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International Symposium on Communication Systems, Networks and Digital Signal Processing, CNSDSP

作者： Mircea Hulea George-Iulian Uleru Alexandru Barleanu Othman Isam Younus Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania Photonics Technology Laboratory Optical Communications Research Group. FEE Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (数字)9798350348743

ISBN: (纸本)9798350348750

The growing demand for energy-efficient communication systems has triggered the interest in using photovoltaic (PV) panels to power optical receivers in Visible Light Communication (VLC) systems. While this approach offers the enticing advantage of self-powered operation, it comes with a significant drawback of limited bandwidth. This limitation severely restricts the maximum data rate achievable through VLC channels, especially when compared to digital audio transmission which requires high bandwidth due to sampling and resolution needs. Therefore, a key challenge lies in finding a balance between the energy-harvesting benefits of PV receivers and the need for a sufficient data rate for specific VLC applications. This work evaluates experimentally the possibility to transmit using VLC the voice in the analogue domain when the transmission of the digital conversion of limited by the bandwidth of the PV-based receiver. The results show that speech can be received properly in analogue domain when the transmission of the digital conversion is not possible due to limited bandwidth of the PV panel. The results show that the PV panel is able to receive speech with cross-correlation factors above 0.94. On the other hand, a 128 KHz digital signal, that is transmitted over the same optical channel, is not visible at the receiver side.

关键词： Wireless communication Photovoltaic systems Stimulated emission Bandwidth Optical receivers Stability analysis Real-time systems

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FPGA-based Real-Time High-resolution Image Processing for rocket on-board processing 17

FPGA-based Real-Time High-resolution Image Processing for ro...

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17th IEEE International Conference on computer Research and Development, ICCRD 2025

作者： Zhou, Hui Wang, Xiaofeng Xie, Yujia Cao, Yongxiang Tang, Yu Huo, Yongchun Wen, Huan Zhao, Xiongbo Li, Chaoran National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China Beihang University Computer Science and Engineering Beijing China Beihang University National Superior College for Engineers Beijing China

ISBN: (纸本)9798331531881

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-Time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-The-Art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-To-end computation time for

关键词： Image enhancement

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FPGA-based Real-time High-resolution Image Processing for rocket on-board processing

FPGA-based Real-time High-resolution Image Processing for ro...

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International Conference on computer Research and Development (ICCRD)

作者： Hui Zhou Xiaofeng Wang YuJia Xie Yongxiang Cao Yu Tang Yongchun Huo Huan Wen Xiongbo Zhao Chaoran Li Beijing Aerospace Automatic Control Institute National key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Computer Science and Engineering Beihang University Beijing China National Superior College for Engineers Beihang University Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for

关键词： YOLO Rockets Power demand Convolution Multicore processing Reviews Scalability Accelerator architectures Real-time systems Field programmable gate arrays

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Solar Irradiance Forecasting using Gaussian Process Regression Model

Solar Irradiance Forecasting using Gaussian Process Regressi...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Nesrine Omri Jannet Jamii Majdi Mansouri Mohamed Faouzi Mimouni The Laboratory of Automatic Electrical System and Environment (LAS2E) National Engineering School of Monastir Tunisia Electrical and Computer Engineering Department Texas A&M University at Qatar Electrical Engineering Department National Engineering School at Monastir (ENIM) Tunisia

Photovoltaic power generation presents a significant problem for power system security due to their stochastic nature. Thus, photovoltaic energy forecasting plays a key role in addressing the challenges of grid stability. Then, accurate PV energy forecasting improve the output power and therefore reduce the need for reserve energy to balance energy. Also, it improves dispatch and planning power. In this paper, we study a short term forecasting of PV power using a Gaussian Process Regression (GPR) model. The meteorological conditions such as temperature is used as input, while solar irradiance, is used as output for GPR *** data set are illustrated from data NOAA'S Surface Radiation (SURFRAD) Network at Bondville station, located in the USA. A comparative study with other machine learning techniques is developed in terms of calculation time (CT), RMSE, MAE, and R 2 .

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms Data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

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Observer Design and Sensorless Control in Electrical Machines: A Brief Overview

Observer Design and Sensorless Control in Electrical Machine...

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International Conference on Sciences and Techniques of automatic Control and computer engineering (STA)

作者： Omar Naifar Mourad Elloumi Hamdi Gassara Control and Energy Management Laboratory National School of Engineering Sfax University Sfax Tunisia Laboratory of Sciences and Technology of Automatic Control and Computer Engineering ENIS University of Sfax Sfax Tunisia

ISBN: (纸本)9781665482622

This review paper discusses the most used observation techniques applied to the electrical machines, namely the direct estimation technique and estimation model based on a mathematical technique. Without a deep investigation, this paper allow readers to take a general overview of the observer techniques utilized to control the electrical machines.

关键词： Computational modeling Estimation Sensorless control Observers Mathematical models Induction machines Standards

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An Effective Random Forest based Long-Term Wind Power Forecasting Technique

An Effective Random Forest based Long-Term Wind Power Foreca...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Jannet Jamii Mohamed Trabelsi Majdi Mansouri Mohamed Faouzi Mimouni Laboratory of Automatic Electrical System and Enviroment (LAS2E) National Engineering School Monastir Monastir Tunisia Engineering Department Electronic and Communications Kuwait College of Science and Technology Kuwait City Kuwait Electrical and Computer Engineering Department Texas A&M University at Qatar Doha Qatar Electrical Engineering Department Laboratory of Automatic Electrical System and Enviroment (LAS2E) National Engineering School Monastir Monastir Tunisia

Accurate Wind Power Forecasting (WPF) has become an essential task for grid operators due to its ability to minimize the effect of wind power intermittency and the need of energy storage deployment. In this paper, a Random Forest (RF)-based long-term WPF technique is proposed. The model inputs consist of some meteorological parameters such as wind speed, temperature, and pressure. The input data are selected from the data NOAA’S Surface Radiation (SURFRAD) network at Bondville station (USA). The RF parameters (number of trees, number of leaves) are optimized to get the best prediction accuracy. The normalized RMSE and MAE metrics are computed to assess the performance of the proposed RF model.

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Roblets: Robotic Tablets That Self-Assemble and Self-Fold into a Robot

Roblets: Robotic Tablets That Self-Assemble and Self-Fold in...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Junyi Han Daniela Rus Shuhei Miyashita Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Insigneo Institute for in silico Medicine

Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for many years. The key challenges include how to synthesize parts in pieces into a three-dimensional functional structure in a practical time, and subsequently, achieve a controlled robotic motion, all with minimal human intervention. This study proposes a method of self-assembling a 3D robot by first self-assembling random parts into a 2D structure and then self-folding it into a 3D shape. Once self-folded, the robot, whose compositional parts contain magnets, becomes capable of performing basic tasks such as block-pushing upon an application of an external magnetic field. Self-assembly from parts into a two-dimensional structure was performed by repeatedly colliding the parts with each other, and combining them with complementary-shaped parts, like matching jigsaw puzzle pieces. Self-folding was performed by shrinking a heat-responsive film attached across the hinge of each assembly part in hot water, causing the entire 2D structure to self-fold. The experiment demonstrated a series of 13 parts self-assembling into the shape of a 3D beetle, then walking and pushing an object in 13 minutes. The self-assembly process is programmed (mechanically) to generate the same geometry even if the number of parts is greater than the necessary number for the structure, thus is capable of generating multiple structures simultaneously.

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H observer-based control for fuzzy Hadamard fractional-order systems

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Computational and Applied Mathematics 2025年第4期44卷 1-14页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad El Hajjaji, Ahmed Ben Makhlouf, Abdellatif Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

While the $$H_\infty $$ observer-based control has found widespread application in the literature for integer-order systems, researchers have shown less interest in addressing the same issue within the fractional-order framework. In this context, this study delves into the application of $$H_\infty $$ observer-based control for the Hadamard fractional-order system (HFOS) described by the Takagi–Sugeno fuzzy models (TSFM). Using Lyapunov approach and by employing a matrix decoupling technique, LMI based conditions ensuring the existence of an observer and controller, are proposed. To minimize the impact of disturbances on the controlled output, $$H_\infty $$ optimization technique is used. The validity of our approach is substantiated through an example, underscoring the robustness and reliability of our proposed findings.

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