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16th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2022

作者： He, Bin Yu, Ning-Mei Xu, Meng Wang, xing-Jia Xi'an University of Technology School of Automation and Information Engineering Xi'an710000 China

ISBN: (数字)9781665469067

ISBN: (纸本)9781665469067

Aiming at the problems of long operation time and poor flexibility of embedded processor, this paper proposes a structure of configurable execution unit based on RISC-V extended instruction set for computing granularity. The structure sets up basic parallel computing units and configuration registers, which can realize a large number of parallel operations such as multiplication, addition, or higher abstractions similar to convolutional neural networks by software configuration. This paper completed the design of this structure and integrated it into a four-stage pipelined RISC-V processor. The white blood cell images collected by lensless system are used to test various image algorithms. The experimental results show that the processor speed designed in this paper is 5.95 times to 22.21 times higher than that of the ordinary pipeline processor, and the number of instructions required is reduced by 1.16 times to 7.49 times. Therefore, the processor has a certain improvement in processing speed and flexibility, which is of great significance for the implementation of various algorithms in embedded systems. © 2022 IEEE.

关键词： Pipeline processing systems

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Study on Optimization of Computer image Structure in Wushu Performance Training Model

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International Journal of High Speed Electronics and systems 2025年第2期34卷

作者： Li, Li PE Department of Shaanxi Xueqian Normal University Shaanxi Xueqian Normal University Shanxi Xi’an710100 China

With the vigorous development of computer technology, its application in various fields is increasingly deepening, especially in the application of martial arts performance training models. This paper focuses on the practical application and far-reaching impact of computer image structure optimization in martial arts performance training models. Computer image structure optimization is a multidisciplinary technique that involves image processing, computer vision, and machine learning, aiming to optimize the visual effects and quality of images through algorithms and models. In the training model of martial arts performance, computer image structure optimization technology plays a crucial role. By utilizing advanced technologies such as color management, light and shadow processing, motion estimation and compensation, the clarity, smoothness, and coherence of martial arts performance images can be effectively improved, thereby enhancing the artistic effect and viewing value of martial arts performances. The study delved into the application effect of computer image structure optimization in martial arts performance training models. Through empirical research, we have found that optimized images can not only more accurately display the details and techniques of martial arts movements, but also create a more stunning performance atmosphere. Meanwhile, computer image structure optimization technology can also provide more accurate and efficient feedback mechanisms for martial arts performance training, helping athletes better adjust and improve their movements. © World Scientific Publishing Company.

关键词： Motion estimation

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Solving Optimization Problems Through Fully Convolutional Networks: An Application to the Traveling Salesman Problem

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第12期51卷 7475-7485页

作者： Ling, Zhengxuan Tao, xinyu Zhang, Yu Chen, xi Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

In the new wave of artificial intelligence, deep learning is impacting various industries. As a closely related area, optimization algorithms greatly contribute to the development of deep learning. But the reverse applications are still insufficient. Is there any efficient way to solve certain optimization problem through deep learning? The key is to convert the optimization to a representation suitable for deep learning. In this article, a traveling salesman problem (TSP) is studied. Considering that deep learning is good at image processing, an image representation method is proposed to transfer a TSP to an image. Based on samples of a ten city TSP, a fully convolutional network (FCN) is used to learn the mapping from a feasible region to an optimal solution. The training process is analyzed and interpreted through stages. A visualization method is presented to show how an FCN can understand the training task of a TSP. Once the training is completed, no significant effort is required to solve a new TSP and the prediction is obtained on the scale of milliseconds. The results show good performance in finding the global optimal solution. Moreover, the developed FCN model has been demonstrated on TSP's with different city numbers, proving excellent generalization performance.

关键词： Urban areas Optimization Deep learning Neural networks Feature extraction image resolution Heuristic algorithms Deep learning fully convolutional network (FCN) mapping optimization traveling salesman

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Aerial Photovoltaic Panel Infrared image Defect Detection Method Based on Improved YOLOv8

Aerial Photovoltaic Panel Infrared Image Defect Detection Me...

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Hongtao Hu Yunxiang Li Junfeng Wang Yong Lu School of Computer Science Xi'an ShiYou University Xi'an China No.3 Oil Production Plant PetroChina Changqing Oilfield Company Xi'an Shannxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Photovoltaic panels are the core equipment of photovoltaic power generation. Defects in photovoltaic panels are generally detected by analyzing infrared images taken by drones. However, the photovoltaic panel defects to be detected in infrared images are small, and traditional target detection algorithms are not sensitive to small targets. Misdetections and missed detections are prone to occur. In response to the above problems, this article proposes an Improved-YOLOv8 model to increase the accuracy of small target detection. Firstly, the CA attention mechanism is integrated into the YOLOv8 model to improve the ability to extract defect target features and locations; secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to replace Concat to reduce the loss of target information and enhance the model's ability to fuse targets of different scales. Maintain the sensitivity of the model to targets of different scales; finally, experiments were conducted using infrared image data of photovoltaic panels taken aerially from a photovoltaic power station. The results show that the accuracy and mAP@0.5 of the Improved-YOLOv8 model reached 88.5% and 95.2% respectively, effectively improving the YOLOv8 model's ability to handle small target defects in photovoltaic panels.

关键词： Photovoltaic systems Accuracy Attention mechanisms Signal processing algorithms Object detection Signal processing Feature extraction Data models Drones Defect detection

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跟踪成像复合轴光学系统设计

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光子学报 2024年第10期53卷 192-202页

作者：许彤裕李旭阳任志广魏锦洋陆志贤边立国姚凯中中国科学院西安光学精密机械研究所空间光学技术研究室西安710119 中国科学院大学北京100049

空间望远镜的部件振动与相对运动会导致像移,为抑制振动引起的像移,可在光路中加入快速反射镜对像移进行抑制(二级稳像),然而快反镜较小的通光口径与稳像时造成的像旋限制了应用范围。为此,围绕以快反镜实现二级稳像的同轴共光路跟踪成... 详细信息

空间望远镜的部件振动与相对运动会导致像移,为抑制振动引起的像移,可在光路中加入快速反射镜对像移进行抑制(二级稳像),然而快反镜较小的通光口径与稳像时造成的像旋限制了应用范围。为此,围绕以快反镜实现二级稳像的同轴共光路跟踪成像复合轴光学系统,分析对比反射式与折反式两种同轴无焦系统各自的特点,选择可实现较大扩束比的折反式无焦系统作为复合轴光学系统的前组,以满足光路中快反镜小通光口径的要求。根据像移补偿与像旋产生的机理构建数学模型,使用MATLAB分析仿真快反镜补偿线性像移时,探测器不同位置上像旋对调制传递函数的影响,并根据后组焦距与边缘视场调制传递函数的关系图,选择合适的后组焦距将像旋对调制传递函数的影响降低在合理范围。设计出焦距为9200 mm、快反镜通光口径小于30 mm的复合轴光学系统,并将像旋造成的调制传递函数下降控制在10%内。

关键词：光学设计快速反射镜无焦系统复合轴大孔径

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A Semantic Feature-Driven Multiscale Grid Dehazing Method

A Semantic Feature-Driven Multiscale Grid Dehazing Method

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Hao Zhang Hongmei Jin Zhanli Li Ning He Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the problems of incomplete dehazing and detail distortion in the dehazing process of existing algorithms, an end-to-end dehazing method SemanticGridDehazeNet is proposed, which is semantic feature-driven and combines multi-scale feature fusion. The method consists of three modules: preprocessing, backbone, and post-processing. First, preprocessing is performed using a newly designed residual dense fusion block (RDFB) and convolutional layer. The Backbone module is based on the extended GridN et, which contains three processing scales, and semantic features extracted based on VGG16 Net are added in front of each scale to enhance learning. Each scale contains five RDFB blocks to fuse the feature map to the last column. Then an RDFB post-processing module improves the output quality. The main innovations include: (1) the introduction of the RDFB fusion module to focus on haze features, dynamically adjust the level of attention paid to channel or pixel locations to more effectively learn and utilize key information about the haze distribution in the input data; (2) Semantic features are integrated into the image dehazing process by incorporating semantic features extracted from VGG16 Net before each processing scale. This enhances multi-scale learning and enables more accurate focus on haze features, improving dehazing effectiveness. Experimental results demonstrate superior performance in target enhancement, robustness, and generalization. The method effectively removes residual hzae and addresses detail distortion issues.

关键词： Measurement Technological innovation image color analysis Fuses Semantics Signal processing algorithms Feature extraction Distortion Robustness Real-time systems

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

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

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International Conference on systems and Informatics (ICSAI)

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

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.

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High-speed image reconstruction for optically sectioned,super-resolution structured illumination microscopy

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Advanced Photonics 2022年第2期4卷 78-90页

作者： Zhaojun Wang Tianyu Zhao Huiwen Hao Yanan Cai Kun Feng Xue Yun Yansheng Liang Shaowei Wang Yujie Sun Piero RBianco Kwangsung Oh Ming Lei Xi’an Jiaotong University School of PhysicsMOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterShaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic DevicesXi’anChina Peking University School of Life SciencesState Key Laboratory of Membrane Biology&Biomedical Pioneer Innovation Center(BIOPIC)BeijingChina Northwest A&F University College of ScienceYanglingChina University of Nebraska Medical Center College of PharmacyDepartment of Pharmaceutical SciencesOmahaNebraskaUnited States University of Nebraska Omaha College of Information Science&TechnologyDepartment of Computer ScienceOmahaNebraskaUnited States

Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living *** date,by using elaborately designed systems and algorithms,SR-SIM can achieve rapid,optically sectioned,SR observation with hundreds to thousands of time ***,real-time observation is still out of reach for most SIM setups as conventional algorithms for image reconstruction involve a heavy computing *** address this limitation,an accelerated reconstruction algorithm was developed by implementing a simplified workflow for SR-SIM,termed joint space and frequency *** algorithm results in an 80-fold improvement in reconstruction speed relative to the widely used ***,the increased processing speed does not come at the expense of spatial resolution or sectioning capability,as demonstrated by live imaging of microtubule dynamics and mitochondrial tubulation.

关键词： real-time structured illumination microscopy high-speed image reconstruction live-cell imaging microtubule dynamics mitochondrial tubulation

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A Review of Intrinsic image Decomposition

A Review of Intrinsic Image Decomposition

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image processing and Media Computing (ICIPMC), International Conference on

作者： Siyuan Liu xiaoyue Jiang Letian Liu Zhaoqiang xia Sihang Dang xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Intrinsic image decomposition (IID) aims to extract intrinsic components from natural images, namely shading and reflectance, which is widely applied in computer vision and image processing tasks for the better understanding of the objects and scenes. However, IID is a severely ill-posed problem, extra constraints should be applied to address the decomposition problem. In this paper, we reviewed recent papers about intrinsic image decomposition. The extra cues from lighting, shape, color and textures are used to constrain the ill-posed problem. These papers can be mainly classified into two categories: physics-based methods and learning-based methods. Physics-based methods mainly introduced extra constraints into an optimization framework to solve the IID problem. While learning-based methods mainly depend on an encoder-decoder network with the explicit constraint from the labeled intrinsic images. Additionally, extra priors are introduced into the network by the form of network structure or specific feature. In order to make the IID problem more practical, researchers may explore how to achieve fast and accurate image decomposition with lower computing resources through optimization algorithms and model design.

关键词： Learning systems Accuracy image color analysis Shape Computational modeling image decomposition Real-time systems

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Enhancing Downhole image Reconstruction in Oil and Gas Drilling through Low Rank Sparse Models

Enhancing Downhole Image Reconstruction in Oil and Gas Drill...

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Digital Society and Intelligent systems (DSInS), International Conference on

作者： Yixi Zhou Lin Li Chaoyang Xue Kexin Ji School of Electronic Engineering Xi’an Shiyou University Xi’an China

Effective subsurface monitoring is vital in oil drilling. This study emphasizes the need for an algorithm to streamline video transmission from underground to the wellsite, overcoming bandwidth and interference challenges for high-quality transmission. It employs low-rank and sparse models to enhance image quality. Comparing three image processing algorithms, the Structured Low Dimensional Representation (SLDR) algorithm stands out. The research aims to improve subsurface image accuracy, ensuring underground pipeline system safety and integrity.

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