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2024 International Conference on electrical Machines, ICEM 2024

作者： Kasagga, Ecem El-Refaie, Ayman Marquette University Milwaukee Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350370607

The increasing demand for electrical machines underscores the urgent need to address their end-of-life disposal. Traditional designs rely on complex assemblies of diverse materials and pose significant challenges for recycling and resource recovery. This review explores current approaches and perspectives on designing recyclable electrical machines, emphasizing the integration of recyclability without sacrificing performance. Key materials are examined for their properties and recyclability. Design for Recyclability principles are highlighted through various case studies. The review concludes with comparative analyses of material choices and their impact on performance and recyclability. Findings demonstrate that low to mid-power applications are more adaptable to recyclable designs. Future research should focus on developing innovative design ideas and assessing their recyclability. © 2024 IEEE.

关键词： design for disassembly electrical machines rare-earth magnets recyclability sustainable materials

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Efficient Onboard SAR Image Processing for Real- Time Ship Detection and Data Reduction

Efficient Onboard SAR Image Processing for Real- Time Ship D...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Madabhushi, Bharadwaj Zhang, Zhehang Alves, Tiago A. O. Tessier, Russell Kundu, Sandip University of Massachusetts Amherst Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331540081

Maritime Authorities face significant challenges in monitoring vast maritime domains and enforcing regulations within their Exclusive Economic Zones (EEZs). Synthetic Aperture Radar (SAR) imaging of Fers an effective means of monitoring maritime activities, including ship traffic, illegal fishing, and piracy. However, SAR images tend to be very large in size, while limited communication bandwidth from aerial vehicles or satellites to ground stations constrains the transmission of high-resolution SAR images. This paper proposes onboard image processing as a solution to overcome these limitations. By processing SAR images directly on the UAV or satellite, we can significantly reduce the amount of raw data transmitted. This enables the transmission of high-resolution images across wider geographic areas while preserving a wider dynamic range for capturing finer details in the scene. However, onboard processing presents its own challenge, namely, limited power availability on satellites and UAV s. To address this, we explore the use of low-power, commercially available of F-the-shelf (COTS) mobile phone processors for onboard SAR image processing. These processors offer a trifecta of benefits: low power consumption, large address space, and compatibility with low-power memory, making them ideal for large-scale image processing onboard. By performing image processing onboard, we optimize data trans-mission, minimize bandwidth usage, and enhance the efficiency of maritime surveillance operations. Our experimental results, using GDAL for image compression and OpenCV for image processing on a Qualcomm Snapdragon Gen 2 mobile processor running Kali Linux, demonstrate a significant improvement in image transmission and storage efficiency, achieving a substantial bandwidth reduction of 91.21 % while maintaining real-time performance and preserving accuracy. Thus our solution paves the way for automated ship detection, tracking, and illegal behavior identification. © 2024 IEEE.

关键词： Crime

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Operation Analysis and Verification of H-Bridge Modular Cascaded Linear Amplifier to Improve Efficiency by Increasing the Number of Cells 50

Operation Analysis and Verification of H-Bridge Modular Casc...

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

作者： Ishida, Shunsuke Obara, Hidemine Department of Electrical and Computer Engineering Yokohama National University Yokohama Japan

ISBN: (纸本)9781665464543

The harmonic and electromagnetic noise issues by switching operations are becoming more serious due to the enhancement of the fast-switching capability of power devices. A modular cascaded linear amplifier (MCLA) has been proposed as a power conversion circuit that does not generate electromagnetic interference without any output filter by utilizing partial linear operation. An H-bridge MCLA can improve efficiency by increasing the number of cascaded H-bridge cells and applying unequal DC capacitor voltages. In this study, the operating modes of the MCLA are analyzed to increase the number of H-bridge cells and enhance the efficiency. Based on the analysis, an extended control scheme including capacitor voltage balancing for the MCLA with four H-bridge cells is developed utilizing a regularity of the operation, and an experiment was conducted using a laboratory prototype. It is verified that the prototype MCLA realizes sinusoidal voltage output with balanced capacitor voltages, and achieves the efficiencies of 88.8% and 92.1% under equal and unequal DC capacitor voltages, respectively, even in a linear amplifier. © 2024 IEEE.

关键词： Capacitor bank

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An Adaptive Acoustic Transceiver Testbed on System-on-Chip for Underwater Communication

An Adaptive Acoustic Transceiver Testbed on System-on-Chip f...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Nges, Boris Bam Moore, Dylan Doucet, Andrew Bhattacharya, Ananya Alloway, Hunter Bousquet, Jean-Francois Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada

ISBN: (纸本)9798331540081

This paper describes a test setup to support the development of a bi-directional acoustic communication link relying on adaptive transceivers. The physical layer relies on the JANUS protocol for discovery of new nodes and assessment of the channel conditions. The transmitter has a bandwidth of 5 kHz at a center frequency of 27.5 kHz, while the receiver relies on broadband hydrophones. The embedded system allows to transmit in diverse underwater scenarios, and the System-on-Chip is programmed to transmit various waveforms including through coded Orthogonal Frequency Division Multiplexing (OFDM). Key elements of the program are described to maintain real-time operation on the embedded systems. At the receiver, the data is sampled at 96 kHz and a 4 Mbps cabled link to a host interface is developed to diagnose the communication link reliability. © 2024 IEEE.

关键词： System-on-chip

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Method of MIMO Capacity Improvement Based on S-parameter Phase Difference 12

Method of MIMO Capacity Improvement Based on S-parameter Pha...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Yang, Danyu Chen, Zhi Ning National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

ISBN: (纸本)9798350351019

In this work, a method to improve multiple-input-multiple-output (MIMO) antenna system channel capacity based on S-parameter phase difference is proposed. Theoretical derivation and numerical analysis show that the 90° phase difference of port reflection and transmission coefficients between two antennas enhances MIMO channel capacity by significantly suppressing the correlation. The proposed method provides a new way to increase the capacity of MIMO systems. © 2024 IEEE.

关键词： Antennas

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Boosting Wind Harvest: FOPID Pitch Angle Controller for Turbines 21

Boosting Wind Harvest: FOPID Pitch Angle Controller for Turb...

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21st International Learning and Technology Conference, L and T 2024

作者： Salem, Nema Jamjoom, Jude Qashqari, Maha Alzahrani, Mariah Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Wind turbine blades are subjected to a variety of loads, including aerodynamic and gravitational loads. These loads produce aerodynamic strain and vibration in the blades, resulting in rotor blade damage and a reduction in the wind turbine's system efficiency. This could be prevented by implementing a proper pitch angle controller that plays a crucial role in boosting the energy capture and overall performance of wind power systems. The conventional Proportional-Integral-Derivative, P ID, controller has been widely utilized for pitch control, but it often faces challenges in meeting the requirements of complex and dynamic wind conditions. To address these limitations, this study explores the implementation of the Fractional Order Proportional-Integral-Derivative, F OP ID, controller for wind turbine pitch control. This paper presents a comparative analysis between the P ID and F OP ID controllers for wind turbine pitch control. The performance of both controllers is evaluated through Simulink. The results demonstrate that the F OP ID controller exhibits superior performance in terms of faster response time, and improved steady-state error compared to the P ID controller. © 2024 IEEE.

关键词： Controllers

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Personalized Federated Learning Techniques: Empirical Analysis

Personalized Federated Learning Techniques: Empirical Analys...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Khan, Azal Ahmad Khan, Ahmad Faraz Ali, Haidar Anwar, Ali University of Minnesota-Twin Cities Department of Computer Science and Engineering United States Virginia Tech Department of Computer Science United States

ISBN: (纸本)9798350362480

Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a careful balancing act between memory overhead costs and model accuracy. This paper delves into the trade-offs inherent in pFL, offering valuable insights for selecting the right algorithms for diverse real-world scenarios. We empirically evaluate ten prominent pFL techniques across various datasets and data splits, uncovering significant differences in their performance. Our study reveals interesting insights into how pFL methods that utilize personalized (local) aggregation exhibit the fastest convergence due to their efficiency in communication and computation. Conversely, fine-tuning methods face limitations in handling data heterogeneity and potential adversarial attacks while multi-objective learning methods achieve higher accuracy at the cost of additional training and resource consumption. Our study emphasizes the critical role of communication efficiency in scaling pFL, demonstrating how it can significantly affect resource usage in real-world deployments. © 2024 IEEE.

关键词： Federated learning

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Technological advancements in the detection and diagnosis of migraine: A review 4th

Technological advancements in the detection and diagnosis of...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Kaur, Shivneet Kaur, Parneet Gosain, Monika Kaur, Roopkamal Department of Computer Science & Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Headaches are the most prevalent medical condition in the globe. The migraine is one of the various types of headaches. Along with their excruciating headaches, migraineurs often have nausea, vomiting fits, and light and sound sensitivities. Individuals have unique symptoms, some of which may be more intense than others. A migraine episode is about to begin when an aura, a symptom, appears. Based on the nature of symptoms, migraines can be divided into two basic categories: aura-producing and aura-less. Every individual has different triggers for migraine. This study provides an overview of the state-of-the-art deep learning and machine learning techniques for migraine identification. The primary subjects of this study include migraine kinds, phases, causes, and symptoms. © 2025 the Author(s).

关键词： Diseases

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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Efficiency Analysis of Lamp System: A Comparative Study of Domain E/E and Distributed Architectures 18

Efficiency Analysis of Lamp System: A Comparative Study of D...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Lim, Se Jeong Oh, Sung Bhin Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350331011

One of the many components of a vehicle is the lamp system. Beyond lamp functions such as DRL (Daytime Running Light) and CHMSL (Center High Mount Stop Light), vehicle developers are applying ADB (Adaptive Driving Beam) combined with ADAS (Advanced Driver Assistance Systems) to lamp systems. ADB operation requires complex control algorithms to be performed, and there are many challenges in applying them to lamp systems with a distributed E/E architecture. To solve this problem, they should configure a lamp domain controller with a domain E/E architecture. Also, it is important to implement validation and experiment before getting the system. The present study will analyze how efficient the Lamp system is in a Domain E/E architecture with an LDC than in a traditional architecture. And these architectures will be experimented in compliance with OSEK NM. © 2024 IEEE.

关键词： Advanced driver assistance systems

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