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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Khanmohammadi, Amir Hossein Sadeghian, Salar Hamzeh, Mohsen Abrishamifar, Adib University of Tehran School of Electrical and Computer Engineering Tehran Iran Iran University of Science and Technology School of Electrical Engineering Tehran Iran

ISBN: (纸本)9798331533946

The boundary conduction mode (BCM) flyback power factor correction (PFC) converter is well-suited for low to medium power-level applications. It isolates input and output voltages while improving the power factor (PF) by modifying the transformer turns ratio. However, the converter switching frequency varies throughout the line cycle, and the frequency range depends on the input voltage and load conditions. Consequently, the electromagnetic interference (EMI) spectra show significant variations, making the EMI filter design more challenging. This paper presents a constant on-time (COT) control strategy that employs a variable inductor (VI) to limit the range of switching frequency fluctuations across the universal input voltage range (85-264 Vrms). © 2025 IEEE.

关键词： Switching frequency

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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Breaking the Shield: Systematic Security Analysis on Pulse Fingerprinting LiDAR Systems for Autonomous Driving

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IEEE Sensors Journal 2025年第11期25卷 20523-20537页

作者： Hayakawa, Yuki Sato, Takami Suzuki, Ryo Ikeda, Kazuma Sako, Ozora Nagata, Rokuto Yoshida, Ryo Chen, Qi Alfred Yoshioka, Kentaro Keio University Department of Electronics and Electrical Engineering Tokyo108-0073 Japan University of California Department of Computer Science IrvineCA92697 United States

LiDAR sensors play a crucial role in autonomous driving (AD) systems because of their high-definition 3D sensing capabilities. While LiDAR spoofing attacks that project fake ranging signals pose a critical security threat, new-generation (new-gen) LiDARs have introduced pulse fingerprinting (PF), an authentication mechanism that distinguishes reflected signals from external interference. Although PF offers a promising defense against spoofing attacks, its security implications remain unexplored. This paper presents the first comprehensive security evaluation of PF features. While current PF-equipped LiDARs use the time difference of pulse pairs as authentication to counter conventional spoofing attacks, we demonstrate their vulnerability to periodic malicious laser pulses through two novel attacks: High-Frequency Removal (HFR) and Adaptive HFR (A-HFR). The basic HFR attack employs high-frequency laser pulses, while A-HFR incorporates weak synchronization and selective range limiting to prevent laser overheating, achieving spoofing frequencies 25 times higher than HFR. Our evaluation of three commercial LiDARs with PF reveals their vulnerability to these attacks, with over 96% of the point cloud data vanishing within the attack target area. Through attack simulations against Autoware using real attack data, we show that successful attacks in over 90% of frames invariably lead to accidents, highlighting significant limitations in current PF implementations. We conclude by proposing enhanced security measures for both AD systems and PF mechanisms. © 2001-2012 IEEE.

关键词： Authentication

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Ultrasound Localization Microscopy using Ensemble Learning

Ultrasound Localization Microscopy using Ensemble Learning

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Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Alqarni, Afnan Almekkawy, Mohamed School of Electrical Engineering and Computer Science Penn State University University Park United States

ISBN: (纸本)9781510686021

Noninvasive imaging of deep-tissue microvascular structures is essential for accurate clinical diagnosis and monitoring. Ultrasound Localization Microscopy (ULM) achieves subwavelength resolution imaging but is hindered by challenges such as extended acquisition times, high microbubble (MB) concentration requirements, and localization inaccuracies. In this study, we propose ENS-ULM, an ensemble model that integrates a Swin transformer with a subpixel Convolutional Neural Network (CNN) to enhance MB localization in ULM. Using synthetic data, we validated ENS-ULM with metrics including the Jaccard index and localization precision. Our ensemble model outperformed traditional approaches, such as Gaussian Fitting and Radial Symmetry, as well as individual CNN and Swin transformer models, achieving superior imaging precision and accuracy. These findings highlight the potential of ensemble methods in advancing MB localization performance for ULM applications. © 2025 SPIE.

关键词： Ultrasonic imaging

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Robust 3D point clouds classification based on declarative defenders

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Neural Computing and Applications 2025年第3期37卷 1209-1221页

作者： Li, Kaidong Zhang, Tianxiao Zhong, Cuncong Zhang, Ziming Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas 1520 West 15th St. LawrenceKS66045 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using lattice point classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://***/KaidongLi/***. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Enhancement Methods for Chiral Optical Signals by Tailoring Optical Fields and Nanostructures

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engineering 2025年第2期45卷 25-43页

作者： Hanqing Cai Liangliang Gu Haifeng Hu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Zhangjiang Laboratory Shanghai 201204China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

The unique property of chirality is widely used in various *** the past few decades,a great deal of research has been conducted on the interactions between light and matter,resulting in significant technical advancements in the precise manipulation of light field *** this review,which focuses on current chiral optics research,we introduce the fundamental theory of chirality and highlight the latest achievements in enhancing chiral signals through artificial nano-manufacturing technology,with a particular focus on mechanisms such as light scattering and Mie resonance used to amplify chiral *** providing an overview of enhanced chiral signals,this review aims to provide researchers with an indepth understanding of chiral phenomena and its versatile applications in various domains.

关键词： Mie scattering Optical chirality Circular dichroism Orbital angular momentum Bound states in the continuum Nonlinear optics

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Gait Pattern Recognition through Force Sensor Platform based on XGBoost Model and Harris’ Hawks Optimization

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IAENG International Journal of Applied Mathematics 2025年第1期55卷 118-125页

作者： Yu, Lie Mei, Pengzhi Ding, Lei School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China

This study developed a gait pattern classification system based on ground contact forces measured by six force sensors embedded inside the shoe sole. The data transmission is facilitated via the Bluetooth module integrated into an STM32 microcontroller. The extreme gradient boosting (XGBoost) algorithm is used to identify the gait patterns, and the basic idea of XGBoost is to use second-order derivatives to make the loss function more precise, incorporate regularization to prevent tree overfitting, and enable block storage for parallel computation. By optimizing the XGBoost algorithm with four algorithms, the exploration capabilities of these algorithms are effectively incorporated into the fusion model. Experimental results indicate that the XGBoost algrithm optimized by Harris’ hawks optimization (HHO) outperforms the other optimization algorithms. Specifically, the HHO-XGBoost achieved high values of 97.41%, 97.03%, and 97.22% severally in the metrics of precision, recall, and F1 score. This research illustrates the HHO-XGBoost method's superiority in gait phase recognition. © (2025), (International Association of Engineers). All rights reserved.

关键词： Gait analysis

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Autonomous embodied navigation task generation from natural language dialogues

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science China(Information sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Modeling the selective laser sintering of printed silver nanoparticle ink on a flexible substrate 19

Modeling the selective laser sintering of printed silver nan...

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Laser-based Micro- and Nanoprocessing XIX 2025

作者： Verma, Sagar Kumar Schlake, Ellie Kandadai, Nirmala School of Electrical Engineering and Computer Science Oregon State University Corvallis97330 United States

ISBN: (纸本)9781510684508

Silver nanoparticles (AgNPs) are widely used in printed electronics because of their high electrical conductivity. Unfortunately, "printing" does not provide a consolidated thin film of the desired material on the flexible substrate but simply delivers the ink to the substrate, leaving the material on the substrate in the form of wet nanoparticle (NP) agglomerates or clusters. A post-sintering or a post-annealing (thermal) step is mandatory to consolidate these NPs into a film for providing connectivity and realizing the desired film properties. Laser sintering is an attractive technology for creating the desired film due to its low power requirements and ability to selectively sinter parts of printed films. In continuous wave laser sintering, the optical radiation directly into the sample converts into a heat wave through a photo-thermal reaction. As a result, thermal penetration into the sample depends on the laser parameters, the thickness of the printed layer, the thermal conductivity of the ink, and the substrate. In this simulation work, we showcase how the thermal penetration for a focused laser signal on silver nanoparticle ink varies with substrate, scanning speed, ink thickness, spot size, and incident laser wavelength. The simulation was performed using finite element-based software COMSOL Multiphysics version 6.2. For the simulation, the laser source was considered a continuous wave laser with a laser wavelength of 445nm. The laser power was varied between 1W to 4W, and spot size was varied between 10μm to 150μm. The effect of different substrate materials was also studied. © 2025 SPIE.

关键词： Silver nanoparticles

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Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents

Robust Defense Against Extreme Grid Events Using Dual-Policy...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Peter, Benjamin M. Korkali, Mert University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65211 United States

ISBN: (纸本)9798331541125

Reinforcement learning (RL) agents are powerful tools for managing power grids. They use large amounts of data to inform their actions and receive rewards or penalties as feedback to learn favorable responses for the system. Once trained, these agents can efficiently make decisions that would be too computationally complex for a human operator. This ability is especially valuable in decarbonizing power networks, where the demand for RL agents is increasing. These agents are well suited to control grid actions since the action space is constantly growing due to uncertainties in renewable generation, microgrid integration, and cybersecurity threats. To assess the efficacy of RL agents in response to an adverse grid event, we use the Grid2Op platform for agent training. We employ a proximal policy optimization (PPO) algorithm in conjunction with graph neural networks (GNNs). By simulating agents' responses to grid events, we assess their performance in avoiding grid failure for as long as possible. The performance of an agent is expressed concisely through its reward function, which helps the agent learn the most optimal ways to reconfigure a grid's topology amidst certain events. To model multi-actor scenarios that threaten modern power networks, particularly those resulting from cyberattacks, we integrate an opponent that acts iteratively against a given agent. This interplay between the RL agent and opponent is utilized in N-k contingency screening, providing a novel alternative to the traditional security assessment. © 2025 IEEE.

关键词： Graph neural networks

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