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GPI-based secrecy rate maximization beamforming scheme for wireless transmission with an-aided directional modulation

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arXiv 2017年

作者： Yu, Hai Wan, Simin Cai, Wenlong Xu, Ling Zhou, Xiaobo Wang, Jin Wu, Yongpeng Shu, Feng Wang, Jiangzhou Wang, Jianxin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China faculty of National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Shanghai Key Laboratory of Navigation and Location-Based Services Shanghai Jiao Tong University Minhang200240 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The first one is the useful precoding vector of transmitting confidential messages to the desired user while the second one is the artificial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for confidential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases. Copyright © 2017, The Authors. All rights reserved.

关键词： Modulation

ML-Powered Biomedical Signals Processing using Hybrid Filters

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ML-Powered Biomedical Signals Processing using Hybrid Filter...

Progress in Applied electrical engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： electrical engineering Filters Biological system modeling Biomedical communication

Developing a denoising filter for electron microscopy and tomography data in the cloud

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Biophysical Reviews 2012年第3期4卷 223-229页

作者： Starosolski, Zbigniew Szczepanski, Marek Wahle, Manuel Rusu, Mirabela Wriggers, Willy Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology 44-100 Gliwice Akademicka 16 Poland School of Biomedical Informatics University of Texas Health Science Center Houston TX 77030 7000 Fannin St. UCT 600 United States Department of Pediatric Radiology Texas Children's Hospital Houston TX 77030 6621 Fannin St. United States Department of Biomedical Engineering Rutgers The State University of New Jersey Piscataway NJ 08854 599 Taylor Road United States Department of Physiology and Biophysics and Institute for Computational Biomedicine Weill Medical College of Cornell University NY NY 10065 1300 York Ave. United States D. E. Shaw Research NY NY 10036 120 West 45th Street United States

The low radiation conditions and the predominantly phase-object image formation of cryo-electron microscopy (cryo-EM) result in extremely high noise levels and low contrast in the recorded micrographs. The process of single particle or tomographic 3D reconstruction does not completely eliminate this noise and is even capable of introducing new sources of noise during alignment or when correcting for instrument parameters. The recently developed Digital Paths Supervised Variance (DPSV) denoising filter uses local variance information to control regional noise in a robust and adaptive manner. The performance of the DPSV filter was evaluated in this review qualitatively and quantitatively using simulated and experimental data from cryo-EM and tomography in two and three dimensions. We also assessed the benefit of filtering experimental reconstructions for visualization purposes and for enhancing the accuracy of feature detection. The DPSV filter eliminates high-frequency noise artifacts (density gaps), which would normally preclude the accurate segmentation of tomography reconstructions or the detection of alpha-helices in single-particle reconstructions. This collaborative software development project was carried out entirely by virtual interactions among the authors using publicly available development and file sharing tools. © 2012 International Union for Pure and Applied Biophysics (IUPAB) and Springer.

关键词： cryo-EM Digital paths Filtering Helix detection Remote collaboration Supervised classification

Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods

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arXiv 2023年

作者： Foszner, Pawel Szczęsna, Agnieszka Ciampi, Luca Messina, Nicola Cygan, Adam Bizon, Bartosz Cogiel, Michal Golba, Dominik Macioszek, Elżbieta Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Institute of Information Science and Technologies National Research Council Pisa Italy QSystems.pro sp. z o.o. Mochnackiego 34 Bytom41-907 Poland Blees sp. z o.o. Zygmunta Starego 24a/10 Gliwice44-100 Poland Department of Transport Systems Traffic Engineering and Logistics Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice Poland

Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA. © 2023, CC BY.

关键词： Deep learning

CrowdSim2: an Open Synthetic Benchmark for Object Detectors

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arXiv 2023年

Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment. © 2023, CC BY.

关键词： Object detection

Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms

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arXiv 2024年

作者： Zhang, Han Lin, Shengxiang Zhang, Xingyi Wang, Yu Zhang, Yangguang College of Aritificial Intelligence and Automation Hohai University Nanjing211100 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Control and Computer Engineering North China Electric Power University Beijing071003 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

In high-energy particle physics, extracting information from complex detector signals is crucial for energy reconstruction. Recent advancements involve using deep learning to process calorimeter images from various sub-detectors in experiments like the Large Hadron Collider (LHC) for energy map reconstruction. This paper compares classical algorithms—MLP, CNN, U-Net, and RNN—with variants that include self-attention and 3D convolution modules to evaluate their effectiveness in reconstructing the initial energy distribution. Additionally, a test dataset of jet events is utilized to analyze and compare models’ performance in handling anomalous high-energy events. The analysis highlights the effectiveness of deep learning techniques for energy image reconstruction and explores their potential in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Modeling Demand for Inventory Management of Slow-Moving Items in Case of Reporting Errors

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IFAC Proceedings Volumes 2004年第4期37卷 27-32页

作者： Alexandre Dolgui Anatoly Pashkevich Maxim Pashkevich Scientific Methods for Industrial Management Dept. (MSGI) Industrial Engineering and Computer Sciences Center (G2I) Ecole de Mines de Saint Etienne 158 Cours Fauriel 42023 Saint Etienne France Robotic Laboratory Department of Automatic Control Belarusian State University of Informatics and Radioelectronics 6 P.Brovka St. Minsk 220027 Belarus Department of Mathematical Modeling and Data Analysis Faculty of Applied Mathematics and Computer Science Belarusian State University Skarina Av. 4 Minsk 220050 Belarus

The paper is devoted to the problem of modeling demand for inventory management of slow-moving items in the case of reporting errors. It is proposed a generalization of the beta-binomial demand model that takes into account possible reporting errors in the learning sample. For the new model, there are developed identification and forecasting algorithms that provide consistent estimators of the model parameters and mean square optimal forecasts. The efficiency of the proposed approach is illustrated by an application example for slow-moving car parts.

关键词： inventory control error correction statistical inference parameter estimation prediction methods

Design and initial study of porous core electromagnet for levitation applications

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AIP Conference Proceedings 2018年第1期2029卷

作者： Adam Piłat Bartosz Sikora 1AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics Mickiewicza 30 30-059 Krakow Poland

A concept of the porous core electromagnet is studied. The form for the electromagnet core was printed using 3D technique. The core was manufactured manually using iron filings and cured in room temperature and air pressure. The aim of this paper is to deliver initial analysis of the proposed powder core together with the modelling procedure by the using finite element method, and to compare the experimental results of the manually manufactured electromagnet. The material property of the numerical model was adjusted to fit the experimental results. Finally, the electromagnet was tested in the levitation real–time experiment.

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Educational Innovation: Interaction and Relationship Inside A Sub-Module

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Procedia - Social and Behavioral sciences 2015年 186卷 395-400页

作者： Fernando Oterino-Echavarri Jose Manuel Lopez-Guede Ekaitz Zulueta Manuel Graña Department of Electronics University College of Engineering of Vitoria Basque Country University (UPV/EHU) C/Nieves Cano 12 01006 Vitoria-Gasteiz Spain Department of Systems Engineering and Automatic Control University College of Engineering of Vitoria Basque Country University (UPV/EHU) C/Nieves Cano 12 01006 Vitoria-Gasteiz Spain Computational Intelligence Group Basque Country University (UPV/EHU) Department of Computer Science and Artificial Intelligence Faculty of Informatics Basque Country University (UPV/EHU) Paseo Manuel de Lardizabal 1 20018 Donostia-San Sebastián Spain

In this paper authors will expose a work developed with the aim to submit an educational innovation project proposal to a competitive call for Educational Innovation Projects 2013-2015 of the Education Advisory Service of the Basque Country University (UPV/EHU, Spain). This project is being carried out in the computer Structure and computer Architecture sub-module of the Degree in computer Management and Information Systems engineering of the University College of engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU). The project is based on the active learning, more specifically, on cooperative learning. In this paper we have given deeper insight the dependencies between all the subjects belonging to the analyzed sub-module, which is composed of two subjects named computer Structure and computer Architecture. We have included in such analysis a previous subject named Principles of Digital Systems Design, which does not belong to that sub-module, but plays an important role in the acquisition of the knowledge and competencies of the two previously referenced subjects.

关键词： Active Learning Cooperative Learning computer engineering computer Structure computer Architecture