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arXiv 2024年

作者： Zhang, Shaojie Wang, Yinghui Liu, Peixuan Li, Wei Yang, Jinlong Yan, Tao Wang, Yukai Huang, Liangyi Wang, Mingfeng Atadjanov, Ibragim R. School of Artificial Intelligence and Computer Science Jiangnan University 1800 Li Lake Avenue wuxi Jiangsu214122 China Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education 1800 Li Lake Avenue wuxi Jiangsu214122 China School of Computing and Augmented Intelligence Arizona State University 1151 S Forest Ave TempeAZ8528 United States Department of Mechanical and Aerospace Engineering Brunel University Kingston Lane Middlesex LondonUB8 3PH United Kingdom

The images captured by Wireless Capsule Endoscopy (WCE) always exhibit specular reflections, and removing highlights while preserving the color and texture in the region remains a challenge. To address this issue, this paper proposes a highlight removal method for capsule endoscopy images. Firstly, the confidence and feature terms of the highlight region’s edges are computed, where confidence is obtained by the ratio of known pixels in the RGB space’s R channel to the B channel within a window centered on the highlight region’s edge pixel, and feature terms are acquired by multiplying the gradient vector of the highlight region’s edge pixel with the iso-intensity line. Subsequently, the confidence and feature terms are assigned different weights and summed to obtain the priority of all highlight region’s edge pixels, and the pixel with the highest priority is identified. Then, the variance of the highlight region’s edge pixels is used to adjust the size of the sample block window, and the best-matching block is searched in the known region based on the RGB color similarity and distance between the sample block and the window centered on the pixel with the highest priority. Finally, the pixels in the best-matching block are copied to the highest priority highlight removal region to achieve the goal of removing the highlight region. Experimental results demonstrate that the proposed method effectively removes highlights from WCE images, with a lower coefficient of variation in the highlight removal region compared to the Crinimisi algorithm and DeepGin method. Additionally, the color and texture in the highlight removal region are similar to those in the surrounding areas, and the texture is continuous. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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An Error-Matching Exclusion Method for Accelerating Visual SLAM

arXiv

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arXiv 2024年

作者： Zhang, Shaojie Wang, Yinghui Ma, Jiaxing Yang, Jinlong Yan, Tao Huang, Liangyi Wang, Mingfeng School of Artificial Intelligence and Computer Science Jiangnan University 1800 Li Lake Avenue wuxi Jiangsu214122 China Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education 1800 Li Lake Avenue wuxi Jiangsu214122 China School of Computing and Augmented Intelligence Arizona State University 1151 S Forest Ave TempeAZ8528 United States Department of Mechanical and Aerospace Engineering Brunel University Middlesex Kingston Lane LondonUB8 3PH United Kingdom

In Visual SLAM, achieving accurate feature matching consumes a significant amount of time, severely impacting the real-time performance of the system. This paper proposes an accelerated method for Visual SLAM by integrating GMS (Grid-based Motion Statistics) with RANSAC (Random Sample Consensus) for the removal of mismatched features. The approach first utilizes the GMS algorithm to estimate the quantity of matched pairs within the neighborhood and ranks the matches based on their confidence. Subsequently, the Random Sample Consensus (RANSAC) algorithm is employed to further eliminate mismatched features. To address the time-consuming issue of randomly selecting all matched pairs, this method transforms it into the problem of prioritizing sample selection from high-confidence matches. This enables the iterative solution of the optimal model. Experimental results demonstrate that the proposed method achieves a comparable accuracy to the original GMS-RANSAC while reducing the average runtime by 24.13% on the KITTI, TUM desk, and TUM doll datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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A Robust Error-Resistant View Selection Method for 3D Reconstruction

arXiv

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arXiv 2024年

作者： Zhang, Shaojie Wang, Yinghui Nan, Bin Yang, Jinlong Yan, Tao Huang, Liangyi Wang, Mingfeng School of Artificial Intelligence and Computer Science Jiangnan University 1800 Li Lake Avenue wuxi Jiangsu214122 China Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education 1800 Li Lake Avenue wuxi Jiangsu214122 China School of Computing and Augmented Intelligence Arizona State University 1151 S Forest Ave AZ Tempe8528 United States Department of Mechanical and Aerospace Engineering Brunel University Kingston Lane London MiddlesexUB8 3PH United Kingdom

To address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method. The method utilizes a triangulation-based computation to obtain an error-resistant model, which is then used to construct an error-resistant matrix. The sorting results of each row in the error-resistant matrix determine the candidate view set for each view. By traversing the candidate view sets of all views and completing the missing views based on the error-resistant matrix, the integrity of 3D reconstruction is ensured. Experimental comparisons between this method and the exhaustive method with the highest accuracy in the COLMAP program are conducted in terms of average reprojection error and absolute trajectory error in the reconstruction results. The proposed method demonstrates an average reduction of 29.40% in reprojection error accuracy and 5.07% in absolute trajectory error on the TUM dataset and DTU dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Errors

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A Feature Matching Method Based on Multi-Level Refinement Strategy

arXiv

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arXiv 2024年

作者： Zhang, Shaojie Wang, Yinghui Ma, Jiaxing Yang, Jinlong Yan, Tao Huang, Liangyi Wang, Mingfeng School of Artificial Intelligence and Computer Science Jiangnan University 1800 Li Lake Avenue Jiangsu Wuxi214122 China Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education 1800 Li Lake Avenue Jiangsu Wuxi214122 China School of Computing and Augmented Intelligence Arizona State University 1151 S Forest Ave TempeAZ8528 United States Department of Mechanical and Aerospace Engineering Brunel University Kingston Lane Middlesex LondonUB8 3PH United Kingdom

Feature matching is a fundamental and crucial process in visual SLAM, and precision has always been a challenging issue in feature matching. In this paper, based on a multi-level fine matching strategy, we propose a new feature matching method called KTGP-ORB. This method utilizes the similarity of local appearance in the Hamming space generated by feature descriptors to establish initial correspondences. It combines the constraint of local image motion smoothness, uses the GMS algorithm to enhance the accuracy of initial matches, and finally employs the PROSAC algorithm to optimize matches, achieving precise matching based on global grayscale information in Euclidean space. Experimental results demonstrate that the KTGP-ORB method reduces the error by an average of 29.92% compared to the ORB algorithm in complex scenes with illumination variations and blur. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Multi-Level Transfer Learning for irregular clinical time series prediction

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Knowledge-Based Systems 2025年 324卷

作者： Xingwang Li Fei Teng Minbo Ma Zhen Qin Jinhong Guo Jie Xu Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science University of Leeds UK

Clinical time series prediction plays a crucial role in precision medicine, enabling early detection of health issues to help formulate effective treatment plans. Most existing studies have focused on capturing irregular temporal dependencies of time series but have overlooked the causes of irregularity. We empirically find that the testing frequency is intimately associated with the patient’s condition. Motivated by this observation, this paper proposes a M ulti- L evel T ransfer L earning framework (MLTL). This framework first categorizes time series based on patient conditions and then performs predictions according to clinical variables (indicators). In particular, we use prediction models that are tailored to various series categories. In addition, we present a pretraining task to capture correlations among indicators to enhance the information density of the series. We further transfer dense representations to models in a fine-tuning manner. To assess the effectiveness of MLTL, we conducted extensive experiments on two clinical benchmark datasets and one real-world dataset. Results demonstrate that MLTL consistently surpasses mainstream regular and irregular prediction methods. In particular, even when 80% of the indicator values are missing from the clinical time series, the MLTL framework achieves a significant 9.4% reduction in mean absolute error, confirming the effectiveness of our transfer learning strategy.

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An Effective Network Repair Strategy Against Both Random and Malicious Edge Attacks

An Effective Network Repair Strategy Against Both Random and...

引用

第40届中国控制会议

作者： Jianda Zhao Shiwen Sun Hao Shen Chengyi Xia School of Computer Science and Engineering Tianjin University of Technology Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education

Attack vulnerability and protection of large-scale complex networks are of great significance in academic and engineering *** this study,the problem of structural vulnerability of complex networks induced by attacks on edges is *** order to restore network’s performance after attacks,a simple two-stage attack-recovery model based on activating backup edges is ***,with the consideration of two different edge attack strategies,the joint influence of repair cost（*** extent of edge redundancy） and repair targets are extensively *** simulations are implemented on artificially generated random networks and real-world networks,which demonstrate the effectiveness of proposed repair *** research results can provide some useful suggestion on constructing robust infrastructure systems,also help to better understand the structural complexity of large-scale networks.

关键词： Complex network attack structural vulnerability repair cost redundant edges

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

arXiv

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arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

arXiv

引用

arXiv 2021年

作者： Zhu, Yanqiao Cui, Hejie He, Lifang Sun, Lichao Yang, Carl Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Computer Science Emory University United States Department of Computer Science and Engineering Lehigh University United States

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. However, how to employ GNNs to extract effective representations from brain networks in multiple modalities remains rarely explored. Moreover, as brain networks provide no initial node features, how to design informative node attributes and leverage edge weights for GNNs to learn is left unsolved. To this end, we develop a novel multiview GNN for multimodal brain networks. In particular, we treat each modality as a view for brain networks and employ contrastive learning for multimodal fusion. Then, we propose a GNN model which takes advantage of the message passing scheme by propagating messages based on degree statistics and brain region connectivities. Extensive experiments on two real-world disease datasets (HIV and Bipolar) demonstrate the effectiveness of our proposed method over state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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Spectrometric Characterization of Suspension Liquid and Light Extinction Model Update

SSRN

引用

SSRN 2022年

作者： Liu, Sheng Shen, Yang Gao, Zihui Gao, Hongwen School of Computer Science and Technology Huaibei Normal University Huaibei235000 China State Key Laboratory of Enviromental Pollution Control and Resource Reuse Tongji University Shanghai200092 China Shanghai GreenEmpire Environmental Science and Technology Corporation Shanghai200092 China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

On the basis of the classical light scattering models, the light extinction model is the first to establish as [[EQUATION]] (, N and γ – average diameter in μm, number and relative refractive index of the suspending particles, λ , A and δ – incident light wavelength in μm, absorbance and optical path in cm of the suspension liquid) by spectrometric characterization of ten standard suspension liquids. It has been used to determine the suspending particles in the calcium oxalate, Formazine, soil, milk and sewage suspension water samples. As the result, the light extinction model method brought out less than 12% error of and 18% error of the suspending particles’ quality by comparing with the conventional methods. It provides a simple and reliable spectroptometric determination of a suspension liquid. Also, it is very potential to in-situ monitor the growth and working state of the suspending particles using in synthesis of materials, culture of cells, treatment of wastewater and safety of drinking water and foods. © 2022, The Authors. All rights reserved.

关键词： Suspensions (fluids)

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Disentangled self-attentive neural networks for click-through rate prediction

arXiv

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arXiv 2021年

作者： Xu, Yichen Zhu, Yanqiao Yu, Feng Liu, Qiang Wu, Shu School of Computer Science Beijing University of Posts and Telecommunications Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Alibaba Group Artificial Intelligence Research Chinese Academy of Sciences

Click-Through Rate (CTR) prediction, whose aim is to predict the probability of whether a user will click on an item, is an essential task for many online applications. Due to the nature of data sparsity and high dimensionality of CTR prediction, a key to making effective prediction is to model high-order feature interaction. An efficient way to do this is to perform inner product of feature embeddings with self-attentive neural networks. To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction. Specifically, the unary term models the general importance of one feature on all other features, whereas the pairwise interaction term contributes to learning the pure impact for each feature pair. We conduct extensive experiments using two real-world benchmark datasets. The results show that DESTINE not only maintains computational efficiency but achieves consistent improvements over state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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