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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 IEEE.

关键词： Man machine systems

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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2023 International Conference on electrical engineering and Informatics, ICEEI 2023

作者： Pranowo, Samuel Raymond Sukra, Zener Lie Asy'ari, Muhammad Zacky Paryasto, Marisa Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Telkom University Computer Engineering Program Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350381290

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%. © 2023 IEEE.

关键词： Lettuce

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Residential Power Satisfaction as a Demand Side Management Approach in Puerto Rico 42

Residential Power Satisfaction as a Demand Side Management A...

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42nd IEEE Central America and Panama Convention, CONCAPAN 2024

作者： Pena Becerra, Carlos A. Pacheco Cano, Willian Sitiriche, Marcel Castro Ortiz, Samira University of Puerto Rico Department of Electrical and Computer Engineering Mayaguez Puerto Rico Universidad Ana G. Méndez - Recinto de Gurabo Electrical and Computer Engineering Program Puerto Rico

ISBN: (纸本)9798350366723

Puerto Rico's electrical infrastructure powers over three million residents, yet energy consumption overcomes generation and continues to rise. This imbalance results in an unreliable power supply and frequent blackouts across the island. One solution to this issue is to increase generation capacity by adding new generating units and enhancing transmission networks, though this is expensive. Another approach is to promote the adoption of rooftop solar generation with storage systems through microgrids. Alternatively, demand-side management (DSM) offers a more cost-effective solution by regulating demand and reducing energy use at the consumer level. This paper explores the unique integration of power satisfaction within DSM strategies to reduce energy consumption while preserving user satisfaction. Utilizing an agent-based model to simulate household appliance behavior, we demonstrate how power satisfaction can reduce energy demand. This innovative approach offers a promising pathway toward a more resilient and sustainable energy future for Puerto Rico. © 2024 IEEE.

关键词： Associative storage

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Effects of Temporal Light Modulation on Cognitive Performance,Eye Movements, and Brain Function

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LEUKOS - Journal of Illuminating engineering Society of North America 2024年第1期20卷 67-106页

作者： Veitch, Jennifer A. Van Roon, Patricia D’Angiulli, Amedeo Wilkins, Arnold Lehman, Brad Burns, Greg J. Dikel, E. Erhan Construction Research Centre National Research Council Canada OttawaON Canada Department of Neuroscience Carleton University OttawaON Canada Department of Psychology University of Essex Colchester United Kingdom Department of Electrical and Computer Engineering Northeastern University BostonMA United States

Temporal light modulation (TLM) describes variations in light output from a light source or lighting system and can be a characteristic of their normal operation. TLM at 100 or 120 Hz can disrupt eye movements and reduce visual performance compared to 40 kHz, but little is known of the effects between those frequencies. Such evidence could provide the basis for lighting product and application standards to support the development of energy-efficient lighting systems. This repeated-measures experiment compared the effects of three stimuli (no TLM [DC], 100 Hz, and 500 Hz [both rectangular-wave, 100% modulation depth, 50% duty cycle]) on state anxiety, eye movements (blinks, saccades, fixations, and pupil size), cognitive performance (Stroop task and sentence reading speed), and visual event-related potentials (ERPs). Post-processing of EEG data included dipole source analysis and estimates of source dipole strength. TLM at 100 Hz was associated with larger pupil sizes and greater ERP dipole strength during 100 Hz exposure than no TLM, particularly in the right hemisphere. Cognitive interference was lower for 500 Hz TLM than 100 Hz. State anxiety was unaffected. The addition of TLM to the light source signal increased arousal and altered brain activity;at the highest frequency tested here this reduced cognitive interference. Arousal theory and stochastic facilitation can provide possible explanations. © 2023 His Majesty the King in Right of Canada. Published by Taylor & Francis.

关键词： Energy efficiency

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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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A Review of Deep Learning for Biomedical Signals:Current Applications,Advancements,Future Prospects,Interpretation,and Challenges

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computers, Materials & Continua 2025年第6期83卷 3753-3841页

作者： Ali Mohammad Alqudah Zahra Moussavi Biomedical Engineering Program University of ManitobaWinnipegMB R3T 2N2Canada Department of Electrical and Computer Engineering University of ManitobaWinnipegMB R3T 2N2Canada

This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation *** systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly *** study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational *** results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)*** is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model *** addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant *** from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal *** advances the field by bridging technical advancements with clinical needs,such as scalability in real-world sett

关键词： Deep learning deep models biomedical signals physiological signals biosignals

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A Low-Noise Capacitively-Coupled Chopper Instrumentation Amplifier with SAR-Assisted Low Current Ripple Reduction Technique 21

A Low-Noise Capacitively-Coupled Chopper Instrumentation Amp...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Jo, Jungkook Jun, Jaehoon Inha University Department of Electrical and Computer Engineering Korea Republic of Inha University Program in Semiconductor and Device Korea Republic of

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377088

This paper presents a novel design technique for reducing ripple in various sensor read-out integrated circuits (IC) by calibrating the amplifier offset through current control in bias resistors. Traditional methods for reducing ripple often involve complex amplifiers consuming static power. In contrast, the proposed ripple reduction technique employs successive-approximation register (SAR) logic to control the current flowing through the bias resistor, thus minimizing static power consumption. Experimental results obtained through simulations in a 28 nm CMOS process demonstrate significant ripple reduction to 8.94 μVpp from 2 mVpp. This approach is suitable for low-power design, particularly in wearable device applications, and can potentially extend to other amplifier configurations utilizing chopping technique. © 2024 IEEE.

关键词： Chopper amplifiers

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Real Time Chest X-Ray Interpretation Applying Vision Transformer for Tuberculosis Detection

Real Time Chest X-Ray Interpretation Applying Vision Transfo...

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2024 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Irhamsyah, Muhammad Roslidar Yunidar Al Lail Qadrillah, M. Nasaruddin Arnia, Fitri Munadi, Khairul Universitas Syiah Kuala Post Graduate Program in Engineering Banda Aceh2311 Indonesia Faculty of Engineering Department of Electrical and Computer Engineering 2311 Indonesia

ISBN: (纸本)9798350368802

Tuberculosis (TB) is an infectious disease that can attack the lungs and other organs. In the diagnosis of TB, the interpretation of X-ray radiological images requires special skills from medical specialists. However, the lack of experience of medical personnel and focus on X-ray examinations can be a challenge for radiologists because it has the potential to cause diagnostic errors in image interpretation. Therefore, we proposed a web-based application of real-time chest X-ray interpreter by integrating the deep learning model of Vision Transformer (ViT) to interpret chest X-ray images. The inference model was trained and tested on 652 normal and 652 tuberculosis X-ray images. The data preprocessing of Contrast Adaptive Histogram Equalization (CLAHE) was implemented to increase chest X-ray image contrast as well as inference model performance. The proposed real time system can inteprete the tested chest x-ray images with an accuracy of 95% and with simulation duration time of 4.7 seconds. © 2024 IEEE.

关键词： Diagnosis

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