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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Khanin Udomchoksakul Orathai Sangpetch Akkarit Sangpetch Electrical and Computer Engineering Carnegie Mellon University Pittsburgh USA Carnegie Mellon-KMITL Thailand Program CMKL University Bangkok Thailand

ISBN: (纸本)9781665463683

The complexity of current Deep learning has been growing rapidly nowadays. Such advancement allows various organizations such as private sectors and government to leverage intelligent systems on their use cases. High Performance Computing (HPC) infrastructure nowadays has pivoted to GPU-oriented systems, enabling developers and researchers to train complex models with large datasets unlike conventional clusters equipped only with CPU cores. However, focus on power efficiency on the HPC system has not been prevalent especially on the new system such as DGX A100 that does not have datapoints on how GPUs consumed power. Even though such HPC cluster can be powerful, always allowing it to run at the maximum capacity results to financial cost to the HPC provider at the end. Therefore, for any organization providing the system, it is crucial for them to balance the cluster capabilities while maintaining overall power consumption which can potentially be costly in the long term. This paper reveals A100 GPU metrics that are relevant to Power usage and explains GPU profiling applied to Deep learning workload on the cluster, saving up to 32% of the power usage while compromising only 11.5% of training time compared to a default profile. Then, the paper investigates literature review that could be learned further adopted to the current system at CMKL university as the next milestone.

关键词： Deep learning Training Measurement Cloud computing Power demand High performance computing Government

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Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

Comparison of Algebraic Reconstruction Technique Methods and...

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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Lubis, A.J. Nasir, N.F. Mohd Zakaria, Z. Jusoh, M. Azizan, M.M. Zakaria, M.Z. Kazim, M.N.F.M. Rahman, S. Faculty of Engineering and Computer Science Universitas Harapan Medan Medan Indonesia Computer Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Biomedical Electronic Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Department of Electrical and Electronic Department Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Negeri Sembilan Malaysia Faculty of Mechanical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Mechatronic Engineering Program Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia

Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution's internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Generative adversarial networks

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Internet of Wearables: Fog Extrapolation for Reduced Data Collection and Expanded Capture Volume in Real-Time Motion Capture Edge Devices

Internet of Wearables: Fog Extrapolation for Reduced Data Co...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Shaun Stevens Paulo Garcia Hyong Kim Carnegie Mellon-KMITL Thailand Program CMKL University Bangkok Thailand Electrical and Computer Engineering Carnegie Mellon University Pittsburgh USA

ISBN: (纸本)9781665463683

The range of applications that make up the Internet-of-Things ecosystem continues to grow. New opportunities present themselves along with new design challenges concerning the efficiency, portability, and processing capabilities of future Internet-of-Things systems. Thus, improving the operational metrics of individual Internet-of-Things devices, particularly across the edge and fog layers, is of paramount *** this manuscript, we present an approach for decreasing data collection at the edge, thus reducing form factor and power consumption of edge devices. This is particularly relevant for our application of interest, wearable motion capture, where human comfort and operational longevity are of prime importance. Our approach extrapolates from reduced edge data by leveraging prior physiological knowledge of the captured entity at the computational stage in the fog. By delegating computation to the fog, we also demonstrate the possibility for expanded capture volumes (operational areas) for future wearable motion capture systems and motion capture systems in *** approach, when prototyped on a millimeter wave sensor edge device and two fog node platforms of different processing tiers, shows that prior knowledge can facilitate a reduction in capture data dimensionality (and an associated decrease in power consumption) with little to no accuracy degradation, when compared to a more data-intensive edge system (Microsoft Kinect).

关键词： Measurement Cloud computing Extrapolation Power demand Millimeter wave devices Wearable computers Data collection

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Temperature Dependent Anomalous Threshold Voltage Modulation of a-IGZO TFT by Incorporating Variant Gate Stresses

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ECS Journal of Solid State Science and Technology 2024年第6期13卷 065008-065008页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan 701401 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO) has recently made significant advancement as a key material for electronic component design owing to its compatibility with complementary metal oxide semiconductor technologies. A comprehensive analysis of reliability-related issues is required to determine the true potential of a-IGZO-based devices for next-generation electronics applications. To address this objective, we electrically characterize scaled-channel a-IGZO thin film transistors (TFTs) under positive bias (temperature) stress (PB(T)S). Both PBS and PBTS are characterized by positive and negative V_th shift, respectively, during the various gate stresses. In particular, the negative V_th shift is explained by the generation of donor-like traps stimulated by ionization of oxygen vacancy/hydrogen at elevated temperature. The TFTs exhibit relatively decent stability during the PBS operation. The analysis of devices with variant channel dimensions implies that long-channel devices exhibit relatively higher stability and performance compared to the short-channel ones. We also observe that the V_th can be controllably adjusted by employing the top gate (TG) with bottom gate sweep. Moreover, the stress-induced partial recovery mechanism is experimentally observed owing to detrapping of charges. Generally, the reported results infer a perceptive understanding of scaled-channel a-IGZO-TFTs which helps with shaping performance-enhancement strategies. © 2024 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited

关键词： amorphous IGZO-TFT electrical instabilities hydrogen-impact scaling impact on reliability

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Deep Learning based Fault Diagnosis in a Grid-Connected Photovoltaic Systems 19

Deep Learning based Fault Diagnosis in a Grid-Connected Phot...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kasserine Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Signal Processing and Image National Engineering School of Monastir Research Laboratory of Automation 5019 Tunisia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Photovoltaic (PV) system fault diagnosis is crucial because it helps PV system operators reduce energy and income losses. It also decreases the risk of fire and electric shock from PV system failures. Thus, the implementation of fault diagnosis tool is required. The most well-known techniques are machine learning models. Although these techniques provided well in terms of diagnosis and classification accuracy, they rely on manual feature extraction, which is time consuming, expensive, and requires diagnostic skill. Manual feature extraction is a problem that has to be addressed. This paper therefore presents a PV system fault diagnosis techniques that can automatically extract features from raw data in order to classify PV system faults. The implemented methods include long short-term memory (LSTM) bidirectional long short-term memory (BiLSTM) networks, which are deep learning based algorithms. Finally, the presented fault diagnosis approaches showed a good diagnosis performances when applied to an emulated GCPV system. © 2022 IEEE.

关键词： Failure analysis

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Interferometry Among Strongly Driven Quasiparticles with Distinct Dynamical and Geometrical Phases

Interferometry Among Strongly Driven Quasiparticles with Dis...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： O'Hara, Seamus Costello, Joe Tian, Qi Wu, Qile West, Ken Pfeiffer, Loren Sherwin, Mark Wu, Liang Department of Physics Institute of Terahertz Science and Technology University of California Santa Barbara Santa BarbaraCA93106 United States Intelligence Community Post-Doctoral Research Fellowship Program PhiladelphiaPA19104 United States Department of Physics University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08542 United States

ISBN: (纸本)9781957171395

In this proceeding, we present polarization data of sidebands emitted from strongly driven quasiparticles, demonstrate the dependence of these data on various parameters of the external driving field, and describe the data using an analytical expression, dependent on driving field parameters and the effective Hamiltonian of the system. We propose using this method on spin-momentum locked systems, such as altermagnets, to observe and reconstruct sidebands which are influenced by the topology of individual bands. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Topology

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Dynamics of COVID-19 Misinformation: An Analysis of Conspiracy Theories, Fake Remedies, and False Reports

Dynamics of COVID-19 Misinformation: An Analysis of Conspira...

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17th International Conference on Social Computing and Social Media, SCSM 2025, held as part of the 27th HCI International Conference, HCII 2025

作者： Thakur, Nirmalya Shao, Mingchen Knieling, Victoria Su, Vanessa Bian, Andrew Jeong, Hongseok Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid CitySD57701 United States Department of Computer Science Emory University AtlantaGA30322 United States Program in Linguistics Emory University AtlantaGA30322 United States Goizueta Business School Emory University AtlantaGA30322 United States

ISBN: (纸本)9783031935350

This paper makes four scientific contributions to the area of misinformation detection and analysis on digital platforms, with a specific focus on investigating how conspiracy theories, fake remedies, and false reports emerge, propagate, and shape public perceptions in the context of COVID-19. A dataset of 5,614 posts on the internet that contained misinformation about COVID-19 was used for this study. These posts were published in 2020 on 427 online sources (such as social media platforms, news channels, and online blogs) from 193 countries and in 49 languages. First, this paper presents a structured, three-tier analytical framework that investigates how multiple motives - including fear, politics, and profit - can lead to a misleading claim. Second, it emphasizes the importance of narrative structures, systematically identifying and quantifying the thematic elements that drive conspiracy theories, fake remedies, and false reports. Third, it presents a comprehensive analysis of different sources of misinformation, highlighting the varied roles played by individuals, state-based organizations, media outlets, and other sources. Finally, it discusses multiple potential implications of these findings for public policy and health communication, illustrating how insights gained from motive, narrative, and source analyses can guide more targeted interventions in the context of misinformation detection on digital platforms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data mining

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Experimental implementation of an efficient test of quantumness

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Physical Review A 2024年第1期109卷 012610-012610页

作者： Laura Lewis Daiwei Zhu Alexandru Gheorghiu Crystal Noel Or Katz Bahaa Harraz Qingfeng Wang Andrew Risinger Lei Feng Debopriyo Biswas Laird Egan Thomas Vidick Marko Cetina Christopher Monroe Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences California Institute of Technology Pasadena California 91125 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA IonQ Inc. College Park Maryland 20740 USA Departments of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Institute for Theoretical Studies ETH Zürich Zürich CH 8001 Switzerland Duke Quantum Center and Department of Physics Duke University Durham North Carolina 27708 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Chemical Physics Program and Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA

A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits nonclassical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efficient verification or noninteractive challenges with inefficient (exponential time) verification. In this paper, we execute an efficient noninteractive test of quantumness on an ion-trap quantum computer. Our results significantly exceed the bound for a classical device's success.

关键词： Quantum cryptography Quantum information with trapped ions Quantum protocols Quantum verification

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Dimensions of Equity: Undergraduate Research through Vertically Integrated Projects at Five Institutions

Dimensions of Equity: Undergraduate Research through Vertica...

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Frontiers in Education (FIE) Conference

作者： Julia Sonnenberg-Klein Edward J. Coyle Jack Bringardner Robert V. Kukta Donna Llewellyn Nichole Ramirez Carla B. Zoltowski VIP Program Georgia Institute of Technology Atlanta GA Electrical & Computer Engineering VIP Program Georgia Institute of Technology Atlanta GA USA Engineering Design & Society Colorado School of Mines Golden CO USA Mechanical Engineering Stony Brook University Stony Brook NY USA Institute for Inclusive & Transformative Scholarship Boise State University Boise ID USA Vertically Integrated Projects Purdue University West Lafayette IN USA School of Electrical & Computer Engineering Purdue University West Lafayette IN USA

In this innovative practice work-in-progress paper, enrollment data from five institutions was used to examine equity in undergraduate research through Vertically Integrated Projects (VIP) program.. VIP is a model for undergraduate research in which large student teams are embedded in faculty-driven projects. The American Association of Colleges and Universities recognizes undergraduate research as a high-impact experience, associated with higher graduation rates and greater learning gains in college. Participation in multiple high-impact experiences yields cumulative gains to students from all backgrounds, and compensatory gains for minoritized and marginalized students. Nationally however, minoritized students, first-generation college students, and transfer students participate in undergraduate research at lower rates than their peers. In this study, VIP enrollments at five institutions (N = 6,651 over two semesters) were compared to demographics of the institutions to determine the degree to which program. achieved equity among historically underserved minorities, transfer students, first-generation college students, and by gender. Analysis accounted for demographics and level of participation of the academic units involved, comparing enrollments with what would be expected under equitable enrollment. Analyses were done for each institution and across the pooled sample. By institution, equity across categories varied. Across the pooled sample, results show small effects sizes for status as a historically underserved minority, very small effect sizes for first-generation students and transfer students, and slightly higher participation among women than men. The large-scale nature of VIP teams enables institutions to scale-up their undergraduate research offerings. This paper begins answering the question of whether this scaling increases access for marginalized populations, and the results are encouraging. The paper is a work-in-progress, because data needs to be

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Dynamics of COVID-19 Misinformation: An Analysis of Conspiracy Theories, Fake Remedies, and False Reports

arXiv

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arXiv 2025年

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