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43rd International Conference on Application and Theory of Petri Nets and Concurrency , Petri Nets 2022

作者： Strand, Ruben Dobler Kristensen, Lars M. Petrucci, Laure Department of Safety Chemistry and Biomedical Laboratory Sciences Western Norway University of Applied Sciences Haugesund Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway LIPN CNRS UMR 7030 Université Sorbonne Paris Nord Villetaneuse France

ISBN: (纸本)9783662681909

Long periods of dry and cold weather conditions significantly increase fire risks for wooden buildings. Recent advances in predictive fire risk models combined with publicly available cloud-based weather data services have enabled the development of smart software systems for location-oriented fire risk notification. We have developed a Coloured Petri Net (CPN) model specifying the software architecture of a microservice-based predictive fire risk notification system. The CPN model captures the set of micro-services provided via REST APIs and the interaction between the constituent services for location tracking and subscription, fire risk computation and data harvesting. As part of the work, we have applied an agile-oriented implementation-first modelling approach and developed a general pattern for REST-based APIs. We apply simulation and state space exploration to validate and verify key behavioural properties of the predictive fire risk notification system. © 2024, The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature.

关键词： Software architecture

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Biological age estimation from ECG using Artificial Intelligence approaches

Biological age estimation from ECG using Artificial Intellig...

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biomedical engineering Conference (JIBEC), Jordanian International

作者： Jana Sleiman Mohamad Daher Mohamad Khalil Ahmad Diab Abdallah Kassem Computer and Communications Engineering Lebanese University Tripoli Lebanon Dep. of Computer Science/Dep. of Computer Science & Information Technology Faculty of Science/Faculty of Applied Sciences & Technology Tripoli Lebanon Electrical Engineering Lebanese University Tripoli Lebanon Biomedical Engineering Lebanese International University Beirut Lebanon ECCE Dept. Faculty Of Engineering Notre Dame University Zouk Mosbeh Lebanon

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R 2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R 2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age.

关键词： Deep learning Maximum likelihood estimation Accuracy Data analysis Biological system modeling Medical services Electrocardiography Data models Convolutional neural networks Random forests

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Camouflage-based location privacy preserving scheme in vehicular ad hoc networks

Camouflage-based location privacy preserving scheme in vehic...

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作者： Benarous, Leila Kadri, Benamar Boudjit, Saadi Department of Computer Science LIM Laboratory Faculty of Science University of Amar Telidji Laghouat Algeria LISSI-Tinc-NET Laboratory University of Paris-Est Creteil Vitry-sur-SeineF-94400 France Department of Biomedical Engineering Faculty of Technology University of Tlemcen Chetouane Algeria L2TI Laboratory University of Paris 13 Villetaneuse93430 France

The location privacy is critical and preserving it is essential. The tracking exposes the real time location, history of visited places and parsed trajectories. Metaphorically speaking, it is the cyber equivalent of the physical stalking and as dangerous as it is. In vehicular networks in particular, this issue is serious because the autonomous vehicles periodically and timely transmit their locations, headings, speed and identity to neighbour vehicles and/or service infrastructures. To preserve the location privacy, various pseudonymbased approaches exist, mainly focusing on unlinkable pseudonym change strategies. In this paper, we propose a camouflage-based solution that prevents the linkability of pseudonyms upon their update even within low density roads where the tracking chances are high. The solution is simulated using NS2 against a global passive attacker that executes the semantic and syntactic linking attacks. The results demonstrate the effectiveness of the solution in protecting the privacy. © 2023 Inderscience Enterprises Ltd.

关键词： Autonomous vehicles

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基于侧基多通道和对称性破缺效应的分子电导调控

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science China Materials 2024年第6期67卷 1994-1999页

作者：郝杰汪博宇赵璁霍雅妮王进莹贾传成郭雪峰 Center of Single-Molecule Sciences Institute of Modern OpticsFrontiers Science Center for New Organic MatterTianjin Key Laboratory of Micro-scale Optical Information Science and TechnologyCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin 300350China Network for Computational Nanotechnology School of Electrical and Computer EngineeringPurdue UniversityIndiana 47907USA Beijing National Laboratory for Molecular Sciences National Biomedical Imaging CenterCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China

化学取代是有效调节分子电导,产生多通道输运、量子干涉效应和其他机制的关键方法.但分子骨架和侧基之间存在复杂的相互作用,目前仍未被研究清楚.在本工作中,我们结合扫描隧道显微镜裂结实验技术和第一性原理计算方法,深入研究了具有不... 详细信息

化学取代是有效调节分子电导,产生多通道输运、量子干涉效应和其他机制的关键方法.但分子骨架和侧基之间存在复杂的相互作用,目前仍未被研究清楚.在本工作中,我们结合扫描隧道显微镜裂结实验技术和第一性原理计算方法,深入研究了具有不同σ和π侧基的分子电导.实验结果及理论分析表明,σ侧基的加入引起了分子前线轨道的对称性破缺并加剧了分子内散射,从而导致电导的降低.相反,π侧基引入了额外的电荷传输通道,实现了电荷传输的多通道效应,增加了分子的电导.这些发现为未来分子功能化和分子电子器件的设计提供了坚实的基础.

关键词： side substituent multiple-channel effect symmetry-breaking effect single-molecule junction

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Using Swarm Intelligence Algorithms for Optimization in IoT Applications 10

Using Swarm Intelligence Algorithms for Optimization in IoT ...

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Roshanzamir, Mohamad Nematollahi, Mohammad Ali Darbandy, Mohammad Tayarani Roshanzamir, Mahdi Alizadehsani, Roohallah Shoeibi, Afshin Ahmadian, Davood Fasa University Faculty of Engineering Department of Computer Engineering Fasa74617-81189 Iran Fasa University Faculty of Science Department of Computer Sciences Fasa74617-81189 Iran Islamic Azad University Taft School of Architecture Taft*** Iran University of Tabriz Department of Electrical and Computer Engineering Tabriz Iran Victoria Australia K. N. Toosi University of Technology FPGA Laboratory Faculty of Electrical Engineering Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Sciences Tabriz Iran

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Internet of Things (IoT) is considered as the next industrial revolution in the years ahead. Many devices will have been connected to the Internet and more and more devices were added to them. The number of these types of devices is increased rapidly. So, management of them needs advanced technologies. Optimization of different problems in IoT is an essential subject of many researches and Swarm Intelligence algorithms (SI) are one of the most successful methods in this field. They were inspired by the collective behavior of simple creatures which communicate directly and indirectly with each other. Although they are considered as simple agents, their collective behavior is smart. They can overcome most problems in their life using collective behavior. In this research, we investigated some of the most common optimization problems in IoT and the SI algorithms which are used to solve them. © 2022 IEEE.

关键词： Internet of things

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Trimmed Non-Local Means Filtering for the Suppression of Mixed Noise in Color Images

Trimmed Non-Local Means Filtering for the Suppression of Mix...

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electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Damian Kusnik Bogdan Smolka Milena Smolka Faculty of Automatics Electronics and Informatics Silesian University of Technology Gliwice Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland

In this paper, a novel approach to the problem of mixed Gaussian and impulsive noise reduction in color images is proposed. The elaborated denoising framework is based on the Non-Local Means technique, which has been shown to effectively suppress Gaussian noise. To overcome the inability of this filter to deal with impulsive distortions, a robust similarity measure between image patches, that is insensitive to the impact of impulsive distortions, was developed. First, the distorted pixels of the image are detected using the concept of a peer group. If the number of neighbors of a pixel at the center of a filtering window, whose Euclidean distance in the RGB color space does not exceed a threshold value, is smaller than a predefined value, then the considered pixel is treated as corrupted. Afterwards, the similarity between image patches are calculated in a standard way omitting the noisy pixels. In this way, only the average of distances between pairs of unaffected pixels are calculated. Thus, the closeness measure of the patches, which serves as the coefficient in weighted averaging of pixels in the processing block, is not affected by impulsive noise and is still able to cope with the Gaussian noise component. The proposed filtering design is capable of reducing even strong mixed noise while preserving image details and edges. Moreover, it is not computationally intensive, so it can be used in real-time image processing scenarios.

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Adaptive Technique of Impulsive Noise Removal in Color Digital Images

Adaptive Technique of Impulsive Noise Removal in Color Digit...

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International Conf on electrical and Electronic engineering (ICEEE)

作者： Bogdan Smolka Monika Mendrela Damian Kusnik Milena Smolka Faculty of Automatics Electronics and Informatics Silesian University of Technology Gliwice Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland

This paper describes a new technique for removing impulsive noise in digital color images. The proposed method uses a measure of the pixel distortion caused by the impulsive noise, expressed as the sum of the Euclidean distances between the central pixel and its closest neighbors in the filtering window. This corruption measure is used to determine a weighted average of the pixels from the local neighborhood that is not affected by the distorted pixels. The filtering output is obtained using a soft switching scheme, where the denoising result is determined as a weighted average of the central pixel of a processing window and its robust estimate. The weighting coefficient is calculated based on the amount of distortion of the central pixel. Thus, if the central pixel is highly distorted, it is replaced by its robust estimate, otherwise it is not significantly changed. The parameters that determine the image enhancement result are adaptively adjusted by using a noise-resistant image contrast measure. The proposed filter design is able to effectively suppress impulsive noise while preserving fine image details and edges. Comparison with existing algorithms shows that the proposed method provides better denoising results than the most efficient filters designed for impulsive noise suppression in color images. Moreover, the new method is very fast and can be used in real-time denoising scenarios.

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