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A Systematic Three-Stage Safety Enhancement Approach for Motor Drive and Gimbal Systems in Unmanned Aerial Vehicles

IEEE Transactions on Power Electronics 2025年第7期40卷 9329-9342页

作者： Jie, Huamin Zhao, Zhenyu Li, Hong Gan, Theng Huat See, Kye Yak Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Zhejiang University College of Electrical Engineering Hangzhou310058 China National University of Singapore Temasek Laboratories 117411 Singapore

Electromagnetic compatibility (EMC) is critical for ensuring the reliability and safety of power electronics-related assets, such as unmanned aerial vehicles (UAVs). EMC encompasses two key aspects: electromagnetic interference (EMI) and electromagnetic susceptibility (EMS). While EMI has been widely studied, EMS in power electronics systems, particularly with sensitive control and sensing modules, is gaining increasing attention due to rising threats from intentional or unintentional electromagnetic (EM) noises. Therefore, enhancing the EM safety of these systems is essential. This article proposes a systematic three-stage safety enhancement approach for power electronics-related systems in UAVs. In stage 1, a quantitative risk assessment strategy based on the EMS test results is introduced, identifying the motor drive and gimbal systems of the UAVs as the most vulnerable components. Stage 2 analyzes their failure mechanisms, offering valuable insights for further hardening. Stage 3 develops a comprehensive lightweight strategy to enhance the UAV safety. Using a DJI UAV as a case study, the proposed approach demonstrates the capability of the proposed EM hardening solutions against external EM noises with the electric field strength up to 200 V/m from 2 MHz to 18 GHz. The total weight increases by only 1.2% with a negligible addition to volume. © 2025 IEEE.

关键词： Antennas

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A Novel Mixed-Signal Flash-based Finite Impulse Response (FFIR) Filter for IoT Applications 25

A Novel Mixed-Signal Flash-based Finite Impulse Response (FF...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Lee, Cheng-Yen Khatri, Sunil P. Ghrayeb, Ali Department of Electrical and Computer Engineering Texas A and M University College StationTX United States Department of Electrical and Computer Engineering Texas A and M University at Qatar Doha Qatar

ISBN: (纸本)9798400706356

In this paper, we present a novel mixed-signal flash-based Finite Impulse Response (FFIR) filter architecture for IoT applications. The FFIR filter is scalable in that it can implement any filter with up to a provisioned maximum number of taps. Our FFIR filter utilizes flash transistors, a type of non-volatile memory (NVM) device, to perform analog computations in the current domain, achieving low power, energy, and area requirements. This design is well-suited for the Internet of Things (IoT) applications and other scenarios where resources are highly constrained. Our FFIR filter consists of several Flash-based Coefficient Multipliers (FCMs). The FIR coefficients of each FCM are stored in its constituent flash transistors, with the threshold voltage (Vt) of the flash transistors serving as a proxy for the filter coefficients. Furthermore, the impact of process or voltage variations is mitigated by precisely tuning the Vt of the flash transistors. The tuning of the Vt of the flash transistors can be performed either in the factory by the manufacturer (to negate process variations), or by the user in the field (to negate voltage variations or aging effects). We evaluate the tolerance of FFIR filters to manufacturing variations through Monte Carlo analysis, demonstrating robustness to process and VDD variations. Our FFIR design achieves a significant improvement over previous approaches. Compared to Digital FIR (DFIR) filters operating at the fastest frequency, we reduce the average of power, energy, and area by 4.05×, 1.95×, and 6.06×, while achieving an average peak signal-to-noise ratio (PSNR) of 38.04 dB and an average effective number of bits (ENOB) of 8.87 bits. In addition, we compare our FFIR filter with state-of-the-art Analog FIR (AFIR) filters as well. Our designs demonstrate significantly improved performance of at least 1.3×, 5.3×, and 18.5× in terms of energy per tap, area, and latency, respectively, when compared with the best among 4 recently published

关键词： Signal to noise ratio

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Optimizing Cloud computing Task Schedules Through Advanced Intelligent Optimization Methods

Optimizing Cloud Computing Task Schedules Through Advanced I...

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International IoT, Electronics and Mechatronics Conference, IEMTRONICS 2024

作者： Ammari, Ahmed Chiheb Al Hmouz, Rami Khriji, Lazhar Zhou, MengChu Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9789819747832

The number of Distributed Green Cloud Datacenters (DGCDs) is globally increasing. Such DGCDs deploy different types of renewable sources to generate clean energy and save money. They are located in different regions depending on the availability of renewable energy sources, costs of bandwidth and grid electricity prices. This paper focuses on applications within them, which are sensitive to delay and looks into a way to schedule different applications with respect to time constraint. The paper uses the Firefly algorithm, bat algorithm and simulated annealing-bat algorithms as optimization techniques to minimize total operational cost of such DGCDs. These algorithms have been compared through data-driven experiments carried out in this study. Of particular note is the Firefly algorithm's superior performance when compared with others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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Strategies for Effective EEG Preprocessing 22

Strategies for Effective EEG Preprocessing

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22nd International Learning and Technology Conference, L and T 2025

作者： Salem, Nema Alamoudi, Nora Mustafa, Lama Bagais, Fatima Effat University Electrical Research Laboratory College of Engineering Jeddah Saudi Arabia Effat University College of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology;however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produces superior outcomes compared to other techniques investigated, achieving a high Signal-To-Noise Ratio (SNR) of 43.23 dB. © 2025 IEEE.

关键词： Anesthesia Discrete Wavelet Transform EEG preprocessing Gaussian Independent Component Analysis Moving average Noise removal SDG 17

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Deep learning and vision transformers-based framework for breast cancer and subtype identification

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Neural computing and Applications 2025年 1-20页

作者： Jahan, Ishrat Chowdhury, Muhammad E. H. Vranic, Semir Al Saady, Rafif Mahmood Kabir, Saidul Pranto, Zahid Hasan Mim, Sabiha Jahan Nobi, Sadia Farhana Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Department of Electrical Engineering Qatar University 2713 Doha Qatar College of Medicine QU Health Qatar University Doha Qatar Department of Electrical and Electronic Engineering University of Dhaka Dhaka1000 Bangladesh

Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer det

关键词： Diseases

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Artificial Intelligence and Machine Learning for Accessibility: Smart Reader as an Assistive Tool for the Visually Impaired 2

Artificial Intelligence and Machine Learning for Accessibili...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Palarimath, Suresh Radhakrishnan, Venkateswaran Veerasamy, Devarajan Valsalan, Prajoona Viswan, Vimbi Thumu, Muni Balaji College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sohar Oman

ISBN: (纸本)9798331505745

The amalgamation of artificial intelligence (AI) and machine learning (ML) has greatly improved accessibility for visually impaired individuals, fostering increased autonomy through pioneering assistive technology. This study introduces the Smart Reader, a device that integrates optical character recognition (OCR) and text-to-speech (TTS) technologies to enhance accessibility to printed documents. The Smart Reader utilizes a camera to take text images, translates them into digital representation through OCR, and converts the text into audible speech via TTS. The system has an intuitive user interface, an automatic page-turning mechanism, and a dictionary lookup function, guaranteeing a seamless reading experience. The findings indicate that the Smart Reader significantly improves access to written information, hence enhancing the quality of life for visually impaired individuals, especially in educational environments such as schools and libraries. The device is engineered for user-friendliness, enhancing accessibility and autonomy. The Smart Reader utilizes AI and ML, marking a significant progression in assistive technology, promoting an inclusive society where visually impaired individuals can engage with printed material with assurance. In addition to functionality, it seeks to empower people by connecting capability with opportunity, guaranteeing equitable access to information for everyone. © 2025 IEEE.

关键词： Contrastive Learning

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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An improved swarm intelligence for power system economic operations based on optimal power generation to control congestion in transmission channels

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Neural computing and Applications 2025年 1-28页

作者： Kumari, Pooja Paul, Kaushik Kumar, Niranjan Sinha, Pampa Agarwal, Krishna Kant Vidyarthi, Ankit Alkhayyat, Ahmed Department of Electrical Engineering NIT Jamshedpur Jamshedpur India Department of Electrical Engineering BIT Sindri Dhanbad828123 India School of Electrical Engineering KIIT University Bhubaneswar751024 India School of Computing Science and Engineering Galgotias University Greater Noida India Department of CSE & IT Jaypee Institute of Information Technology Uttar Pradesh Noida201309 India College of Technical Engineering The Islamic University Najaf Iraq

Power system congestion is a key challenge in the deregulated era. Finding ways to transmit power without congestion is crucial. Achieving optimal generator power output with the application of evolutionary algorithms is a primary method to manage this issue. This paper proposes a novel modified whale optimization algorithm (MWOA) for the optimal power generation to address the power system economic operations for the nonlinear transmission congestion management cost problem. The MWOA has been developed by introducing two correction factors in the exploration and exploitation phases that enhances its performance and coordination in these two phases. The MWOA strikes a good balance between exploring and exploiting WOA phases. Its capabilities are validated using benchmark functions and compared to other heuristic algorithms. The MWOA has been applied to accomplish minimum congestion cost by optimal adjustment in the generator power output while alleviating congestion in the lines. Generator sensitivity factors are computed to identify the most sensitive generators that are participating in the power rescheduling process. The results show that MWOA lowered congestion costs by 6.00%, 4.45%, 3.14%, and 2.30% for the 39-bus system, 7.91%, 4.29%, 2.57%,1.69% for the 30-bus system, and 10.44%, 8.48%, 7.36%, 5.17%, 2.00% for 118-bus system when compared to FEP, GA, DE, and original WOA. Statistical and comparative analysis has shown that with MWOA there has been a considerable reduction in the congestion cost, system losses, amount of power rescheduled, and improved in the system bus voltages when contrasted with other referred optimization algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Swarm intelligence

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Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8 24

Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8

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7th International Conference on Information Science and Systems, ICISS 2024

作者： Ortega, Gerson Itliong Ordas, Amiel Mir Odiamar Villaverde, Jocelyn Flores Juanatas, Roben Antalan Juanatas, Irish Castrojeres School of Electrical Electronics and Computer Engineering Mapua University Philippines College of Computing in Information Technologies National University Philippines Department of Information Technology FEU Diliman Philippines

ISBN: (纸本)9798400717567

The advancement of YOLO and similar computer vision models represents a technological era that could revolutionize numerous aspects of day-to-day life, including traffic management. This study explores the feasibility of utilizing YOLOv8 for pedestrian detection and counting to control traffic light timing sequences at isolated pedestrian crossings. By employing the Raspberry Pi 4 Model B with a camera module, real-time pedestrian video data can be captured and processed through YOLOv8. For the hardware, three LEDs and a Dot Matrix Module replicate conventional traffic light signals and timing, effectively simulating a traffic light system. The capabilities of the custom YOLOv8 model are evaluated through pedestrian detection accuracy, calculated by percentage error across twenty samples. Results reveal an average percent error of 7.33%, indicating 92.67% accuracy in pedestrian detection and counting. These findings underscore the model’s competence for pedestrian-based adaptive traffic light control, highlighting its potential to enhance practicality and safety at pedestrian crossings by dynamically adjusting signal timings based on real-time pedestrian activity. © 2024 Copyright held by the owner/author(s).

关键词： Pedestrian safety

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Understanding Cybercrime: A Study of Malware Analysis, Phishing Attacks, and Sentiment Trends 3

Understanding Cybercrime: A Study of Malware Analysis, Phish...

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3rd International Conference on Intelligent and Innovative Technologies in computing, electrical and Electronics, IITCEE 2025

作者： Kumaran, U. Sreeraag, G. Narendran, Advik Meena, V.P. Gupta, Sushil Kumar Appaji, Abhishek Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India National Institute of Technology Department of Electrical Engineering Jharkhand Jamshedpur831014 India Bms College of Engineering Department of Medical Electronics Engineering Bangalore India

ISBN: (纸本)9798331515911

With the rapid evolution of computer systems and technologies, individuals face an increasing threat of cybercrime. This research explores the changing landscape of cybercrime by focusing on three key areas: malware analysis, phishing attack analysis, and sentiment analysis. The study aims to identify malware strategies for evading detection and analyze the prevalence of spyware by examining diverse malware samples. Additionally, trend analysis highlights the rising complexity and frequency of phishing attacks, with a notable shift toward targeting reputable platforms. Sentiment analysis of media headlines surrounding data breaches uncovers a paradox: while concerns about cybersecurity remain, there is a growing confidence in the ability to manage cybersecurity crises. These findings provide insights into emerging threats and evolving public perceptions, offering a comprehensive understanding of the current cyber- crime landscape. © 2025 IEEE.

关键词： Phishing

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