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Ultrasonics Symposium (LAUS), IEEE UFFC Latin America

作者： Atsushi Hosokawa Department of Electrical and Computer Engineering Nationl Institute of Technology! Akashi College Akashi Japan

ISBN: (数字)9798350349085

ISBN: (纸本)9798350349092

A piezoelectric finite-difference time-domain (PE-FDTD) method was used to simulate the piezoelectric signals generated in cancellous bone by ultrasound irradiation. The changes in the piezoelectric and ultrasound signals with the cancellous bone thickness were observed to clarify the relationship between these signals. In the ultrasound direction parallel to the strong trabecular orientation, the contributions of the fast and slow waves in the ultrasound signal to the piezoelectric signal were investigated. As a result, two waves could be identified in the piezoelectric signal. The amplitude of the former wave little varied with the cancellous bone thickness, but the amplitude of the later wave randomly varied although the variation was small. It was considered that the former wave corresponded to the local piezoelectric signal generated at the shallow depth of cancellous bone by majorly the fast wave and that the latter wave corresponded to the signal by majorly the slow wave.

关键词： Radiation effects Ultrasonic imaging Acoustics Cancellous bone Time-domain analysis Finite difference methods

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A Description of the Texas A&M University Electric Grid Test Case Repository for Power System Studies

A Description of the Texas A&M University Electric Grid Test...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Sanjana Kunkolienkar Farnaz Safdarian Jonathan Snodgrass Adam Birchfield Thomas Overbye Department of Electrical and Computer Engineering Texas A&M University College Station TX

A repository of diverse electric grid test cases, varying in size and characteristics, has been developed for application in power system studies. The purpose of this repository is to provide public access to realistic electric grid models of varying sizes to help facilitate research and education. This repository is regularly updated with new test cases or modifications of existing ones as a result of continuing research. This paper outlines the necessity for this repository, details the electric grids it contains, and provides explanations for the types of cases, highlighting their relevance to different applications.

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Highly Efficient and Power-dense GaN-Based Drive-train Inverter for Light Electric Vehicles

Highly Efficient and Power-dense GaN-Based Drive-train Inver...

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International Conference on Power Electronics and Motion Control (IPEMC)

作者： Jaydeep Saha Rahul Bhujade Prasanth Sundararajan Sai Srinivas Manohar Sanjib Kumar Panda Department of Electrical and Computer Engineering (ECE) College of Design and Engineering (CDE) National University of Singapore (NUS)

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

Though a great amount of literature is available on wide-bandgap (WBG) devices based drive-train inverter development for medium or high power electric-vehicles (EVs), there is meagre literature on the same for light electric-vehicles (lEVs). This paper investigates the optimal design, operation and hardware development of a GaN-based drive-inverter for a typical lEV drive-train. It can be observed that for a GaN-based 10 kVA drive-inverter with 72 V DC-link voltage, optimized design and operation can lead to an efficiency of ~99% and power-density of ~48 kVA/L. A GaN-based, 72 V DC-link, 10 kVA drive-train inverter’s experimental prototype is fabricated and operated according to the obtained optimized parameters, and its associated experimental results provide comprehensive verification of the drive-inverter’s performance.

关键词： Space vector pulse width modulation Power system measurements Costs Simulation Prototypes Inverters Hardware

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Variable-Friction In-Hand Manipulation for Arbitrary Objects via Diffusion-Based Imitation Learning

arXiv

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arXiv 2025年

作者： Yan, Qiyang Ding, Zihan Zhou, Xin Spiers, Adam J. Manipulation and Touch Lab Department of Electrical and Electronic Engineering Imperial College London United Kingdom Electrical and Computer Engineering Department Princeton University United States

Dexterous in-hand manipulation (IHM) for arbitrary objects is challenging due to the rich and subtle contact process. Variable-friction manipulation is an alternative approach to dexterity, previously demonstrating robust and versatile 2D IHM capabilities with only two single-joint fingers. However, the hard-coded manipulation methods for variable friction hands are restricted to regular polygon objects and limited target poses, as well as requiring the policy to be tailored for each object. This paper proposes an end-to-end learning-based manipulation method to achieve arbitrary object manipulation for any target pose on real hardware, with minimal engineering efforts and data collection. The method features a diffusion policy-based imitation learning method with co-training from simulation and a small amount of real-world data. With the proposed framework, arbitrary objects including polygons and non-polygons can be precisely manipulated to reach arbitrary goal poses within 2 hours of training on an A100 GPU and only 1 hour of real-world data collection. The precision is higher than previous customized object-specific policies, achieving an average success rate of 71.3% with average pose error being 2.676 mm and 1.902◦. Code and videos can be found at: https://***/view/vf-ihm-il/home. © 2025, CC BY.

关键词： Contrastive Learning

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Subjective QoE Assessment for Virtual Reality Cloud-based First-Person Shooter Game

Subjective QoE Assessment for Virtual Reality Cloud-based Fi...

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IEEE International Conference on Communications (ICC)

作者： Henrique Souza Rossi Karan Mitra Samuel Larsson Christer Åhlund Irina Cotanis Department of Computer Science Electrical and Space Engineering Pervasive and Mobile Computing Luleå University of Technology Infovista AB Sweden

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Quality of experience (QoE) is an essential metric for stakeholders to understand how customers perceive the quality of their products or services. Gaming-as-a-Service (GaaS) is a challenging model to deliver efficiently to customers worldwide since it involves the joint force of cloud service providers, network operators, and game developers. The recent move of the cloud gaming (CG) industry to virtual reality (VR) platforms brings the benefits of the cloud to the most immersive quality of service (QoS) and QoE-sensitive VR content. Virtual reality cloud-based gaming (VRCG) necessitates understanding of stochastic broad-band network connections on users' QoE so that stakeholders can deliver quality content by optimizing their services to underlying QoS conditions. Very few studies exist in the literature that study the impact of network QoS on users' QoE for VRCG. This paper presents subjective tests (N=30) and investigates the effect of network-emulated QoS metrics (N=28) on the commercial Nvidia CloudXR service and their impact on the users' perceived QoE while playing Serious Sam VR shooter game. Our findings reveal that QoE was most affected by round trip time (RTT) $\geq 75$ ms or packet loss (PL) $> 6\%$ . Random jitter (RJ) caused QoE degradation for values more significant than one standard deviation, while the combined RTT and PL degraded QoE the most for RTT $\geq 25$ ms and PL $\geq 4\%$ , Finally, based on actual network traffic data between Sweden and various data centers in Europe, we suggest VRCG can be hosted anywhere in these data centers with minimal impact on QoE for wired connections. However, for 4G and 5G networks, high jitter values could pose a challenge to VRCG services.

关键词： Data centers Solid modeling Stochastic processes Quality of service Virtual reality Telecommunication traffic Jitter

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Radio frequency-based periodic cloud data analysis for smart farming

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International Journal of Cloud computing 2022年第5-6期11卷 568-577页

作者： Jayanthiladevi, A. Maurya, Sudhanshu Murugan, Sivaram Deva Brinda, M. Kannadasan, R. Narmadha, R. Computer Science and Information Science Srinvas University Karnataka India Ministry of Education Malaysia School of Computing Universiti Malaysia Perlis Malaysia Graphic Era Hill University Bhimtal Campus Uttarakhand India College of Engineering and Computer Science Lebanese French University Erbil Iraq Electrical and Electronics Engineering AMET University Chennai India University Vellore India School of Electrical and Electronics Engineering Sathyabama Institute of science and technology Chennai India

Radio frequency-based smart farming is revolutionising agriculture. To meet the growing demand of food, farm productivity should be improved by predicting crop performance in diverse environmental conditions. Farming becomes smarter with automation and radio frequency technology. Radio frequency collect and manage data about crop performance, climate change, livestock welfare, resource shortages and atmosphere that frequently influence farming construction via smartphone applications where the data container remains and is retrieved wherever. It also aids in animal nursing and termination. With smart sensors and equipment, farmers can increase crop production with reduced cost and time. Smart farming along with sensors offers the guarantee of expanding efficiency while diminishing generation costs and limiting ecological effects. The sensor data will be collected and stored in the cloud. Analysis of farming is done with computing the data stored in the cloud. Based on these cloud data, farmers perform farming in a proper annual time. Cloud-based storage is more appropriate for these computations. Various climatic conditions for farming are analysed with these cloud data. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Climate change

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SAE: Single Architecture Ensemble Neural Networks

arXiv

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arXiv 2024年

作者： Ferianc, Martin Fan, Hongxiang Rodrigues, Miguel Department of Electronic and Electrical Engineering University College London London United Kingdom Department of Computing Imperial College London London United Kingdom

Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks. To improve the hardware efficiency of ensembles of separate NNs, recent methods create ensembles within a single network via adding early exits or considering multi input multi output approaches. However, it is unclear which of these methods is the most effective for a given task, needing a manual and separate search through each method. Our novel Single Architecture Ensemble (SAE) framework enables an automatic and joint search through the early exit and multi input multi output configurations and their previously unobserved in-between combinations. SAE consists of two parts: a scalable search space that generalises the previous methods and their in-between configurations, and an optimisation objective that allows learning the optimal configuration for a given task. Our image classification and regression experiments show that with SAE we can automatically find diverse configurations that fit the task, achieving competitive accuracy or confidence calibration to baselines while reducing the compute operations or parameter count by up to 1.5∼3.7×. Copyright © 2024, The Authors. All rights reserved.

关键词： Calibration

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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computer Systems Science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Successive Cancellation Sampling Decoder: An Attempt to Analyze List Decoding Theoretically

Successive Cancellation Sampling Decoder: An Attempt to Anal...

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IEEE International Symposium on Information Theory

作者： Hsin-Po Wang Venkatesan Guruswami Department of Electrical Engineering and Computer Science Simons Institute for the Theory of Computing University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Successive cancellation list (SCL) decoders of polar codes excel in practical performance but pose challenges for theoretical analysis. Existing works either limit their scope to erasure channels or address general channels without taking advantage of soft information. In this paper, we propose the successive cancellation sampling (SCS) decoder. SCS hires iid “agents” to sample codewords using posterior probabilities. This makes it fully parallel and amenable for some theoretical analysis. As an example, when comparing SCS with $\boldsymbol{a}$ agents to any list decoder with list size $\boldsymbol{\ell}$ , we can prove that the error probability of the former is at most $\boldsymbol{\ell}/\boldsymbol{ae}$ more than that of the latter. In this paper, we also describe how to adjust the “temperature” of agents. Warmer agents are less likely to sample the same codewords and hence can further reduce error probability.

关键词： Convolutional codes Error probability Posterior probability Picture archiving and communication systems Decoding Polar codes

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