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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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GPU-Accelerated Vecchia Approximations of Gaussian Processes for Geospatial Data using Batched Matrix Computations

GPU-Accelerated Vecchia Approximations of Gaussian Processes...

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ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

作者： Qilong Pan Sameh Abdulah Marc G. Genton David E. Keyes Hatem Ltaief Ying Sun Division of Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Statistics Program Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Jeddah Saudi Arabia

Gaussian processes (GPs) are commonly used for geospatial analysis, but they suffer from high computational complexity when dealing with massive data. For instance, the log-likelihood function required in estimating the statistical model parameters for geospatial data is a computationally intensive procedure that involves computing the inverse of a covariance matrix with size $n$ × n, where $n$ represents the number of geographical locations in the simplest case. As a result, in the literature, studies have shifted towards approximation methods to handle larger values of $n$ effectively while maintaining high accuracy. These methods encompass a range of techniques, including low-rank and sparse approximations. Among these techniques, Vecchia approximation is one of the most promising methods to speed up evaluating the log-likelihood function. This study presents a parallel implementation of the Vecchia approximation technique, utilizing batched matrix computations on contemporary GPUs. The proposed implementation relies on batched linear algebra routines to efficiently execute individual conditional distributions in the Vecchia algorithm. We rely on the KBLAS linear algebra library to perform batched linear algebra operations, reducing the time to solution compared to the state-of-the-art parallel implementation of the likelihood estimation operation in the ExaGeoStat software by up to 700X, 833X, 1380X on 32GB GV100, 80GB A100, and 80GB H100 GPUs, respectively, with the largest matrix dimension that can fully fit into the GPU memory in the dense Maximum Likelihood Estimation (MLE) case. We also successfully manage larger problem sizes on a single NVIDIA GPU, accommodating up to 1 million locations with 80GB A100 and H100 GPUs while maintaining the necessary application accuracy. We further assess the accuracy performance of the implemented algorithm, identifying the optimal settings for the Vecchia approximation algorithm to preserve accuracy on two real geos

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Outage Performance Analysis of Terahertz Band Drone-to-Ground Communications

Outage Performance Analysis of Terahertz Band Drone-to-Groun...

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Akhtar Saeed Ozgur Gurbuz Fatih Alagoz Department of Electrical Engineering DHA Suffa University Karachi Pakistan Electronics Engineering Program Sabanci University Istanbul Turkiye Department of Computer Engineering Bogazici University Istanbul Turkiye

Recent studies have shown the potential of em-ploying the Terahertz (THz) band for 6G non-terrestrial communications including drones. This paper assesses the outage performance of the THz band Drone-to-Ground (D2G) vertical communications up to 100 m altitude at four practically feasible THz transmission windows (TWs): f 1 = 0.140 THz; [0.110-0.170] THz, f 2 = 0.300 THz; [0.275-0325] THz, f 3 = 0.850 THz; [0.790-0.910] THz, and f 4 = 0.935 THz; [0.930-03940] THz. We consider vertical D2G communication, where realistic THz-based absorption gains of the four THz TWs at the drone altitudes are obtained via the International Telecommunications Union (ITU) model using the Standard Weather Profile. Additionally, multipath (MP) fading and beam misalignment (BM) fading are incorporated into the overall channel gain for assessing the outage performance. Numerical results reveal that under severe BM fading and Rayleigh fading, THz band D2G communication employing 60 dBi total antenna gains at $f_{1},\ f_{2},\ f_{3}$, and f 4 can be reliably established up to the drone altitudes: 76.9 m, 59.2m, 34.4 m, and 43.2 m, and up to 85.1 m, 60.3m, 36.9m, 45.5m under the severe BM fading and the presence of a strong Line-of-Sight component, truly showcasing the achievable altitude and range limits for the THz band D2G communications among 6G non-terrestrial cognitive networks.

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Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment

arXiv

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arXiv 2024年

作者： Yu, Sangwon Song, Jongyoon Hwang, Bongkyu Kang, Hoyoung Cho, Sooah Choi, Junhwa Joe, Seongho Lee, Taehee Gwon, Youngjune L. Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Samsung SDS Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

A binary decision task, like yes-no questions or answer verification, reflects a significant real-world scenario such as where users look for confirmation about the correctness of their decisions on specific issues. In this work, we observe that language models exhibit a negative bias in the binary decisions of complex reasoning tasks. Based on our observations and the rationale about attention-based model dynamics, we propose a negative attention score (NAS) to systematically and quantitatively formulate negative bias. Based on NAS, we identify attention heads that attend to negative tokens provided in the instructions as answer candidate of binary decisions, regardless of the question in the prompt, and validate their association with the negative bias. Additionally, we propose the negative attention score alignment (NASA) method, which is a parameter-efficient fine-tuning technique to address the extracted negatively biased attention heads. Experimental results from various domains of reasoning tasks and large model search space demonstrate that NASA significantly reduces the gap between precision and recall caused by negative bias while preserving their generalization abilities. Our codes are available at https://***/ysw1021/NASA. Copyright © 2024, The Authors. All rights reserved.

关键词： NASA

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Enhancement of Antenna Gain With M&W Slot Model on EBG Reflector Structure Utilizing Reflection Phase Diagram Analysis

Enhancement of Antenna Gain With M&W Slot Model on EBG Refle...

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13th International electrical engineering Congress, iEECON 2025

作者： Chakunna, Payu Phusam, Nopphadon Mahatthanajatuphat, Pisit Lee, Wilaiporn Suangun, Thanakarn University of Phayao School of Engineering Phayao Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

This research demonstrates the improvement in antenna gain by utilizing an EBG reflector. The EBG reflector includes a unit cell with grooves shaped like the letters M and W to mitigate surface waves at a frequency of 2.45 GHz. The suggested EBG reflector is constructed on an FR-4 substrate exhibiting a uniform dielectric constant of 4.3, a loss tangent of 0.025, and a thickness of 1.6 mm. Furthermore, the impact of boosting the antenna gain can be analyzed by the reflection phase diagram, which exhibits values ranging from +90° to -90° at the resonant frequency of the reflector. The proposed EBG reflector comprises 5x5 unit cells. The testing results of the reflector's performance, when integrated with a general dipole antenna, indicate that it can effectively respond to the resonant frequency of 2.45 GHz, with a |S11| level below -10 dB within the frequency range of 2.2 GHz to 2.6 GHz, thereby adequately covering the IEEE 802.11 b/g/n band. The suggested EBG reflector exhibits a gain of around 6.5 dBi at 2.45 GHz, indicating its efficacy in enhancing gain relative to the standard PEC reflector. Moreover, the radiation pattern is also directional. © 2025 IEEE.

关键词： Antenna reflectors

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Mitigating electrical Losses Through a program.able Smart Energy Advanced Metering Infrastructure System

Mitigating Electrical Losses Through a Programmable Smart En...

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International Conference on Prognostics and Health Management, PHM

作者： Daniel O. Williams Zhaojun Steven Li Afsaneh Ghanavati Industrial Engineering Department Western New England University Springfield MA USA Electrical and Computer Engineering Program School of Engineering Wentworth Institute of Technology Boston MA USA

With the current alarming exponential increase in global energy demand chiefly due to population growth, electrification, and the issues associated with fossil generation, utilities are reinvesting their returns in alternative ways of clean power generation. Although, finding alternative ways to provide clean energy and to advance the power grid are of the main interest globally, many countries face power theft as a frequent problem. in Ghana, power losses in the distribution system cost the nation over a billion Ghana Cedis in the country's total annual revenue, of which power theft plays a predominant role. This paper presents an electricity theft mitigation technique through a program.able smart energy meter. The proposed method is such that interruptions are added to the smart energy meters in order to detect input signals from an added current sensor placed at the terminal point of the service line, from where in-between the sensor and the meter, illegal connections are made. The proposed Advanced Metering Infrastructure (AMI) system will provide smart services, including calculating consumed energy in kWh and generating a bill sent to the utility station. After which, the AMI system will disconnect the power supply from the meter.

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Automatic 2D Material Detection in Optical Images using Deep Learning Based computer Vision 4

Automatic 2D Material Detection in Optical Images using Deep...

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AI and Optical Data Sciences IV 2023

作者： Ramezani, Fereshteh Parvez, Sheikh Fix, J. Pierce Battaglin, Arthur Whyte, Seamus Borys, Nicholas J. Whitaker, Bradley Electrical and Computer Engineering Department Montana State University Bozeman United States Department of Physics Montana State University Bozeman United States Materials Science Program Montana State University Bozeman United States Optical Technology Center Montana State University Bozeman United States

ISBN: (纸本)9781510659810

computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. In order to use these materials for research and product development, single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of 2D materials based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectorRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes ( © 2023 SPIE.

关键词： Object detection

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Real-Time Computation of E-Field for Transcranial Magnetic Stimulation

Real-Time Computation of E-Field for Transcranial Magnetic S...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Nahian I. Hasan Moritz Dannhauer Dezhi Wang Zhi-De Dengv Luis J. Gornez Elmore Family School of Electrical and Computer Engineering Purdue University Indiana WL USA National Institute of Mental Health Intramural Research Program National Institutes of Health USA

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique employing coils to induce electric fields (E-fields) in the brain, influencing its activity. Accurately determining the dosage of E-field induced in targeted brain areas during TMS is crucial for evaluating its effects and optimizing parameters such as coil current amplitude and placement. We present a method capable of computing induced E-fields for arbitrary coil positions in real-time (within 4 milliseconds) within the brain. Initially, white noise currents, defined on a surface delineating the head and its surrounding medium, are employed to generate a comprehensive basis set of mode field patterns representing TMS-induced brain E-fields. Subsequently, electromagnetic reciprocity and Huygens' principles are leveraged to establish the relationship between expansion coefficients for these modes and TMS-induced electric fields in free space. Our real-time TMS solver predicts E-fields with comparable accuracy to existing solvers using fewer than 400 modes, rendering it suitable for neuronavigation systems and real-time adjustment of coil dosing parameters.

关键词： Coils Transcranial magnetic stimulation White noise Rendering (computer graphics) Computational electromagnetics Real-time systems Magnetic heads

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Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://***/HongyangDu/DistributedDiffusion. © 2023, CC BY.

关键词： Wireless networks

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Enhancement of Antenna Gain With M&W Slot Model on EBG Reflector Structure Utilizing Reflection Phase Diagram Analysis

Enhancement of Antenna Gain With M&W Slot Model on EBG Refle...

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International electrical engineering Congress (iEECON)

作者： Payu Chakunna Nopphadon Phusam Pisit Mahatthanajatuphat Wilaiporn Lee Thanakarn Suangun School of Engineering University of Phayao Phayao Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This research demonstrates the improvement in antenna gain by utilizing an EBG reflector. The EBG reflector includes a unit cell with grooves shaped like the letters M and W to mitigate surface waves at a frequency of 2.45 GHz. The suggested EBG reflector is constructed on an FR-4 substrate exhibiting a uniform dielectric constant of 4.3, a loss tangent of 0.025, and a thickness of 1.6 mm. Furthermore, the impact of boosting the antenna gain can be analyzed by the reflection phase diagram, which exhibits values ranging from +90° to -90° at the resonant frequency of the reflector. The proposed EBG reflector comprises 5x5 unit cells. The testing results of the reflector's performance, when integrated with a general dipole antenna, indicate that it can effectively respond to the resonant frequency of 2.45 GHz, with a |S 11 | level below -10 dB within the frequency range of 2.2 GHz to 2.6 GHz, thereby adequately covering the IEEE 802.11 b/g/n band. The suggested EBG reflector exhibits a gain of around 6.5 dBi at 2.45 GHz, indicating its efficacy in enhancing gain relative to the standard PEC reflector. Moreover, the radiation pattern is also directional.

关键词： Slot antennas Dipole antennas Resonant frequency Reflector antennas Metamaterials Reflection Gain Standards Periodic structures Antenna radiation patterns

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