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Adversarial Attacks Against Double RIS-Assisted MIMO Systems-based Autoencoder in Finite-Scattering Environments

IEEE Wireless communications Letters 2024年第6期14卷 1593-1597页

作者： Son, Bui Duc Khanh, Ngo Nam Van Chien, Trinh In Kim, Dong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The School of Information and Communication Technology Hanoi University of Science and Technology Hanoi100000 Viet Nam

Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This paper explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-assisted multiple-input and multiple-output (MIMO)-based autoencoder, where an adversary employs encoded and decoded datasets to create adversarial perturbation and fool the system. Because of the complex and dynamic data structures, adversarial attacks are not unique, each having its own benefits. We, therefore, propose three algorithms generating adversarial examples and perturbations to attack the RIS-MIMO-based autoencoder, exploiting the gradient descent and allowing for flexibility via varying the input dimensions. Numerical results show that the proposed adversarial attack-based algorithm significantly degrades the system performance regarding the symbol error rate compared to the jamming attacks. © 2012 IEEE.

关键词： Decoding Vectors Reconfigurable intelligent surfaces Finite element analysis Fading channels Correlation MIMO communication Jamming Optimization Channel state information

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Research on Artificial Intelligence (AI) Recognition Automated Soft-Shell Crab Breeding System under 'Internet +' technology 3

Research on Artificial Intelligence (AI) Recognition Automat...

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3rd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Zheng, Lei Gong, Mingmin Li, Zhenyang Yang, Mingming School of Business Shenzhen Institute of Technology Shenzhen China School of Information Engineering Wuhan College Wuhan China School of Information and Communication Shenzhen Institute of Technology Shenzhen China

ISBN: (纸本)9798350380989

This project aims to use artificial intelligence technology to raise crabs in land equipment, so as to easily obtain soft-shell crabs, to increase the supply of new aquatic products in the market, to meet the needs of market consumption upgrading, and finally to achieve the goal of economic benefits. This project is based on artificial intelligence to identify automatic soft-shell crab cultivation technology, combined with scientific breeding management methods, the general crab to soft-shell crab cultivation. The equipment uses the Internet of Things, big data, cloud computing, computer vision and other technologies to design five systems, including soft shell crab mounting identification system, water quality monitoring system, three-dimensional breeding system, automatic feeding system, integrated control system, to realize the local and remote all-round intelligent monitoring of soft-shell crab breeding base. In this paper, STC15F2K60S2 as the core of the design of MCU based hardware platform is presented. RS485 communication mode is used to collect data of each sensor in real time, and multiple environmental parameters can be monitored in real time. Using video surveillance technology, the pond can be secured, and the pond can be photographed on the surface and underwater. The feeding device is controlled by PLC and the feeding machine and aerator are controlled intelligential. The network uses ESP8266 wireless network to realize network connection to AP. Through the establishment of the 'server' management software, we enable users to remote query through the computer browser, mobile APP and other ways in the case of network. © 2024 IEEE.

关键词： Automatic recognition monitoring system sensor soft shell crab breeding system water quality monitoring

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Unsupervised Fast Scene Depth Estimation Based on Laplacian Pyramid 5

Unsupervised Fast Scene Depth Estimation Based on Laplacian ...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Peng, Zhiyong Yang, Guixu Qin, Zujun Qiao, Yulong Guilin University of Electronic Technology School of Optoelectronic Engineering Guilin China Harbin Engineering University School of Information and Communication Engineering Harbin China

ISBN: (纸本)9798350373820

Currently, most scene depth estimation based on deep learning relies on supervised methods, which require a large number of real labels. However, obtaining such labels in practice is often challenging. Existing unsupervised methods have problems such as low accuracy, significant artifacts, and slow inference speed. Therefore, addressing these challenges, we propose an unsupervised fast depth estimation network based on the Laplace pyramid. This network is trained by minimizing losses related to sparse matching point pair disparity using Scale-invariant Feature Transform (SIFT), image reconstruction and the consistency of left and right depth maps generated by the weight-sharing sub-network. During deployment, only one image needs to be input to complete the testing. Experimental results on the KITTI dataset demonstrate that our method outperforms the compared unsupervised depth estimation algorithms in terms of depth map accuracy, image clarity, and inference speed. For images with a resolution of 1152 × 256, the inference speed on an RTX2080Ti graphics card reaches 46fps. © 2024 IEEE.

关键词： Image reconstruction

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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Computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Anti-swarm UAV radar system based on detection data fusion

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Journal of Systems engineering and Electronics 2024年第5期35卷 1167-1176页

作者： WANG Pengfei HU Jinfeng HU Wen WANG Weiguang DONG Hao School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China China Airborne Missile Academy Luoyang 471009China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324003China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

There is a growing body of research on the swarm unmanned aerial vehicle(UAV)in recent years,which has the characteristics of small,low speed,and low height as radar *** confront the swarm UAV,the design of anti-UAV radar system based on multiple input multiple output(MIMO)is put forward,which can elevate the performance of resolution,angle accuracy,high data rate,and tracking flexibility for swarm UAV *** resolution and detection are the core problem in detecting the swarm *** distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array *** MIMO radar has better performance in resolution,swarm UAV detection still has difficulty in target *** paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection ***,signal processing and data processing based on the detection fusion algorithm above are designed,forming a high resolution detection *** simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

关键词： swarm radar high resolution deep neural network fusion algorithm

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An Efficient Attention Deficit Hyperactivity Disorder (ADHD) Diagnostic Technique Based on Multi-Regional Brain Magnetic Resonance Imaging

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Journal of Computing Science and engineering 2023年第3期17卷 135-143页

作者： Sachnev, Vasily Mahanand, B.S. School of Information Communication and Electronics Engineering Catholic University of Korea Bucheon Korea Republic of Department of Information Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysuru India

In this paper, an efficient technique for the diagnosis of attention deficit hyperactivity disorder (ADHD) was proposed. The proposed method used features/voxels extracted from structural magnetic resonance imaging (MRI) scans of seven brain regions and efficiently classified three subtypes of ADHD: ADHD-C, ADHD-H, and ADHD-I, as well as the typically developing control (TDC). Training and testing data for experiments were obtained from ADHD-200 database, and 41,721 features/voxels were extracted from sMRI by using region-of-interest (ROI). The proposed ADHD diagnostic technique built an efficient ADHD classifier in two steps. In the first step, a proposed regional voxels selection method (rVSM) selected an optimal set of features/voxels from seven brain regions available in ADHD-200, i.e., the Amygdala, Caudate, Cerebellar Vermis, Corpus Callosum, Hippocampus, Striatum, and Thalamus. In the second step, voxels/features selected by rVSM were used together to form a unified set of voxels. The unified set of voxels was used by a multiregion voxels selection method to train an efficient classifier using the extreme learning machine (ELM). Finally, the proposed method selected a unique set of voxels from the seven brain regions and built a final ELM classifier with maximum accuracy. Experiments clearly indicated that the proposed method produced better results than existing methods. © 2023. The Korean Institute of information Scientists and Engineers

关键词： Magnetic resonance imaging

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An edge-cloud collaborative industrial Internet of Things network architecture for complex scenarios 8

An edge-cloud collaborative industrial Internet of Things ne...

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8th IEEE Symposium on Wireless technology and Applications, ISWTA 2024

作者： Luo, Meiqi Yan, Ming Yang, Hongbin Li, Lifen School of Information and Communication Engineering Communication University of China Beijing100024 China China Mobile Digitalization Technology Company Beijing100080 China School of Information Science and Technology Yanching Institute of Technology Langfang065201 China

ISBN: (纸本)9798350351354

The 5th generation (5G) wireless network technology drives the rapid development of Industrial Internet of Things (IIoT). Edge computing technology based on 5G networks can effectively alleviate the pressure on the cloud, thus providing reliable low-latency network services for IIoT. However, in complex industrial scenarios such as mines, an optimized design of the network architecture is required to activate the performance of edge-cloud cooperative networks. In this paper, we first investigate the key technologies related to edge-cloud collaboration. Based on analyzing technologies such as edge computing, an edge-cloud cooperative IIoT architecture is designed to cope with various scenarios. Finally, we give a network architecture for complex scenarios in mines and analyze its main functional modules. This network architecture can effectively respond to the needs of IIoT in complex scenarios. © 2024 IEEE.

关键词： Cloud computing architecture

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Design of Belt No-load Detection System Based on Image Processing technology

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第11期51卷 1731-1739页

作者： Sun, Jiali Xu, Shaochuan Liu, Dehua He, Jing School of Electronic Information University of Science and Technology Liaoning Anshan114051 China School of Control Science and Engineering University of Science and Technology Liaoning Anshan114051 China Anshan Liaoning University of Science and Technology 114051 China

Belts are mainly used for transporting coal. During the conveying process, the belt may be idling. Manual inspections not only lead to reduced efficiency, but also pose safety hazards. As a result, it is proposed a belt no-load monitoring system based on the improved algorithm of support vector machine. The belt is used for on-site monitoring of belt idling problems. An initial image of the belt is acquired by a camera unit and the image is preprocessed. According to the belt images of different working conditions, feature selection is performed for images affected by external light sources, belt surface trace features and ore state distribution. Then support vector machine models using different kernel functions are constructed to classify the belts. Therefore, the belt no-load detection method proposed in this paper can to a certain extent solve the problem that artificial cannot monitor the belt status in real time and timely warning. This method reduces the chance of belt conveyor failures and makes the belt work more efficiently. © (2024), (International Association of Engineers). All rights reserved.

关键词： Belt conveyors

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A Dynamic Bayesian-Based Comprehensive Trust Evaluation Model for Dispersed Computing Environment

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China communications 2023年第2期20卷 278-288页

作者： Hongwen Hui Zhengxia Gong Jianwei An Jianzhong Qi School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate School of University of Science and Technology Beijing Guangdong 528399China China Academy of Information and Communications Technology(CAICT) Beijing 100191China Department of Information Engineering North China University of TechnologyBeijing 100144China

Dispersed computing is a new resourcecentric computing *** to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its *** trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing *** this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing ***,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the *** the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding ***,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary *** experiments are introduced to verify the performance of the *** with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.

关键词： dispersed computing trust evaluation model malicious node interaction success rate detection rate

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Enhance Statistical Features with Changepoint Detection for Driver Behaviour Analysis 21st

Enhance Statistical Features with Changepoint Detection for ...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Maktoubian, Jamal Tran, Son N. Shillabeer, Anna Amin, Muhammad Bilal Sambrooks, Lawrence School of Information and Communication Technology University of Tasmania Hobart Australia School of Information Technology Deakin University VIC Australia School of Computing Mathematics and Engineering Charles Sturt University NSW Australia

ISBN: (纸本)9789819601240

Driver behaviour modelling is a critical field that addresses complex and dynamic driving behaviours on roads with the goal of enhancing road safety, reducing air pollution, and improving vehicle performance. Recent advancements in sensor technology and machine learning (ML) techniques have facilitated the capture and analysis of driver behaviour patterns. Nonetheless, the efficacy of ML models heavily relies on the quality of the data used. Therefore, developing feature extraction techniques that provide high-quality inputs is crucial. In this paper, we conceptualised, implemented, and evaluated a novel feature model called Changepoint-based Statistical Feature (C-bSF). Initially, we extracted various statistical functions from raw sensor data, which were then aggregated using lagging windows. Following this, a changepoint detection method was used to derive the C-bSF feature. We compared the performance metrics of this new approach with other feature extraction methods, demonstrating the superiority of C-bSF in driver behaviour classification tasks across three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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