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7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Younis, Mohammad Nabil Sleiman, Sary Haj Khadra, Salma Zaylaa, Amira Daher, Alaa Ayache, Mohammad Beirut Arab University Biomedical Program Depatment of Electrical and Computer Engineering Debbieh Lebanon

ISBN: (纸本)9798350325850

Development of Brain computer Interface (BCI) has been rapid since the mid 1990's. There are three criteria for BCI, (i) comfortability and possession of a suitable signal acquisition device, (ii) system validation and dissemination, and (iii) reliability and potentiality. As there are no BCI possessing the optimal criteria, it was essential to consider building a new one. Thereby, the paper investigates building BCI based on the utilization of EEG signals to translate brainwave patterns into actionable commands. The primary objective is to enhance communication capabilities for individuals afflicted with neurological disorders, empowering them to command external devices and engage more effectively with their surroundings. We built our model on EEG online dataset for the purpose of feature extraction and classification. Statistical features and Discrete Wavelet Transform (DWT) have been applied for feature selection. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were the classifiers involved. Results showed that the proposed architecture of MLP and RBF were able to classify the EEG signals into two classes (open eye and closed eye). Results also showed that the proposed approach, which is based on the combination of statistical features and DWT for features selection using AF3 and AF4 channels by the application of MLP, has 98% succession rate. BCI system based on Arduino circuit has been built after the classification Further algorithms and system evaluation need to be considered as future work. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Non-Invasive and Cost-Effective Monitoring of Street Lighting Photometric Performance 11

Non-Invasive and Cost-Effective Monitoring of Street Lightin...

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11th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Khan, Esrat Ba'ba, Leen Alshatri, Ruqaia Parvez, Saba Gad, Abdalla Jama, Mohamed College of Engineering Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362541

The United Arab Emirates has a large and sophisticated street lighting infrastructure;therefore, the photometric performance of the street lighting systems must be prioritized. Locating the malfunctions in a large street lighting infrastructure can be time-consuming and arduous. In this paper, a low-cost street lighting performance monitoring system is designed, implemented, and tested. The proposed system comprises three main components: an illuminance sensor, a spectrometer, and a GNSS receiver. These components build a light and robust system to mount on vehicles' rooftops. Additionally, a comprehensive data analysis and inference engine is developed to determine the photometric performance of the lighting poles by identifying the number and the position of the defective luminaires, under-performing luminaires, poles obstructed by vegetation, and lighting poles that suffer from a complete power outage due to electrical faults in lighting control cabinets. Lastly, the field survey results are demonstrated in reports and interactive online visualization using Google Maps, in which the obtained information can be converted into performance metrics and indexes overlayed on urban maps. © 2024 IEEE.

关键词： Poles

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Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations

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biomedical engineering Frontiers 2022年第1期3卷 381-393页

作者： Erica Skerrett Zichen Miao Mercy N.Asiedu Megan Richards Brian Crouch Guillermo Sapiro Qiang Qiu Nirmala Ramanujam Department of Biomedical Engineering Duke UniversityDurhamNCUSA Department of Electrical and Computer Engineering Purdue UniversityWest LafayetteINUSA Department of Computer Engineering Massachusetts Institute of TechnologyCambridgeMAUSA Department of Electrical and Computer Engineering Department of Biomedical EngineeringDepartment of Computer ScienceDepartment of MathematicsDuke UniversityDurhamNCUSA

Objective and Impact *** use deep learning models to classify cervix images—collected with a low-cost,portable Pocket colposcope—with biopsy-confirmed high-grade precancer and *** boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs,which come at no additional cost to the *** the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low-or middle-Human Development Indices,an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual *** dataset consists of cervical images(n=1,760)from 880 patient *** optimizing the network architecture and incorporating a weighted loss function,we explore two methods of incorporating green light image pairs into the network to boost the classification performance and sensitivity of our model on a test *** achieve an area under the receiver-operator characteristic curve,sensitivity,and specificity of 0.87,75%,and 88%,*** addition of the class-balanced loss and green light cervical contrast to a Resnet-18 backbone results in a 2.5 times improvement in *** methodology,which has already been tested on a prescreened population,can boost classification performance and,in the future,be coupled with Pap smear or HPV triaging,thereby broadening access to early detection of precursor lesions before they advance to cancer.

关键词： boost network backbone

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Detection, Identification and Tracking of Anomaly in Medical Images 1

Detection, Identification and Tracking of Anomaly in Medical...

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1st IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Bouzid-Daho, Abdellatif Siarry, Patrick Faculty of Electrical and Computer Engineering Biomedical Engineering Department Tizi-Ouzou Algeria France

ISBN: (纸本)9798350344158

In this work, we will develop an algorithm based on unsupervised region segmentation for the detection, identification and traking of anomalies in a set of medical images. We created an algorithmic system based on k-means segmentation to identify cancerous regions and track the progression of infected people. Several experiments were carried out based on images to guarantee the reliability of our software tool. The results are satisfactory and robust for aiding medical diagnosis. © 2023 IEEE.

关键词： Diagnosis

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Assessment of Neuromuscular Function in Patients with Long-Term Symptoms of COVID-19 9th

Assessment of Neuromuscular Function in Patients with Lon...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Cremasco, Emilia F. Zambalde, Ellen P. Germer, Carina M. Elias, Leonardo A. Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas Campinas Brazil Department of Biomedical Engineering Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9783031494062

Part of individuals infected by SARS-CoV-2 experiences long-lasting effects that include a wide range of symptoms, with fatigue being one of the most common complaints. Self-reported fatigue can have different manifestations, and although chronic fatigue has been reported as a long-term effect of other viral infections, the etiology of this complaint is still unclear. In this work, we employed an experimental protocol to assess the neuromuscular function in participants who had COVID-19 (12 women, 5–30 weeks after COVID-19 diagnosis) and age-matched controls (9 women). The COVID participants presented a high degree of variability in their symptoms, and the majority (91%) had mild to moderate manifestations. Participants performed an abduction force of the index finger in three different motor tasks, namely isometric force steadiness, reaction time, and time-to-task failure (fatigue task). The experiment was performed in three stages: pre-fatigue, post-fatigue, and after a resting interval of 5 min. We found significant differences between groups for the maximum voluntary contraction, indicating the COVID group had a lower force generation capacity. Nonetheless, no significant difference was found in measures related to force control;however, a larger and more homogeneous number of participants is needed to confirm these observations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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Spatial Variability Learning of Biomechanical Dynamics in Daily Lives 14

Spatial Variability Learning of Biomechanical Dynamics in Da...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Liu, Ming Zhang, Qingxue Purdue School of Engineering and Technology Department of Electrical and Computer Engineering IndianapolisIN United States Purdue School of Engineering and Technology Department of Biomedical Engineering IndianapolisIN United States

ISBN: (纸本)9798350360134

With the rapid development of modern electronics and computation capability, biomechanical mining is attracting more and more attention. Due to the complexity and inter-person variance among the biomechanical dynamics, it is important to study the spatial variability to not only optimize the activity recognition performance but also deepen the understanding of inter-sensor-location and inter-subject differences. We here propose a system with both wearable motion sensing and deep learning, for comprehensive biomechanical dynamics understanding. More specifically, the system allows collection of motion data from diverse body locations. Further, the deep learning algorithm then learns the signals and yields the physical activity types. The experiments have promisingly indicated the spatial variability of biomechanical dynamics capturing and analysis. This study will benefit the understanding of biomechanical dynamics. © 2024 IEEE.

关键词： Dynamics

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A Comparison of Induction Motor Fault Detection Using Single and Double-Channel Phase Currents

A Comparison of Induction Motor Fault Detection Using Single...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wright, Robert Fajri, Poria Fu, Xingang Asrari, Arash University of Nevada Reno Department of Electrical and Biomedical Engineering Reno United States Purdue University Northwest Department of Electrical and Computer Engineering Hammond United States

ISBN: (纸本)9798350376067

Rotating machines, such as Induction Motors (IMs), play critical functions for industrial equipment, owing to their reliability and robustness. However, costly downtime occurs when these motors sustain faults, such as damaged bearings. Condition-Based Monitoring (CBM) methods and early fault detection are paramount in safeguarding machinery from unexpected damage and downtime. With the rapid development of artificial intelligence, Machine Learning (ML) techniques have emerged at the forefront of monitoring the health of IMs. This paper contributes a study comparing single and double-channel phase current approaches towards Convolutional Neural Networks (CNN) employing Short-Time Fourier Transform (STFT) based spectrograms. Motor current signals can be readily and non-invasively collected from the motor compared to other signals. Regarding classification accuracy, the double-channel method is an effective approach for bearing fault classification using motor phase current signals. © 2024 IEEE.

关键词： Convolutional neural networks

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Induction Motor Fault Diagnosis Using Shifting Window Data Augmentation of Current Signals

Induction Motor Fault Diagnosis Using Shifting Window Data A...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

ISBN: (纸本)9798350376067

Deep learning models have been shown to be capable of enhancing Condition-Based Monitoring (CBM) methods for rotating machinery, such as Induction Motors (IMs). However, the performance of these models heavily depends on the training set's size as well as how the signals are presented to the model. Once a model is trained on segmented signals over a fixed period, its accuracy can decline when the system is tested on randomly sampled signals. Meanwhile, models trained using data augmentation techniques can be generalized better to unforeseen conditions. This paper introduces a method to augment the phase current signals during training using a shifting window technique. Regarding classification accuracy, this method is demonstrated as a practical approach for generalizing the model and is shown to perform better than the current techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of a Standalone Hybrid Renewable Energy System - A Hospital in Lafayette, Indiana, USA 5

Design of a Standalone Hybrid Renewable Energy System - A Ho...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Thapa, Samir Asrari, Arash Naderi, Ehsan Fajri, Poria Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States Arkansas State University College of Engineering and Computer Science Department of Electrical Engineering JonesboroAR United States University of Nevada Department of Electrical and Biomedical Engineering RenoNV United States

ISBN: (纸本)9798350379884

This paper presents the design of a standalone hybrid renewable energy system (HRES) for a hospital in Lafayette, Indiana, USA, to enhance energy reliability and reduce costs. The proposed system integrates photovoltaic (PV) panels, wind turbines, batteries, and a backup diesel generator. Using Hybrid Optimization Model for Electric Renewables (HOMER) software, the system is optimized for cost-effectiveness and sustainability, while electrical Transient Analyzer program (ETAP) software is employed to ensure a safe operation under varying conditions. Results verify that the developed HRES meets the hospital's energy needs, significantly reduces operational costs, and minimizes carbon emissions, offering a sustainable model for healthcare facilities. © 2024 IEEE.

关键词： Windmill

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Comparative Analysis of Photoplethysmogram (PPG) Waveform Characteristics Across Various Body Sites Under Normal and Apneic Conditions 46

Comparative Analysis of Photoplethysmogram (PPG) Waveform Ch...

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

作者： Rahme, Joe Saleh, Sahera Al-Sadek, Tamara Amatoury, Jason Khraiche, Massoud American University of Beirut Biomedical Engineering Program Beirut Lebanon American University of Beirut Department of Electrical Engineering Beirut Lebanon

ISBN: (纸本)9798350371499

Detecting sleep apnea through wearable devices poses challenges due to the condition's variability across populations and the inconsistencies in measurements attributed to current wearable technologies. This study aims at comparing photoplethysmogram (PPG) waveform characteristics in healthy subjects, including the change in amplitude, width, and time to peak (Tp) of the signal. PPG signals were recorded at six different body sites (wrist upper, wrist lower, ring finger, thumb, neck, and head) under both simulated normal and apneic conditions. A key objective of this work was to identify optimal LED intensities for detecting these waveform features at each site, providing valuable insights for future development of PPG hardware by pinpointing the most effective intensities. Additionally, the research aims for a better understanding of the variation of the PPG waveform between different body sites. © 2024 IEEE.

关键词： Photoplethysmography

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