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Conference on Vehicle control and Intelligence (CVCI)

作者： Xu Zhao Jingzheng Guo Lianbing Wang Hongyan Guo Zhenze Liu Yiming Zhang College of Communication Engineering Jilin University Changchun China Department of Aircraft Control and Information Engineering Jilin Institute of Chemical Technology Changchun China State Key Laboratory of Automotive Simulation and Control and the College of Communication Engineering Jilin University Changchun China SAIC Motor R&D Innovation Headquarter Jilin University Shanghai China

Designing a controller to ensure vehicle stability under snow and ice-rutted road conditions, in the absence of accurate model knowledge and with potential sampling data delay, poses a significant challenge. To address this, this paper presents a novel data-driven parametric approach that integrates linear matrix inequalities (LMIs) methods. This approach allows for the determination of an appropriate sampling time interval. By iteratively solving LMIs, a state-feedback controller is derived, which guarantees both system stability and robustness. The effectiveness of the proposed approach is demonstrated through numerical simulations conducted on the MATLAB/CarSim platform. The results highlight the good control performance and robustness achieved by the proposed method.

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Artificial Intelligence-Aid OTFS Receiver: Design and Performance Evaluation

Artificial Intelligence-Aid OTFS Receiver: Design and Perfor...

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Ubiquitous communication (Ucom), International Conference on

作者： Chenxi Wang Li Ping Qian Huijie Zhu College of Information Engineering Zhejiang University of Technology Hangzhou China Science and Technology on Communication Information Security Control Laboratory The 36th Research Institute of China Electronics Technology Jiaxing China

OTFS technology is attracting much attention due to its strong adaptability to high-speed mobility and high spectrum efficiency. Studies show that applying deep learning (DL) to traditional communication systems significantly improves their performance. To this end, this paper proposes an AI-aid OTFS receiver to enhance the performance of OTFS systems in high-speed mobile communication scenarios. The proposed receiver employs two networks - a channel estimation network called TFNet and a decoding network called RecovNet. In the channel estimation module, TFNet performs more accurate channel estimation based on the results of traditional channel estimation methods. In the decoding module, RecovNet decodes the signal based on the equalized signals. To validate the superiority of our proposed receiver, we conduct comparative testing with a traditional receiver under the integer Doppler channel. The experimental results demonstrate that integrating DL technology in OTFS systems effectively enhances their performance.

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An Enhanced Analysisof Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Mary, S.J. Jereesha Anitha, C. Logeshwaran, Jaganathan Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Annai Velankanni College of Engineering Potalkulam Kanyakumari629401 India Saveetha School of Engineering Chennai600124 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore641202 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Center University of Anbar Baghdad55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk Iraq

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating theserich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets. © 2024, CC BY-NC-ND.

关键词： Radial basis function networks

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Spectrum Sensing for Cognitive Radio Based on Feature Extraction and Deep Learning 11

Spectrum Sensing for Cognitive Radio Based on Feature Extrac...

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2022 11th International Conference on Information and Electronics engineering, ICIEE 2022

作者： Geng, Yue Huang, Jingyi Yang, Jianxin Zhang, Sen School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518061 China School of Mechanical Engineering & Automation Beihang University Beijing100191 China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin150080 China

In cognitive radio, spectrum sensing is used to determine whether the primary user is using the spectrum based on the signal received on a specific frequency band, thereby determining whether the secondary user can use the spectrum. The main problem faced by spectrum sensing is how to identify the existence of the primary signal under the condition of low signal-to-noise ratio (SNR). Compared with traditional technologies, deep learning methods can identify the features of input data more efficiently and accurately. Based on convolutional neural network (CNN), This paper regard spectrum sensing as a binary classification problem. In the method we proposed, different features of received are extracted, and a dataset of feature matrices obtained under different SNRs is constructed for the training of the CNN network. Experiment results show that under the condition of low signal-to-noise ratio, the performance of our method is improved compared with the traditional method, and the combination of different features can improve the sensing accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Cognitive radio

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Non-Singular Fixed-Time Robust Containment control for Autonomous Surface Vehicles

SSRN

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SSRN 2023年

作者： Ma, Junda Sun, Hefang Tan, Chong Zhao, Tianyuan Liu, Ke College of Automation Harbin University of Science and Technology Harbin150000 China Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150000 China College of Journalism and Communication Jilin University Changchun130000 China College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150000 China

For the containment control problem of autonomous surface vehicles with external disturbances, a novel non-singular fixed-time control scheme is developed, where the multi-ship system consists of real leaders and followers. In contrast with traditional containment cases studied in most of the existing literature, the real leaders are required to track the desired trajectories, and the followers need to get into the convex hull spanned by the real leaders in fixed time. The method for solving the fixed-time containment control problem is divided into the following steps: Firstly, a fixed-time disturbance observer is designed to estimate the external disturbance for the leaders and the followers, and the estimation result is used to compensate for the input of the control system;Second, combined with fixed-time stability theory and a switch function, a non-singular fixed-time trajectory tracking controller for leaders is designed, such that the position tracking error could be bounded within a fixed time;Third, to achieve formation efficiently, a non-singular fixed-time containment controller strategy incorporating with graph theory is proposed for coordinating followers, such that the fixed-time stability can be ensured. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Graph theory

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A Novel Consecutive Artificial Noise Secrecy Enhancing Scheme for RIS Aided MIMO System With Imperfect CSI

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IEEE Transactions on Vehicular technology 2025年

作者： Shang, Pingping Tao, Zhuofan Xie, Zhonghui Huang, Min Sui, He Li, Jun Hai, Han Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Civil Aviation University of China College of Aeronautical Engineering Tianjin300399 China Guangzhou University School of Electronics and Communication Engineering Guangzhou510006 China Donghua University College of Information Science and Technology Shanghai201620 China

Reconfigurable intelligent surface (RIS) is a technology with great promise, which is widely used in wireless networks to improve the coverage and enhance the performance. However, security concerns pose a major challenge for wireless networks, especially given inevitable errors in channel estimation. This paper proposes a high spectrum efficiency security scheme using generalized spatial modulation (GSM) and space-time block coding (STBC) for scenarios with imperfect channel state information (CSI). In this scheme, GSM uses RIS block indices as unique identifiers, ideal for secure scenarios. It operates by activating different RIS blocks over two time slots to reflect unmodulated carrier signals, while STBC is designed based on the phase shifts of the RIS. To ensure secure communication, a low-complexity continuous artificial noise (CAN) is transmitted with STBC signals to interfere with eavesdroppers and prevent them from decoding the signals. The ergodic secrecy rate is deduced, and the bit error rate (BER) is validated through experiments. The simulation results indicate that the proposed solution can remarkably improves the security performance of the RIS-assisted wireless communication systems. © 1967-2012 IEEE.

关键词： Space-time block coding (STBC)

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Deep Architecture of Neural Networks Using Stochastic Integral Computation in VLSI 1

Deep Architecture of Neural Networks Using Stochastic Integr...

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1st International Conference on Advances in Computational Science and engineering, ICACSE 2020

作者： Sumathy, B. Venusamy, Kanagaraj David Neels Ponkumar, D. Malathi, P. Department of Instrumentation and Control Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Department of Engineering University of Technology and Applied Science-Al Mudabah Muladdah Musanna Oman Department of Electronics and Communication Engineering Tamirabharani Engineering College Tamil Nadu Tirunelveli India Department of Electronics and Communication Engineering Prathyusha Engineering College Tamil Nadu Thiruvallur India

Recently considerable attention has been paid to the deep neural networks many implementations currently need greater processes that match system architecture. However, multiple components and complex interconnections are usually needed, resulting in a wide range of occupational and abundant energy consumption. Stochastic technology clearly shows impressive outcomes for reduced area-efficient hardware implementations, although current stochastic techniques need lengthy flows that activate deep delays. We recommend a numerical model of stochastic computing in this paper and add a few simple circuits. We now suggest an effective execution of an integral SC-based DNN. The system framework was deployed on a Virtex7 Xilinx FPGA based on the average area and latency decrease of 45 % and 62 %, especially compared to the best validation design in the writings. We also consider semi-integration attributed to the fault-tolerant nature of stochastic structures, resulting in a 33 % reduction in energy consumption regarding the design of discrete datatype without even any compromising on efficiency. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Next Generation of Smart Wheelchair with Speech Command 21

Next Generation of Smart Wheelchair with Speech Command

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7th International Conference on engineering and MIS, ICEMIS 2021

作者： Elsokah, Moeid Zerek, Amer R. Hamdona, Hanin M. Osman, Asaad H. Mejber, Anas D. AL-Ojeli, Amna J. Department of Communication Engineering College of Electronic Technology Tripoli Libya Faculty of Engineering EE Department Zawia University Zawia Libya Department of Electrical Engineering College of Engineering Technology Jounzur Libya University Technology Malaysia Tripoli Libya Department of Control Engineering College of Electronic Technology Tripoli Libya Department of Computer Sciences University of Benghazi Benghazi Libya

ISBN: (纸本)9781450390446

In recent times, most of the industries in their manufacture have moved on the technological development around the world. People who are a partially paralyzed or physically disabled feel uncomfortable and more dependent on others using several types of wheelchairs, and they need outside help to perform the activities of their daily lives. Electric wheelchairs are designed to assist paraplegics. This project aims to controls a wheelchair with a human voice. The prototype of the prototype that we designed and it is working an idea based on the ability of a movement by voice commands. In the order to do this, we implemented software portable on Arduino circuit connected to voice recognition where the signal drives a motor to control the movement of the chair. DC's motors are used in a conjunction with a motor drive to apply torque to the wheels, so that, a directional control can be assuring. The proposed a design supports a voice activation system for the physically challenged with the incorporation of a manual operation. Arduino microcontroller and an amplifier-based a voice recognition processor was used to support the movement of the wheelchair. The wheelchair does not respond to a wrong speech command. Keywords Automation, an internet of Things(IOT), Solar Plates, Android, Temperature, Agriculture, Greenhouse, controller, Smart Hydroponics, a smart farm, Sensor. © 2021 ACM.

关键词： Microcontrollers

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Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

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International Journal of Information technology (Singapore) 2021年第6期13卷 2363-2369页

作者： Sharma, Lakhan Dev Chhabra, Himanshu Chauhan, Urvashi Saraswat, Ritesh Kumar Sunkaria, Ramesh Kumar School of Electronics Engineering VIT-AP University Amaravati India Electronics and Communication Engineering M.L.V. Textile and Engineering College Bhilwara India Instrumentation and Control Engineering Division Netaji Subhas University of Technology Delhi India Electronics and Communication Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

Cognitive load recognition during mental arithmetic activity facilitates to observe and identify the brain’s response towards stress stimulus. As a result, an efficient mental load characterization approach using electroencephalogram (EEG) signal and Bayesian optimized K-Nearest Neighbor (BO-KNN) has been proposed in this work. The study has been conducted on a recorded EEG dataset of 30 healthy subjects who were exposed to an arithmetic questioner. To obtain artifacts free EEG signal, the Savitzky–Golay filtering approach has been utilized. Further, the decomposition of the extracted EEG signal has been carried out using stationary wavelength transform. In this work, the entropy based feature extraction has been performed followed by F-score based feature selection. Top 40 features having the highest precedence have been used for classification using BO-KNN. The rigorous experimental analysis has been performed to analyze the effectiveness of the proposed method over other state-of-the-art methods and it shows that the classification accuracy is substantially improved. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Bayesian optimized K-nearest neighbor (BO-KNN) Cognitive task EEG Stationary wavelet transformation

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Evolving Multi-Access Edge Computing (MEC) for Diverse Ubiquitous Resources Utilization: A Survey

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Telecommunication Systems 2025年第2期88卷 1-41页

作者： Khattak, Muhammad Ilyas Yuan, Hui Khan, Ajmal Ahmad, Ayaz Ullah, Inam Ahmed, Manzoor School of Control Science and Engineering Shandong University Shandong Province Jinan China Communication and Information Research Center Sultan Qaboos University Muscat Oman Department of Electrical Engineering College of Engineering King Faisal University Al-Ahsa Saudi Arabia Jinan China School of Computer and Information Science and AI Industrial Technology Research Hubei Engineering University Xiaogan China

The surge in smart device usage and the demand for high-throughput, low-latency mobile networks have accelerated the adoption of distributed smart applications and Collaborative Resource Management (CRM) techniques such as Multi-Access Edge Computing (MEC). With its ability to handle large data volumes, computational demands, scalability, and high energy consumption, MEC represents the next generation of Mobile Cloud Computing (MCC), addressing key challenges in emerging smart applications. MEC supports real-time, high-throughput, and energy-efficient techniques like task offloading and ultra-reliable low-latency communication (URLLC). However, as these techniques become more widespread, their diversity increases, leading to complexities that make them harder to understand and validate in different applications. This includes their use in areas like assistance for unmanned vehicles (UVs), autonomous driving, unmanned aerial vehicles (UAVs), high altitude platforms (HAPs), underwater unmanned vehicles (UUVs), satellite air-ground communication (SAGC), and other emerging systems that rely on Collaborative Resource Management (CRM). Although a number of review articles pertaining to MEC systems exist, we analyzed techniques beyond task offloading that explore MEC system applicability in diverse domains, including UUVs, vehicular edge computing, Re-configurable Intelligent Surfaces (RIS), Massive MIMO, autonomous driving, HAPs, and SAGC. From the perspective of these optimization techniques, this article highlights their role in shaping MEC’s evolution, focusing on parameters such as real-time response, energy efficiency, and other critical factors in task offloading processes. We also explored the role of Standard Development Organizations (SDOs), such as ETSI and IEEE, in advancing MEC systems. This included an analysis of offloading techniques based on their principles, features, and trade-offs, using key performance indicators (KPIs) for fair comparison, while ident

关键词： Computation offloading

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