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Tm-doped Chirped-pulse-amplification Fiber Laser System at 1.9 μm

Tm-doped Chirped-pulse-amplification Fiber Laser System at 1...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Xu, Shutao Turnali, Ahmet Lim, Timothy Sander, Michelle Y. Department of Electrical and Computer Engineering BU Photonics Center Boston University BostonMA02215 United States Division of Materials Science and Engineering Boston University BrooklineMA02446 United States Department of Biomedical Engineering Boston University BostonMA02215 United States

We report a high-energy Tm-doped chirped-pulse-amplification fiber laser system seeded by dissipative solitons at 1902 nm. The system provides output pulses with a pulse energy of 120 nJ and a pulse duration of 940 fs... 详细信息

ISBN: (纸本)9781957171258

关键词： Fiber lasers

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SF(DA)2: SOURCE-FREE DOMAIN ADAPTATION THROUGH THE LENS OF DATA AUGMENTATION 12

SF(DA)2: SOURCE-FREE DOMAIN ADAPTATION THROUGH THE LENS OF D...

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12th International Conference on Learning Representations, ICLR 2024

作者： Hwang, Uiwon Lee, Jonghyun Shin, Juhyeon Yoon, Sungroh Division of Digital Healthcare Yonsei University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domains without requiring access to source domain data. Although the potential benefits of applying data augmentation to SFDA are attractive, several challenges arise such as the dependence on prior knowledge of class-preserving transformations and the increase in memory and computational requirements. In this paper, we propose Source-free Domain Adaptation Through the Lens of Data Augmentation (SF(DA)2), a novel approach that leverages the benefits of data augmentation without suffering from these challenges. We construct an augmentation graph in the feature space of the pretrained model using the neighbor relationships between target features and propose spectral neighborhood clustering to identify partitions in the prediction space. Furthermore, we propose implicit feature augmentation and feature disentanglement as regularization loss functions that effectively utilize class semantic information within the feature space. These regularizers simulate the inclusion of an unlimited number of augmented target features into the augmentation graph while minimizing computational and memory demands. Our method shows superior adaptation performance in SFDA scenarios, including 2D image and 3D point cloud datasets and a highly imbalanced dataset. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep learning

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Comparative Analysis of Clusterization Methods Applied to a Solid-State LiDAR

Comparative Analysis of Clusterization Methods Applied to a ...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Henrique N. Poleselo Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Object segmentation is one of the main activities for the robot to create a sense of its environment. This task is a precursor to other activities, such as autonomous navigation in a given environment. Through sensors such as LiDAR, it's possible to generate high-resolution three-dimensional maps of the environment in which the robot is located, thus enabling their interpretation so that tasks such as object segmentation can be performed. In this article, the DBSCAN and HDBSCAN unsupervised clustering methods are explored. Results in a simulated environment in Gazebo together with Robot Operating System ROS framework for capturing sensory data from LiDAR Livox Mid-70 coupled to a mobile robot show the performance of such techniques through comparisons.

关键词： Laser radar Operating systems Clustering methods Object segmentation Robot sensing systems Sensors Mobile robots Autonomous robots

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Automatic Identification of Abnormal Lung Sounds Using Time-Frequency Analysis and Convolutional Neural Network 15

Automatic Identification of Abnormal Lung Sounds Using Time-...

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15th International Conference on Information Technology and electrical engineering, ICITEE 2023

作者： Phettom, Rattanathon Theera-Umpon, Nipon Auephanwiriyakul, Sansanee Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand Graduated School Chiang Mai University Chiang Mai Thailand Department of Electrical Engineering Chiang Mai University Chiang Mai Thailand Department of Computer Engineering Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9798350304466

This research focuses on the development of a method utilizing signal processing and machine learning techniques to identify abnormal lung sounds, specifically adventitious lung sounds, for diagnosis and monitoring. The proposed algorithm combines short-time Fourier transform (STFT) with convolutional neural networks (CNN) to automatically analyze breath sounds captured by a stethoscope. By employing a band pass filter, noise is effectively removed, facilitating accurate identification of lung sounds. The algorithm classifies abnormal lung sounds, such as crackles and wheezes, with an impressive accuracy rate of 85.27%. This research not only enhances the efficiency of physical examinations but also enables the recording and analysis of lung sounds, thereby offering valuable insights into the progression of treatments. Furthermore, the development of this medical device has significant implications for advancing human healthcare and information retrieval in the field of respiratory medicine. © 2023 IEEE.

关键词： Convolution

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Scalable machine learning architecture for neonatal seizure detection on ultra-edge devices 2

Scalable machine learning architecture for neonatal seizure ...

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2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022

作者： Nagarajan, Vishal Muralidharan, Ashwini Sriraman, Deekshitha Kumar, Pravin S. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Biomedical Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

ISBN: (纸本)9781665442909

Neonatal seizures are a commonly encountered neurological condition. They are the first clinical signs of a serious neurological disorder. Thus, rapid recognition and treatment are necessary to prevent serious fatalities. The use of electroencephalography (EEG) in the field of neurology allows precise diagnosis of several medical conditions. However, interpreting EEG signals needs the attention of highly specialized staff since the infant brain is developmentally immature during the neonatal period. Detecting seizures on time could potentially prevent the negative effects on the neurocognitive development of the infants. In recent years, neonatal seizure detection using machine learning algorithms have been gaining traction. Since there is a need for the classification of bio-signals to be computationally inexpensive in the case of seizure detection, this research presents a machine learning (ML) based architecture that operates with comparable predictive performance as previous models but with minimum level configuration. The proposed classifier was trained and tested on a public dataset of NICU seizures recorded at the Helsinki University Hospital. Our architecture achieved a best sensitivity of 87%, which is 6% more than that of the standard ML model chosen in this study. The model size of the ML classifier is optimized to just 4.84 KB with minimum prediction time of 182.61 milliseconds, thus enabling it to be deployed on wearable ultra-edge devices for quick and accurate response and obviating the need for cloud-based and other such exhaustive computational methods. © 2022 IEEE.

关键词： Electrophysiology

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Fuzzy Support Vector Regression with Grey Wolf Optimization in Stock Market Prediction

Fuzzy Support Vector Regression with Grey Wolf Optimization ...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Titimakan Sonsupap Sansanee Auephanwiriyakul Nipon Theera-Umpon Computer Engineering Department Faculty of Engineering Graduate School Chiang Mai University Chiang Mai Thailand Computer Engineering Department Faculty of Engineering Biomedical Engineering Institute Biomedical Engineering and Innovation Research Center Chiang Mai University Chiang Mai Thailand Electrical Engineering Department Faculty of Engineering Biomedical Engineering Institute Biomedical Engineering and Innovation Research Center Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

The stock market price is influenced by many factors both domestic and international factors. To help stock traders in make buying or selling decision, a stock price prediction model is needed. In this paper, a stock closing price prediction system is introduced. We utilized 17 financial factors, i.e., 8 technical analysis factors (Crude oil price, Gold spot, Thai Baht to US Dollar exchange rate, High price, Low price, Opening price, Closing price, and Volume) and 9 fundamental factors (Exponential moving average, Relative strength index, Current ratio, Return on equity ratio, Return on assets ratio, Net profit margin ratio, Debt to equity ratio, Price to earning ratio, and Price to book value) from 18 previous working days as our inputs. We then computed chaos centroids from each factor. Finally, to predict the Monday closing price, the fuzzy support vector regression with the grey wolf optimization is utilized. There are 29 companies in 6 industry groups, i.e., RESOURCE, SERVICE, INDUS, PROPCON, AGRO, and TECH, considered in this research. We found that the minimum root mean square error of the blind test set is 0.3698 and the maximum of that is 11.66. However, the prediction trend of each company is very similar to the real closing price.

关键词： Support vector machines Industries Gold Oils Companies Market research Indexes

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HyperScore: A unified measure to model hypertension progression using multi-modality measurements and semi-supervised learning

HyperScore: A unified measure to model hypertension progress...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Alkhodari, Mohanad Lapidaire, Winok Xiong, Zhaohan Kart, Turkay Iturria-Medina, Yasser Hadjileontiadis, Leontios Khandoker, Ahsan Lewandowski, Adam J. Banerjee, Abhirup Leeson, Paul University of Oxford Cardiovascular Clinical Research Facility Radcliffe Department of Medicine Oxford United Kingdom Khalifa University Healthcare Engineering Innovation Center Department of Biomedical Engineering Abu Dhabi United Arab Emirates Imperial College London Biomedical Image Analysis Group Department of Computing London United Kingdom McGill University Neuroinformatics for Personalized Medicine Lab Montreal Canada Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece University of Oxford Institute of Biomedical Engineering Department of Engineering Science Oxford United Kingdom

ISBN: (纸本)9798350337488

Hypertension is a serious medical condition that affects over a billion people worldwide. The proper management of disease progression requires an extended knowledge of the overall functional and structural changes in the whole body in response to the hypertension. Here, we propose HyperScore, an integrative and unified measure of hypertension progression relative to multi-organ and multi-modality clinical measurements and based on a semi-supervised machine learning (ML) approach. We developed the measure based on a large participating cohort from the UK Biobank database (n=27,099) with over 500 imaging and clinical variables from multiple modalities. The semi-supervised approach was developed based on the contrastive trajectory inference mechanism to provide a score that reflects the proximity of a participant to the disease state (range: 0-1). Modelling revealed that majority of hypertensive participants had scores above 0.25, whereas normotensives had scores below this threshold. The sensitivity and specificity were above 89%, with an area under the receiver operating characteristics of 96.4%. The modelling showed a stable performance when evaluating hidden testing sets on a 10-fold cross-validation scheme with nearly 0.1 error. There was a strong association (r2>0.6) between HyperScore and organs' phenotypic patterns, especially for variables such as white matter hyperintensity and body mass index. This study is the first to potentiate ML-based modelling of hypertension progression from a multi-organ perspective, which could significantly aid in clinical decision making to save lives. © 2023 IEEE.

关键词： blood pressure contrastive PCA Hypertension machine learning semi-supervised learning

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PERFORMANCE OPTIMIZATION OF THE STAGNANT CAP HYDRODYNAMIC RETARDATION EFFECT DETECTOR (SHRED) 26

PERFORMANCE OPTIMIZATION OF THE STAGNANT CAP HYDRODYNAMIC RE...

引用

26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Fatima, Afreen Basu, Amar S. Department of Electrical and Computer Engineering Wayne State University DetroitMI United States Department of Biomedical Engineering Wayne State University DetroitMI United States

ISBN: (纸本)9781733419048

SHRED detects analytes through a unique mechanism based on the stagnant cap retardation effect. This paper discusses methods to enhance device performance by studying the effect of the geometry and pressure conditions on signal amplitude, speed and stability. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Drops

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Intelligent ADHD Detection Using Explainable Multimodal Fusion Model

Intelligent ADHD Detection Using Explainable Multimodal Fusi...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ho-Jung Tsai Hsiao-Kuang Wu Chun-Chuan Chen Chen-Hsin Yeh Tzu-Yueh Chu Shih-Ching Yeh Department of Computer Science and Information Engineering National Central University Department of Biomedical Sciences and Engineering National Central University College of Computer Science and Electrical Engineering Hunan University

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Deep learning has been proved to diagnose Attention Deficit/Hyperactivity Disorder (ADHD) accurately, but it has raised concerns about trustworthiness because of the lack of explainability. Fortunately, the development of explainable artificial intelligence (XAI) offers a solution to this problem. In this study, we employed a VR-based GO/NOGO task with distractions, capturing participants' eye movement, head movement, and electroencephalography (EEG) data. We used the collected data to train an explainable multimodal fusion model. Besides classifying between ADHD and normal children, the proposed model also generates explanation heatmaps. The heatmaps provide the importance of specific variables and timestamps in the EEG data to help us analyze the patterns captured by the model. According to our observations, the model identified specific time intervals that related to specific event-related potentials (ERPs) components. The heatmaps also demonstrate that the impacts of distractions vary between not only the GO and NOGO events but also ADHD and normal children.

关键词： Heating systems Deep learning Pediatrics Accuracy Explainable AI Brain modeling Electroencephalography Data models Physiology Information technology

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Field Uniformity Optimization for Integrated Capacitance Sensing of Tumor Treating Field-Treated Cancer Cell Cultures

Field Uniformity Optimization for Integrated Capacitance Sen...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Yann Gilpin Joseph Yankel Marc Dandin Department of Electrical and Computer Engineering Department of Biomedical Engineering Carnegie Mellon University Pittsburgh USA

An emerging modality in cancer treatment is tumor treating fields (TTF). It consists of electric fields of approximately 1 V RMS /cm in strength and of around 200 kHz in frequency. When these fields are aligned with a cancer cell's mitotic spindle, they can impede cell division. This method has improved overall patient survival in glioblastoma patients, remarkably with limited side effects. However there are still many unknowns associated with this therapy. Thus, to assist the discovery effort and facilitate further studies, we previously designed and successfully tested an in vitro integrated capacitance sensing TTF microsystem for autonomous and label-free detection of the effect TTFs on breast cancer cells. Herein, we propose an update to the microsystem's electrode geometry. Our novel approach eases the trade-offs between size, material usage, and consistency of treatment. Based on simulation studies and a physical model, we show that the new approach reduced an applied field's magnitude variance by 63% and angle variance by 54%. These gains should translate to improved repeatability, looser manufacturing tolerances, and smaller designs.

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