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arXiv 2023年

作者： Shi, Yifan Wei, Kang Shen, Li Li, Jun Wang, Xueqian Yuan, Bo Guo, Song The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Jd Explore Academy Beijing China The Department of Computing Hong Kong Polytechnic University 999077 Hong Kong The School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing China Shenzhen Wisdom and Strategy Technology Co. Ltd. Shenzhen518055 China

Federated learning (FL) is a collaborative learning paradigm for decentralized private data from mobile terminals (MTs). However, it suffers from issues in terms of communication, resource of MTs, and privacy. Existing privacy-preserving FL methods usually adopt the instance-level differential privacy (DP), which provides a rigorous privacy guarantee but with several bottlenecks: severe performance degradation, transmission overhead, and resource constraints of edge devices such as MTs. To overcome these drawbacks, we propose Fed-LTP, an efficient and privacy-enhanced FL framework with Lottery Ticket Hypothesis (LTH) and zero-concentrated DP (zCDP). It generates a pruned global model on the server side and conducts sparse-to-sparse training from scratch with zCDP on the client side. On the server side, two pruning schemes are proposed: (i) the weight-based pruning (LTH) determines the pruned global model structure;(ii) the iterative pruning further shrinks the size of the pruned model's parameters. Meanwhile, the performance of Fed-LTP is also boosted via model validation based on the Laplace mechanism. On the client side, we use sparse-to-sparse training to solve the resource-constraints issue and provide tighter privacy analysis to reduce the privacy budget. We evaluate the effectiveness of Fed-LTP on several real-world datasets in both independent and identically distributed (IID) and non-IID settings. The results clearly confirm the superiority of Fed-LTP over state-of-the-art (SOTA) methods in communication, computation, and memory efficiencies while realizing a better utility-privacy trade-off. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing

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Quality Improvements in the electrical Design of Typical DC-DC Electronic Power Converter for High-Voltage Applications

Quality Improvements in the Electrical Design of Typical DC-...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： King Harold A. Recto Miguel T. Escoto Noel B. Hernandez Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines Post-doctoral Program in Quality Management of the Graduate School Centro Escolar University Manila Philippines

ISBN: (纸本)9781665491075

The green transition has brought about a worldwide-shift to the use of renewables as alternative energy sources. Because of this, high voltage DC has been a field of interest in power electronics due to its capability in efficiently transmitting power to reach remote areas. Typical DC-DC electronic power converter design has the capability of boosting voltage level to a certain extent but not enough for high-voltage applications. Using the DMAIC Approach of the Six Sigma, quality improvements in the design was proposed to stabilize operating frequency, bring up the output voltage from 100 VDC to 4,000 VDC and provide at least 10% margin of error in the apparent power measured at the output side to offset inherent losses in the electrical design. These proposed quality improvements were sustained by designing a control circuit that would ensure the attainment of the parameters set in the Define Phase of the DMAIC Approach. The result of the study is a practical 12 VDC to 4,000 VDC boost converter design that has incorporated all the proposed quality improvements.

关键词： Renewable energy sources Voltage measurement Power measurement Measurement uncertainty Green products High-voltage techniques Loss measurement

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Near-field microwave microscopy of nanostructured materials: Modeling and experiment

Near-field microwave microscopy of nanostructured materials:...

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Low-Dimensional Materials and Devices 2021

作者： Lopez, Diego C. Ohlberg, Douglas A. A. Giraldo, Brian Ramirez, Jhonattan C. Kobayashi, Nobuhiko P. Do Rego, Cássio G. Medeiros-Ribeiro, Gilberto Electrical Engineering Graduate Program Universidade Federal de Minas Gerais Belo HorizonteMG 31270-901 Brazil Microscopy Center Universidade Federal de Minas Gerais Belo HorizonteMG 31270-901 Brazil Department of Electrical and Computer Engineering Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Computer Science Department Universidade Federal de Minas Gerais Belo HorizonteMG 31270-901 Brazil

ISBN: (数字)9781510644397

ISBN: (纸本)9781510644380

Synthesis of novel material often times requires novel analysis and characterization techniques. The possibility of combining sputtering (SPU) and Atomic Layer Deposition (ALD) in the same chamber, Sputtering Atomic Layer Augmented Deposition (SALAD), has produced interesting meta-dielectric nanocomposite systems that have unique optical and electronic properties, which may find novel applications [1]. Scanning Microwave Impedance Microscopy (sMIM) is a relatively novel characterization tool which permits assessment of local impedance. More recently, the utilization of microwaves in the near field regime has been an exciting topic in the field of high-resolution microscopy. We were able to demonstrate 1 nm resolution using scanning Microwave Impedance Microscopy (sMIM) where a spontaneously forming water meniscus concentrated the microwave fields in small regions [2]. Here we analyzed numerically sMIM with Finite Element Method (FEM) to investigate complex metal-dielectric structures created in a SALAD system. sMIM measurements provide information on both real and imaginary parts of the reflected microwave signal, which can be associated with the local conductivity and permittivity. Yet, these quantities can be influenced by the local topography, so extraction of the electronic contribution is a challenge. In this work, we perform tip-surface distance scans in order to gain a better understanding of the substrate response and compare with the FEM results. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Finite element method

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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm

Impact of Image Enhancement for Osteoporosis Detection Based...

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Computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Riza Dianil Iman Roslidar Roslidar Kahlil Muchtar Amalia Amalia Mohammad Syaryadhi Alfatirta Mufti Novi Maulina Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an Fl-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance.

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Parallel DICE Cells and Dual-Level CEs based 3-Node-Upset Tolerant Latch Design for Highly Robust Computing

Parallel DICE Cells and Dual-Level CEs based 3-Node-Upset To...

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International Test Conference in Asia (ITC-Asia)

作者： Aibin Yan Zijie Zhai Lele Wang Jixiang Zhang Ningning Cui Tianming Ni Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China College of Electrical Engineering Anhui Polytechnic University Wuhu China Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781665413350

With the rapid advancement of design and manufacturing technologies of nano-scale CMOS circuits, latches are becoming increasingly sensitive to multiple-node-upsets caused by harsh radiation effects. In this paper, a Parallel Dual-interlocked-storage-cells (DICEs) and Dual-level C-elements (CEs) based 3-node-upset (3NU)-Tolerant Latch, namely PDDCTL, design for highly robust computing, is proposed. The latch comprises five transmission gates, two DICEs and three CEs. Due to the use of two single-node-upset self-recoverable DICEs and three error-interceptive CEs, the latch can provide complete 3NU-tolerance with low cost. Simulation results not only confirm the 3NU-tolerance of the proposed latch but also demonstrate that the delay-power-area product of the PDDCTL latch is reduced by 68.82% on average compared with the state-of-the-art 3NU hardened latch designs.

关键词： Radiation effects Latches Simulation Logic gates Reliability engineering Silicon Power dissipation

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Giant long-range dipole-dipole interactions in a plasmonic lattice

Giant long-range dipole-dipole interactions in a plasmonic l...

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Frontiers in Optics + Laser Science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Boddeti, Ashwin K. Guan, Jun Sentz, Tyler Juarez, Xitali Newman, Ward Cortes, Cristian Odom, Teri W. Jacob, Zubin School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Intel Corporation BeavertonOR97007 United States Center for Nanoscale Materials Argonne National Laboratory LemontIL60439 United States Graduate Program in Applied Physics Department of Materials Science and Engineering Department of Chemistry Northwestern University EvanstonIL60208 United States

We observe long-range dipole-dipole interactions in a plasmonic lattice. Fluorescence lifetime measurements show density-dependent non-exponential decay dynamics over 800nm mean nearest-neighbour separation distances ... 详细信息

ISBN: (纸本)9781557528209

关键词： Plasmonics

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Unsupervised Domain Adaptation for Segmentation with Black-box Source Model

arXiv

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arXiv 2022年

作者： Liu, Xiaofeng Yoo, Chaehwa Xing, Fangxu Kuo, C.-C. Jay Fakhri, Georges El Kang, Je-Won Woo, Jonghye Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School BostonMA United States Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of Dept. of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches. © 2022, CC BY.

关键词： Distillation

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A Study on Giant Magnetostrictive Actuator for Interior Sound Control System of the Ultra-Compact Mobility: Fundamental Consideration on Formation of the Giant Magnetostrictive Phases Depending on Heat Treatment Condition 27

A Study on Giant Magnetostrictive Actuator for Interior Soun...

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27th International Conference on electrical Machines and Systems, ICEMS 2024

作者： Kato, Taro Naganuma, Ryusei Bando, Koki Sawada, Kentaro Wenbao, Wu Kobayashi, Ikkei Kuroda, Jumpei Uchino, Daigo Ogawa, Kazuki Ikeda, Keigo Endo, Ayato Kato, Hideaki Narita, Takayoshi Furui, Mitusaki Tokyo University of Technology Department of Mechanical Engineering 1404-1 Katakura Hachioji-shi Tokyo192-0982 Japan Sustainable Engineering Program Graduate School of Engineering Tokyo University of Technology 1404-1 Katakura Hachioji-shi Tokyo192-0982 Japan Tokai University Kitakaname 4-1-1 Kanagawa Hiratsuka-shi259-1292 Japan Tokai University Kitakaname 4-1-1 Kanagawa Hiratsuka-shi259-1292 Japan Numazu College Department of Mechanical Engineering Ohoka 3600 Shizuoka Numazu-shi410-0022 Japan Aichi University of Technology Department of Electronic Robotics 50-2 Manori Nishisako-machi Aichi Gamagori-shi443-0047 Japan Hokkaido University of Science Department of Mechanical Engineering 7-Jo 15-4-1 Maeda Teine Hokkaido Sapporo006-8585 Japan Fukuoka Institute of Technology Department of Electrical Engineering Wajiro Higashi 3-30-1 Higashi-ku Fukuoka Fukuoka-shi811-0295 Japan Tokai University Department of Mechanical System Engineering Kitakaname 4-1-1 Kanagawa Hiratsuka-shi259-1292 Japan

ISBN: (纸本)9784886864406

An active noise control system using giant magnetostrictive actuator has been proposed as a countermeasure for interior noise of the ultra-compact mobilities, which is a new transportation tools. In this paper, as basic research toward the development of the proposed giant magnetostrictive actuator for active noise control, we conduct experiments on the material properties when terbium, dysprosium, and iron powder, which make up the giant magnetostrictive material, are mixed and the heat treatment conditions are changed. In this study, the heat treatment temperature was varied from 800°C to 1100°C in 100°C incrementsbased on the equilibrium state temperature of each material, and the heat treatment time was set in 30-minute increments from 1 hour to 3 hours. As a result, TbFe2 was confirmed after heat treatment at an electric furnace temperature of 1000°C for 3 hours, although its strength was insufficient. © 2024 The Institute of electrical Engineers of Japan.

关键词： Active noise control

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Maximal determinants of matrices over the roots of unity

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Linear Algebra and its Applications 2025年

作者： Guillermo Nuñez Ponasso Graduate School of Information Sciences Division of Mathematics Tohoku University Sendai Miyagi Japan Dept. of Electrical & Computer Engineering Worcester Polytechnic Institute Worcester MA USA

We study the maximum absolute value of the determinant of matrices with entries in the set of ℓ -th roots of unity — this is a generalization of D -optimal designs and Hadamard's maximal determinant problem, which involves ±1 matrices. For general values of ℓ , we give sharpened determinantal upper bounds and constructions of matrices of large determinant. The maximal determinant problem in the cases ℓ = 3 , ℓ = 4 is similar to the classical Hadamard maximal determinant problem for matrices with entries ±1, and many techniques can be generalized. For ℓ = 3 we give an additional construction of matrices with large determinant, and calculate the value of the maximal determinant of matrices with entries in the third-roots of unity for all orders n < 14 . Additionally, we survey the case ℓ = 4 and exhibit an infinite family of maximal determinant matrices of odd order over the fourth roots of unity.

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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