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检索条件"任意字段=Conference on Nonlinear Image Processing XI"

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Research on nonlinear Programming Problems Based on Random Forest Model and Genetic Algorithm 5

Research on Nonlinear Programming Problems Based on Random F...

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5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

作者： Li, Yanhui Qing, Yuhua Huang, Wentao Qi, Kun Du, Ke Li, Guodong Guilin University of Electronic Technology School of Mathematics and Computing Science Guilin541004 China Guilin University of Electronic Technology School of Foreign Languages Guilin541004 China Xi'an Aeronautical University School of Mechanical Engineering Xi'an710077 China

ISBN: (纸本)9798350374407

In the realm of daily life planning, the challenge of optimizing decisions is frequently encountered, with traditional methods often failing to achieve desired outcomes. To address this issue, this research proposes an innovative approach that combines random forest and genetic algorithms to obtain optimal solutions for nonlinear programming problems. The aim is to enhance the precision of solving nonlinear programming problems and improve the effectiveness of decision optimization in daily life planning. The research introduces important constraints on influencing factors in the design of the random forest model for nonlinear programming and utilizes genetic algorithms to solve the nonlinear programming problems, resulting in more accurate optimal solutions. The research findings demonstrate that this method not only optimizes the impact structure of various factors on the outcomes but also enhances the precision of the optimal solutions through genetic algorithms. © 2024 IEEE.

关键词： genetic nonlinear programming optimal solution optimization decision random forest model

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Application of Artificial Intelligence-Based nonlinear Filtering Techniques in Chaotic Communication

Application of Artificial Intelligence-Based Nonlinear Filte...

引用

2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： Guo, Chengbo Wu, Ruihan Ji, Hongying Xi'an University of Posts & Telecommunications Xi'an China Institute of Disaster Prevention Sanhe China Yanbian University Yanji China

ISBN: (纸本)9798350314670

Chaotic communication, due to its good interference immunity and privacy, is widely used in confidential communication. However, the existence of real-life nonlinear channels interferes strongly with the actual communication. Conventional adaptive filters have good handling effect on nonlinear channels. However, the traditional LMS adaptive filter cannot satisfy both the adaptive filter convergence speed and steady-state error. The NLSM can better handle nonlinear data and capture the noise in the data, but the accuracy of the algorithm needs to be improved for pseudo-random sequences. Therefore, a nonlinear filtering algorithm based on LSTM neural network is proposed in this paper based on common algorithms of artificial intelligence in order to get better recovery of signals passing through nonlinear channels. The algorithm combines the ability of LSTM with strong generalization and can handle complex nonlinear relationships. Simulation results show that the algorithm proposed in this paper recovers the signal with less error and more accurate results, and can make the signal through the nonlinear channel get better recovery. The results verify the superiority of the algorithm in this paper and show that the LSTM neural network nonlinear filtering algorithm has a broader application prospect in the field of chaotic communication. © 2023 IEEE.

关键词： Adaptive filters

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A Gray-level Morphological Operation Method Based on the Umbra Notion 18

A Gray-level Morphological Operation Method Based on the Umb...

引用

18th IEEE International conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Zhang, Yimeng Wang, Junping Li, Yuyu School of Telecommunications Engineering Xidian University Xi'an China

ISBN: (纸本)9798331505639

Mathematical morphology as a nonlinear image processing theory has been used in various aspects of image processing. However, with the emergence of morphological networks and the increasing resolution of images, the demand for the speed of morphological image processing is increasing. In this paper, we propose an implementation method of binary operations for gray-level morphology based on the umbra notion. In this method, firstly, the gray-level image to be calculated and the structural element image are converted into the corresponding binary umbra image according to the umbra notion. Then, dilation, erosion, opening and closing operations are performed on the umbra of the structural element image and the image to be processed to obtain the corresponding umbra result image. Finally, the umbra result image is converted into its top surface based on the linkage of the binary image with the pixel value of the gray-level image to obtain the corresponding gray-level image dilation, erosion, opening and closing operational results. The experimental results on one-dimensional and two-dimensional images demonstrate the effectiveness of the method, which can lay a foundation for the fast calculation of gray-level morphological images. © 2024 IEEE.

关键词： Binary images

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Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration 17

Multi-scale Frequency Attention Fusion Network for Optical a...

引用

17th IEEE International conference on Signal processing, ICSP 2024

作者： Hu, xin Wu, Yan Li, Zhikang Yang, Zhifei Li, Ming Xidian University School of Electronic Engineering Xi'an China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350387384

image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences between optical and synthetic aperture radar (SAR) images and speckle noise in SAR, which limits the ability of pure convolutional neural networks (CNNs) to obtain modality-shared features. To solve these problems, this paper proposes a Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration (MFAF-OS-RegNet), which includes a Multi-scale Frequency Channel Attention Fusion (MFCAF) module and a Momentum Contrastive Learning (MCL) strategy. Firstly, we extract repeatable keypoints distributed on the edges and develop a novel MFCAF module to obtain modality-shared features. In the MFCAF module, multi-scale feature fusion is used to improve the positioning accuracy of matching points, and frequency channel attention is used to mine more favorable features. Secondly, we design a MCL strategy to reduce the interference of speckle noise in network training. The momentum teacher corrects handcrafted labels by learning the essential characteristics of the data for robust matching. Finally, we filter outliers and estimate transformation parameters based on the initial matching correspondence output by MFAF-OS-RegNet. Experimental results on a publicly available OS-dataset show that the proposed method outperforms other state-of-the-art methods. © 2024 IEEE.

关键词： image fusion

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Communication Base Station Traffic Prediction Based on nonlinear Regression-LSTM Decomposition Algorithm 4

Communication Base Station Traffic Prediction Based on Nonli...

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4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Tao, Yanhe School of Marine Science and Technology Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9798331528676

Communication base station traffic prediction can help network service providers predict the peak hours of network traffic, so as to carry out network expansion and load balancing in advance, and keep the users getting a smooth network experience even during the peak hours of the network. In order to accurately predict traffic usage in different time periods, we first constructed a nonlinear regression model, then solved the parameters in the algorithm, and finally got the image of our predicted nonlinear function and compared it with the real value, and found that there are some small swings. Finally, we use the LSTM recurrent neural network to predict the swing term. The final flow prediction is obtained which is very close to the real situation. The model performance was verified with MAE, MAPE and RMSE metrics respectively, and the results showed that the values of the three metrics were 0.1873, 0.0978 and 0.2260 respectively. ©2024 IEEE.

关键词： Prediction models

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Spatial resolution enhancement method for hyperspectral image based on spatial-spectral feature fusion 2

Spatial resolution enhancement method for hyperspectral imag...

引用

2nd International conference on Electrical, Electronics, and Information Engineering, EEIE 2023

作者： Yi, Meichen Yi, Chen Li, Longjie Gan, Yuquan Xu, Jian School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Shaanxi Xi'an China School of Energy and Electrical Engineering Chang'an University Shaanxi Xi'an China

ISBN: (纸本)9781510672840

Hyperspectral imaging has many applications including target detection, classification and environmental monitoring. Whereas, the spectral resolution and spatial resolution of hyperspectral image are mutually constrained due to the limited imaging conditions and imaging devices, so it is hard to directly acquire hyperspectral image with both high spatial resolution and spectral resolution. This paper aims to merge low-spatial resolution hyperspectral image with high-spatial resolution multispectral image to obtain high-spatial resolution hyperspectral image. The proposed algorithm divides the hyperspectral image into overlapping bands and non-overlapping bands depending on the band range of hyperspectral image and multispectral image. On the overlapping bands, a non-negative dictionary learning method is used to achieve the fusion result of hyperspectral image and multispectral image. On the non-overlapping bands, a neural network is learned to express nonlinear mapping between overlapping bands and non-overlapping bands of hyperspectral image, which is exploited to acquire the high quality non-overlapping hyperspectral bands. Finally, the processing results of overlapping and non-overlapping bands are combined to acquire a hyperspectral image with high spatial resolution. Experiments on both simulated and real datasets show that the proposed method can acquire better or comparable fusion result with the current state-of-the-art methods. © 2024 SPIE.

关键词： Hyperspectral imaging

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A Gradient-based Method with Denoising for Target Shadow Profile Retrieval in Forward Scatter Radar 2

A Gradient-based Method with Denoising for Target Shadow Pro...

引用

2nd IEEE International conference on Signal, Information and Data processing, ICSIDP 2024

作者： Shen, xi Huang, Defeng The University of Western Australia Department of Electrical Electronic and Computer Engineering Perth Australia

ISBN: (纸本)9798331515669

The existing nonlinear least-squares method for target shadow profile retrieval in forward scatter radar only considers noiseless samples of the forward scatter shadow ratio. Inspired by optical diffraction tomography, in this paper we propose to use a gradient-based method with denoising to tackle scenarios involving noisy samples. Simulation results demonstrate its superiority over the conventional least-squares method. More specifically, an essential prerequisite for the least-squares method, i.e., the number of samples being greater than or equal to the number of pixels, is no longer required by the proposed method. Moreover, the proposed method retrieves the target image with a higher peak signal-to-noise ratio than that using the least-squares method. © 2024 IEEE.

关键词： Forward scattering

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Evaluating multi-modal image matching algorithms for remote sensing applications 6

Evaluating multi-modal image matching algorithms for remote ...

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6th International conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Liu, Zhanquan Han, Yilong Fu, Mingsheng Song, Baiwan Gong, Danchao Ming, Yanfang Sun, Lin College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266590 China Shandong Province Research Center of Engineering and Technology for GPS RS and GIS Qingdao266590 China Star-Rising Technologies Co. Ltd. Qingdao266590 China Chinese Academy of Surveying and Mapping Beijing100036 China Shandong GEO-Surveying & Mapping Institute Jinan250000 China State Key Laboratory of Geo-Information Engineering Xi’an710054 China Xi’an Institute of Surveying and Mapping Xi’an710054 China

ISBN: (纸本)9781510688018

image matching is a fundamental task in image processing, particularly in photogrammetry and remote sensing. Conventional image matching methods often struggle with multi-modal images due to significant geometric distortions and nonlinear radiometric effects. To address these challenges, researchers have developed a variety of algorithms specifically tailored for multi-modal data. Despite this, there is a notable lack of comprehensive theoretical summaries of multi-modal image matching. This paper aims to bridge this gap by discussing the complexities of matching multi-modal images and providing a succinct overview of existing methods. We evaluate three Conventional algorithms—OS-SIFT, RIFT, and POS-GIFT—and two deep learning methods—SemLA and SuperPoint+SuperGlue. These methods are assessed using six types of multi-modal remote sensing datasets: Optical-Optical, Optical-Infrared, Optical-Depth, Optical-Map, Optical-Synthetic Aperture Radar (SAR), and Day-Night. The results indicate that the POS-GIFT algorithm outperforms others, achieving an average of 1217 correct matches with a success rate of 96.70%. This paper concludes by discussing the challenges of multi-modal image matching and suggesting future research directions, serving as a valuable resource for researchers and practitioners in remote sensing. © 2025 SPIE.

关键词： image matching

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Prediction and Modeling of Reservoir Properties in Ultra-Low Permeability Reservoirs with Edge and Bottom Water Based on Deep Learning 2

Prediction and Modeling of Reservoir Properties in Ultra-Low...

引用

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Yang, Jieyou Liu, Hong Liu, Feilong Yang, Huan Chen, Qiu Han, Dongmei The Fifth Oil Production Plant of Petrochina Changqing Oilfield Company Shaanxi Xi'an China

ISBN: (纸本)9798350360240

The reservoir properties of reservoirs in edge and bottom water reservoirs have complex and nonlinear characteristics, and traditional prediction methods have low accuracy and cannot accurately predict them. This study adopted a CNN algorithm based method for predicting the reservoir properties of reservoirs in edge and bottom water reservoirs. Firstly, a large amount of reservoir data was collected and prepared, including physical properties and geological features. Then, CNN models were used for feature learning and predictive modeling. The model performed deep learning and feature extraction on the data through convolutional and pooling layers, and predicted attributes through fully connected layers. The experimental results showed that the prediction accuracy range of CNN was 95%-99%. The prediction model has achieved significant results in predicting reservoir properties in reservoirs with edge and bottom water. It can accurately predict reservoir properties and achieve lower error rates in both positive and negative class predictions. © 2024 IEEE.

关键词： Prediction models

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An Imaging Method for Ship Target Based on Rotation Parameters Estimation and Extended nonlinear Chirp Scaling 6

An Imaging Method for Ship Target Based on Rotation Paramete...

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6th International conference on Information Communication and Signal processing, ICICSP 2023

作者： Chen, Tao Lin, Jinsong Yang, Qing Li, Zhongyu Wu, Junjie Yang, Jianyu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350339994

In this paper, the problem of imaging targets with uniform rotation is investigated, and a solution method based on genetic algorithm and extended nonlinear chirp scaling is proposed. Firstly, the keystone transform and high-order range cell migration are performed to accomplish motion compensation. Then, the differential evolution (DE) algorithm, a mutant of the genetic algorithm, is utilized to search the optimal rotation parameters. In DE algorithm, the image entropy is adopted as the object function. Simulation results validate that the proposed method can estimate rotation parameters accurately and achieve desirable peak side-lobe ratio and integration side-lobe ratio. © 2023 IEEE.

关键词： differencial evolution algorithm extended nonlinear chirp scaling rotating targets spatially variant Doppler coefficients synthetic aperture radar

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