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arXiv 2024年

作者： Jang, Hojun Kim, Young Min Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University Korea Republic of

We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a robust spatio-temporal motion prior can encapsulate underlying 3D dynamics applicable to various sensor modalities. We learn the rich motion prior from a sequence of complete parametric models of posed human body shape. Our prior can easily estimate poses in missing frames or noisy measurements despite significant occlusion by employing a temporal attention mechanism. More interestingly, our prior can guide the system with incomplete and challenging input measurements to quickly extract critical information to estimate the sequence of poses, significantly improving the training efficiency for mesh sequence recovery. ReMP consistently outperforms the baseline method on diverse and practical 3D motion data, including depth point clouds, LiDAR scans, and IMU sensor data. Project page is available in https://***/ReMP. Copyright © 2024, The Authors. All rights reserved.

关键词： Reusability

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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION CONTROL & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts. Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan. Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan. Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan. Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan. Infection Control Unit Massachusetts General Hospital Boston Massachusetts. Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts. Harvard Medical School Boston Massachusetts.

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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Comparative Analysis of Real-Time Multi-Person Pose Detection in electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose 4

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Wu, Zong-Rong Lin, Pin-Hsuan Chen, Chin-Hsiung Chien, Wei Chang, Yu-Cheng School of Art and Design Sanming University Fujian365004 China National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan National Central University Department of Business Administration Taoyuan Taiwan Commercial Industrial Vocational Senior High School Department of Electrical Engineering New Taipei Municipal Tamsui New Taipei Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350360721

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy. © 2024 IEEE.

关键词： Accident prevention

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A Fingerprint Based Indoor Visible Light Positioning System for Tilted Receivers Equipped with a Single Photo Detector

A Fingerprint Based Indoor Visible Light Positioning System ...

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IEEE Conference on Wireless Communications and Networking

作者： Ibrahim M. Abou-Shehada Ali H. Muqaibel Wessam Mesbah Ki-Hong Park Mohamed-Slim Alouini Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical Engineering Department Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Indoor positioning systems (IPS) are gaining higher attention recently due to the increased demand for indoor location aware services. Visible light communication (VLC) is a promising technology to use for IPS. In particular, received signal strength (RSS) based visible light positioning (VLP) systems are gaining high attention due to their low complexity and cost, in addition to higher positioning accuracy compared to their radio frequency (RF) counterparts. One of the main challenges in RSS based VLP systems is encountered when the receiver (the target) is tilted and not placed in parallel with the transmitters (the anchors). RSS based trilateration techniques require a computationally expensive and time-consuming process to solve the nonlinear problem of tilted receivers. Fingerprint based systems generally provide high positioning accuracy with short positioning time, and maybe used to circumvent the need to deal with the high complexity associated with tilted receivers. However, the design of a fingerprinting VLP system for tilted receiver has not been explored yet as far as receivers with a single photodetector (PD) are concerned. In this work, a fingerprint based VLP system for tilted receivers using artificial neural networks (ANN) is proposed, where different types of input features for training the positioning algorithm are studied. We show that using the components of the normal vector to the PD's surface in addition to RSS values provides an excellent positioning accuracy with an average positioning error of 25.41 cm and a remarkably low average positioning time less than $\mathbf{5} \boldsymbol{\mu} \boldsymbol{s}$ . In addition, important research directions for future work are discussed.

关键词： Training Accuracy Receivers Artificial neural networks Fingerprint recognition Vectors Real-time systems

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Real Time Monitoring System of Drinking Water Quality Using Internet of Things 24

Real Time Monitoring System of Drinking Water Quality Using ...

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24th International Electronics Symposium, IES 2022

作者： Abidin, Zainul Maulana, Eka Nurrohman, Muhammad Yogi Wardana, Fitri Candra Warsito Universitas Brawijaya Faculty of Computer Science Department of Computer Engineering Malang Indonesia Universitas Brawijaya Faculty of Engineering Department of Electrical Engineering Malang Indonesia Universitas Brawijaya Master Program in Environmental Resources and Development Management Postgraduate School Malang Indonesia Universitas Brawijaya Faculty of Mathematics and Natural Sciences Department of Chemistry Malang Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

In order to reduce Polyethylene Terephthalate (PET) bottle plastic waste, Universitas Brawijaya (UB) provided reverse osmosis drinking water in some buildings. Water monitoring is needed to keep the water quality. Therefore, a water monitoring system based on Total Dissolved Solids (TDS), pH, and water flow was proposed. Since the UB buildings have good internet connection (WiFi), Internet of Things (IoT) was employed to the system to support real time monitoring. So, data of TDS, pH, and water flow can be periodically sent to cloud and displayed to a specific website. The system worked very well and the data can be summarized and used by operator as indicators of performance of the reverse osmosis machine. When water quality and performance decrease, operator must check filters membrane or water pump for further maintenance to support long life use. Although the sensors have been calibrated properly, some measurement errors occur. The measurement error percentages of TDS and pH are 0.62% and 0.94%, respectively. © 2022 IEEE.

关键词： Internet of things

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Dynamic Mesh Recovery from Partial Point Cloud Sequence

Dynamic Mesh Recovery from Partial Point Cloud Sequence

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International Conference on computer Vision (ICCV)

作者： Hojun Jang Minkwan Kim Jinseok Bae Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

The exact 3D dynamics of the human body provides crucial evidence to analyze the consequences of the physical interaction between the body and the environment, which can eventually assist everyday activities in a wide range of applications. However, optimizing for 3D configurations from image observation requires a significant amount of computation, whereas real-world 3D measurements often suffer from noisy observation or complex occlusion. We resolve the challenge by learning a latent distribution representing strong temporal priors. We use a conditional variational autoencoder (CVAE) architecture with a transformer to train the motion priors with a large-scale motion dataset. Then our feature follower effectively aligns the feature spaces of noisy, partial observation with the necessary input for pre-trained motion priors, and quickly recovers a complete mesh sequence of motion. We demonstrate that the transformer-based autoencoder can collect necessary spatio-temporal correlations robust to various adversaries, such as missing temporal frames, or noisy observation under severe occlusion. Our framework is general and can be applied to recover the full 3D dynamics of other subjects with parametric representations.

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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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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Multi-Domain Emotion Recognition Enhancement: A Novel Domain Adaptation Technique for Speech-Emotion Recognition

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 528-541页

作者： Ammar Amjad Sucharita Khuntia Hsien-Tsung Chang Li-Chia Tai Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan Taiwan Department of Physical Medicine and Rehabilitation Chang Gung Memorial Hospital Taoyuan Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

As artificial intelligence advances, speech-emotion recognition (SER) has become a critical research area. Traditional SER methods typically rely on homogeneous domain data for training and testing. This practice requires adaptation when confronted with real-world data's heterogeneous linguistic, methodological, and speaker-related attributes. These variances can degrade the accuracy and generalization of SER models. To address this gap, we introduce a novel domain adaptation technique, multi domain emotion recognition enhancement (MDERE), which utilizes a non-negative matrix to reduce the inflexibility of the conventional binary label matrix for source domain data. This process yields a label matrix that better adapts to the nuances of the source labels while preserving their original structure. This framework refines SER methods by fine-tuning a transformation matrix for enhanced emotion discrimination. Elastic net regularization, which combines L1 and L2 penalties, enriches the transformation matrix, selectively emphasizing relevant features to enhance the robustness of emotion detection. The framework constructs customized similarity and dissimilarity graphs to reconcile the differences between source and target domains, enabling nuanced cross-domain data analysis. Extensive testing on multiple cross-domain SER tasks has shown that MDERE substantially improves recognition accuracy, surpassing several state-of-the-art algorithms. These results demonstrate MDERE's ability to effectively align domain variations enhances the generalizability of SER systems.

关键词： Emotion recognition Speech processing Speech recognition Training Adaptation models Testing Sparse matrices Linear regression Accuracy Vectors

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Ontology-Driven IoT System for Monitoring Hypertension 25

Ontology-Driven IoT System for Monitoring Hypertension

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： de Souza, Pedro Lopes de Souza, Wanderley Lopes Pires, Luís Ferreira Moreira, João Luiz Rebelo da Silva Rodrigues, Ronitti Juner Ciferri, Ricardo Rodrigues Semantics Cybersecurity & Services Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede Netherlands Ubiquitous Computing Group Graduate Program in Computer Science Computing Department Federal University of São Carlos P.O. Box 676 São Carlos Brazil

ISBN: (纸本)9789897586484

Hypertension is a noncommunicable disease (NCD) that causes global concern, high costs and a high number of deaths. Internet of Things, Ubiquitous Computing, and Cloud Computing enable the development of systems for remote and real-time monitoring of patients affected with NCDs like hypertension. This paper reports on a system for monitoring hypertension patients that was built by employing these techniques. This system allows the vital signs of a patient (blood pressure, heart rate, body temperature) to be captured via sensors built in a wearable device similar to a wristwatch. These signals are transmitted to the patient's mobile device for processing, and the generated clinical data are sent to the cloud to be properly presented and analysed by the health professionals responsible for the patient. To deal with semantic interoperability issues that arise when multiple different devices and system components must interoperate, a semantic model was conceived for this system in terms of ontologies for diseases and devices. This paper also presents the semantic module that we developed and implemented in the cloud to perform reasoning based on this model, demonstrating the potential benefits of incorporating semantic technologies in our system. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Ontology

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