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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgenij S. Barbin Ivan V. Kulinich Aleksej N. Koleda Ayan Myrzakhmetov Anton Perin Laboratory of Radiophotonics The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Intelligent Computer Systems Institute of Optics and Atmosphere Siberian Branch of the Russian Academy of Sciences Tomsk Russia Laboratory of Photonic Integrated Circuits The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

This paper presents the results of modeling a grating element for input/output of optical radiation into Si 3 N 4 /SiO 2 waveguide structures in the wavelength range from 1.5 to $1.6\ \mu \mathrm{m}$ . The results of the study of the geometric parameters of a diffraction grating on the optical transmission coefficient, in particular: the grating period, undercutting period, etching depth, filling factor, are presented. For a wavelength of $1.55\ \mu \mathrm{m}$ the maximum optical transmission coefficient is observed at: grating period $0.875\ \mu \mathrm{m}$ ; etching depth $0.181\ \mu \mathrm{m}$ ; fill factor of $0.595\ \mu \mathrm{m}$ ; undercutting $\pm 0.015\ \mu \mathrm{m}$ . The greatest contribution to the change in the transmission coefficient is made by the change in the grating period and is $\pm 0.0022\ \mu \mathrm{m}/{\%}$ and the etching depth is $\pm 0.00762\ \mu \mathrm{m}/{\%}$ . Based on the results of simulations, taking into account the operating wavelengths and refractive indices of Si 3 N 4 /SiO 2 films, the following permissible deviations of diffraction lattice parameters were put forward: grating period $\pm 0.022\ \mu \mathrm{m}$ ; etching depth $\pm 0.0762\ \mu \mathrm{m}$ ; filling factor ±20%; undercutting period $\pm 0.14\ \mu \mathrm{m}$ . Permissible insertion angles were 10–12 degrees. The reflection loss at the input of light into the waveguide was −17 dB, and at the output from −20 to −35 dB. Transmission loss at −3dB was $0.09\ \mu \mathrm{m}$ .

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Pedestrian detection with high-resolution event camera

arXiv

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arXiv 2023年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow Poland

Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges. A video stream captured by traditional cameras is often prone to problems such as motion blur or degraded image quality due to challenging lighting conditions. In addition, the frame rate – typically 30 or 60 frames per second – can be a limiting factor in certain scenarios. Event cameras (DVS – Dynamic Vision Sensor) are a potentially interesting technology to address the above mentioned problems. In this paper, we compare two methods of processing event data by means of deep learning for the task of pedestrian detection. We used a representation in the form of video frames, convolutional neural networks and asynchronous sparse convolutional neural networks. The results obtained illustrate the potential of event cameras and allow the evaluation of the accuracy and efficiency of the methods used for high-resolution (1280 x 720 pixels) footage. © 2023, CC BY.

关键词： Cameras

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Pedestrian detection with high-resolution event camera

TechRxiv

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TechRxiv 2023年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow Poland

关键词： Cameras

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Computational imaging based on single-photon detection: a survey

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Artificial Intelligence Review 2025年第8期58卷 1-54页

作者： Pu, Yanyun Zhu, Chengyuan Yao, Gongxin Li, Chao Pan, Yu Yang, Kaixiang Yang, Qinmin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Zheda Road Zhejiang Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Zheda Road Zhejiang Hangzhou310027 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational imaging applications, including bioimaging and remote sensing. In this review, we utilize the CiteSpace tool to create knowledge maps and perform a bibliometric analysis of this research area. Furthermore, we provide a comprehensive overview of the key challenges associated with computational imaging using temporal single-photon counting. We also highlight how these challenges have been addressed under extreme conditions to establish a reference model for future imaging solutions. We examine the performance evaluation parameters of single-photon detectors to enhance the understanding of detector array scaling and their application in constructing efficient computational imaging systems. Lastly, we aim to elucidate the current technical challenges in single-photon detector-based computational imaging and explore their potential future developments. © The Author(s) 2025.

关键词： Computational Imaging

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tatsukichi Shibuya Nakamasa Inoue Rei Kawakami Ikuro Sato Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in fine-tuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO

关键词： Training Speech coding Memory management Signal processing algorithms Signal processing Classification algorithms Servers Speech processing Optimization Image classification

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Parameter Uncertainty Analysis in Precise Pointing control of Flexible Spacecraft

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IFAC-PapersOnLine 2022年第20期55卷 241-246页

作者： János Bezsilla Béla Takarics Bálint Vanek Jian Guo Systems and Control Laboratory ELKH Institute for Computer Science and Control Budapest Hungary Faculty of Aerospace Engineering Delft University of Technology Delft The Netherlands

This article focuses on the validation of a classical PID controller scheme for flexible spacecraft with regards to the effect of parameter uncertainty on system stability and pointing precision. A high-fidelity simulation environment with external disturbances was built in Simulink using a control-oriented model of an Earth-observing satellite with a flexible appendage and on-board microvibration sources in orbit around the planet. Then, a PID control loop was designed with sensor dynamics, time delay behaviour, and a smooth trajectory generator. After declaring the natural frequencies, damping ratio, and rotation angle of the appendage, as well as the propellant tank mass to be uncertain, two worst-case scenarios were identified. Comparing the response of worst-case systems with nominal settings, only a minor drop has been found in the phase margins, with little to no difference in the pointing errors (smaller than ±2 arcsec for both roll and pitch).

关键词： flexible spacecraft nonlinear dynamics attitude tracking precise pointing control PID control parameter uncertainty

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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An Approach to Formation control of UAVs Based on Applying Adapted Kohonen Neural Network

An Approach to Formation Control of UAVs Based on Applying A...

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Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

作者： Mikhail Khachumov Intelligent Control Laboratory Ailamazyan Program Systems Institute of RAS Pereslavl-Zalessky Russia Federal Research Center” Computer Science and Control” of RAS Moscow Russia

The paper gives a statement and considers the solution of an urgent scientific problem of formation control for a group of unmanned aerial vehicles (UAVs) operating in an unstable environment. To construct the reference trajectories and allocate UAVs to positions of a given structure, an original approach is proposed based on the adapted Kohonen neural network with a set of metrics, including Euclidean, Mahalanobis and Euclidean-Mahalanobis distances. To implement the movement of a UAV group in a complex environment, we apply the principles of intelligent-geometric control, which allows to combine flexible intelligent and precise geometric control methods within one concept. When moving along a given route, UAVs use a set of allowable control strategies. The developed modeling scheme is designed to take into account wind loads and possibly dangerous rapprochement of vehicles. To reflect the dynamics of UAVs, a special module is used that contains transfer functions integrated into a single stabilization system with an autopilot. The proposed approach has shown promise when simulating a series of formation control problems.

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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