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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Mohammed Saleh Ali Muthanna Abdulfattah Mohammed Obadi Musaeed Ammar Muthanna Maria Filimonova Yaqoob Ahmed Hazzaa Esmail Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Faculty of Infocommunication Networks and Systems Saint -Petersburg State University of Telecommunications Saint Petersburg Russia Department of Information and Communication Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

The growing interest in video-based applications has resulted in a rapid increase in wireless data traffic and meeting the stringent quality-of-experience (QoE) demands for such type of traffic poses a great challenge given the scarce spectrum. Millimeter waves suffers from the huge losses during the propagation in comparison with other communications. However, with smaller cell sizes, spectral efficiency increases, atmospheric absorption and rain do not yield additional losses on the track for cells of 200 m size. Therefore, mmWave communication is mainly used for the internal environment, as well as access to small cells and feedback of data with cell sizes of 200 m. To this end, this paper presents models for transmission of video traffic in 5G networks based on millimeter waves in conjunction with cloud units on RAN boundary to offload the underlaying network. The system achieves higher efficiency in terms of unloading, overloading, coverage and delay. The system is modeled in terms of the probability of blocking for three scenarios i.e. 1) Using mmWave radio interface 2) Using D2D technology 3) Without using D2D technology; each of them is a separate radio interface. The simulation results show that the worst-case scenario is to use a network without D2D and cloud layers.

关键词： Device-to-device communication Millimeter wave communication 5G mobile communication Millimeter wave technology Relays Task analysis Cloud computing

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Research and development of a stereo encoder of a FM-transmitter based on FPGA 1st

Research and development of a stereo encoder of a FM-transmi...

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

作者： Artem, Volokyta Volodymyr, Shymkovych Ivan, Volokyta Vladyslav, Vasyliev Department of Computer Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 37 Prosp. Peremohy Kyiv Ukraine Department of Automation and Control in Technical Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 37 Prosp. Peremohy Kyiv Ukraine Royal Caribbean International FL United States

ISBN: (纸本)9783319910079

This paper describes the research and development of the digital stereo encoder FM transmitter including its implementation on Field Programmable Gate Array (FPGA). The development of structural schemes of the digital part of the FM transmitter exciter, the stereo encoder and the digital synthesizer of the sine wave on the FPGA is also described. Paper includes a description of development of a digital synthesizer of a sinusoidal signal on a FPGA, which uses a tabular method to generate the sinusoidal wave, fourth part of its period is written to memory and the rest of the period is calculated using the first part. To generate five sinusoids of different frequencies in a stereo encoder, only one table in memory used. The simulation of the stereocoder on the FPGA in carried out in the specialized Computer-Aided Design (CAD). The graphs of the left and right balance signals, the sum and difference of these signals, the signal of difference of the two sine waves which have their amplitude modulated and with a carrier signal of 19 kHz and the complex stereo signal are constructed. After receiving a complex stereo signal its frequency modulation is carried out with a carrier signal of 100 MHz, the FPGA chip outputs the frequency code of modulated signal. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Digital signal processing

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Affinely Parametrized State-space Models: Ways to Maximize the Likelihood Function

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IFAC-PapersOnLine 2018年第15期51卷 718-723页

作者： Wills, Adrian Yu, Chengpu Ljung, Lennart Verhaegen, Michel School of Electrical Engineering and Computer Science University of Newcastle Australia School iof Automation Beijing Institute of Technology China Division of Automatic Control Department of Electrical Engineering Linkoping University Sweden Delft Center for Systems and Control Delft University Delft2628CD Netherlands

Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique. The likelihood function is a nonconvex function and care must be exercised in the numerical maximization. Here the focus will be on affine parameterizations which allow some special techniques and algorithms. Three approaches to formulate and perform the maximization are described in this contribution: (1) The standard and well known Gauss-Newton iterative search, (2) a scheme based on the EM (expectation-maximization) technique, which becomes especially simple in the affine parameterization case, and (3) a new approach based on lifting the problem to a higher dimension in the parameter space and introducing rank constraints. © 2018

关键词： Maximum likelihood estimation

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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General higher-order majorization-minimization algorithms for (non)convex optimization

arXiv

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arXiv 2020年

作者： Necoara, Ion Lupu, Daniela Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

Majorization-minimization algorithms consist of successively minimizing a sequence of upper bounds of the objective function so that along the iterations the objective function decreases. Such a simple principle allows to solve a large class of optimization problems, even nonconvex and nonsmooth. We propose a general higher-order majorization-minimization algorithmic framework for minimizing an objective function that admits an approximation (surrogate) such that the corresponding error function has a higher-order Lipschitz continuous derivative. We present convergence guarantees for our new method for general optimization problems with (non)convex and/or (non)smooth objective function. For convex (possibly nonsmooth) problems we provide global sublinear convergence rates, while for problems with uniformly convex objective function we obtain locally faster superlinear convergence rates. We also prove global stationary point guarantees for general nonconvex (possibly nonsmooth) problems and under Kurdyka-Lojasiewicz property of the objective function we derive local convergence rates ranging from sublinear to superlinear for our majorization-minimization algorithm. Moreover, for unconstrained nonconvex problems we derive convergence rates in terms of first- and second-order optimality conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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3D Deep Learning for 3D Printing of Tooth Model

3D Deep Learning for 3D Printing of Tooth Model

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Liu, Yiwen Shang, Xiuqin Shen, Zhen Hu, Bin Wang, Zhihai Xiong, Gang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Faculty of Information Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Beijing University of Technology Faculty of Information Beijing China

ISBN: (纸本)9781728128535

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives. © 2019 IEEE.

关键词： 3D printers

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A Geometrical Criterion for the Synchronization of Dynamic Networks with Input Saturation

A Geometrical Criterion for the Synchronization of Dynamic N...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Juncen Ye Haoyi Que Longhua Ma Ming Xu Hongye Su Polytechnic institute Zhe-jiang University Ningbo Campus Ningbo Zhejiang PR China Institute of Intelligence Science and Engineering Shenzhen Polytechnic Shenzhen Guangdong PR China Institute of automation and electrical engineeringZhejiang University Ningbo Institute of Technology Ningbo Zhejiang PR China National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Yuquan Campus Hangzhou Zhejiang PR China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

The paper investigated the global synchronization control problem of dynamic networks with discrete-time communications and input saturation. Based on a geometrical criterion of certain non-linear non-autonomous systems, we proposed a sufficient condition for stability of saturating linear control for a linear networked system. The stability could be ensured simply from a range of gains rather than a complex Lyapunov function. A numerical example is offered to show the effectiveness and the relative analysis.

关键词： Synchronization Sun Sufficient conditions Stability criteria Neural networks Motion control Vehicle dynamics

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A Novel Line Feature Description and Matching Method for Visual-Aided Inertial Navigation System

A Novel Line Feature Description and Matching Method for Vis...

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2019 3rd International Conference on Artificial Intelligence, automation and control Technologies (AIACT 2019)

作者： Xingxing Guang Yanbin Gao Pan Liu Guangchun Li College of Automation Harbin Engineering University Department of Electrical and Computer Engineering University of Calgary Beijing Institute of Control and Electronic Technology

With the rapid development of machine vision technology, more and more attention has been paid to the visual-aided inertial navigation system. It is important that to extract and track the line features at the dynamic situation in the visual-aided inertial navigation system which is based on visual line feature information to compensate attitude errors. A novel line feature description is proposed that use the SURF points to mark the LSD lines. Then, through coarse matching and fine matching, the function of continuously tracking the one line features in different images was realized. These line feature description and tracking method are applied in the visual-aided inertial navigation system, and its effectiveness is verified by the vehicle experiment.

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Power Network Modelling for Hardware in the Loop and Real Time Applications 4

Power Network Modelling for Hardware in the Loop and Real Ti...

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4th IEEE International Forum on Research and Technologies for Society and Industry, RTSI 2018

作者： Janda, Zarko Dragosavac, Jasna Ciric, Zoran Dragicevic, Miroslav Electrical Engineering Institute Nikola Tesla Automation Control Department Belgrade Serbia

ISBN: (纸本)9781538662823

In the paper the approximate model of power system elements adjusted for reactive power flow and voltage simulations is presented and the use of these models is demonstrated. The developed approximate models of power system elements are simple but when used in circuit simulations the fast and accurate reactive power flow solution is obtained. The performed reactive power flow calculations take into account corrections due to predetermined active power flow through circuit branches. The so derived values are accurate enough (error less than 1%) in cases where voltage angle across inductance model is less than 15 deg. The main advantage of the proposed approach is that the solution is directly calculated in one step, rather than using iterations. Due to this the proposed approach is handy and numerically efficient for use in power network modelling for hardware in the loop and real time applications, implemented on industrial grade programmable logic controllers (PLCs). Also the proposed approach can be efficiently used in projective model control applications. To demonstrate the versatility of the proposed modelling approach, these models are used to build up the circuit used for the simulation of voltage area controller operation. The voltage area control is modelled by two voltage control modes. The first one is keeping the area total exchange of reactive power close to zero until pilot node (PN) voltage is within the certain voltage band and the second one is imposing the predetermined the PN voltage reactive droop. Simulation results of area voltage control operating under different external and internal transient reactive sinks are presented and thoroughly explained. © 2018 IEEE.

关键词： Voltage control

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