support for nonresistive loads. An in-depth circuit analysis of the proposed converter is provided based on the proposed switch modulation strategies. The guidelines for component design/selection are discussed, followed by the comparisons with state-of-the-art three-level ac-ac converters. Finally, practical circuit verifications are performed on a laboratory-assembled prototype. © 1986-2012 IEEE.

关键词： Buck-Boost converter

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Optimal Volume-Sensitive Bounds for Polytope Approximation 39

Optimal Volume-Sensitive Bounds for Polytope Approximation

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39th International Symposium on Computational Geometry, SoCG 2023

作者： Arya, Sunil Mount, David M. Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9783959772730

Approximating convex bodies is a fundamental question in geometry and has a wide variety of applications. Consider a convex body K of diameter ∆ in Rd for fixed d. The objective is to minimize the number of vertices (alternatively, the number of facets) of an approximating polytope for a given Hausdorff error Ε. It is known from classical results of Dudley (1974) and Bronshteyn and Ivanov (1976) that (Equation presented) vertices (alternatively, facets) are both necessary and sufficient. While this bound is tight in the worst case, that of Euclidean balls, it is far from optimal for skinny convex bodies. A natural way to characterize a convex object's skinniness is in terms of its relationship to the Euclidean ball. Given a convex body K, define its volume diameter ∆d to be the diameter of a Euclidean ball of the same volume as K, and define its surface diameter (Equation presented) analogously for surface area. It follows from generalizations of the isoperimetric inequality that (Equation presented). Arya, da Fonseca, and Mount (SoCG 2012) demonstrated that the diameter-based bound could be made surface-area sensitive, improving the above bound to (Equation presented). In this paper, we strengthen this by proving the existence of an approximation with (Equation presented) facets. This improvement is a result of the combination of a number of new ideas. As in prior work, we exploit properties of the original body and its polar dual. In order to obtain a volume-sensitive bound, we explore the following more general problem. Given two convex bodies, one nested within the other, find a low-complexity convex polytope that is sandwiched between them. We show that this problem can be reduced to a covering problem involving a natural intermediate body based on the harmonic mean. Our proof relies on a geometric analysis of a relative notion of fatness involving these bodies. © Sunil Arya and David M. Mount;licensed under Creative Commons License CC-BY 4.0.

关键词： Approximation algorithms

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A Novel Approximation Algorithm for Max-Covering Circle Problem 16th

A Novel Approximation Algorithm for Max-Covering Circle Pro...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Zhang, Kaiqi Zhang, Siyuan Gao, Jirun Wang, Hongzhi Gao, Hong Li, Jianzhong School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Computer Science and Technology Zhejiang Normal University Jinhua China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783031496103

We study the efficient approximation algorithm for max-covering circle problem. Given a set of weighted points in the plane and a circle with specified size, max-covering circle problem is to find the proper place where the center of the circle is located so that the total weight of the points covered by the circle is maximized. Our core approach is to approximate the circle with a symmetrical rectilinear polygon (SRP). We first present a method to construct the circumscribed SRP of a given circle and disclose their area relationship. Then, we convert max-covering SRP problem to SRP intersection problem, which can be efficiently solved with simple partition and modification based on the existing method. Finally, the optimal solution returned from max-covering SRP problem can be used to produce an approximate answer to max-covering circle problem. We prove that for most of the inputs, our algorithm can give a (1 - Ε) approximation to the optimal solution, which only needs O(nΕ-1logn+nΕ-1log(1Ε)) time for unit points and o(nΕ-2logn) time for weighted points. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Testing the Security of Deep Learning Systems Based on Chest X-ray Images 24

Testing the Security of Deep Learning Systems Based on Chest...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： Li, Yang Liu, Shaoying Guo, Lisen Graduate School Of Advanced Science And Engineering Hiroshima University Japan School Of Computer Science South China Business College Of Guangdong University Of Foreign Studies China

ISBN: (纸本)9798400708329

In recent years, deep learning has experienced rapid advancement and significant success, particularly in the field of medical image analysis. However, emerging studies have highlighted a critical vulnerability: deep learning systems are susceptible to adversarial attacks, which employ slight perturbations to deceive these systems. This vulnerability is notably pertinent to medical deep learning systems, thereby raising substantial concerns regarding their security. In our research, we focus on medical deep learning systems developed for chest X-ray image analysis. We subject these systems to adversarial attack and further examine the transferability of this attack across various medical deep learning models. Given the direct correlation between the accuracy of medical image diagnoses and patient health outcomes, enhancing the safety and reliability of medical deep learning systems before their practical implementation is of paramount importance and profound significance. © 2024 ACM.

关键词： Learning systems

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FMCW Monopulse Radar with an Orthogonally Polarized Dual-Beam Dielectric Resonator Antenna

FMCW Monopulse Radar with an Orthogonally Polarized Dual-Bea...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Lee, Chanhee Shin, Kyeong-Han Lee, Ji-Hoon Lee, Hyo-Won Kim, Jeong-Wook Yu, Jong-Won School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Electrical and Computer Engineering Ajou University Suwon Korea Republic of

ISBN: (纸本)9798350364774

In this paper, frequency-modulated continuous-wave (FMCW) radar system using dielectric resonator antenna (DRA), which has two beams with orthogonal linear polarization to each other, is introduced to function as a monopulse radar. The DRA operates at 24.3 GHz with a 16.8% bandwidth and provides over 40 dB isolation between V-pol and H-pol ports. Simulations demonstrate effective target detection and advanced directional information extraction, similar to monopulse radar, using a signal processing technique to address overlapping beam regions. © 2024 IEEE.

关键词： Monopulse radar

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Weed detection in precision agriculture: leveraging encoder-decoder models for semantic segmentation

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3547-3561页

作者： Thiagarajan, Shreya Vijayalakshmi, A. Grace, G. Hannah Shell India Markets Private Limited Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Division of Mathematics School of Advanced Sciences Vellore Institute of Technology Chennai India

Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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science China(Information sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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