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

作者： Kala, Annapurna Chong, Daniel Baqui, Abdullah H. Ahmed, Salahuddin Islam, Asmd Ashraful Chowdhury, Nabid Roy, Arunangshu McCollum, Eric D. Elhilali, Mounya Johns Hopkins University Lab. for Computational Audio Perception Dpt. Electrical & Computer Engineering United States Projahnmo Research Foundation Dhaka Bangladesh Johns Hopkins University Bloomberg School of Public Health Dpt. International Health United States Johns Hopkins School of Medicine Global Program in Pediatric Respiratory Sciences Eudowood Division of Pediatric Respiratory Sciences Dpt. Pediatrics Baltimore United States

ISBN: (纸本)9798350371499

While chest auscultations provide an accessible and low-cost tool for pediatric pneumonia diagnosis, its subjectivity and low reliability continues to hinder its inclusion in global pneumonia guidelines;eventhough more robust tools like chest radiography also suffer from cost and accessibility issues. Advances in computer-aided analytics is offering more robust tools for interpreting digital auscultation signals though little has been done to explore variations of lung sounds across different chest positions and the correspondence between auscultations and specific radiographic findings. The present study explores interpretation of lung auscultations across chest positions in a pediatric pneumonia population, using a deep neural network classification of normal and abnormal breathing patterns. The results reveal a strong alignment between computer-aided auscultation findings and radiographic interpretation not only in terms of presence of adventitious lung patterns, but also in terms of localizing the abnormality along the left or right lung. Though evaluated in a small clinical population, this research underscores the potential of computer-aided auscultation analysis as a cost-effective substitute for radiography in resource-limited settings. © 2024 IEEE.

关键词： Deep neural networks

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Control Design for 3-Phase Bidirectional Battery Chargers with Multiple Battery Charging Capabilities for Electric Vehicle Fleet Applications

Control Design for 3-Phase Bidirectional Battery Chargers wi...

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2021 North American Power Symposium, NAPS 2021

作者： Okon, Queen Urquizo, Javier Kondrath, Nisha Singh, Pritpal College of Engineering Sustainable Engineering Program Villanova University Department of Electrical and Computer Engineering VillanovaPA United States L3Harris Technologies Maritime Power Systems PhiladelphiaPA United States

ISBN: (纸本)9781665420815

This paper describes a control structure for a bidirectional battery charger model design which consists of three-phase PWM ac/DC converters linked to a DC/DC converter with a CLLC-type resonant tank circuit. It presents a control structure that accommodates: constant current, constant voltage (CCCV) charging algorithm of an Energy Storage System (ESS), a multiple battery charger setup, and the automation of the combined control system based upon certain conditions being satisfied. The proposed control scheme was simulated and validated for a system integrating two battery charger modules using the PLECS software tool while charging a Li-ion battery model. The interactions between the two modules during the charging process were investigated with and without the introduction of a DC bus. © 2021 IEEE.

关键词： Charging (batteries)

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Epilayer on NEAT Grown GaN Schottky Diodes for Radiation Detection

Epilayer on NEAT Grown GaN Schottky Diodes for Radiation Det...

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2023 Transactions of the American Nuclear Society Annual Meeting and Technology Expo, ANS 2023

作者： Remy, Jarod Zhang, Yuxuan Talesara, Vishank Zhao, Hongping Lu, Wu Hashimoto, Tadao Cao, Lei R. Nuclear Engineering Program Department of Mechanical and Aerospace Engineering 281 W. Lane Ave. ColumbusOH43210 United States Department of Electrical and Computer Engineering 281 W. Lane Ave ColumbusOH43210 United States Department of Material Science and Engineering The Ohio State University 281 W. Lane Ave ColumbusOH43210 United States SixPoint Materials Inc 37 Industrial Way STE 106 BuelltonCA United States

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Growing Our PEARLS and ASSETS Through a Support Ecosystem for Low-Income Academically Talented Students 54

Growing Our PEARLS and ASSETS Through a Support Ecosystem fo...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Lopez Del Puerto, Carla Jimenez, Manuel Alfaro, Monica Bellido, Carmen Cafaro, Matias J. Univ. of Puerto Rico Mayaguez Civil Engineering Department Mayaguez00681-9000 Puerto Rico Univ. of Puerto Rico Mayaguez Electrical & Computer Eng. Department Mayaguez00681-9000 Puerto Rico Univ. of Puerto Rico Mayaguez Biology Department Mayaguez00681-9000 Puerto Rico Teacher Preparation Program Univ. of Puerto Rico Mayaguez Mayaguez00681-9000 Puerto Rico

ISBN: (纸本)9798350351507

This research to practice paper describes two NSF-funded projects: The 'program.for engineering Access, Retention, and LIATS Success' (PEARLS) and the 'Academic and Socioemotional Support Ecosystem for Talented low-income STEM Students' (ASSETS). These initiatives aim to support low-income, academically talented students pursuing STEM degrees, offering academic, socio-emotional, and financial assistance. Both projects provide mentoring, workshops, mental health services, and scholarships. Outcomes have shown increased engagement, retention, and graduation rates among participants compared to non-participants. Retention and persistence rates among PEARLS participants reached 97% and 96.3% respectively, showcasing the model effectiveness in keeping students focused on their studies. Graduation rates saw remarkable improvements, with on-time graduation rates soaring to 4.17 times that of non-participating students. The paper presents a robust framework for an integrated support ecosystem, emphasizing scalability and replicability across diverse institutions. By addressing the challenges faced by underprivileged students during environmental crises, this framework aims to foster equity and inclusion in higher education, ensuring all students have the support they need to succeed. © 2024 IEEE.

关键词： Students

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Bayesian Parameter Inference for Partially Observed SDEs driven by Fractional Brownian Motion

arXiv

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arXiv 2022年

作者： Maama, Mohamed Jasra, Ajay Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling;the approximation reduces the time (in terms of total observation time T) by O(T). This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error (MSE) versus using a single time discretization. Our methods are illustrated on simulated and real data. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

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Coordinated Feeder Reconfiguration, Capacitor Placement and Allocation of Distributed Energy Resources for Loss Reduction Under Variable Loading Conditions

Coordinated Feeder Reconfiguration, Capacitor Placement and ...

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IEEE Kansas Power and Energy Conference

作者： Meisam Mahdavi Augustine Awaafo Sasa Z. Djokic Konrad Schmitt Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain School of Engineering The University of Edinburgh Edinburgh Scotland UK Electrical & Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock USA

Reduction of active power losses in distribution networks is an important issue, which is usually addressed through a separate, sequential, or simultaneous feeder reconfiguration (FR), volt-var regulation (e.g., capacitor placement, CP), and control of distributed energy resources (DERs). However, finding optimal solution for a coordinated FR, CP, and DER control requires significant computational efforts, which are additionally increased when variations in loading conditions are considered. Therefore, load variations are usually neglected in related analysis, although changes in power flows influence changes in network losses and, consequently, optimal controls for FR, CP and DERs. This paper presents the analysis of both benefits and disadvantages of including not only the peak demands, but also different loading conditions for a coordinated implementation of FR, CP and DER controls for the reduction of losses. The analysis is illustrated on the several distribution test networks and using representative load patterns from the existing literature. The obtained results suggest that the inclusion of load variations requires significantly longer computational times, but in some cases affects optimal solutions for FR, CP and DER controls (their numbers, locations and settings).

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Additive manufacturing for multimodal photoacoustic ophthalmoscopy

Additive manufacturing for multimodal photoacoustic ophthalm...

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Photons Plus Ultrasound: Imaging and Sensing 2023

作者： Haindl, Richard Bellemo, Valentina Rajendran, Praveenbalaji Tan, Bingyao Liu, Mengyang Leitgeb, Rainer Drexler, Wolfgang Schmetterer, Leopold Pramanik, Manojit School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University 62 Nanyang Drive Singapore Singapore Centre for Medical Physics and Biomedical Engineering Medical University of Vienna Währinger Gürtel 18-20/4L Vienna Austria Program Nanyang Technological University Singapore Singapore Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Electrical and Computer Engineering Iowa State University AmesIA United States

ISBN: (纸本)9781510658639

Photoacoustic ophthalmoscopy in rodents is gaining research momentum, due to advancement in transducer shape and technology. Needle transducers emerged as most valuable tool for photoacoustic retinal imaging and have proven to be sensitive enough to resolve retinal vasculature in-vivo. Nevertheless, placement of the eye and screening of the retina remains challenging, since needle transducers must remain static during image acquisition, while the optical field of view is limited. Such restriction mandates movement of the mouse to rotate the eye and therefore the imaging area on the retina. The needle transducer needs to be temporarily detached during this process to avoid damage to the eye or the transducer. Re-attachment involves additional application of ultrasound gel and doesn’t guarantee ideal placement for optimized imaging performance. Additive manufacturing can help to tackle those challenges and allows to design novel rotational rodent holders for imaging. Hence, we present a fully 3D printable rotatable tip/tilt mouse platform with the eye in the center of rotation, combined with a printable needle transducer holder. Such system guarantees optimal placement of the needle transducer during imaging and rotation of the mouse eye, avoiding detachment of the transducer and effortless screening of the retina. The capabilities for retinal screening are demonstrated by a multimodal optical coherence photoacoustic ophthalmoscopy system employing two separated wavelengths, 1310 nm for optical coherence and 570 nm for photoacoustic ophthalmoscopy. © 2023 SPIE.

关键词： Optical tomography

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TinyML for Safe Driving: The Use of Embedded Machine Learning for Detecting Driver Distraction

TinyML for Safe Driving: The Use of Embedded Machine Learnin...

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Metrology for Automotive (MetroAutomotive), IEEE International Workshop on

作者： Thommas Flores Marianne Silva Mariana Azevedo Thais Medeiros Morsinaldo Medeiros Ivanovitch Silva Max Mauro Dias Santos Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal Federal University of Technology Ponta Grossa Brazil

Among the main causes of road accidents, one of the most significant is related to driver distraction while driving, which is responsible for 18% of car accidents worldwide. This situation has demanded the development of mechanisms to automatically detect this dangerous behavior while driving. One of the computational solutions that has been considered viable to detect situations like this is the use of Convolutional Neural Networks (CNN), but some complex issues arise when deploying CNN models in microcontroller-based embedded devices with constrained processing and memory capabilities. In this context, this paper proposes a driver distraction detection system that achieves high accuracy (99.3%) and low latency (72ms) while requiring minimal computational resources (Peak- RAM of 164 KB and Flash of 52.7 KB). This solution exploiting Tiny Machine Learning (TinyML) algorithms was developed with the support of the Edge Impulse platform, used to perform the entire ML pipeline, from data pre-processing and ML model creation to deployment into an Arduino Portenta H7 board. By designing a driver assistance system that can be integrated into vehicles, it is expected that an affordable solution based on embedded machine learning is provided, tackling a real-world problem by potentially reducing accidents caused by driver distractions.

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Edge Device for Ultraviolet Fluorescence Inspection of Photovoltaic Panels

Edge Device for Ultraviolet Fluorescence Inspection of Photo...

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IEEE SENSORS

作者： André Biffe Di Renzo Carlos Ruiz Zamarreño Cicero Martelli Jean Carlos Cardozo Da Silva Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Instituto Federal do Federal do Paraná Curitiba Paraná Brazil Dep. de Ingeniería Eléctrica Electrónica y de Comunicación Universidad Pública de Navarra Pamplona Spain Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Curitiba Paraná Brazil

Regular inspection of photovoltaic panels plays a key role in maximizing performance, ensuring safety, and extending the life of solar plants. This paper presents the construction of a 6W 365 nm ultraviolet light source for ultraviolet fluorescence (UVF) inspections coupled with an edge device used to capture and process the fluorescence images. In addition, an artificial intelligence (AI) algorithm was applied to identify and classify automatically healthy and defective cells in the captured images. The trained AI presents a precision of 89%, and this result shows that the development of an ultraviolet light source coupled with an edge device for automatic cell classification could help with the maintenance staff to make routine UVF inspections to identify possible defects in cell structure, which is the main contribution of the presented work.

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