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Comparative Analysis of a Resource Loss System with the Finite Buffer and Different Service Disciplines 1

26th International Conference on Distributed computer and Communication Networks: control, Computation, Communications, DCCN 2023

作者： Stepanov, V.A. Daraseliya, A.V. Sopin, E.S. Shorgin, S.Ya. Moscow Russia Institute of Informatics Problems Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

Fifth-generation (5G) networks are expected to revolutionize wireless communication by enabling faster and more reliable data transfer rates. However, the use of millimeter-wave frequencies in 5G networks introduces unique challenges in data transmission due to phenomena such as dynamic signal blockage, which can result in sudden and intense resource demands for customers. This paper presents a mathematical model of a 5G base station using a resource allocation system with waiting and non-homogeneous resource requirements for customers. The study aims to determine the most efficient way to select customers from the waiting buffer to increase system performance and minimize the probability of blocking data customers and reduce waiting time in the waiting buffer. As a result of the research, it was found, that the service discipline that prioritized customers with the highest resource requirements and took them from the waiting buffer until the system reached its limit, demonstrates the best system performance under certain parameters. In particular, in this paper, an analytical model was obtained for one of the service disciplines with the calculation of a system of equilibrium equations and performance measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 5G mobile communication systems

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Self-Timed Circuit Emulation on FPGA

Self-Timed Circuit Emulation on FPGA

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Stepchenkov, Yuri Diachenko, Yuri Tyurin, Sergei Institute of Informatics Problems Frc 'Computer Science and Control' of Ras Moscow Russia National Research Polytechnic University Perm Russia

Field programmable gate arrays (FPGAs) serve as a convenient tool for prototyping and debugging developed digital circuits. Their circuitry is optimized for synchronous unit implementation. computer-aided design systems for digital circuits on FPGAs are also focused on synchronous projects. However, one can also use FPGAs for prototyping self-timed (ST) circuits in the mode of emulating a single-stage representation of some logical and indication cells. The paper analyzes the ST circuit indication cell implementations on the FPGAs and proposes a technique for emulating ST circuits on typical FPGAs. The paper estimates a hardware redundancy of the ST circuit implementations on FPGAs and the results of prototyping an ST unit multiplying two operands with subsequent third operand addition and subtraction without intermediate rounding on the Intel Cyclone and Arria families FPGAs. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1527-1538页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russian Federation Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russian Federation

The widespread dissemination of misinformation and propaganda has become a crucial issue in societal conversations. This study presents an innovative framework to counter propaganda within information warfare using a modified logistic differential equation integrated with a Physics-Informed Neural Network (PINN). Employing the LIAR dataset, which reflects real-world political discourse, our model captures the intricate dynamics of propaganda. The novel approach effectively estimates underlying dynamics and unknown parameters, yielding superior accuracy (97.32%) compared to benchmark models. A parameter sensitivity analysis reveals the significant impact of the spread rate parameter, shedding light on aggressive propaganda dissemination. This research paves the way for robust methods in combating propaganda, demonstrating the effectiveness and superiority of the proposed PINN-based model over established benchmark models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Information warfare Nonlinear differential equations PINN Propaganda Random forest classifier TF-IDF

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Asymptotic Approximations of a Regulator and a Convergent Iteration Process for a Singularly Perturbed Problem 16

Asymptotic Approximations of a Regulator and a Convergent It...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Dmitriev, Mikhail Makarov, Dmitry Moscow Institute of Physics and Technology Federal Research Center 'Computer Science and Control' of Ras Moscow Russia

ISBN: (纸本)9798350337907

The paper considers the construction of asymptotic expansions in a singularly perturbed weakly nonlinear control problem to design an approximate controller. The asymptotic expansions are used to construct interpolation and extrapolation procedures and are obtained by applying the SDRE technique and analyzing a convergent iterative process. The experiments performed showed the efficiency of the proposed approach. © 2023 IEEE.

关键词： controllers

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ITLP-Campus: A Dataset for Multimodal Semantic Place Recognition 1

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8th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Melekhin, Alexander Bezuglyj, Vitaly Petryashin, Ilia Muravyev, Kirill Linok, Sergey Yudin, Dmitry Panov, Aleksandr Moscow Institute of Physics and Technology Dolgoprudny Russia Fedral Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9783031776885

ISBN: (纸本)9783031776878

Data is an important aspect of modern deep learning research, particularly in Place Recognition, which plays a pivotal role in various applications such as robotics, augmented reality, and autonomous navigation. In this paper, we introduce the ITLP-Campus dataset captured by a mobile robot equipped with front and back cameras and a LiDAR sensor. The dataset encompasses diverse outdoor and indoor environments within a university campus setting. Spanning different times of day, seasons, and weather conditions, ITLP-Campus offers a rich and varied set of scenes for analysis. The dataset includes unique features such as strategically placed ArUco markers along routes, automatically generated semantic masks and textual descriptions of scenes. Moreover, indoor tracks are annotated with scene text, enhancing the dataset’s utility for tasks involving text recognition and understanding. We provide detailed insights into the dataset’s acquisition process, annotation procedures, and potential applications. Additionally, we conduct extensive experiments by testing popular Place Recognition methods on ITLP-Campus, demonstrating its effectiveness and versatility for advancing research in multimodal semantic place recognition and related fields. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： deep learning mobile robotics multimodal dataset place recognition

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Evaluating A* and RRT for High-DoF Path Planning 1

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8th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Onegin, Aleksandr Kerimov, Nuraddin Yakovlev, Konstantin Moscow Institute of Physics and Technology Moscow Russia Federal Research Center for Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9783031776885

ISBN: (纸本)9783031776878

Path planning is crucial for various robots, including manipulators, that autonomously perform non-repetitive tasks in obstacle-rich environments. In this paper we focus on path planning for an n-DoF (degrees of freedom) manipulator, i.e. the one that consists of n links and n joints. We evaluate and compare head-to-head two approaches that are the most commonly used to solve this problem: search-based planning in the discretized configuration space and sampling-based planning in the continuous space. We evaluate the planners in challenging obstacle-rich scenes while varying the dimensionality of the configuration space (the number of joints/links). The memory consumption, quality of the resultant paths, runtime are tracked. Our evaluation reveals that, despite the common belief, search-based methods can outperform the sampling-based ones for 3-DoF and even 4-DoF manipulators, however for higher dimensional spaces (5-DoF and more) sampling methods are, indeed, more preferable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Degrees of freedom (mechanics)

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Leveraging Single and Multi-task Reinforcement Learning Algorithms for Autonomous Mobile Aloha Robot 1

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8th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Narendra, Aditya Makarov, Dmitry Panov, Aleksander I. Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control RAS Moscow117312 Russia Artificial Intelligence Research Institute Moscow105064 Russia

ISBN: (数字)9783031776885

ISBN: (纸本)9783031776878

Traditional Reinforcement Learning (RL) methods often require careful algorithm design, hyper-parameter tuning, and experimentation to perform optimally across multiple tasks. Multi-task models, however, offer increased efficiency, better generalization, and improved resource utilization, which are crucial for robots performing diverse autonomous tasks. On the other hand, single-task models often demonstrate better results and more robust task-specific policies. In this paper, we demonstrate the versatility of these models through experiments on the Mobile Aloha robot, which has both manipulation and navigation capabilities. The main idea behind our work is to demonstrate the use of various types of RL algorithms (single and multi-task) for multi-control robots (in our case, Mobile Aloha) which has not been explored much in the past. We conclude the paper with comparative results of experiments with state-of-the-art RL algorithms across different types of tasks including navigation, manipulation, and a combination of both. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Self-assembly Phenomenon Implementation Problems in Recurrent-Dynamic Architecture

Self-assembly Phenomenon Implementation Problems in Recurren...

引用

2023 Seminar on Information Computing and Processing, ICP 2023

作者： Khilko, Dmitry V. Stepchenkov, Yuri A. Orlov, Georgy A. Shikunov, Yury I. Institute of Informatics Problems Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798350369830

This paper covers the research of self-assembly phenomenon implementation problems within the recurrent-dynamic approach to the construction of computing architectures. The self-assembly phenomenon that occurs during the nanostructure formation process is approached in relation to the computing systems. An approach to implement the computational process based on this phenomenon has been previously proposed. The recurrent-dynamic approach has been developed as a step towards the implementation of the self-assembly phenomenon. Its main principles are self-sufficient data and recurrence, which are covered within the paper. Based on this approach the prototype of multi-core recurrent data-flow architecture has been developed and a description of its features is provided. An analysis of the existing method for implementing the self-assembly phenomenon within the architecture prototype is also presented. Self-assembly phenomenon implementation issues and their possible solutions are formulated. © 2023 IEEE.

关键词： Self assembly

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Advancing Network Anomaly Detection: Comparative Analysis of Machine Learning Models

Advancing Network Anomaly Detection: Comparative Analysis of...

引用

International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russia

ISBN: (纸本)9789819706402

In the rapidly evolving realm of cyber-security, the detection of network anomalies serves as a pivotal line of defense against a myriad of malicious activities and cyberthreats. This research undertakes the task of enhancing the accuracy and efficacy of network anomaly detection by employing a comparative analysis of various individual machine learning models. The study delves into the performance of distinct models, including Random Forest, Gradient Boosting, AdaBoost, neural networks, and SVM, meticulously scrutinizing their capabilities in detecting network anomalies. The crux of this study lies in its meticulous evaluation of each individual model on the revered NSL-KDD dataset—an established benchmark within the field of network intrusion detection. Through a systematic blend of rigorous mathematical frameworks, precise model implementations, and comprehensive experimental assessments, this research offers a deep understanding of the inner workings of each algorithm. The pivotal aspect of this study revolves around the comprehensive comparative analysis of these standalone models. Going beyond the mere quantifica-tion of accuracy, the exploration delves into aspects of precision, recall, F1-score, and more, shedding light on their diverse facets of performance. With achieved accuracies of 99.2419% for Random Forest, 99.5197% for Gradient Boosting, 86.6044% for AdaBoost, 84.00% for neural networks, and 87.00% for SVM, this research underlines the distinctive attributes and potential of each model in the context of network anomaly detection. As the study unravels the distinct strengths and limitations of each model, it contributes to the broader landscape of cyber-security by providing insights into the efficacy of individual machine learning approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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