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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Tsai, Wen-Shan Huang, Syue-Guan Huang, Kuan-Chih Chen, Jung-Chih Lin, Chin-Teng National Yang Ming Chiao Tung University Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Brain Science and Technology Center College of Electrical and Computer Engineering Hsinchu300093 Taiwan Faculty of Engineering and Information Technology UltimoNSW2007 Australia

ISBN: (纸本)9798350305791

Due to advancements in brain signal application technology, there has been a growing focus on the human speech Brain-computer Interface (BCI) in recent years. An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing. To ensure the decoding capability of human speech BCI, researchers have to investigate the differences between overt speech and imagined speech, and they also need to find the best paradigm to build a reliable BCI. Thus, this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words ""(up), ""(down), ""(left), and ""(right). We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca's and Wernicke's areas (specifically F5, F7, FC3, FC5, and C5), to analyze the brain activity changes while speaking or imagining speech. Second, we used Daubechies 4 (db4) wavelet to do a 7-level decomposition to the EEG signal. The corresponding discrete wavelet transform (DWT) coefficients were further calculated with six statistical features for support vector machine (SVM) classification. We found that the "speak (overt speech)"task and the "imagine (imagined speech)"task resulted in significantly different alpha band power changes, and the beta band shows the same phenomenon. The result of average classification accuracy is 71.97% for "speak"and "imagine"distinction. In conclusion, we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features. This may pave the way to establish a Mandarin imagined speech BCI. © 2023 IEEE.

关键词： Electroencephalography

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Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Heart Diseases Prediction Using Machine Learning Techniques:...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Safaqah, Mohammed Abu Alzoubi, Haneen Omar, Hani Al-Safarini, Maram Y. Elsoud, Esraa Abu Elrashidi, Ali Alluwaici, Mo'Ath Owaida, Suhaila Abu Zarqa University Faculty of It Department of Data Science and Ai Zarqa Jordan Department of Mechanical Engineering Muscat Oman Applied Science Private University Faculty of It Department of Computer Science Amman Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan

ISBN: (纸本)9798331540012

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan. © 2024 IEEE.

关键词： Adversarial machine learning

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Secondary Electron Yield Reduction in High Porosity Surfaces and Its Application for Multipactor Suppression 50

Secondary Electron Yield Reduction in High Porosity Surfaces...

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50th IEEE International Conference on Plasma science, ICOPS 2023

作者： Iqbal, A. Mirmozafari, M. Zhang, P. Behdad, N. Booske, J.H. Verboncoeur, J. University of Wisconsin-Madison Electrical and Computer Engineering MadisonWI United States Michigan State University Electrical and Computer Engineering East LansingMI United States Michigan State University Computational Mathematics Science and Engineering East LansingMI United States

ISBN: (纸本)9798350302660

We investigate secondary electron yield (SEY) reduction in high porosity surfaces consisting of periodic through-holes. Using Vaughan's empirical model of SEY [1], [2], we perform two-dimensional Monte Carlo (MC) simulation [3], [4] of the electron trajectory in a single well and determine the effective SEY of a porous surface. © 2023 IEEE.

关键词： Porosity

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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

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computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

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Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets 24

Avian Activity Classification Using Recurrent Networks to Fu...

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10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Wang, Xijun Szymanski, Adam Hamada, Yuki Katsaggelos, Aggelos K. Dept. of Computer Science Northwestern University EvanstonIL United States Argonne National Lab LemontIL United States Dept. of Electrical and Computer Engineering Northwestern University EvanstonIL United States

ISBN: (纸本)9798400717055

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale. © 2024 Owner/Author.

关键词： Solar power generation

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FastPlane: A Fully Convolutional Network for Real-time 3D Plane Segmentation

FastPlane: A Fully Convolutional Network for Real-time 3D Pl...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zhang, Haijun Ren, Qingji Dong, Li Tsang, Kim Fung Harbin Institute of Technology Shenzhen China Department of Computer Science Shenzhen China Shenzhen Technology University The College of Big Data and Internet Shenzhen China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China

ISBN: (纸本)9798350324136

3D plane segmentation is a challenging computer vision task that involves the detection of planar regions in a scene and the prediction of their spatial position. Recent methods have addressed this problem as a multi-task learning problem in which convolutional neural networks with separate branches predict the plane segmentation mask and the dense depth maps, or plane parameters. However, existing methods often rely on a multi-stage object detection framework for plane segmentation, resulting in slow inference speeds that hinder real-time applications. To address this issue, we propose FastPlane, a real-time 3D plane segmentation network based on a fully convolutional structure. FastPlane comprises a shared backbone, a fully convolutional plane segmentation network, and a lightweight depth estimation network. Given a single image as input, FastPlane outputs both the plane segmentation mask and dense depth maps. Extensive experiments on public datasets demonstrate the effectiveness and efficiency of FastPlane, outperforming existing methods in terms of plane segmentation and depth estimation metrics. Notably, FastPlane achieves an impressive inference speed of 45.0 FPS, making it suitable for real-time applications in robotics, augmented reality, and virtual reality. © 2024 IEEE.

关键词： 3D plane segmentation depth estimation multitask learning scene reconstructing

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Attention-Driven Causal Discovery: From Transformer Matrices to Granger Causal Graphs for Non-Stationary Time-series Data

Attention-Driven Causal Discovery: From Transformer Matrices...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhu, Jiageng Li, Kehao Mai, Zheda Xie, Hanchen AbdAlmageed, Wael Abraham, Zubin Information Sciences Institute University of Southern California United States Sunnyvale United States Department of Computer Science and Engineering Ohio State University United States Holcombe Department of Electrical and Computer Engineering Clemson University United States

ISBN: (纸本)9798350368741

Causal discovery in non-stationary time series data is crucial for understanding complex systems but remains challenging due to evolving relationships over time. This paper presents a novel two-stage approach for causal discovery in non-stationary multivariate time series data. The first stage employs a Temporal Attention Forecasting Network (TAFNet), a modified Transformer architecture, to capture complex temporal dependencies and generate informative attention matrices. The second stage utilizes these matrices in an iterative process for Granger causality discovery, refining the predicted causal graph while improving forecasting accuracy. The proposed method addresses the limitations of existing approaches and provides a more complete understanding of causal relationships in non-stationary systems. Extensive experiments demonstrate the method's superior performance compared to state-of-the-art approaches, particularly in handling non-linear relationships and scaling to high-dimensional data. © 2025 IEEE.

关键词： Causal Discovery Non Stationary Time Series

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Performance Comparison of dSPACE and MCU MPC Implementation in PV Power Optimizer

Performance Comparison of dSPACE and MCU MPC Implementation ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Metry, Morcos Guler, Naki Rohouma, Wesam Balog, Robert S. Texas A&m University Department of Electrical and Computer Engineering Doha Qatar Gazi University Department of Electrical-Electronic Engineering Technology Faculty Ankara Turkey University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar Texas A&m University Department of Electrical and Computer Engineering College StationTX United States

ISBN: (纸本)9798350316445

Finite control set model predictive control (MPC) can be computationally intensive especially when scaled up for more complicated converters since each state variable in the system is evaluated for all possible switching configurations. Multi-objective optimization of the cost function adds to the complexity of MPC. Much of the MPC literature in power electronics relies on expensive rapid prototyping (RP) platforms (RPP) such as dSPACE and OPAL RT. This paper aims to bridge the gap between expensive RPP and low-cost MCUs by exploring the feasibility of rapid prototyping (RP) on the low-cost F28379D launchpad. This paper is aimed toward the practitioner or researcher who desires to reduce to practice the MPC methods developed on a RPP to a lower-cost embedded controller. The suitability of a low-cost Texas Instrument (TI) launchpad to perform the same rapid-prototyping functions needed for MPC regulator development in a full-featured, high-end RPP is discussed. Experimental results using the dSPACE 1006 and the F28379D Launchpad showing steady-state performance and dynamic response are illustrated and compared. Comparative results show the F28379D launchpad and dSPACE 1006 offer similar performance markers. © 2023 IEEE.

关键词： Low-cost controller Model Predictive Control Power Electronics rapid prototyping platforms

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Flexible multimaterial fibers in modern biomedical applications

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National science Review 2024年第10期11卷 110-127页

作者： Jongwoon Kim Xiaoting Jia The Bradley Department of Electrical and Computer Engineering Virginia Tech School of Neuroscience Virginia Tech Department of Materials Science and Engineering Virginia Tech

Biomedical devices are indispensable in modern healthcare, significantly enhancing patients' quality of ***, there has been a drastic increase in innovations for the fabrication of biomedical devices. Amongst these fabrication methods, the thermal drawing process has emerged as a versatile and scalable process for the development of advanced biomedical devices. By thermally drawing a macroscopic preform, which is meticulously designed and integrated with functional materials, hundreds of meters of multifunctional fibers are produced. These scalable flexible multifunctional fibers are embedded with functionalities such as electrochemical sensing, drug delivery, light delivery, temperature sensing, chemical sensing, pressure sensing, etc. In this review, we summarize the fabrication method of thermally drawn multifunctional fibers and highlight recent developments in thermally drawn fibers for modern biomedical application, including neural interfacing, chemical sensing, tissue engineering, cancer treatment, soft robotics and smart ***, we discuss the existing challenges and future directions of this rapidly growing field.

关键词： biomedical fiber thermal drawing functional fiber biomedical devices

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Epileptic Seizure Detection using Denoising Autoencoder 4

Epileptic Seizure Detection using Denoising Autoencoder

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Joshi, Sukumar Rout, Pravat Kumar Samanta, Indu Sekhar Cherukuri, Murthy Swain, Kunjabihari Siksha 'o' Anusandhan University Dept. of Electrical Engineering Bhubaneswar India Siksha 'o' Anusandhan University Department of Electrical and Electronics Engineering India Siksha 'o' Anusandhan University Dept. of Computer Science and Engineering Bhubaneswar India Nist Institute of Science and Technology Dept. of Electrical and Electronics Engineering Berhampur India

ISBN: (纸本)9798350349856

Epileptic seizures are a common neurological disorder characterized by abnormal brain activity. Early and accurate detection of seizures plays a crucial role in effective treatment and improving the quality of life for individuals with epilepsy. This research paper proposes a innovative strategy for epileptic seizure detection utilizing a denoising autoencoder (DAE). The DAE is a feature extraction technique to enhance the discriminatory power of electroencephalogram (EEG) signals, commonly used for seizure detection. The suggested method's excellent sensitivity and specificity in correctly recognizing epileptic seizures is supported by the results of experiments. © 2024 IEEE.

关键词： Feature extraction

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