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European Control Conference (ECC)

作者： Miron Mihaela Anisia Luiza Culea-Florescu Simona Moldovanu Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Dunarea de Jos University of Galati 47 Domneasca Str. Galati Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Galați Dunarea de Jos University of Galati 47 Domneasca Str. Galati Romania

The objective of this paper is to investigate a new technique for skin lesion classification based on discrete wavelet transforms and convolutional networks. More precisely, for preprocessing the images, two 2D family wavelets and Low-Low (LL), High-High (HH) bands were used to generate subimages for each skin lesion image as melanoma and normal nevi. On each subimage datasets of nevi and melanoma, five convolutional neural networks (CNNs) were tested. The experiments were conducted on the public Mednode dataset which contains 170 images (70 melanoma and 100 nevi cases). The accuracy, precision, recall, and F1-score metrics were extracted from confusion matrix for each test performed on the proposed CNN architecture. All metrics show that LL subimages are recommended to be used in classifying melanoma vs. nevi.

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Attention Predictions Related to Cinematic Montage Using Biometric Markers with Application in ADHD Disorder

Attention Predictions Related to Cinematic Montage Using Bio...

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E-Health and Bioengineering Conference (EHB)

作者： Emanuela Motrescu Oana Geman “Babes Bolyai” University Cluj Romania Department of Electronic Computers and Automation NeuroaestheticsLab Faculty of Electric Engineering and Computer Science “Stefan cel Mare” University Suceava Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

The following case study targets the live deconstruction of a visual experience correlating attention-related body sensations that emerge with external visual stimuli resulting the improvement of attention. The attention deficit and hyperactivity disorder (ADHD) is one of the most common mental health disorders among children. It includes symptoms such as difficulty paying attention, hyperactivity and impulsive behavior. The disease appears before age of seven and lasts till adulthood. In contrast with traditional treatment (medication) our work focuses on the usage of biometric marker such as heart rate variability, applied in recovery through other treatments such as art therapy based on cinematic language techniques. We will be able to create a database that can be used to forecast pathologies within the autism spectrum and ADHD using various machine learning algorithm, by expanding the pilot project and including more participants.

关键词： Training Visualization Pediatrics Pathology Autism Art Machine learning algorithms

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Development of the Module «version the Visually Impaired» for the Websites of Organizations 3

Development of the Module «version the Visually Impaired» ...

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3rd International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2021

作者： Chernomordov, Sergey Viktorovich Sinyukov, Alexey Vladimirovich Sinyukova, Tatyana Viktorovna Belyaev, Dmitry Anatolievich Bunin Yelets State University Institute of Mathematics Natural Science and Technology Yelets Russia Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia Lipetsk State Pedagogical University Institute of History Law and Social Sciences Lipetsk Russia

ISBN: (纸本)9781665439817

When a modern user visits a website, it is important not only the quality and volume of information, but also its visual display. This is of particular importance for people with poor and impaired vision. There are various modules 'Version for the visually impaired A fundamentally new plugin is proposed, which, unlike existing analogues, can work with all browsers: ***, Google Chrome, Mozilla Firefox Opera, Microsoft Edge, Internet Explorer. The proposed module is easy to use, has a clear interface, can work both on regular websites and in combination with any CMS. © 2021 IEEE.

关键词： Websites

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Typical curve with G1 constraints for curve completion

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 283-294页

作者： Chuan He Gang Zhao Aizeng Wang Fei Hou Zhanchuan Cai Shaolin Li School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

This paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic *** G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a *** proposed algorithm was applied to a curve completion *** main advantages of the proposed method are its simple construction,compatibility with NURBS,and monotonic curvature.

关键词： Typical curves Monotonic curvature G1 interpolation Curve completion Euler spiral

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Image and Feature Space Based Domain Adaptation for Vehicle Detection

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computers, Materials & Continua 2020年第12期65卷 2397-2412页

作者： Ying Tian Libing Wang Hexin Gu Lin Fan School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China School of Mechanical Engineering and Automation University of Science and Technology LiaoningAnshan114051China Faculty of Business Economics&LawThe University of QueenslandBrisbaneQLD 4072Australia

The application of deep learning in the field of object detection has experienced much ***,due to the domain shift problem,applying an off-the-shelf detector to another domain leads to a significant performance drop.A large number of ground truth labels are required when using another domain to train models,demanding a large amount of human and financial *** order to avoid excessive resource requirements and performance drop caused by domain shift,this paper proposes a new domain adaptive approach to cross-domain vehicle *** approach improves the cross-domain vehicle detection model from image space and feature *** employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image *** feature space,we align feature distributions between the source domain and the target domain to improve the detection *** are carried out using the method with two different datasets,proving that this technique effectively improves the accuracy of vehicle detection in the target domain.

关键词： Deep learning cross-domain vehicle detection

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Safety System for Industrial Robots Based on Human Detection Using an RGB-D Camera

Safety System for Industrial Robots Based on Human Detection...

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Proceedings of the International Convention MIPRO

作者： Valentin Šimundić Dario Mihelčić Damian Svirac Petra Đurović Robert Cupec Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Chair of Automation and Robotics

A robotic system, in general, should be safe for the operators who are using it and, if possible, it should be safe for the system itself and the environment it interacts with. This paper investigates three methods for human detection by an RGB-D camera and their adjustments for monitored robot movement. The methods are implemented in ROS and integrated into a robotic system consisting of an industrial robot arm ABB IRB-2400L and an RGB-D camera. Validation and comparison of the implemented algorithms was performed on the recorded video dataset containing people in the robot working area with per-frame annotations of human presence, velocity and proximity to the robot. The algorithms detect humans with highest recall of 98%.

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 IEEE.

关键词： Stereo image processing

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An e-Business Services Composition Framework

An e-Business Services Composition Framework

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International Conference on Communications (COMM)

作者： Eugen Pop Daniela Gî fu Automatics and Industrial Informatics Department Faculty of Automatic Control and Computers University &#x201C Politehnica&#x201D of Bucharest R&#x0026 D Institute for Automation-IPA SA Bucharest Romania Institute of Computer Science of Romanian Academy - Iasi Branch Faculty of Computer Science of Alexandru Ioan Cuza University of Iasi Iasi Romania

E-business offers to economic agents the opportunity to increase the efficiency of their activity, promote and offer their products and services on a large and diversified market. Often a single service, separately, does not meet all the needs of various categories and groups of customers. In such a situation, service composition can provide more technical, commercial, informational facilities than each separate component service can offer. There are many web-based frameworks used for e-business, but still with limitations. The paper presents a Services Composition Framework and a wise algorithm for e-business models, based on REST and SOAP web services. It is intended to be used for the development of e-business models, comprising stock market exchange, e-commerce, e-banking, etc. The customers can access the framework services, through Smartphones, on mobile and wireless communication channels. Within a Quality-of-Service performance analysis of the e-business Service Composition Framework, the response time and throughput for multiuser access simultaneous requests were evaluated. A stock market exchange service is considered, as an e-business service example, while Bucharest Stock Exchange information is the digital content source for data transactions.

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