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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Control of Grid-Connected Inverters Using PLL for Synchronization and Harmonic Mitigation

Control of Grid-Connected Inverters Using PLL for Synchroniz...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Yousefpour, Kamran Yousefpour, Mohammad Molla, Seyyed Javad Hosseini Andani, Mansour Taheri Varnousdaderani, Sepideh Alaei Andani, Hamid Taheri Mississippi State University Department of Electrical and Computer Engineering United States Azad University Iran Babol Noshirvani University of Technology Department of Electrical and Computer Engineering Iran School of Manufacturing Systems and Networks Arizona State University United States Azad University Department of Computer Engineeering Iran Isfahan University of Technology Department of Electronic Engineering Iran

ISBN: (纸本)9798331541125

This paper presents the design and simulation of a single-phase grid-connected inverter control system, focusing on enhancing power quality and dynamic performance. The control system comprises a resonant controller for real and reactive power regulation, ensuring sinusoidal current injection into the grid. Additionally, a Phase-Locked Loop (PLL) is incorporated to improve synchronization with the grid voltage, generating accurate reference signals for current control and mitigating harmonic disturbances. The PLL design leverages specific gain parameters, μ1,μ2, and μ3, optimized for a critically damped response. A comprehensive simulation study evaluates the system's performance under different conditions, including soft-start operation, transient power commands, and grid voltage distortion with low-order harmonics (Sth, 7th, LIth, and 13th). Results highlight the system's robust dynamic response, low total harmonic distortion (THD), and effective harmonic attenuation, demonstrating its suitability for grid-connected distributed energy resource (DER) applications. This work provides a scalable and reliable control framework for next-generation inverter systems operating in modern, complex grid environments. © 2025 IEEE.

关键词： Harmonic distortion

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A survey on hyperspectral image restoration: from the view of low-rank tensor approximation

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Science China(Information Sciences) 2023年第4期66卷 33-63页

作者： Na LIU Wei LI Yinjian WANG Ran TAO Qian DU Jocelyn CHANUSSOT School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Department of Electrical and Computer Engineering Mississippi State University GIPSA-Lab University of Grenoble Alpes Aerospace Information Research Institute Chinese Academy of Sciences

The ability to capture fine spectral discriminative information enables hyperspectral images(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent the true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors' hardware limitations. These degradations include but are not limited to complex noise, heavy stripes, deadlines,cloud/shadow occlusion, blurring and spatial-resolution degradation, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation(LRTA) is such an emerging technique, having gained much attention in the HSI restoration community, with an ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation(LRMA),LRTA characterizes more complex intrinsic structures of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: denoising, fusion,destriping, inpainting, deblurring, and super-resolution. For each topic, state-of-the-art restoration methods are introduced, with quantitative and visual performance assessments. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration, and application concerning the interpretation requirements.

关键词： hyperspectral image image restoration low-rank tensor approximation multisource fusion remote sensing

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Assessing the Performance of AI-Generated Code: A Case Study on GitHub Copilot 35

Assessing the Performance of AI-Generated Code: A Case Study...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Li, Shuang Cheng, Yuntao Chen, Jinfu Xuan, Jifeng He, Sen Shang, Weiyi Wuhan University School of Computer Science Wuhan China University of Arizona Department of Systems and Industrial Engineering Tucson United States University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350353884

The integration of Large Language Models (LLMs) into software development tools like GitHub Copilot holds the promise of transforming code generation processes. While AI-driven code generation presents numerous advantages for software development, code generated by large language models may introduce challenges related to security, privacy, and copyright issues. However, the performance implications of AI-generated code remain insufficiently explored. This study conducts an empirical analysis focusing on the performance regressions of code generated by GitHub Copilot across three distinct datasets: HumanEval, AixBench, and MBPP. We adopt a comprehensive methodology encompassing static and dynamic performance analyses to assess the effectiveness of the generated code. Our findings reveal that although the generated code is functionally correct, it frequently exhibits performance regressions compared to code solutions crafted by humans. We further investigate the code-level root causes responsible for these performance regressions. We identify four major root causes, i.e., inefficient function calls, inefficient looping, inefficient algorithm, and inefficient use of language features. We further identify a total of ten sub-categories of root causes attributed to the performance regressions of generated code. Additionally, we explore prompt engineering as a potential strategy for optimizing performance. The outcomes suggest that meticulous prompt designs can enhance the performance of AI-generated code. This research offers valuable insights contributing to a more comprehensive understanding of AI-assisted code generation. © 2024 IEEE.

关键词： Software design

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Robust Anomaly Detection of Adventitious Auscultation Signals using Bayesian Belief Tracking 46

Robust Anomaly Detection of Adventitious Auscultation Signal...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Kala, Annapurna Elhilali, Mounya Johns Hopkins University Laboratory for Computational Audio Perception Department of Electrical & Computer Engineering United States

ISBN: (纸本)9798350371499

Stethoscope screening serves as a primary method for diagnosing pulmonary infections, with medical professionals actively listening for signs of pathologies in breathing sounds like wheezing and crackling, which carry different clinical interpretations. Environmental conditions during auscultation recordings often share similarities with these abnormal lung sounds, and can mask or confound their presence making their detection highly sensitive to surrounding factors. To automate this process, a robust anomaly detection scheme with resilience to ambient backgrounds and high precision is essential. In this study, we propose an unsupervised framework for anomaly detection where statistics of a deep neural network embeddings are tracked using a Bayesian belief model in order to flag variations that are deemed anomalous, hence facilitating detection of adventitious auscultation events. The proposed scheme leverages two key principles: (1) learning of statistics of normal auscultation patterns using variational constraints, and (2) tracking changes in the statistics using Bayesian beliefs that interpret anomalies as deviations from normal statistics. This approach is shown to be very effective in detecting adventitious auscultations under various noise levels hence ensuring its resilience to environmental conditions. © 2024 IEEE.

关键词： Deep neural networks

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mURLLC Wireless Networks 57

Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mUR...

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57th Annual Conference on Information Sciences and systems, CISS 2023

作者： Zhang, Xi Zhu, Qixuan Poor, H. Vincent Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9781665451819

The sixth generation (6G) mobile wireless networks are expected to provide massive ultra-reliable and low-latency communications (mURLLC) for data services, which require extremely stringent quality-of-services (QoS) with massive connectivity. Rate splitting (RS) multiple access schemes have been proposed recently for tackling the difficulty of the massive access yielded by a large number of mobile users (MUs) requesting to connect to a single base station. Massive multiple-input and multiple-output (massive-MIMO) communication is considered a promising solution to serve massive MUs in 6G wireless networks. However, how to apply RS schemes into massive-MIMO techniques to support the massive access has not been sufficiently studied. In this paper, we propose to integrate RS schemes with massive-MIMO techniques to achieve mURLLC transmissions under a Nakagamir $m$ fading wireless channel. First, we establish the system model for the integrated RS and massive-MIMO schemes. Then, we derive closed-form expressions for the signal-to-interference-plus-noise ratios (SINRs) under a Nakagami- $-m$ fading channel and obtain the channel capacity. Finally, we use numerical analyses to validate and evaluate our proposed RS and massive-MIMO integrated schemes. © 2023 IEEE.

关键词： Wireless networks

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

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CSEE Journal of Power and Energy systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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Analytical Modeling of Electromagnetic Fields Scattered from a Target in the Presence of a Rough Surface

Analytical Modeling of Electromagnetic Fields Scattered from...

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2024 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2024

作者： Johnson, Joel T. Gedney, Joseph Burkholder, Robert J. ElectroScience Laboratory Department of Electrical and Computer Engineering Ohio State University ColumbusOH United States

ISBN: (纸本)9781946815194

The prediction of electromagnetic fields scattered from a target located above a statistically described rough surface is of interest in multiple applications. The resulting electromagnetic fields must be described statistically given the stochastic nature of the rough surface, making quantities such as field probability density functions and moments (i.e. mean, variance, correlations, etc.) of interest. In time harmonic problems, the associated moments can be taken with or without the inclusion of field phases, resulting in identification of the coherent field (the mean of the complex valued field so that phase is included) and the incoherent power (proportional to the mean of the field amplitude squared). These moments can then be used to identify a coherent radar cross section (RCS) that is associated with the coherent field and a mean RCS that includes both coherent and incoherent contributions. © 2024 IEEE. All rights reserved.

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Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks 57

Heterogeneous Statistical QoS Provisioning for Scalable Soft...

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57th Annual Conference on Information Sciences and systems, CISS 2023

ISBN: (纸本)9781665451819

Due to the explosively increasing number of mobile users and the new types of data demands in the fifth generation (5G) mobile wireless network, research in wireless networks has shifted toward the development of the sixth-generation (6G) wireless network. Although the research for software-defined network (SDN) architectures in 5G mainly focuses on the dynamic programming for the internet backbone, these software programming techniques can be also applied at the network edge to support the exponentially increasing demands from mobile users under constrained wireless resources. In order to study the interference problem resulted by massive mobile users, the scaling law is a powerful tool to show how fast the levels of network imperfections can be tolerated as the number of mobile users increases. In this paper, we investigate the scaling behavior of software-defined architectures over 6G wireless networks. We consider the wireless channel in three scenarios: single-input-single-output (SISO), multiple-input-single-output (MISO), and multiple-input-multiple-output (MIMO), where we derive the corresponding scaling law for each scenario, respectively. Our derived scaling law shows how the network performance scales with the number of mobile users in a wireless network. Then, we propose a software-defined network slicing scheme to select the optimal mobile users and derive their optimal resource allocations, according to our derived scaling law, under SISO, MISO, and MIMO wireless channel, respectively. Finally, we validate and evaluate the derived scaling behavior of the software-defined architecture over 6G wireless networks through numerical analyses. © 2023 IEEE.

关键词： Wireless networks

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