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IEEE International Conference on Big Data

作者： Tan Tran Lin-Ching Chang Peter Lum Department of Electrical Engineering and Computer Science The Catholic University of America Washington DC USA Department of Informatics New Jersey Institute of Technology Newark NJ USA Department of Biomedical Engineering The Catholic University of America Washington DC USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors – challenges in capturing the full context of functional movements – this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments.

关键词： Deep learning Accelerometers Torso Pose estimation Manuals Stroke (medical condition) Motors Cameras Wearable devices Videos

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ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization

arXiv

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arXiv 2023年

作者： Shivashankara, Kshama Kodthalu Deepanshi Shervedani, Afagh Mehri Clifford, Gari D. Reyna, Matthew A. Sameni, Reza The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States The Electrical and Computer Engineering Department University of Illinois Chicago ChicagoIL United States The Department of Biomedical Informatics Emory University AtlantaGA United States The Department of Biomedical Engineering Georgia Institute of Technology GA United States

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and the electrocardiogram (ECG) plays a crucial role in diagnosing CVDs. For decades, ECGs have been recorded in printed formats, and their digitization offers significant potential for training machine learning (ML) models in algorithmic ECG diagnosis. However, the valuable information in physical ECG archives risks deterioration, leading to a considerable loss in CVD research, particularly in low and low-middle-income countries where paper-based ECGs are common. Furthermore, merely scanning printed ECGs is inadequate, as most ML models require ECG time-series data. Therefore, digitizing and converting paper ECG archives into time-series data is essential. In this context, deep learning models for image processing are promising. Nonetheless, the scarcity of clinical ECG archives with reference time-series data presents a challenge. To overcome this, data augmentation techniques using digital twins offer a potential solution. We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic ECG images with realistic artifacts from time-series data, and showcase its application in developing algorithms for ECG image digitization. Synthetic data is generated by producing distortionless ECG images on a standard ECG paper background. Subsequently, various distortions, including handwritten text artifacts, wrinkles, creases, and perspective transformations, are applied to these ECG images. The artifacts and text are synthetically generated, excluding personally identifiable information. The toolbox is used for data augmentation in the 2024 PhysioNet Challenge on Digitization and Classification of ECG Images. As a case study, we employed ECG-Image-Kit to create an ECG image dataset of 21,801 records from the PhysioNet QT database [1, 2]. A denoising convolutional neural network (DnCNN)-based model was developed and trained on this synthetic dataset and used to convert the synthetically ge

关键词： Electrocardiograms

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

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International computer Sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. Control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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Observation of Su-Schrieffer-Heeger Topological Model Band Structure in Integrated LNOI Coupled Ring Cavities 16

Observation of Su-Schrieffer-Heeger Topological Model Band S...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Dinh, Hiep X. Balcytis, Armandas Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Rmit University Integrated Photonics and Applications Centre MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan The University of Tokyo Research Center for Advanced Science and Technology Tokyo153-8904 Japan

We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonic integrated circuits

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Age-of-Information and Energy Optimization in Digital Twin Edge Networks

arXiv

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arXiv 2024年

作者： Guo, Yongna Fu, Yaru Zhang, Yan Quek, Tony Q.S. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Informatics University of Oslo Oslo Norway Singapore University of Technology and Design Singapore

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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Weaving Indigeneity and Culture into the Fabric of HCI Futures

Weaving Indigeneity and Culture into the Fabric of HCI Futur...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Farao, Jaydon Pillai, Ajit G. Mthoko, Hafeni Samuel, Marly Muudeni Elmimouni, Houda Lazem, Shaimaa HCI Lab. The University of Cape Town Western Cape Cape Town South Africa Affective Interactions Lab. The University of Sydney SydneyNSW Australia Independent Institute of Education Cape Town South Africa School of Simulation and Visualisation Glasgow School of Art Glasgow United Kingdom Department of Computer Science University of Manitoba WinnipegMB Canada Informatics Research Institute City of Scientific Research and Technology Applications New Borg El-Arab Alexandria Egypt

ISBN: (纸本)9798400713958

Interconnectedness and relationality have become integral to technology development and innovation, which has led to indigenous and cultural philosophies and approaches becoming important in developing effective and inclusive design practices. This workshop challenges the dominant paradigms in Human-computer Interaction (HCI) by exploring the potential of indigeneity and culture to inform design methodologies. We invite participants to reflect on how such approaches can move HCI research toward more inclusive, culturally diverse, and mutually beneficial synergies. Through constructive conflict and co-creation, we aim to illuminate the potential of indigenous and cultural approaches in HCI practice, inspiring vital shifts towards decolonial and pluralistic design. We welcome HCI researchers and practitioners and indigenous scholars and experts interested in disrupting traditional methods and embracing indigenous and cultural lenses to join this critical conversation. © 2025 Copyright held by the owner/author(s).

关键词： community connectedness futuring indigenous practices

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Assessment of Post-Disaster Concerns using Social Network Analysis and Machine Learning: A Study of 2018 Palu Disaster

Assessment of Post-Disaster Concerns using Social Network An...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Tyanita Puti Marindah Wardhani Subhajyoti Samaddar Hirokazu Tatano Zulkifli Tahir Sudip Roy Department of Informatics Faculty of Engineering Universitas Hasanuddin Makassar Indonesia Disaster Prevention Research Institute Kyoto University Kyoto Japan Centre of Excellence in Disaster Mitigation and Management (CoEDMM) Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (数字)9798350367928

ISBN: (纸本)9798350367935

Post-disaster needs assessment plays a critical role in developing emergency response programs and disaster preparedness. An assessment of needs identifies possible interventions or assistance and the necessary resources for emergency responses. This emergency need assessment assists stakeholders in defining possible response options, evaluating the priorities of needs of affected communities, and determining what support will be required immediately. The prevalent use of social media in Indonesia highlights its significance as a crucial means of acquiring prompt, effortless, individualized, and geo-specific information regarding the demands and requirements of communities impacted by a calamity. This study aims to analyze the tweets posted by local communities affected by the 2018 Central Sulawesi earthquakes, tsunami, and liquefaction using the Social Network Analysis (SNA) method based on machine learning and natural language processing. Through this analysis, we intend to observe the spatial interaction and relationship among the affected areas of communities for the needs and concerns after the disaster to enhance the relief operation. We found that social media users, government, and humanitarian organizations effectively shared information during the disaster. This study also underlined that the behavioral responses of individuals to disasters were not limited to the disaster-stricken areas but extended far beyond them. This action suggests that they were willing to exhibit a more empathetic, benevolent, and philanthropic demeanor in the face of such traumatic events.

关键词： Social networking (online) Disasters Government Information sharing Machine learning Tsunami Natural language processing Stakeholders Open data Videos

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