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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

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Energy-Efficient Communication Protocols for IoT Devices 9

Energy-Efficient Communication Protocols for IoT Devices

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rahamathunnisa, U. Vivekanand, Chettiyar Vani Chithras, T. Sreedevi, E. Kiran, Venneti Rajendiran, M. School of Computer Science Engineering And Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Thiruvallur India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Electrical And Electronics Engineering Chennai Tamil Nadu Avadi600062 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram India Andhra Pradesh Surampalem India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

The development and optimisation of energy-efficient communication protocols that are specifically adapted for Internet of Things (IoT) devices is the focus of this research. The proliferation of interoperable bias has given rise to the converse girding energy consumption, which has come a consummate concern in the contemporary period. The primary end of this bid is to probe innovative approaches and protocols with the capacity to reduce the energy consumption of Internet of Effects(IoT) connections. As a result, the bias will retain the capability to attain an extended functional lifetime while coincidently producing a lowered environmental footmark. The presented protocols aim to achieve a result that balances the utilisation of energy coffers with the conservation of dependable communication. A thorough examination of the presently employed protocols, coupled with the perpetration of sophisticated optimisation ways, will be the styles employed to achieve this thing. Theprovides a significant donation to the development and prosecution of communication protocols that effectively address the unique energy constraints encountered by Internet of effects(10T)- connected ecosystems. This contributes to the enhancement of effectiveness and sustainability in the world of networked bias, which is expanding at an accelerated rate. © 2024 IEEE.

关键词： Energy efficiency

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Efficient Evaluation of Short-Circuit Faults in Active Distribution Networks 11

Efficient Evaluation of Short-Circuit Faults in Active Distr...

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

作者： Meena, Ghanshyam Mathur, Akhilesh Singh, V.P. Murari, Krishna Kamalasadan, Sukumar Malaviya National Institute of Technology Department of Electrical Engineering Rajasthan Jaipur 302017 India The University of Toledo Electrical Engineering and Computer Science Department Toledo 43606 OH United States University of North Carolina at Charlotte Power Energy and Intelligent Systems Laboratory Epic Department of Electrical Engineering Charlotte 28223 NC United States

Short-circuit problems in unbalanced radial distribution networks have become more common as a result of distributed generation connectivity and smart grid advancements. An accurate and effective strategy for assessing these faults due to short circuits is presented in this study. In contrast to traditional methods, this approach uses relationship matrices that are created expressly to take use of distribution network's radial characteristics. Through the examination of variations in bus voltages, bus current injections, and branch currents during fault conditions, the suggested method formulates the analysis of various fault types. The accuracy of developed method has been validated on IEEE-123 bus modified test network by comparing the obtained results with PSCAD/EMTDC simulation results. © 2024 IEEE.

关键词： Distributed Generation Short-circuit Analysis Unbalanced

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Shared Information Under Simple Markov Independencies

Shared Information Under Simple Markov Independencies

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IEEE International Symposium on Information Theory

作者： Madhura Pathegama Sagnik Bhattacharya Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College Park MD USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Shared information is a measure of mutual dependence among $m\geq 2$ jointly distributed discrete random variables. We show that the shared information of a Markov random field in which the underlying graph has at least one cut vertex, is the same as the minimum shared information of its blocks (also called biconnected components). This generalizes prior results on shared information of Markov random fields to a much wider class of nontree graphs.

关键词： Random variables Markov random fields Information theory

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer systems Science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Local Fading Memory Effects in a Tantalum Oxide ReRAM Cell from Hewlett Packard Labs 2

Local Fading Memory Effects in a Tantalum Oxide ReRAM Cell f...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Ascoli, A. Schmitt, N. Messaris, I. Demirkol, A.S. Tetzlaff, R. Strachan, J.P. Chua, L.O. Institute of Circuits and Systems Technische Universität Dresden Faculty of Electrical and Computer Engineering Dresden Germany Peter Grünberg Institute Forschungszentrum Jülich GmbH Jülich Germany Rwth Aachen University Aachen Germany University of California Berkeley Department of Electrical Engineering and Computer Sciences BerkeleyCA94720 United States

ISBN: (纸本)9798350300802

Memristors are nano-devices, which, in some of their physical realisations, feature multiple operating modes, allowing data to be alternatively sensed, stored, or processed. Their multi-functionality, which resembles the operating principles of neural structures in the brain, in conjunction with the ease of their fabrication, which is typically carried out on top of underlying CMOS circuitry, at the regularly-spaced spaced crossings of crossbar matrices, composed of two vertically-adjacent and perpendicular sets of parallel metal lines, makes their use in integrated circuit design ideally suitable for enhancing the performance of state-of-the-art computing machines without the need to shrink the sizes of CMOS transistors any further. Given the extraordinary opportunities, which memristors open up in the field of Artificial Intelligence, gaining a deep understanding of their inherently-nonlinear dynamics is of primary importance. Fading memory is the capability of a system to display a unique steady state under a stimulus from a given class. Fading memory effects in a memristive medium were first discovered in 2016 through a comprehensive experimental and theoretical investigation of the switching dynamics of a TaOx ReRAM cell. Recently, a bifurcation study of a predictive model of the same nanostructure revealed how it may also exhibit bi-stability under suitable AC periodic excitation. Bi-stability is essentially a local form of fading memory. This paper gains a deeper insight into the local fading memory properties of the TaOx ReRAM cell, demonstrating how the existence domain for its memory state may be in fact partitioned in a tuneable number of basins of attraction, each of which includes all the initial conditions, from which the memristor would settle into one of the admissible locally-stable oscillatory steady states. © 2023 IEEE.

关键词： Fading Memory Local Fading Memory Memory Memristor Multi-stability Non-volatility ReRAM

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques 7

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Anjaneya, L.H. Maheswari, K. Revathi, N. Siva, K.P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Bapuji Institute of Engineering And Technology Department of Electrical And Electronics Engineering Karnataka Davangere577004 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Panimalar Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Department of Mechatronics Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score. © 2024 IEEE.

关键词： Genetic algorithms

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Blackout Mitigation via Physics-guided RL

arXiv

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arXiv 2024年

作者： Dwivedi, Anmol Paternain, Santiago Tajer, Ali Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute TroyNY12180 United States

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system’s stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black-box counterparts. One important observation is that strategically removing transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts. © 2024, CC BY.

关键词： Electric load flow

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Towards Finding the Sources of Polymorphism in Polymorphic Gates (Invited)

Towards Finding the Sources of Polymorphism in Polymorphic G...

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Asia and South Pacific Design Automation Conference

作者： Timothy Dunlap Zelin Lu Gang Qu Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College Park MD USA

A polymorphic gate can change its functionality based on external conditions such as temperature, voltage, and external signals. Although this concept was proposed more than two decades ago and has found success in designing circuits for area minimization and security applications, how to construct polymorphic gates remains a challenge because they do not have the structure of conventional CMOS gates. Without knowing the causes of polymorphism, the majority of reported polymorphic gates are generated in the ad-hoc fashion, using evolutionary algorithms and the time-consuming Spice simulation. In this paper, we study thousands of candidate circuits that we have created for the sources of polymorphism. We observe several features that are not present in traditional CMOS gates. Circuit analysis suggests that these features are potential sources of polymorphism, which is confirmed as they exist in the polymorphic gates reported in the literature. Furthermore, we demonstrate with examples that polymorphic gates can be effectively constructed using these features as guidance.

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