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Finite-time Frequency Estimator for Harmonic Signal

IFAC-PapersOnLine 2020年第2期53卷 584-589页

作者： Alexey A. Bobtsov Anastasiia O. Vediakova Nikolay A. Nikolaev Olga V. Slita Anton A. Pyrkin Alexey A. Vedyakov Department of Control Systems and Robotics ITMO University Saint Petersburg Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Applications and Systems Saint Petersburg State University Saint Petersburg Russia

This paper is devoted to a frequency estimation of a pure sinusoidal signal in finite-time. The parameterization is based on applying delay operators to a measurable signal. The result is the first-order linear regression model with one parameter, which depends on the signal frequency. The proposed method of finite-time estimation consists of two steps. On the first step, the standard gradient descent method is used to estimate the regression model parameter. On the next step using algebraic equations, finite-time frequency estimate is found. The described method does not require measuring or calculating derivatives of the input signal and uses one integrator for the gradient method and another one for the finite-time estimation. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

关键词： parameter estimation observers frequency estimation finite-time estimation

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Robust Output Regulation of Permanent Magnet Synchronous Motors by Enhanced Extended Observer ⁎

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IFAC-PapersOnLine 2020年第2期53卷 4881-4886页

作者： Oleg I. Borisov Anton A. Pyrkin Alberto Isidori ITMO University Faculty of Control Systems and Robotics St. Petersburg Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia University of Rome “La Sapienza” Department of Computer Control and Management Engineering “A. Ruberti” Rome Italy

This paper presents an application of the enhanced extended observer design with regard to the problem of robust output regulation of permanent magnet synchronous motors. The control framework is further advanced by means of the internal model so as to cope with disturbances represented by the time-varying load torque. The paper shows simulation of regulating a motor for different cases of torque loads and references and provides an explicit procedure of the recursive calculation of the enhanced extended observer coefficients.

关键词： Dayawansa’s lemma Robust observers Nonlinear systems High-gain observers Permanent magnet synchronous motors

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A global to local double embedding method for multi-person pose estimation

arXiv

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arXiv 2021年

作者： Xu, Yiming Li, Jiaxin Peng, Yiheng Ding, Yan Wei, Hualiang Yingcai Honors College University of Electronic Science and Technology of China Chengdu China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China School of Computer Science and Technology Donghua University Shanghai China Department of Automatic Control and Systems Engineering University of Shefield ShefieldS1 3JD United Kingdom

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Intelligent Synthesis of Individual Rehabilitation Plans for Patients after Joint Endoprosthesis Based on computer Vision Technologies

Intelligent Synthesis of Individual Rehabilitation Plans for...

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International Conference on Information Technologies in engineering Education (Inforino)

作者： D.G Arseniev V.V. Borisov A.E. Misnik E.A Gomzhina M.A. Shalukhova Scientific Innovation Institute “Mathematical Modeling and Intelligent Control Systems” Peter the Great St. Petersburg Polytechnic University St. Petersburg Russian Federation Department of Computer Engineering National Research University “MPEI” Branch in Smolensk Smolensk Russia Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University” Mogilev Belarus Early Medical Rehabilitation FSBI “FCTOE” of the Ministry of Health of the Russian Federation Smolensk Russian Federation Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University Peter the Great St. Petersburg Polytechnic University Mogilev Belarus

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article presents an innovative approach to the rehabilitation process for patients with joint endoprosthetics, focusing on the integration of a specialized software module based on computer vision technologies and the ontological approach. Developed for the Federal Centre for Traumatology, Orthopaedics, and Endoprosthesis in Smolensk, Russia, this system is designed to improve the quality of life for patients by offering personalized rehabilitation plans. The core of the study lies in the application of ontological models and meta-associative graphs for synthesizing individual rehabilitation plans, which streamline the recovery process and reduce the workload on healthcare professionals. This method allows for the processing of large amounts of information, providing detailed conclusions and recommendations for functional correction of the musculoskeletal system. The software utilizes computer vision to analyze patient movement and develop tailored rehabilitation strategies, overcoming the limitations of traditional diagnostic tools like MRI, which may not be suitable for all patients. By incorporating artificial intelligence and augmented reality, the system enhances the accuracy of patient assessments and the effectiveness of rehabilitation plans. Furthermore, the article discusses the application of this technology in the educational process at the Belarusian-Russian University, where students are introduced to cutting-edge software tools that assist in creating personalized rehabilitation plans. This educational integration not only enriches the students’ learning experience but also fosters innovation in the development of new applications for computer vision in medical rehabilitation. The ontological approach, combined with modern technology, represents a significant advancement in the management of musculoskeletal system diseases, offering a more efficient, personalized approach to rehabilitation and recovery.

关键词： computer vision Musculoskeletal system Technological innovation Computational modeling Medical services Propulsion Trajectory

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Management of services of a hyperconverged infrastructure using the coordinator 1st

Management of services of a hyperconverged infrastructure us...

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1st International Conference on computer Science, engineering and Education Applications, ICCSEEA2018

作者： Rolik, Oleksandr Telenyk, Sergii Zharikov, Eduard Department of Automation and Control in Technical Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Theoretical Electrical Engineering and Computer Science Faculty of Electrical and Computer Engineering Cracow University of Technology Cracow Poland

ISBN: (纸本)9783319910079

Modern data centers’ providers are gradually moving away from traditional and multi-vendor IT infrastructures to open, standardized and interchangeable solutions that are based on a software defined approach to managing data center resources. The authors analyze the architectural features, requirements, limitations, hardware and software of hyperconverged infrastructures and their advantages in comparison with traditional and converged architectures deployed in data centers. The authors propose to employ two-level coordination schema to manage compute, storage, network and virtualization subsystems of hyperconverged infrastructure along with the self-management algorithms inside these subsystems. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Digital storage

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Adaptive fuzzy convolution networks for uncertainty-aware image analysis in ambiguous environments

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Expert systems with Applications 2025年 292卷

作者： Saeed Iqbal Xiaopin Zhong Muhammad Attique Khan Zongze Wu Amir Hussain Shrooq Alsenan Weixiang Liu College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia School of Computing Merchiston Campus Edinburgh Napier University Edinburgh EH10 5DT Scotland UK Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh 11671 Saudi Arabia

In complex and ambiguous situations, effective image analysis is severely hampered by the instability and unpredictability of convolutional frameworks and fuzzy systems, especially when it comes to managing noise-induced uncertainties and nonlinear interactions. The innovative framework Adaptive Fuzzy Convolution Networks (AFCN) is presented in this paper to tackle the problems of nonlinear interactions and noise-induced uncertainties in image processing in ambiguous situations. To improve feature extraction and uncertainty management, the suggested technique combines context-aware fuzzy aggregation operators, adaptive kernel parameterization, and dynamic nonlinearity modulation. Stability in fuzzy systems under different noise situations is ensured by the framework’s use of a feedback-driven method to modify the nonlinearity parameter ( γ k ) depending on noise characterisation. Multiscale fuzzy convolution with adaptive kernel parameters (center ξ s , k ( x ) and spread σ s , k ( x ) ) that react to localized uncertainty gradients are also incorporated into the model, allowing for reliable feature extraction in noisy environments. A hybrid regularization approach that combines fuzzification and L 2 regularization is presented in order to smooth uncertainty propagation over multiscale convolutions and reduce overfitting. The capacity of AFCN to dynamically adjust to noise while maintaining structural integrity and reducing error measures like F-MAE and F-MSE is what makes it innovative. State-of-the-art performance is demonstrated by experimental assessments on several datasets, with an accuracy of 97.2 %, an F1 Score of 97.3 %, and exceptionally low error metrics like F-MAE of 0.005 and F-MSE of 0.010. These developments make AFCN a potent instrument for practical uses like autonomous driving, where resilience to background noise is crucial, and medical imaging, where accurate feature recognition is crucial.

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A spectral small-gain condition for input-to-state stability of infinite networks

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IFAC-PapersOnLine 2020年第2期53卷 5303-5308页

作者： Christoph Kawan Andrii Mironchenko Abdalla Swikir Navid Noroozi Majid Zamani Institute of Informatics Ludwig Maximilian University Munich Germany Faculty of Computer Science and Mathematics University of Passau Germany Department of Electrical and Computer Engineering Technical University of Munich Germany Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Germany Computer Science Department University of Colorado Boulder USA

This paper presents a tight small-gain theorem for networks composed of infinitely many finite-dimensional subsystems. Assuming that each subsystem is exponentially input-to-state stable, we show that if the gain operator, collecting all the information about the internal Lyapunov gains, has a spectral radius less than one, the overall infinite network is exponentially input-to-state stable. We illustrate the effectiveness of our result by applying it to traffic networks.

关键词： Nonlinear systems small-gain theorems infinite-dimensional systems input-to-state stability Lyapunov methods large-scale systems

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Bio-inspired polarization event camera

arXiv

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arXiv 2021年

作者： Haessig, Germain Joubert, Damien Haque, Justin Chen, Yingkai Milde, Moritz B. Delbruck, Tobi Gruev, Viktor AIT Austrian Institute of Technology Center for Vision Automation & Control High-performance Vision Systems Vienna Austria Institute of Neuroinformatics University of Zurich ETH Zurich Switzerland International Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Sydney Australia Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States

The stomatopod (mantis shrimp) visual system0 has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors0, enabling solutions to challenging medical0 and remote sensing problems0. However, these bioinspired sensors lack the high dynamic range (HDR) and asynchronous polarization vision capabilities of the stomatopod visual system, limiting temporal resolution to ∼12 ms and dynamic range to ∼72 dB. Here we present a novel stomatopod-inspired polarization camera which mimics the sustained and transient biological visual pathways to save power and sample data beyond the maximum Nyquist frame rate. This bio-inspired sensor simultaneously captures both synchronous intensity frames and asynchronous polarization brightness change information with sub-millisecond latencies over a million-fold range of illumination. Our PDAVIS camera is comprised of 346x260 pixels, organized in 2-by-2 macropixels, which filter the incoming light with four linear polarization filters offset by 45°. Polarization information is reconstructed using both low cost and latency event-based algorithms and more accurate but slower deep neural networks. Our sensor is used to image HDR polarization scenes which vary at high speeds and to observe dynamical properties of single collagen fibers in bovine tendon under rapid cyclical loads. © 2021, CC BY.

关键词： Cameras

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EEG-based affective brain-computer interfaces: recent advancements and future challenges

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Journal of neural engineering 2025年第3期22卷 2025 Jun 9页

作者： Yuxin Chen Yong Peng Jiajia Tang Tracey A Camilleri Kenneth P Camilleri Wanzeng Kong Andrzej Cichocki Hangzhou Dianzi University Hangzhou dianzi University Hangzhou 310018 CHINA. Hangzhou Dianzi University Hangzhou Dianzi University Hangzhou 310018 CHINA. School of Computer Science and Technology Hangzhou Dianzi University 1158 No.2 Avenue Hangzhou 310018 CHINA. Department of Systems and Control Engineering University of Malta University of Malta Msida 2080 MALTA. Centre for Biomedical Cybernetics Department of Systems and Control Engineering University of Malta University of Malta Msida 2080 MALTA. School of Computer Science Hangzhou Dianzi University Hangzhou Dianzi University Hangzhou 310018 CHINA. Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw 01-447 POLAND.

As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e., depression, anxiety) are detected, which are considered as the two basic functions of aBCI system. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems. Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders. Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information','outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis a

关键词： Affective brain-computer interface EEG Emotion recognition and regulation Inter-subject variability

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Automatic brain tumor segmentation using 3D architecture based on ROI extraction

Automatic brain tumor segmentation using 3D architecture bas...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Huang, Jing Zheng, Minhua Liu, Peter X. Beijing Jiaotong University School of Mechanical Electronic and Control Engineering No. 3 Shangyuancun Haidian District Beijing100044 China Carleton University Department of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9781728163215

MRI (magnetic resonance image) analysis is crucial for diagnosis, monitoring, and treatment of brain tumors. Manual MRI segmentation of brain tumors requires professional knowledge and costs huge amount of time. Automatic segmentation for multimodal 3D MR images is thus very desirable for clinical applications. In this paper, we present an automatic brain tumor segmentation method based on the U-Net architecture, which is composed of ROI (region of interest) extraction and 3D segmentation networks with long and short skip connections. The introduced method is able to capture detailed features and improves the problem of the imbalanced classification of tumors' sub-regions. The method was evaluated on the BRATS 2017 dataset and the results were promising. © 2019 IEEE.

关键词： Magnetic resonance imaging

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