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Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

computer Modeling in engineering & sciences 2025年第5期143卷 2459-2489页

作者： Karim Gasmi Olfa Hrizi Najib Ben Aoun Ibrahim Alrashdi Ali Alqazzaz Omer Hamid Mohamed O.Altaieb Alameen E.M.Abdalrahman Lassaad Ben Ammar Manel Mrabet Omrane Necibi Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia REGIM-Lab National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia College of Computing and Information Technology University of BishaBisha61922Saudi Arabia Cybersecurity Department College of Engineering and Information TechnologyBuraydah Private CollegesBuraydah51418 Saudi Arabia Department of Computer Science Prince Sattam bin Abdulaziz UniversityAl-Kharj1942Saudi Arabia

The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical *** self-reporting is the foundation of conventional pain assessment methods,which may be *** learning is a promising alternative to resolve this limitation through automated pain *** paper proposes an ensemble deep-learning framework for pain *** framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)*** integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)*** then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s *** improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature *** enables us to reduce the features’dimensionality without sacrificing the classification’s *** improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning *** our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment ***,due to differences in validation protocols,comparisons with previous studies are still *** deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification *** evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.

关键词： Pain assessment ensemble learning deep learning optimal algorithm feature selection

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Parasitic Parameters Effect on the Operation of High Frequency Electric Drive System

Parasitic Parameters Effect on the Operation of High Frequen...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Abdo, Fawzy A. Tawfiq, Kotb B. Sergeant, Peter Systems and Metal Engineering Ghent University Department of Electromechanical Ghent9000 Belgium Flanders Make@UGent Core Lab MIRO Leuven3001 Belgium Menoufia University Department of Electrical Engineering Menoufia32511 Egypt Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350370607

The rapid switching of semiconductor power modules in electric drive systems generates high frequency (HF) common mode voltage at the motor terminals which causes bearing failure and winding degradation. Therefore, accurate characterization of the inverter and motor parameters - including parasitic effects - is vitally important to understand the high frequency performance as well as to model and mitigate the switching oscillations of the electric drive system. In this paper, the high frequency operation of the inverter in the electric drive system, as well as the direct effect of the parasitic components on the complete system are thoroughly investigated. Moreover, a detailed extraction process of the high frequency parasitic parameters is introduced. An equivalent circuit model of the complete drive system based on the impedance characteristics is introduced. Additionally, a HF model of a 750W three-phase induction motor is developed with parameter values determined from measurements on this motor. © 2024 IEEE.

关键词： Terminals (electric)

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IoT-Enabled Driver Drowsiness Detection Using Machine Learning

IoT-Enabled Driver Drowsiness Detection Using Machine Learni...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Mrinmoy Guria Biswajit Bhowmik Department of Computer Science and Engineering National Institute of Technology Karnataka BRICS Laboratory Mangalore Bharat

ISBN: (纸本)9781665454025

The most important procedure for preventing traffic accidents in recent years, maybe on a global scale, is the identification of sleepy drivers. Every day, over 350 people are killed in traffic accidents, and almost 1,000 more suffer injuries. Recent technological advancements could reduce this tendency by 40%. It is still possible to get these benefits despite significant challenges. This paper develops an intelligent alerting method to prevent accidents caused by drivers falling asleep at the wheel. As part of smart cars, the proposed method with the total capacity prevents sleepy driver impairment automatically. The proposed approach detects drowsiness in analyzing the live streaming of drivers’ videos. Eye Aspect Ratio (EAR) and the Euclidean distance of the eye are used to analyze the input video stream to identify sleepy drivers. Experimental results show that the proposed scheme can lower dangerous accidents and injuries caused by road traffic.

关键词： Road accidents Machine learning algorithms Roads Wheels Machine learning Prediction algorithms Automobiles

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Exploiting Differential-Based Data Encoding for Enhanced Query Efficiency 25

Exploiting Differential-Based Data Encoding for Enhanced Que...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Liu, Fangxin Wang, Zongwu Xu, Peng Huang, Shiyuan Jiang, Li Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Qi Zhi Institute China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China

ISBN: (纸本)9798400706356

Storing large-scale high-dimensional data, which is rapidly generated by both industry and academia, poses substantial challenges, primarily in terms of storage and maintenance costs. While data compression techniques offer a potential solution to these challenges, they must overcome two critical hurdles: 1) preserving data integrity within lossless bounds and 2) maintaining query performance on compressed *** address the above challenges, we propose a novel approach based on differential techniques that combine high-dimensional data compression with an efficient query engine. Specifically, Our methodology begins by employing vector quantization to transform high-dimensional vectors into compact integer sequences, known as quantization codes - -a classical and versatile compression method. Subsequently, we introduce DCQ, which builds upon the differential concept to perform lossless compression on quantization codes. DCQ organizes quantization codes into a tree structure and constructs a hierarchy of trees by analyzing the dissimilarity between two quantization codes. To minimize the impact on query performance, we have developed an adaptive reconstruction algorithm that partitions and reconstructs the original tree structure. This algorithm effectively balances the workload for searching each sub-tree, optimizing the compression-performance trade-off. Finally, we adopt a storage format based on multiple subtrees for rapid searching, thereby enabling differential storage to facilitate efficient queries. Extensive experiments on various large-scale real-world datasets show that DCQ achieves a compression ratio of up to 2.5 on the quantization codes and achieves > 50× performance improvement compared to the state-of-the-art general-purpose lossless compression techniques. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Trees (mathematics)

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Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound 22

Exploring Test Time Adaptation for Subcortical Segmentation ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Omolegan, Joshua Yeung, Pak Hei Wyburd, Madeleine K. Hesse, Linde Haak, Monique Namburete, Ana I. L. Dinsdale, Nicola K. University of Oxford Oxford Machine Learning in NeuroImaging Lab United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore Leiden University Medical Center Department of Obstetrics and Fetal Medicine Netherlands

ISBN: (纸本)9798331520526

Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions is a challenging task, but recent work has shown that it can be automated using deep learning. However, applying pretrained models to unseen freehand US volumes often leads to a degradation of performance due to the vast differences in acquisition and alignment. In this work, we first demonstrate that test time adaptation (TTA) can be used to improve model performance in the presence of both real and simulated domain shifts. We further propose a novel TTA method by incorporating a normative atlas as a prior for anatomy. In the presence of various types of domain shifts, we benchmark the performance of different TTA methods and demonstrate the improvements brought by our proposed approach, which may further facilitate automated monitoring of fetal brain development. Our code is available at https://***/joshuaomolegan/TTA-for-3D-Fetal-Subcortical-Segmentation. © 2025 IEEE.

关键词： Image segmentation

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GUI: A Comprehensive Dataset of Global Urban Infrastructure Based on Geospatial Visual Foundation Models 24

GUI: A Comprehensive Dataset of Global Urban Infrastructure ...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Han, Zhenyu Zhang, Xin Xi, Yanxin Luo, Yan Xia, Tong Li, Yong Department of Electronic Engineering BNRist Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Computer Science University of Helsinki Helsinki Finland Media Lab Massachusetts Institute of Technology Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798400711077

The substantial social and financial costs of infrastructure identification impede in-depth analyses of sustainable urban design, especially in developing countries. In this paper, we present a novel framework with interactive web visualization based on geospatial visual foundation models. Leveraging this framework, we examine the urban infrastructure information in 1,178 cities worldwide, covering 93, 088 km2 areas. Cross-validation reveals that the overall accuracy of identified infrastructure achieves 67.0%. It sheds light on the sustainable development of cities and exposes the stark inequity in urban infrastructure provision for vulnerable populations. The identified urban infrastructure dataset of this study are available at https://***/tsinghua-fib-lab/GUI, and the interactive web application is at https://***/yz7xbfy3. © 2024 Copyright held by the owner/author(s).

关键词： Artificial Intelligence

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Design and Development of Novel Routing Protocols for Ubiquitous Network 7

Design and Development of Novel Routing Protocols for Ubiqui...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Chavan, Chandrashekhar Pomu Talabattula, Srinivas Pes University Department of Computer Science and Engineering Bangalore India Electrical Communication Engineering Indian Institute of Science Applied Photonics Lab Bangalore India

ISBN: (纸本)9781665421683

In this paper, we designed and developed ECA (Event Condition Action) scheme based novel Improved AODV Position and Speed Aware Routing (ECA-IAODV-PSAR) and Improved AODV Edge Connectivity (ECA-IAODV-EC) routing protocols. In specific, these two routing protocols use the speed and position information of the node received from the Global Positioning System (GPS) to deal with routing control packet overhead during inflated mobility. The primary routing protocol ECA-IAODV-PSAR is an alteration of the PSAR routing protocol. The ECA-IAODV-PSAR protocol set limits flooding of the route query packet to the tiny zone of the ubiquitous network to select the optimal route towards the ultimate destination. The results produced in the simulation demonstrate the feasibility of the scheme proposed. © 2022 IEEE.

关键词： Routing protocols

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Generating Por table Test Cases for IEC 61499 FBs from Interface Behaviour Specifications 28

Generating Por table Test Cases for IEC 61499 FBs from Inter...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Wiesmayr, Bianca Xavier, Midhun Patil, Sandeep Zoitl, Alois Vyatkin, Valeriy Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden Johannes Kepler University LIT CPS Lab Linz Austria Johannes Kepler University CDL VaSiCS Linz Austria Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9798350339918

IEC 61499 is an executable, event-based language for control software that allows visual and textual implementation of individual software components (Function Blocks, FBs). The standardized visual service sequence model specifies the expected input/output behaviour of a component, thus supporting model-based testing. We present our approach for testing an FB on various platforms, which helps manage the variations in execution semantics between different vendors. First, service sequences are generated manually or derived from an existing (partial) implementation. Then, these service sequences serve as unit tests for this implementation. Finally, we create a test application that is executable on any IEC 61499-compliant platform. Executing tests directly in the target platform helps validate the correct functionality of an FB before deploying the control software to a cyber-physical system. © 2023 IEEE.

关键词： Software testing

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CPPJoules: An Energy Measurement Tool for C++

arXiv

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arXiv 2024年

作者： Shivadharshan, S. Akilesh, P. Chattaraj, Rajrupa Chimalakonda, Sridhar Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

With the increasing complexity of modern software and the demand for high performance, energy consumption has become a critical factor for developers and researchers. While much of the research community is focused on evaluating the energy consumption of machine learning and artificial intelligence systems—often implemented in Python—there is a gap when it comes to tools and frameworks for measuring energy usage in other programming languages. C++, in particular, remains a foundational language for a wide range of software applications, from game development to parallel programming frameworks, yet lacks dedicated energy measurement solutions. To address this, we have developed CPPJoules, a tool built on top of Intel-RAPL to measure the energy consumption of C++ code snippets. We have evaluated the tool by measuring the energy consumption of the standard computational tasks from the Rosetta Code repository. The demonstration of the tool is available at https://***/watch?v=GZXYF3AKzPk and related artifacts at https://***/CPPJoules/. © 2024, CC BY.

关键词： C++ (programming language)

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An AI Architecture for Power MOS Rds-on-Driven Lifetime Estimation

An AI Architecture for Power MOS Rds-on-Driven Lifetime Esti...

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2023 Smart Systems Integration Conference and Exhibition, SSI 2023

作者： Pino, Carmelo Rundo, Francesco Spampinato, Concetto Battiato, Sebastiano STMicroelectronics Adg Power and Discretes R&d Division Catania Italy University of Catania PerCeiVe Lab Group Department of Electrical Electronics and Computer Engineering Catania Italy University of Catania IP-LAB Group Department of Mathematics and Computer Science Catania Italy

ISBN: (纸本)9798350325065

The components lifetime estimation is one of the main challenge in all engineering process specially in power electronics. The problem involves several domains of interest and is particularly felt, because, the estimation of the residual life with an acceptable degree of confidence, allows to carry out preventive measures in order to avoid the consequences of the malfunction and failure of the device at the end of its life. Considering a Power MOS, the distinctive element for understanding the end of life of this component can be obtained by observing the Rdson curve during its life. The Rdson curve works in an operating range during the correct life cycle, this curve exceeds a certain threshold when the device begins to have the first signs of degradation. The proposed method allows to evaluate the variation of the Rdson by making predictions for understand when the device may stop working well. The method is based on a deep learning model trained with a dataset containing the cycle of life of several Power MOS. The pipeline is based on Artificial Intelligence techniques with regularization of the Jacobian of the gradient, designed to compensate for the inevitable measurement and conversion noises present in the system based on ST MCU. The experiments concern predictions of the Rdson at 10, 15 and 20 steps and allow to obtain a good degree of accuracy for predicting the RDS on at least 20 samples before. This allows to guarantee a preventive intervention when the prediction exceed the threshold considered for the correct operating range. The proposed pipeline allows to robustly characterize the threshold voltage of the device by means of a simple acquisition of voltages and currents at poweron, being able to monitor the evolution of this voltage over time and each time the device is used. The obtained results and the cross-validation performances are very promising and allow us to state that the proposed method is really robust. © 2023 IEEE.

关键词： Threshold voltage

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