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Symmetric Positive Definite Convolution network for Polarimetric SAR Image Classification

Symmetric Positive Definite Convolution Network for Polarime...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junfei Shi Keyan Shen Haiyan Jin Wei Wang Zhenghao Shi Haonan Su Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Deep learning models have been widely applied to Polarimetric Synthetic Aperture Radar (PolSAR) image classification due to their excellent performance. However, unlike natural images, PolSAR data is a 3×3 covariance matrix for each resolution unit. Existing deep learning methods generally convert the covariance matrix into a vector as the input of neural networks, which destroys the correlation between channels and distorts the matrix structure. To alleviate this issue, we explore a Symmetric Positive Definite (SPD) convolution network for PolSAR images, which directly inputs the PolSAR complex matrix into the network to learn the geometric features in Riemannian space. Furthermore, a CNN-enhanced SPDnet is designed to further learn the contextual high-level features, which can convert Riemannian matrix features into Euclidean space and apply them for classification. Experimental results on real PolSAR data sets demonstrate the proposed method can achieve better performance than the state-of-the-art methods.

关键词： Deep learning Symmetric matrices Convolution Semantics Neural networks Vectors Radar polarimetry

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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Laryngeal Leukoplakia Classification Via Dense Multiscale Feature Extraction in White Light Endoscopy Images 48

Laryngeal Leukoplakia Classification Via Dense Multiscale Fe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： You, Zhenzhen Yan, Yan Shi, Zhenghao Zhao, Minghua Yan, Jing Liu, Haiqin Hei, Xinhong Ren, Xiaoyong Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an China Xi'An Jiaotong University Second Affiliated Hospital of Medical College Department of Otorhinolaryngology Xi'an China

ISBN: (纸本)9781728163277

Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of this paper is to classify laryngeal leukoplakia in white light endoscopy images into six classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia and squamous cell carcinoma. We proposed a dense multiscale convolutional neural network including parallel multiscale convolution, dense convolution and recurrent convolution in favor of extracting dense multiscale features of laryngeal leukoplakia for fine classification. The proposed network achieved an overall accuracy of 0.8958 for the six-class classification. It has high sensitivity and specificity for each class which are, respectively, 1.0000 and 0.9394 for normal tissues, 0.6667 and 1.0000 for inflammatory keratosis, 0.8889 and 0.9744 for mild dysplasia and moderate dysplasia, 0.7500 and 1.0000 for severe dysplasia, 1.0000 and 0.9767 for squamous cell carcinoma. The experimental results show that our proposed model is superior to the state-of-the-art deep learning-based models. © 2023 IEEE.

关键词： Endoscopy

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Design and implementation of handlebar defect detection method based on machine recognition

Design and implementation of handlebar defect detection meth...

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2024 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zhang, Tong Ren, Sunan Zhang, Wenjing Liu, Yajun School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9781510689275

As an important part of electric vehicles, handlebar installation defects are an important factor in the failure of electric vehicles. In the production process, many large-scale manufacturers still assemble the handlebar manually by workers, and the workload is large, and there may always be errors such as tilting, concave and other assembly defects, and due to the large number of assemblies, the identification of the handlebar assembly defects through manual work will have a significant impact on work efficiency. Therefore, the use of machine recognition instead of traditional manual recognition has become the trend of the development of the industry. In this study, we designed a defect detection method based on machine recognition. First, we collect the image data of the electric vehicle and extract the region of interest group of the electric vehicle through image preprocessing. Then, according to the area of interest information, the electric vehicle baseline group is determined, and the electric vehicle baseline group is used to determine the basis for the handlebar assembly defects. Finally, the angle and distance information between the reference lines are calculated to determine the defects of the handlebar installation. Experiments show that our method can not only reduce the consumption of human resources in the defect detection of electric vehicle handlebar installation by electric vehicle manufacturers, but also use strict filtering conditions to obtain reliable reference lines and detection points in the region of interest, so that the subsequent detection results are more reliable and credible, and the detection error is reduced. © 2025 SPIE.

关键词： Defect detection Image processing Image filtering Edge detection Tunable filters Gaussian filters Image quality Denoising Feature extraction Manufacturing

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Cross Elitist Learning Multifactorial Evolutionary Algorithm 1

引用

3rd International Conference on Neural computing for Advanced Applications, NCAA 2022

作者： Li, Wei Luo, Haonan Wang, Lei School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China

ISBN: (数字)9789811961427

ISBN: (纸本)9789811961410

Multifactorial Evolutionary Algorithm (MFEA) is a popular optimization algorithm in recent years. It has implicit parallelism and can solve different problems at the same time in the same search space. However, premature convergence is a significant shortcoming of MFEA. The main reason for this phenomenon is the lack of population diversity and the inability to migrate effectively between tasks. To address this shortcoming, this paper proposes a cross elitist learning multifactorial evolutionary algorithm (CEL-MFEA). Firstly, the cross elitist strategy guides the individuals to learn from the elite individual, which can improve the convergence of the proposed CEL-MFEA. Secondly, the Nelder-Mead algorithm is used to provide an effective knowledge transfer between different tasks. Finally, the opposition learning mechanism is employed to ensure population diversity. The comprehensive ability of the proposed CEL-MFEA is evaluated by 9 classical multifactorial optimization problem test sets. Experimental results show that the CEL-MFEA is more competitive than MFEA algorithm and several popular multifactorial optimization algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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Adaptive Compression-Encryption Scheme for Medical Image Based on Improved Compressive Sensing and Deoxyribonucleic Acid Coding-Compression

Adaptive Compression-Encryption Scheme for Medical Image Bas...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xue, Xianglian Jin, Haiyan Zhou, Changjun Xi'an University of Technology School of Computer Science and Engineering Xi'an China Shaanxi University of Chinese Medicine Section of Computer Teaching and Research Xianyang China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Zhejiang Normal University School of Computer Science and Technology Jinhua China

ISBN: (纸本)9798350386226

Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm's security. © 2024 IEEE.

关键词： Encoding (symbols)

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Activity Recognition Method Based on Kernel Supervised Laplacian Eigenmaps

Activity Recognition Method Based on Kernel Supervised Lapla...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pengjia Tu Cheng Tian Dandan Du Junhuai Li Huaijun Wang School of Computer Science and Engineering Xi’an University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology China

Laplacian dimensionality reduction can effectively achieve feature transformation and preserve the important structure of high-dimensional features. However, the trained model with this method usually require better generalization ability to new samples. Hence, a human activity recognition method based on kernel-supervised laplacian eigenmaps (KSLE) by combining the kernel method, laplacian mapping, and supervised learning is proposed. Firstly, the adjacency distance relationship of original samples features in the high-dimensional kernel space is obtained. Secondly, the category labels in the high-dimensional feature set are incorporated in the manifold learning for dimensionality reduction. Then, kernel trick is utilized to directly solve the low-dimensional embedding structure of the test feature set. Finally, the obtained low-dimensional feature set is input into the classifier for recognition. Extensive experiments conduct on two public datasets confirm the effectiveness of the proposed approach for improving the generalization ability of new samples.

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Crucial Interaction Elements Detection for 5G Core network based on the Complex network Model

Crucial Interaction Elements Detection for 5G Core Network b...

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International Conference on networking and network Applications (NaNA)

作者： Ruinan Peng Xinhong Hei Yichuan Wang Xiaoxue Liu Yeqiu Xiao School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

This paper focuses on the issue of crucial interaction elements detection in the 5G core network. Given the current absence of an effective method for quantification evaluating the importance of interaction elements, we define the crucial interaction elements as crucial nodes in this paper, and also undertake a comprehensive exploration of various data interaction scenarios within the 5G core network. This aims to overcome the limitations of traditional network analysis methods, which often operate independently in multi-layer data processing. By extracting multilayer data interaction elements across diverse scenarios, these elements are characterized within a complex network framework. Subsequently, a quantification scoring system for crucial nodes is constructed, incorporating multiple centrality indicators and node weights. Notably, the fitness function within the genetic algorithm is improved to facilitate a dynamic weight optimization strategy, responding to real-time changes in network topology. The optimized weight combination is then allocated to various evaluation indicators within the system, culminating in the design of a quantification scoring model for crucial node. Based on this model, a suite of 5G core network crucial point detection methods, tailored to different data scenarios is proposed. This method enable the quantification evaluation of each node’s importance, realizes crucial interaction elements detection. Such an approach serves to bolster the stable operation of the 5G core network, thereby more effectively meeting modern society’s security imperatives concerning information transmission.

关键词： 5G mobile communication Transfer learning Complex networks Reinforcement learning Nonhomogeneous media Data models Robustness

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A Novel Auralization Model for network security Situation Assessment

A Novel Auralization Model for Network Security Situation As...

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International Conference on networking and network Applications (NaNA)

作者： Yeqiu Xiao Yibin Ma Yichuan Wang Kun Jiang Lanlan Cui School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Situation assessment is one of indispensable part for protecting network security. However, with the development of network attacks, the timeliness of attack threat assessment using traditional mathematical methods has gradually become difficult to apply to large-scale and high-speed network attacks. In order to solve this problem, this paper explores the relationship between the influencing factors of network threats and music elements, studies a new method of situation assessment that combines network situation with MIDI music, and builds a network situation auralization model. First, for the network situation assessment model, this paper starts from the perspective of network attacks, designs and extracts attack attributes, and constructs a sliding window-based attack threat dynamic assessment strategy to evaluate risk levels and reflect the current network security status. Secondly, for the MIDI music mapping model, extract key attributes in music, design an emotion classification model based on MIDI music and build a MIDI music library, and finally map it to the network situation. Experimental results show that the auralization model in this paper is more efficient in situation assessment and presentation.

关键词： Deep learning Accuracy High-speed networks network security Threat assessment Libraries

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A Gradient-Guided Method for Hyperspectral Band Selection Based on Spatial-Spectral Weighting

A Gradient-Guided Method for Hyperspectral Band Selection Ba...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jing Hu Ke Lit Yunfei Ju Minghua Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Hyperspectral image(HSI) band selection is a crucial task in image processing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key information, thereby improving image processing efficiency. Aiming at the differences in reflectance characteristics of targets in different regions in hyperspectral images(HSls) and the strong correlation between bands, this paper proposes a gradient- guided spatial-spectral weighted hyperspectral band selection method. Firstly, gradient information is introduced to enhance the edge texture features of different targets. Then, the entropy rate superpixel segmentation algorithm is used to segment the first principal component of the image and divide multiple uniform regions. Secondly, to more comprehensively capture the global relationship between all bands in the image, a multi-graph complementary strategy is proposed to construct a regional similarity graph, aiming to maximize the mutual contribution between superpixels with spatial-spectral similarity. Finally, a unified clustering graph is generated by spectral clustering. After normalized cutting, the band with the least noise information in each sub-cube is selected to form a new band subset. The effectiveness of the proposed method is verified by classification results and data analysis.

关键词： Reflectivity Dimensionality reduction Image segmentation Correlation Data analysis Image edge detection Noise Smart systems Entropy Hyperspectral imaging

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