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A Concept of Visual Programming Tool for Learning VHDL

IOP Conference Series: Materials Science and engineering 2021年第1期1031卷

作者： Aneliya Ivanova University of Ruse Faculty of Electrical Engineering Electronics and Automation Department of Computing 8 Studentska Str. 7017 Ruse Bulgaria

Due to the COVID-19 pandemic, distance education starts playing a crucial role in higher education and the need for development of educational tools, helping the students learn better at home cannot be ignored. Teaching programming languages online is a complicated task and when the course subject is programmable logic design through Hardware Description Languages (HDLs), online teaching becomes a complex challenge. In this paper is presented a concept of a training environment that uses the visual programing technique to help the students create VHDL models of various digital devices. The students construct VHDL models by combining simple visual objects while the environment is providing guidance in real time and is preventing a wrong match of VHDL operators and signals. The strategy for building a model of a compex digital circuit by moving and connecting visual objects will help the students with visual kinaesthetic learning style to internalize the concept and the structure of the VHDL model. The training environment will benefit mostly the students, learning in distance mode, but it is also useful for face-to-face students, who find it difficult to assimilate the specifics of VHDL modelling.

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3D Face Reconstruction System From A Single Photo Based On Regression Neural Network

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IFAC-PapersOnLine 2020年第5期53卷 71-76页

作者： Yatai Ji Keqiang Li Huaiyu Wu Gang Xiong Zhen Shen Xiuqin Shang Bangwen Xi The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Automation Tsinghua University Beijing 100084 China University of Chinese Academy of Sciences Beijing 100049 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China

3D human face modeling is one of the most popular research directions in the field of computer stereo vision. On the one hand, the complex physiological structure of human face as well as various expressions and attitude changes bring great challenges to three-dimensional modeling, which makes the modeling method of human face have high research value and reference significance. On the other hand, 3D face reconstruction has a broad application prospect, and the application demand attracts more researchers to invest in it. Based on the 3DMM and 3DDFA method, this paper utilizes regression neural network to realize end-to-end face reconstruction. After that, we build a visualization program with the training model to realize face modeling based on a single face image of any pose or expression. It’s a 3D face reconstruction system which has functions such as face alignment, face rotation and so on.

关键词： face reconstruct 3DMM 3DDFA regression network

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Security boundaries of an optical-power limiter for protecting quantum-key-distribution systems

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Physical Review Applied 2024年第1期21卷 014026-014026页

作者： Qingquan Peng Binwu Gao Konstantin Zaitsev Dongyang Wang Jiangfang Ding Yingwen Liu Qin Liao Ying Guo Anqi Huang Junjie Wu Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China School of Automation Central South University Changsha 410083 China China Greatwall Research Institute China Greatwall Technology Group Co. Ltd. Shenzhen 518057 China Russian Quantum Center Skolkovo Moscow 121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Vigo Quantum Communication Center University of Vigo 36310 Vigo Spain Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo 36310 Vigo Spain atlanTTic Research Center University of Vigo 36310 Vigo Spain College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 China

Unauthorized light injection has always been a vital threat to the practical security of a quantum-key-distribution (QKD) system. An optical-power limiter (OPL) based on the thermo-optical defocusing effect has been proposed and implemented, limiting the injected hacking light. As a hardware countermeasure, the performance of the OPL under various light-injection attacks will be tested to clarify the security boundary before it is widely deployed. To investigate the security boundary of the OPL in quantum cryptography, we comprehensively test and analyze the behavior of the OPL under continuous-wave (cw) light-injection attacks and pulse-illumination attacks with pulse repetition rates of 0.5 Hz, 40 MHz, and 1 GHz. The test results illuminate the security boundary of the OPL, which allows one to properly employ the OPL in use cases. The methodology of testing and analysis proposed here is applicable to other power-limitation components in a QKD system.

关键词： Optical quantum information processing Photonics Quantum communication Quantum cryptography Devices

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Future Networks 2030: Challenges in Intelligent Transportation Systems

Future Networks 2030: Challenges in Intelligent Transportati...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Mădălin-Dorin Pop Jitendra Pandey Velmani Ramasamy Automation and Applied Informatics University of Timișoara Timișoara România Department of Computing Middle East College Muscat Sultanate of Oman Department of Electronics and Communication Engineering Siddhartha Institute of Technology and Sciences Narapally Hyderabad India

ISBN: (数字)9781728170169

ISBN: (纸本)9781728170176

Many cities around the world implement the Intelligent Transportation Systems (ITS) concept to reduce road traffic congestion. This concept implies the usage of Internet of Things (IoT) technologies to ensure a better traffic flow, especially in crowded urban areas. Sensors networks became part of road infrastructure and have as main responsibilities to collect real-time traffic data and to send them to Traffic Management Centres (TMCs) which control further the traffic lights timings. Today's technologies meet some limitations in terms of efficiency. This paper starts with an overview of these limitations to create a path for discussing the challenges that the development of ITS will meet in the context of Future Networks (FN) 2030 concept. Based on this development path, the identification of current limitations and possible future challenges consists of an important step of this research. Further, the main contribution in this research is to show how the ITS shall adapt to meet the requirements of FN 2030 by providing an architecture for Future ITS 2030 that meets the general ITS needs.

关键词： Security Roads Sensors Computer architecture Real-time systems Smart cities Wireless communication

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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A Data-driven Approach to Solve a Production Constrained Build-order Optimization Problem

A Data-driven Approach to Solve a Production Constrained Bui...

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第三十八届中国控制会议

作者： Pengpeng Wang Yifeng Zeng Bilian Chen Langcai Cao Department of Automation School of Aerospace Engineering Xiamen University School of Computing Teesside University

Production constrained build-order optimization problems challenge artificial intelligence research in computer game applications due to an uncertain set of constraints. Traditional approaches provide subjective values in the constraint formulation therefore resulting in unexpected performance of the optimal build-order in a game. In this article, we propose a data-driven approach to solve a build-order optimization problem in StarCraft. We formulate the constraint by learning the parameter values from game replay data, which complements more precise problem formulation. To solve the optimization, we use the improved genetic algorithm by learning initial solutions from the data. We show the performance of the data-driven methods in a StarCraft simulation platform.

关键词： Build-order Data-Driven Optimization StarCraft Improved Genetic Algorithm

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Robust multi-objective control for vehicle active suspension systems with structured uncertainties

Robust multi-objective control for vehicle active suspension...

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第三十九届中国控制会议

作者： Zhong Cao Sheng-Guo Wang Zhaohui Chen Xiaorong Hou Wenjing Zhao School of Physics and Electronic Engineering Guangzhou University College of Engineering and College of Computing Informatics University of North Carolina at Charlotte Department of Mathematics and Physics Chongqing University of Science and Technology School of Automation Engineering University of Electronic Science and Technology of China Lab Center Guangzhou University

A robust structural control method is proposed to solve the problems of structured uncertainties and multi-objective control for vehicle active suspension systems. The considered model is a quarter model of vehicle subject to unknown ground disturbance and structured uncertainties. The proposed method is based on robust structured control well supported by singular value decomposition(SVD) method, yielding a multi-objective control design approach, applicable to almost any active suspension systems. Some experiments show that the control performance of robust structural control for vehicle active suspension systems with structured uncertainties is significantly improved comparing with passive controlled system and existing other controlled active suspension systems.

关键词： Active suspension systems Robust structural control singular value decomposition structured uncertainties

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[email protected] , an ecosystem of virtual environments and tools for realistic indoor robotic simulation

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Expert Systems with Applications 2022年 208卷

作者： David Fernandez-Chaves Jose-Raul Ruiz-Sarmiento Alberto Jaenal Nicolai Petkov Javier Gonzalez-Jimenez Machine Perception and Intelligent Robotics group (MAPIR) Department of System Engineering and Automation Biomedical Research Institute of Malaga (IBIMA) University of Malaga Spain Johann Bernoulli Institute of Mathematics and Computing Science University of Groningen The Netherlands

Simulations and synthetic datasets have historically empower the research in different service robotics-related problems, being revamped nowadays with the utilization of rich virtual environments. However, with their use, special attention must be paid so the resulting algorithms are not biased by the synthetic data and can generalize to real world conditions. These aspects are usually compromised when the virtual environments are manually designed. This article presents [email protected] , an ecosystem of virtual environments and tools that allows for the management of realistic virtual environments where robotic simulations can be performed. Here “realistic” means that those environments have been designed by mimicking the rooms’ layout and objects appearing in 30 real houses, hence not being influenced by the designer’s knowledge. The provided virtual environments are highly customizable (lighting conditions, textures, objects’ models, etc.), accommodate meta-information about the elements appearing therein (objects’ types, room categories and layouts, etc.), and support the inclusion of virtual service robots and sensors. To illustrate the possibilities of [email protected] we show how it has been used to collect a synthetic dataset, and also exemplify how to exploit it to successfully face two service robotics-related problems: semantic mapping and appearance-based localization.

关键词： Virtual environments Robotic simulation Service robots Semantic maps Robotic dataset Unity

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Learning-Based Predictive Control via Real-Time Aggregate Flexibility

arXiv

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arXiv 2020年

作者： Li, Tongxin Sun, Bo Chen, Yue Ye, Zixin Low, Steven H. Wierman, Adam The Computing + Mathematical Sciences Department California Institute of Technology PasadenaCA91125 United States The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Electrical Engineering Department California Institute of Technology PasadenaCA91125 United States

Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator. However, most of existing aggregate flexibility measures often are slow-timescale estimations and much less attention has been paid to real-time coordination between an aggregator and an operator. In this paper, we consider solving an online optimization in a closed-loop system and present a design of real-time aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In addition to deriving analytic properties of the MEF, combining learning and control, we show that it can be approximated using reinforcement learning and used as a penalty term in a novel control algorithm - the penalized predictive control (PPC), which modifies vanilla model predictive control (MPC). The benefits of our scheme are (1). Efficient Communication. An operator running PPC does not need to know the exact states and constraints of the loads, but only the MEF. (2). Fast Computation. The PPC often has much less number of variables than an MPC formulation. (3). Lower Costs. We show that under certain regularity assumptions, the PPC is optimal. We illustrate the efficacy of the PPC using a dataset from an adaptive electric vehicle charging network and show that PPC outperforms classical MPC. © 2020, CC BY.

关键词： Reinforcement learning

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Real-time implementation of model predictive control law for direct current regulation of a DC-DC boost converter used in renewable energy conversion system

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Results in engineering 2025年 27卷

作者： Badraddine Bezza Abdelhalim Borni Mohcene Bechouat Moussa Sedraoui Abdelhak Bouchakour Layachi Zaghba Sherif S.M. Ghoneim Muhannad Alshareef Takele Ferede Agajie Ahmed B. Abou Sharaf Unité de Recherche Appliquée en Energies Renouvelables URAER Centre de Développement des Energies Renouvelables CDER 47133 Ghardaïa Algeria Department of Automatics and Electromechanics Faculty of Science and Technology University of Ghardaia 47000 Ghardaia Algeria Department of Electrical and Automation Engineering Faculty of Science and Technology University of 8 mai 1945 Guelma Guelma Algeria Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia Department of Electrical Engineering College of Engineering and Computing in Al-Qunfudhah Umm al-Qura University Mecca Saudi Arabia Department of Electrical and Computer Engineering Faculty of technology DebreMarkos University Debre Markos 269 Ethiopia Ministry of Higher Education & Scientific Research Industrial Technical Institute in Mataria Cairo 11718 Egypt Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

While Model Predictive Control (MPC) has been widely studied in power electronics, its real-time implementation on DC-DC boost converters—particularly under variable loading conditions—remains limited. This paper proposes a new real-time implementation of the Model Predictive Control (MPC) law for a DC-DC boost converter connected to variable loads. This implementation ensures precise current regulation through accurate duty cycle control updates, enabling the inverter's frequency switching to be activated or deactivated as needed. This is achieved by proposing a predictive model of the current occurring in the first channel of the converter model, where a fitness function—comprising reference tracking and control effort—is minimized. Compared to the proportional-integral (PI) controller, the MPC law proves more efficient, particularly in preventing oscillations in both transient and steady-state output current responses. This advantage is validated through experimental tests for either a current inductance load or a resistive load. Since this type of real-time implementation has not been previously applied on this converter, it constitutes the main contribution of this paper.

关键词： PI controller DC-DC boost converters Model predictive control (MPC) Experimental validation

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