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ISAR Super-Resolution Imaging Approach Based on Low-Rank Sparse Joint Constraint Under Low SNR

IEEE SENSORS JOURNAL 2024年第3期24卷 3191-3201页

作者： Zhou, Peng Zhu, Yanli Ma, Mingyu Zhang, Zhenhua Zhang, xi Zhang, Jie China Univ Petr Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Beijing Res Inst Telemetry Beijing 100076 Peoples R China Minist Nat Resources Peoples Republ China Inst Oceanog 1 Qingdao 266061 Peoples R China

A super-resolution imaging approach for inverse synthetic aperture radar (ISAR) systems under the condition of low signal-to-noise ratio (SNR) is proposed based on the theory of low-rank and sparse matrix decomposition. The approach unites low-rank constraints and sparse constraints, while taking noise constraints into account. The regularization terms corresponding to the above constraints are added to the objective function of the ISAR super-resolution imaging optimization model. The constructed objective function is then solved using the linear alternating direction multiplier method with an adaptive penalty function to realize the ISAR super-resolution imaging under low SNR. The performance of the proposed approach under low SNR is verified by simulation and real data processing. Our proposed approach achieves better super-resolution imaging quality than those of commonly used algorithms including the orthogonal matching pursuit (OMP) algorithm, the split Bregman iteration (SBI) algorithm, and an approach using low-rank and sparsity priors (LRSPs) under the same low SNR, which is verified by comprehensively comparing the target-to-background ratio (TBR), the peak SNR (PSNR), and the image visual effect. Furthermore, the range of motion compensations errors that the proposed LSJA-LS approach can adapt to is also presented.

关键词： Imaging Signal to noise ratio Superresolution Radar imaging Sensors Radar Matching pursuit algorithms Adaptive penalty function inverse synthetic aperture radar (ISAR) low signal-to-noise ratio (SNR) low-rank sparse joint constraint super-resolution imaging

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An Improved Gaussian-Mapping Algorithm for Edge Detection Based on FPGA 9

An Improved Gaussian-Mapping Algorithm for Edge Detection Ba...

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9th International Conference on systems and Informatics, ICSAI 2023

作者： xie, Dihong Wu, Jin Zhao, Bo Pang, Wenting Xi'an University of Posts and Telecommunications Dept. School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350383706

As a method of image processing, edge detection is widely used in object detection, image recognition, machine vision and other fields. At present, the research of edge detection algorithm is still a hot problem in the field of image processing. At the same time, in order to solve the problem that the edge detection operator is difficult to identify due to noise interference and low edge contrast in the image, in this paper, a contrast enhancement processing algorithm, based on Gaussian filter, is used to preprocess the image. In this paper, an FPGA-based grayscale conversion algorithm is used to convert the original image into a grayscale image, and then an improved algorithm based on Gaussian filtering is used to successfully enhance the slope of the edge in the original image. In order to process the edge detection of the image faster, use FPGA to realize the algorithm of edge detection of the image, and the result is output and displayed through VGA display. Finally, by comparing the experimental results of Roberts, Prewitt and Sobel operators, the advantages and disadvantages of several different algorithms are obtained. After enhancing the slope, using the edge detection algorithm, it is found that the three edge detection operators after enhancing have better detection results without wasting extra time. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Potential Themes and Trends of Chinese Language Education Technology Under the STM Structured Topic Model 2

Potential Themes and Trends of Chinese Language Education Te...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Zhao, Kang Xi'an Fanyi University XI'an China

ISBN: (纸本)9798350317558

In view of the potential themes and trends of Chinese language education technology, this paper proposes a technical potential theme and trend scheme based on STM structure topic model. Firstly, the influencing factors is accurately located through the gradient descent theory, and the indicators is reasonably divided to reduce the interference, and the STM structure topic model is used to construct the potential themes and trends of the technology. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional image recognition algorithm in terms of the accuracy of potential themes and trends of the technology, and the processing time of influencing factors, which has obvious advantages. The potential themes and trends of technology play an extremely important role in Chinese language education, which can accurately predict and optimize the growth characteristics and product generation of Chinese language education. However, traditional image recognition algorithms have certain limitations in solving potential theme simulation problems, especially when dealing with complex problems. In this paper, this paper proposes a technical potential theme and trend scheme based on the STM structure topic model to better solve this problem. In this scheme, the influencing factors is accurately located through the gradient descent theory, so as to determine the division of indicators, and the STM structure topic model is used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on the STM structure topic model can better solve the limitations of traditional image recognition algorithms and improve the accuracy and efficiency of simulation in the potential themes and trends of Chinese language education technology. © 2024 IEEE.

关键词： Chinese Language Education Gradient descent theory Potential Topics STM Structural Topic Model Trend

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Underwater Imaging Using Low Rank Matrix and Sparse Representation for Oil Pipeline Inspection 7

Underwater Imaging Using Low Rank Matrix and Sparse Represen...

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7th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023

作者： Zhou, Mingshuo Zhang, Qizhi School of Electronic Engineering Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350382877

Due to the critical importance of underwater pipeline integrity, particularly in the oil and gas transportation sector. This paper addresses the significance of applying low-rank matrix and sparse representation theories in the context of underwater imaging, with a primary focus on oil pipeline inspection. By comparing the effects of three algorithms on image processing, the superiority of the Structured Low-Dimensional Representation (SLDR) algorithm is demonstrated. The research aims to enhance the accuracy and quality of underwater images, ensuring the safety and integrity of subaqueous pipeline systems. © 2023 IEEE.

关键词： image enhancement

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Construction of Deep ReLU Nets for Spatially Sparse Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2023年第10期34卷 7746-7760页

作者： Liu, xia Wang, Di Lin, Shao-Bo Xian Univ Technol Sch Sci Xian 710048 Peoples R China Xi An Jiao Tong Univ Ctr Intelligent Decis Making & Machine Learning Sch Management Xian 710049 Peoples R China

Training an interpretable deep net to embody its theoretical advantages is difficult but extremely important in the community of machine learning. In this article, noticing the importance of spatial sparseness in signal and image processing, we develop a constructive approach to generate a deep net to capture the spatial sparseness feature. We conduct both theoretical analysis and numerical verifications to show the power of the constructive approach. Theoretically, we prove that the constructive approach can yield a deep net estimate that achieves the optimal generalization error bounds in the framework of learning theory. Numerically, we show that the constructive approach is essentially better than shallow learning in the sense that it provides better prediction accuracy with less training time.

关键词： Deep learning Neural networks Training Spatial resolution Partitioning algorithms Signal resolution Optimization Constructive deep net (CDN) deep learning learning theory spatial sparseness

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Security Risk Assessment of image Classification Model Based on ANP-TOPSIS

Security Risk Assessment of Image Classification Model Based...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Zhiheng Liu, Zeyu Yang, Li Zhe, Tingbo Wu, Jian School of Computer Science and Technology Xidian University Xi'an China Guiyang Aviation Motor Co. Ltd Guizhou China

ISBN: (纸本)9798350327380

Recently, machine learning algorithms have been widely used in the fields of image processing, network security and natural language processing, etc., profoundly affecting human life. However, machine learning algorithms have the characteristics of uncertain output, vulnerability to adversarial attacks, and unexplained decision-making processes, which seriously threaten the security of machine learning-based face recognition, Malware detection, and autonomous driving. Hence, it is imperative for the security practitioners to evaluate algorithm security to ensure that security needs are met. In this article, the authors propose a set of security assessment index systems and methods for machine learning algorithms for image classification scenarios: Refer to the security specification of machine learning algorithms and requirements to construct the security index system of image classification model. Furthermore, The Analytic Network Process(ANP) is applied to quantify the index weights and the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) is applied to screen the optimal model, and finally the sensitivity analysis is applied to prove the stability of the proposed method. Experimental results show that this method has certain value and effect in assessing the security and model screening of image classification models. © 2023 IEEE.

关键词： image classification

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image Recognition and Position Technology Based on Super-pixel Fuzzy C-Means Clustering in Industrial Assembly System 3

Image Recognition and Position Technology Based on Super-pix...

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3rd International Conference on image processing and Intelligent Control, IPIC 2023

作者： Yuan, Hailiang Sun, Weitao Wang, Hailing Tianjin Vocational College of Mechanics and Electricity Tianjin China Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781510668140

Improved fuzzy c-means (FCM) clustering algorithms have been widely used for image recognition and localization. However, in industrial assembly systems, the unsatisfactory pixel merging and segmentation results between local adjacent windows, combined with the differences in the shape, size, and material of parts, as well as variations in lighting conditions, make target image recognition and localization a challenge. Most algorithms struggle to achieve the expected results and have high computational complexity. In this study, we propose a super-resolution-based FCM clustering algorithm that is faster and more accurate for image recognition and localization in industrial assembly systems with irregular part sizes. We first use multiscale morphological gradient operations to obtain high-resolution images. Then, we use the fast FCM clustering algorithm to achieve the recognition and extraction of specific target images. Finally, we use the Sobel operator to determine the target's position. The experimental results demonstrate that the proposed algorithm shows higher accuracy and efficiency in image recognition and localization for industrial assembly systems. © 2023 SPIE.

关键词： image recognition

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Artificial intelligence in tunnel construction: A comprehensive review of hotspots and frontier topics

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Geohazard Mechanics 2024年第1期2卷 1-12页

作者： Lianbaichao Liu Zhanping Song Xu Li School of Civil Engineering Xi'an University of Architecture and TechnologyXi'an710055China Shaanxi Key Lab of Geotechnical and Underground Space Engineering Xi'an710055China Institute of Tunnel and Underground Structure Xi'an University of Architecture and TechnologyXi'an710055China

Application of Artificial Intelligence(AI)in tunnel construction has the potential to transform the industry by improving efficiency,safety,and *** paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction.A total of 554 articles published between 2011 and 2023 were collected from the Web of Science(WOS)core collection database and analyzed using CiteSpace *** analysis identified three main study areas:Tunnel Boring Machine(TBM)performance,construction optimization,and rock and soil *** review highlights the advancements made in each area,focusing on design and operation,performance prediction models,and fault detection in TBM performance;computer vision and image processing,neural network algorithms,and optimization and decision-making in construction optimization;and geo-properties and behaviours,tunnel stability and excavation,and risk assessment and safety management in rock and soil *** paper concludes by discussing future research directions,emphasizing the integration of AI with other advanced technologies,realtime decision-making systems,and the management of environmental impacts in tunnel *** comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolvingfield.

关键词： Literature review Underground construction CiteSpace Artificial intelligent Bibliometrics analysis

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Design of Human Resource Management System Based on Ant Colony Algorithm

Design of Human Resource Management System Based on Ant Colo...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Jia, Yanling xing, Yao Xi'an Polytechnic University School of Management Shaanxi 710600 China

ISBN: (纸本)9798350382693

Aiming at the limitations of traditional image recognition algorithms in the design of human resource management system, a design scheme of resource management system based on ant colony algorithm was proposed. Firstly, the influencing factors is accurately located through the colony foraging theory, and the indicators is reasonably divided to reduce interference, and the ant colony algorithm is used to construct the design scheme of the resource management system. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional image recognition algorithm in terms of the design accuracy of the resource management system and the processing time of influencing factors, and has obvious advantages. The design of resource management system plays an extremely important role in human resources, which can accurately predict and optimize the growth characteristics and product generation of human resources. However, traditional image recognition algorithms have certain limitations in solving resource management simulation problems, especially when dealing with complex problems. In this paper, a resource management system design scheme based on ant colony algorithm is proposed to better solve this problem. In this scheme, the influencing factors were accurately located through the swarm foraging theory, so as to determine the division of indicators, and the ant colony algorithm was used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on ant colony algorithm in the design of human resource management system can better solve the limitations of traditional image recognition algorithms and improve the simulation accuracy and efficiency. © 2024 IEEE.

关键词： Ant colony optimization

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A Highway Traffic image Enhancement Algorithm Based on Improved GAN in Complex Weather Conditions

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第8期24卷 8716-8726页

作者： Cheng, xin Zhou, Jingmei Song, Jiachun Zhao, xiangmo Changan Univ Sch Informat Engn Xian 710064 Peoples R China Traff Management Res Inst Minist Publ Secur Wuxi 214151 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Xian Technol Univ Sch Elect Informat Engn Xian 710021 Peoples R China

To address the problems of low resolution and blurred details in highway images caused by factors such as rain and fog, illumination interference, and nighttime lighting, this paper proposes a highway traffic image enhancement algorithm based on improved GAN in complex weather conditions. The attention mechanism and the multiscale feature fusion were combined to improve the generator network, which could effectively reduce noise while improving the attention of high-frequency region information. The improved PatchGAN in the discriminator used a local discrimination strategy to distinguish the generated image from the real image, and then the Nash equilibrium was achieved through the continuous interaction between the generator and the discriminator, to ensure the integrity and authenticity of the restored image. Compared with other image enhancement algorithms, using PSNR and SSIM as measurement indicators, the experimental results showed that the proposed algorithm's results were, respectively, 21.97% and 12.89% higher in nighttime enhancement, 26.16% and 12.75% higher in rain removal, and 26.56% and 12.1% higher in fog removal. The proposed algorithm can not only retain the image details and feature information, but also produce effective denoising, which increases the reliability of image-based traffic information processing and analysis.

关键词： image enhancement Meteorology Rain Generators Generative adversarial networks image color analysis Feature extraction Traffic image enhancement improved GAN attention mechanism multiscale feature

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