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Sensing With Random Communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

Incremental dissipativity based control of discrete-time nonlinear systems via the LPV framework

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arXiv 2021年

作者： Koelewijn, Patrick J.W. Tóth, Roland Weiland, Siep Control Systems Group Faculty of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

Unlike for Linear Time-Invariant (LTI) systems, for nonlinear systems, there exists no general framework for systematic convex controller design which incorporates performance shaping. The Linear Parameter-Varying (LPV) framework sought to bridge this gap by extending convex LTI synthesis results such that they could be applied to nonlinear systems. However, recent literature has shown that naive application of the LPV framework can fail to guarantee the desired asymptotic stability guarantees for nonlinear systems. Incremental dissipativity theory has been successfully used in the literature to overcome these issues for Continuous-Time (CT) systems. However, so far no solution has been proposed for output-feedback based incremental control for the Discrete-Time (DT) case. Using recent results on convex analysis of incremental dissipativity for DT nonlinear systems, in this paper, we propose a convex output-feedback controller synthesis method to ensure closed-loop incremental dissipativity of DT nonlinear systems via the LPV framework. The proposed method is applied on a simulation example, demonstrating improved stability and performance properties compared to a standard LPV controller design. Copyright © 2021, The Authors. All rights reserved.

关键词： Nonlinear systems

Analyzing Hepatotoxicity of Marine Venoms Using Artificial Intelligence: An Ecoinformatics Perspective

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Analyzing Hepatotoxicity of Marine Venoms Using Artificial I...

Information Technology (ACIT)

作者： Heider Wahsheh Mohammad Wahsha Tariq Al-Najjar Maroof Khalaf Haya Al-Tarawneh Department of Information Systems College of Computer Science and Information Technology King Faisal University Al-Ahsa Saudi Arabia Marine Science Station The University of Jordan Aqaba Branch Aqaba Jordan Faculty of Basic and Marine Sciences The University of Jordan Aqaba Branch Aqaba Jordan Control Health Division Aqaba Special Economic Zone Authority Aqaba Jordan

Marine toxins present considerable challenges to public health due to their intricate biochemical profiles that complicate effectual analysis. In addressing this, our study utilizes a pioneering Ecoinformatics method, employing artificial intelligence to meticulously examine the hepatotoxic effects of stonefish venom on murine models. The convergence of biochemical assays, histopathological scrutiny, and cutting-edge machine learning algorithms is strategically designed to unravel the complex modalities of venom-induced toxicity. Our findings offer unprecedented insight into the hepatotoxic dynamics of marine venoms, underscoring the utility of AI in advancing marine toxin research. This multifaceted research not only deepens our comprehension of venom pathology but also forges a pathway toward enhanced antivenom solutions, thereby reinforcing public health measures in marine and coastal ecosystems.

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Multi-Agent Economic Modeling Automation Technologies

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Multi-Agent Economic Modeling Automation Technologies

International Conference on control, Decision and Information Technologies (CoDIT)

作者： Aleksandra A. Zhukova Nikolai P. Pilnik Ivan G. Kamenev Batyrzhan B. Iusup-Akhunov Department of Mathematical Modelling in Economic Social and Ecological Systems Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russian Federation Department of Mathematical Modelling of Economic Systems Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russian Federation Department of Applied Economics Faculty of Economic Sciences HSE University Moscow Russian Federation Department of Fundamental Sciences-11 FSBEIHE Bauman Moscow State Technical University Moscow Russian Federation The Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Region Russian Federation

Complex economic models often describe the agent's decisions, i.e. choice of some controlled variables in accordance with criteria set by agents. Unlike purely statistical ones, agent-based models allow to describe processes with different modes, in which the agent's behavior changes quali-tatively when the environment (including the actions of other agents) change. The practical use of mathematical models of complex systems with a number of interacting agents is limited by complexity of their construction and possible errors when dealing with multitude of expressions. computer tools, such as the “Ecomod” system discussed in the article, can substantially reduce the time spent on model creation and processing, and help to coordinate the work of several researchers on the model.

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Maximum Power Point Tracking Using Cross-Correlation Algorithm for Pv System

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SSRN

SSRN 2022年

作者： Abulanwar, Sayed Shahin, Ahmed Gad, Hesham H. Rizk, Mohammad E.M. Hu, Weihao Electrical Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Engineering Horus University Egypt

This paper suggests an online, rapid, dynamic, and pragmatic cross-correlation (CC) technique to eﬀectively realize maximum power point tracking (MPPT) of the photovoltaic (PV) array. The PV voltage and current are extracted, averaged, and rate of change of the power and terminal voltage dynamics are integrally cross-correlated to regulate the duty ratio of the converter. According to this approach, the maximum power point (MPP) is defned in terms of the polarity of the CC coefcient (rcc). If the CC coefcient is positive, the operating point is at left of the MPP, while a negative CC coefcient indicates that the operating point is located at right of the MPP, otherwise the MPP is attained zero CC coefcient with lower steady-state ﬂuctuations. In this regard, a tracking optimization can be realized with confned drift either under gradual or step solar irradiance changes. The proposed CC technique quickly converges to the MPP without identifcation of PV array parameters. Besides, the proposed CC tracking algorithm tracks MPP with wide-range small step-size iterations since the search area is restricted to the vicinity of the MPP rather than the entire PV curve. Moreover, the proposed technique is featured by robustness against system parameters and load variations with simple hardware implementation as compared to several existing techniques. The eﬀectiveness of the proposed technique is thoroughly explored via time-domain, experimental, and hardware-in-loop investigations and meanwhile compared to the two most popular techniques: P&O INC methods, for both gradual and step irradiance changes and also shadow eﬀects. © 2022, The Authors. All rights reserved.

关键词： Time domain analysis

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

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Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data ...

2022 Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Principal component analysis

Distributed formation control of networked mechanical systems

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arXiv 2022年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach. © 2022, CC BY.

关键词： Networked control systems

CrowdSim2: an Open Synthetic Benchmark for Object Detectors

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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

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

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

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arXiv 2023年

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