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Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data

Bag-Level Multiple Instance Learning for Acute Stress Detect...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Brügge, Nele Sophie Korda, Alexandra Borgwardt, Stefan Andreou, Christina Giannakakis, Giorgos Handels, Heinz German Research Center for Artificial Intelligence AI in Medical Image and Signal Processing Lübeck Germany Translational Psychiatry Department of Psychiatry and Psychotherapy University of Luebeck Lübeck23562 Germany Heraklion Greece Department of Electronic Engineering Hellenic Mediterranean University Chania Greece Institute of Agri-food and Life Sciences University Research and Innovation Center Hellenic Mediterranean University Heraklion Greece Institute of Medical Informatics University of Luebeck Lübeck Germany

Stress detection is a complex challenge with implications for health and well-being. It often relies on sensors recording biomarkers and biosignals, which can be uncomfortable and alter behaviour. Video-based facial feature analysis offers a noninvasive alternative. This study explores video-level stress detection using top-k Multiple Instance Learning applied to medical videos. The approach is motivated by the assumption that subjects partly show normal behaviour while performing stressful experimental tasks. Our contributions include a tailored temporal feature network and optimised data utilisation by additionally incorporating bottom-k snippets. Leave-five-subjects-out stress detection results of 95.46% accuracy and 95.49% F1 score demonstrate the potential of our approach, outperforming the baseline methods. Additionally, through multiple instance learning, it is possible to show which temporal video segments the network pays particular attention to. © 2025 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Adversarial machine learning

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Orientation-aware People Detection and Counting Method based on Overhead Fisheye Camera

Orientation-aware People Detection and Counting Method based...

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IEEE international conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Cao, Hu Peng, Boyang Jia, Linxuan Li, Bin Knoll, Alois Chen, Guang Tech Univ Munich Chair Robot Artificial Intelligence & Realtime Sy Munich Germany Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

The rise of intelligent vision-based people detection and counting methods will have a significant impact on the future security and space management of intelligent buildings. The current deep learning-based people detection algorithm achieves state-of-the-art performance in images collected by standard cameras. Nevertheless, standard vision approaches do not perform well on fisheye cameras because they are not suitable for fisheye images with radial geometry and barrel distortion. Overhead fisheye cameras can provide a larger field of view compared to standard cameras in people detection and counting tasks. In this paper, we propose an orientation-aware people detection and counting method based on an overhead fisheye camera. Specifically, an orientation-aware deep convolutional neural network with simultaneous attention refinement module (SARM) is introduced for people detection in arbitrary directions. based on the attention mechanism, SARM can suppress the noise feature and highlight the object feature to improve the context focusing ability of the network on the people with different poses and orientations. Following the collection of detection results, an internet of Things (IoT) system based on Real Time Streaming Protocol (RTSP) is constructed to output results to different devices. Experiments on three common fisheye image datasets show that under low light conditions, our method has high generalization ability and outperforms the state-of-the-art methods.

关键词： Focusing Streaming media Cameras Feature extraction internet of Things Convolutional neural networks Servers

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Emergent Converging Technologies and Biomedical systems Select Proceedings of the 2nd international conference, ETBS 2022

Emergent Converging Technologies and Biomedical Systems Sele...

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Emergent Converging Technologies and Biomedical systems Select Proceedings of the 2nd international conference, ETBS 2022

ISBN: (纸本)9789819922703

The proceedings contain 55 papers. The special focus in this conference is on Emergent Converging Technologies and Biomedical systems. The topics include: Development of a Coconut De-Husking Machine;health Sector: An Overview of Various Smartphone Apps;survey on IoT based Secure Health Care Framework Using Block Chain technology;a Methodology for Increasing Precision for the Efficient Implementation of Sigmoid Function;fog-based Smart Healthcare Architecture in IoT Environment;COVID-19 and SDG 3 in Bangladesh: A Statistical and Machine Learning Approach;Analysis of Power Consumption and IoT Devices Using SPSS Tool;analyzing Optimal Environment for the Text Classification in Deep Learning;estimating Factors of Agile Software Development Using Fuzzy Logic: A Survey;hyperspectral image and Deep Learning Methodology for Water Evaporation Prediction and Control System;computational System based on Machine Learning with Hybrid Security Technique to Classify Crime Offenses;Analysis of Robotically Controlled Percutaneous Needle Insertion into Ex Vivo Kidney Tissue for Minimally Invasive Percutaneous Nephrolithotomy (PCNL) Surgery;location-based Attendance Monitoring System with Facial Authentication;load Balancing in Cloud Computing Using Multi-agent-based Algorithms;Viable Methods Adopted for Reducing SAR Value in Mobile Phone Antenna: A Review;numerical Modelling of Cu2O-based Gas Sensor for Detection of Volatile Organic Compounds in Human Breath;significance of Convolutional Neural Network in Fake Content Detection: A Systematic Survey;wavelet and Savitzky–Golay Filter-based Denoising of Electrocardiogram signal: An Improved Approach;Heart Disease Detection Using Phonocardiogram (PCG) signals;enigmas of Various Techniques to Implementing Authentication and Integrity in Blockchain-based Wireless Sensor Networks;data Integration in IoT Using Edge Gateway.

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Machine Learning and internet of Things-based Driver Safety and Support System 8

Machine Learning and Internet of Things-based Driver Safety ...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Rakesh, S. Jeyageetha, K. Nishant, S. Nageswari, D. Prakash, R.B.R. Mary, S. Suma Christal Nitte Institute of Professional Education Nitte University Nitte Institute of Professional Education Mangalore campus pumpwell circle Mangalore India Ramco Institute of Technology Department of Computer Science and Engineering Virudhunagar district Rajapalayam India St. Joseph's College of Engineering Electrical & Electronics Engineering Omr Chennai119 India Rmk College of Engineering & Technology Gummidipoondi Taluk Thiruvallur Puduvoyal601206 India Koneru Lakshmaiah Education Foundation Electrical and Electronics Engineering Vaddeswaram522302 India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India

ISBN: (纸本)9798350396638

Accident patterns can be determined through driving analysis. In environmental, safety, and fleet management applications, accident detection can decrease potential risks to vehicles and drivers, enhance service quality, and save money. Earlier automatic accident detection methods relied on traffic data or external sensors. However, traffic data is difficult to obtain, and external sensors might be untrustworthy and inconvenient. Since traditional techniques have been constrained by a shortage of accident data, cutting-edge technologies such as machine learning (ML) have been underutilized. The most challenging aspect of the task is detecting roadside barriers. The proposed idea is to create something that can identify barriers while driving a car. A Raspberry Pi Camera module aids in the object detection and identification process. The most effective technique for detecting image boundaries is determined by testing on an example image. This study recommends an Advanced Classifier to determine different categories. The proposed technology can identify cars, animals, people, etc. If an object is in the user's route, the system will warn them to slow down. In particular, the proposed Auto-Alert uses both CNN and LSTM models. Importantly, this requires the design and proof of concept of a credible traffic monitoring and density estimation system. The proposed work shows how important CNN and LSTM networks are to help drivers in traffic. Results show that the suggested method is more accurate and faster than the present methodology. © 2023 IEEE.

关键词： Object detection

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Deep Learning-based Adaptive Semantic Coding for image Transmission in Satellite-Integrated internet

Deep Learning-based Adaptive Semantic Coding for Image Trans...

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IEEE international conference on Communications in China (ICCC)

作者： Guangwei Yuan Jian Jiao Heqing Zhang Ke Zhang Ye Wang Qinyu Zhang Peng Cheng Laboratory Shenzhen China Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Satellite-integrated internet will enable image transmission for ubiquitous terrestrial users in extreme climates, environments, and terrain in the upcoming 5G advanced (5GA) and 6 G network. However, the challenges posed by the limited onboard resources and bandwidth of satellite-to-ground link (SGL) in satellite-integrated internet present significant obstacles for image transmission. To address these challenges, we propose a deep learning-based adaptive semantic coding network (ASCN) for image transmission in satellite-integrated internet over shadowed-Rician (SR) channel. Our ASCN utilizes a single deep neural network (DNN) to adaptive adjust transmission rate based on input image features and time varying channel state information (CSI) under three types of SR fading levels. Specifically, we design a SR channel ModNet to match the semantic of transmitted image features based on CSI, which can achieve an optimized tradeoff between the image reconstruction quality and network utility. Experimental results demonstrate that our ASCN can effectively learn to match the semantic of transmitted image features based on CSI under three types of SR fading levels, and the image reconstruction quality significantly outperforms the state-of-the-art schemes.

关键词： Fading channels Measurement Adaptive systems image coding image communication Semantics Noise

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Passage Detection of a Train via a Reference Point

Passage Detection of a Train via a Reference Point

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international conference on Advanced Engineering Theory and Applications, AETA 2022

作者： Rejfek, Lubos Pidanic, Jan Stursa, Dominik Nguyen, Tan N. Tran, Phuong T. Nemec, Zdenek Zalabsky, Tomas Faculty of Electrical Engineering and Informatics University of Pardubice nám. Čsl. Legií 595 Pardubice530 02 Czech Republic Wireless Communications Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City729000 Viet Nam Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819987023

A reference point detection system for position validation of a mobile object was developed for verification of experiments. The detection is based on a classic image processing algorithm and a processing algorithm using neural networks. Both approaches are compared. High-precision concept of the system is based on a camera sensor and automatic processing of video frames for position evaluation. The designed system was tested on a real application proving correct operation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Smart Traffic System with Green Time Optimization Using Fuzzy Logic 1st

Smart Traffic System with Green Time Optimization Using Fuzz...

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1st EAI international conference on Intelligent systems and Machine Learning, ICISML 2022

作者： Kavin, A.K. Radha, R. Prasad, Vishnu Murali, Bharathwaj School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9783031350771

Traffic Congestion has been a very big issue due to the large number of vehicles being utilized in cities. It is necessary to control the number of vehicles getting in and out of the crossing such that the city traffic congestion decreases in an efficient way. In this paper, we try to improvise the timings of green signals using fuzzy logic based on the number of vehicles, speed limit, and length of the crossing. We find out the number of vehicles using computer vision dynamically. We validate our system in two different locations in Chennai against the manual green signal duration that is set manually and we prove that our method improves the waiting time significantly. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： internet of things

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Research on Brain-Computer Interface Classification Algorithm Supported by Motion Imagination

Research on Brain-Computer Interface Classification Algorith...

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Computer Applications and Information technology (ISCAIT), international Symposium on

作者： Hulin Jin Zhiran Jin Yong-Guk Kim Yutong Zhou Kang Zhang School of Computer Science and Technology Anhui University Hefei China Foothill Preparatory School Temple City California USA Department of Computer Engineering Sejong University seoul South Korea

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

In recent years, brain-computer interface, as a new way of human-computer interaction, is considered to be another important technological revolution after the internet, mobile communication and the internet of Things. The performance of BCI system depends on the performance of signal classification algorithm. From the perspective of classification algorithm, it is difficult for traditional signal analysis methods to be directly applied to brain-computer interface systems, so new methods need to be studied to solve the above problems. In recent years, brain-computer interface technology has been widely concerned by scholars at home and abroad. In this paper, we introduce the classical method of motion image signal analysis, and give a new classification algorithm based on fuzzy logic reasoning.

关键词： Fuzzy logic Support vector machines Accuracy Transforms Feature extraction Brain-computer interfaces Cognition Classification algorithms signal analysis Principal component analysis

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Adaptive Resolution Imaging Algorithm for Helicopter-Borne Arc Antenna Array based on Grid Layout Optimization 14

Adaptive Resolution Imaging Algorithm for Helicopter-Borne A...

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14th international conference on signal Processing systems, ICSPS 2022

作者： Zhiqi, Gao Shumeng, Liu Huang, Ping-Ping Yun, Su College of Information Engineering University of Technology Inner Mongolia Hohhot010080 China Technology and Application Inner Mongolia Key Laboratory of Radar Inner Mongolia Hohhot010051 China

ISBN: (纸本)9798350336313

The imaging of arc array radar is complicated due to its special structure. Backward projection (BP) is a high-resolution imaging algorithm, which can solve the coupling problem of range and azimuth in the arc antenna array SAR imaging. However, BP algorithm has huge computation and nonuniform image resolution distribution in the imaging of arc array antenna radar. In this paper, an improved BP algorithm for arc antenna array imaging is studied. By redesigning and optimizing the grid layout, the image resolution balance in the arc imaging region can be achieved, and the grid layout can be adjusted according to the required image resolution adaptively. The computation of the algorithm before and after adaptive adjustment are analyzed and compared, which shows the computation of the improved algorithm is reduced significantly. Finally, the effectiveness of the proposed method is verified by simulation. © 2022 IEEE.

关键词： image resolution

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3rd international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2021

3rd International Conference on 3D Imaging Technologies- Mul...

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3rd international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2021

ISBN: (纸本)9789811924477

The proceedings contain 24 papers. The special focus in this conference is on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning. The topics include: Grid False Data Intrusion Detection Method based on Edge Computing and Federated Learning;comparative Analysis of Automatic Poetry Generation systems based on Different Recurrent Neural Networks;citrus Positioning Method based on Camera and Lidar Data Fusion;network Intrusion Detection based on Apriori-Kmeans Algorithm;research on the Adaptive Matching Mechanism of Graphic Design Elements based on Visual Communication technology;research on Graphic Design of Digital Media Art based on Computer Aided Algorithm;water Environmental Quality Assessment and Effect Prediction based on Artificial Neural Network;full-Focus Imaging Detection of Ship Ultrasonic-Phased Array based on Directivity Function;research on Visual Communication of Graphic Design based on Machine Vision;dynamic Grey Wolf Optimization Algorithm based on Quasi-Opposition Learning;innovative Design of Traditional Arts and Crafts based on 3D Digital technology;Insulator Detection Study based on Improved Faster-RCNN;image-based Physics Rendering for 3D Surface Reconstruction: A Survey;Global Analysis of Discrete SIR and SIS systems;a Fast Heuristic k-means Algorithm based on Nearest Neighbor Information;Color Restoration of RGB-NIR images in Low-Light Environment Using CycleGAN;preface;longitudinal Structure Analysis and Segmentation Algorithm of Dongba Document;development of Mobile Food Recognition System based on Deep Convolutional Network;a Novel Space Division Rough Set Model for Feature Selection;Overview of SAR image Change Detection based on Segmentation;image Recognition Methods based on Deep Learning.

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