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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Shi, Siyu Shao, Huikai Zhong, Dexing School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Xi’an Yizhanghui Technology Co. Shannxi Xi’an712000 China Pazhou Laboratory Guangzhou510335 China Research Institute of Xi’an Jiaotong University Zhejiang311215 China

ISBN: (纸本)9789819610679

Palmprint recognition is an emerging biometric technology with many advantages. However, there is some sensitive personal information in palmprint images. Once accessed by unauthorized malicious third parties, it may lead to privacy breaches. Current palmprint recognition methods focus on recognition accuracy but ignore the privacy protection issue. In this paper, we propose a novel privacy-protecting palmprint recognition method, named Pruning Frequency Channels (PFC). Based on the difference in image perception between the human eye and the model, we eliminate the privacy information in palmprint images by pruning the low-frequency components that can be perceived by humans. Subsequently, we propose a pruning strategy for determining the pruned channels based on the energy of the frequency channels. We explore the impact of the energy ratio threshold on PFC performance and find an energy ratio threshold that is universal to different datasets. Adequate experiments conducted on multiple databases show that our PFC method can perform better than other methods and effectively hide personal privacy information with almost no damage to recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Frequency learning Palmprint recognition Privacy protection

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Revisiting Stochastic Multi-Level Compositional Optimization

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5613-5624页

作者： Wei Jiang Sifan Yang Yibo Wang Tianbao Yang Lijun Zhang National Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Key Laboratory for Novel Software Technology School of Artiicial Intelligence Nanjing University Nanjing China Department of Computer Science and Engineering Texas A&M University College Station TX USA

This paper explores stochastic multi-level compositional optimization, where the objective function is a composition of multiple smooth functions. Traditional methods for solving this problem suffer from either sub-optimal sample complexities or require huge batch sizes. To address these limitations, we introduce the Stochastic Multi-level Variance Reduction (SMVR) method. In the expectation case, our SMVR method attains the optimal sample complexity of $\mathcal {O}(1/\epsilon ^{3})$ to find an $\epsilon$ -stationary point for non-convex objectives. When the function satisfies convexity or the Polyak-Łojasiewicz (PL) condition, we propose a stage-wise SMVR variant. This variant improves the sample complexity to $\mathcal {O}(1/\epsilon ^{2})$ for convex functions and $\mathcal {O}(1/(\mu \epsilon ))$ for functions meeting the $\mu$ -PL condition or $\mu$ -strong convexity. These complexities match the lower bounds not only in terms of $\epsilon$ but also in terms of $\mu$ (for PL or strongly convex functions), without relying on large batch sizes in each iteration. Furthermore, in the finite-sum case, we develop the SMVR-FS algorithm, which can achieve a complexity of $\mathcal {O}(\sqrt{n}/\epsilon ^{2})$ for non-convex objectives, $\mathcal {O}(\sqrt{n}/\epsilon \log (1/\epsilon ))$ for convex functions and $\mathcal {O}(\sqrt{n}/\mu \log (1/\epsilon ))$ for objectives satisfying the $\mu$ -PL condition, where $n$ denotes the number of functions in each level. To make use of adaptive learning rates, we propose the Adaptive SMVR method, which maintains the same complexities while demonstrating faster convergence in practice.

关键词： Complexity theory Optimization Convex functions Convergence Training Stochastic processes Lower bound Jacobian matrices Adaptive learning Storms

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model 1

Emergency Transportation and Structural Prediction and Optim...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Nie, Yiyang Chen, Xiang Wu, Xuanli Deng, Zihao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology Shcool of Mathematics and Computational Science Xiangtan China

ISBN: (纸本)9798350323795

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model;second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed;finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes. © 2023 IEEE.

关键词： Multiobjective optimization

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Node Coupling for Inferring Networks from Short Time Series 5th

Node Coupling for Inferring Networks from Short Time Series

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Li, Hang Wang, Li Xia, Chengyi Tianjin University of Technology Tianjin300382 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Beijing China

ISBN: (纸本)9789811939976

Commonly abstracted as a set of units, the complex systems in the real world are interconnected with each other using the specific dynamical methods. However, in practice, there is nothing else but the performances of the system known, nor are the network structure and the dynamical rules observed. In order to solve this problem, a research has been conducted on network reconstruction, so as to recover the potential network structure and network dynamics from the observed time series. In this paper, a new method is proposed, namely, slope difference similarity (SDS), which is a way of thinking in reverse. Based on the trend of change in nodes, the coupling degree between nodes is judged and the network is reconstructed. By comparing the SDS and Kendall rank correlation method with experiments, the experimental results are obtained in this paper to demonstrate the effectiveness of SDS network reconstruction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Design and analysis of GPS satellite-based augmentation system based on low earth orbit constellations 3

Design and analysis of GPS satellite-based augmentation syst...

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2024 3rd International Conference on Aerospace and Control engineering, ICoACE 2024

作者： Yang, Jianlei Jin, Zhengzhen Zheng, Shuaiyong Huang, Shiyong Ma, Guoyu Ji, Shiran Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang050081 China The 54 Research Institute China Electronics Technology Group Shijiazhuang050081 China School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China College of Software Tianjin Normal University Tianjin300387 China

Addressing the need for improved GPS accuracy, especially for low-dynamic users, this study introduces a GPS augmentation system employing Low Earth Orbit (LEO) satellites. We detail the principles of satellite navigation and augmentation and simulate the GPS-LEO constellation to analyse its performance in terms of satellite visibility and Position Dilution of Precision (PDOP). The results demonstrate substantial augmentation in navigation accuracy with the integrated system, as verified by UAV flight experiments. The integration of LEO satellites is anticipated to advance global navigation capabilities, with implications for intelligent transportation, logistics, and precision agriculture. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Global positioning system

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3D Multi-Modal Feature Interaction Fusion Network for Early Alzheimer's Disease Diagnosis 22

3D Multi-Modal Feature Interaction Fusion Network for Early ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Cai, Gege Guan, Zhenghua Cai, Xiangwen Cheng, Nina Hu, Wenzheng Zhang, Tengda Liu, Yi Lei, Baiying College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging China

ISBN: (纸本)9798331520526

Positron Emission Tomography (PET) and Structural Magnetic Resonance Imaging (sMRI) are widely used for early Alzheimer's disease (AD) diagnosis, providing anatomical and metabolic insights. However, current fusion methods, like simple addition or concatenation, fail to leverage their complementary information fully. To address it, we proposed a 3D multi-modal feature interaction fusion network for AD diagnosis. Specifically, we leverage a robust 3D ResNet backbone network as the architecture for feature extraction. To enable the model to dynamically focus on the areas of interest, we added a local attention module at each layer of the network. We further introduced a collaborative attention module to facilitate the interaction and fusion of features between PET and sMRI modalities, thereby enhancing the overall capability of the model to utilize complementary information from both modalities. Finally, an enhanced feature fusion module based on Transformer is integrated into the network to further strengthen the representation of the fused features. The experiment shows that our model performs better than other representative models and validates the effectiveness of the proposed method on the ADNI dataset. © 2025 IEEE.

关键词： Positron emission tomography

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Communication Interference Recognition Based on Improved Deep Residual Shrinkage Network 28

Communication Interference Recognition Based on Improved Dee...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Wu, Xiaojun Lu, Yaya Tang, Zhenghan Wu, Daolong Xiao, Haitao Sun, Zhongzheng School of Software Engineering Xi'An Jiaotong University Xi'an China 20th Institute Key Laboratory of Technology on Datalink Xi'an China School of Information and Communication Engineering Xi an Jiaotong University Xi an China

ISBN: (纸本)9798350300482

In complex battlefield environments, Flying Ad-hoc NETwork (FANET) faces challenges of manually extracting communication interference signals features, low recognition rate in strong noise environment, and inability to recognize unknown interference types. To solve these problems, one Simple Non-local Correction Shrinkage (SNCS) module is constructed, which modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local Importance-based Pooling (LIP) is introduced to enhance the useful features of interference signals to reduce noise in the downsampling process, and the joint loss function is constructed by combining cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed, and the One Class Support Vector Machine Simplified Non-local Residual Shrinkage Network (OCSVM-SNRSN) model with the ability of both known class recognition and new class rejection is constructed by combining OCSVM and SNRSN. Experimental results show that the recognition accuracy of the OCSVM-SNRSN model is the highest in the scenario of low Jamming Noise Ratio (JNR). The accuracy is increased by about 4%-9% compared with other methods on the known class interference signal dataset, and the recognition accuracy reaches 99% when the JNR is -6dB. At the same time, compared with other methods, the False Positive Rate (FPR) for recognizing unknown class interference signals drops to 9%. © 2023 IEEE.

关键词： Shrinkage

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A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks 10

A Heuristic-based Dynamic Scheduling and Routing Method for ...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Chen, Honglong Liu, Mindong Huang, Jing Zheng, Zhiling Huang, Weihong Xiao, Yufeng Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Hunan Industry Polytechnic Information Center Changsha410125 China

ISBN: (纸本)9798350312461

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization. © 2023 IEEE.

关键词： Heuristic algorithms

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A mesh optimization method using machine learning technique and variational mesh adaptation

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Chinese Journal of Aeronautics 2022年第3期35卷 27-41页

作者： Tingfan WU Xuejun LIU Wei AN Zenghui HUANG Hongqiang LYU MIITKey Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical *** light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational *** the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD ***,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is *** regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression *** the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area *** examples are first used to verify the feasibility and effectiveness of the proposed *** then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our *** show that the proposed method significantly improves the accuracy of the local flow features on the adaptive *** work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.

关键词： CFD Flow field Machine learning Moving mesh method Regression models Variational mesh adaptation

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Wireless Channel Estimation and Equalization Based on Deep Learning

Wireless Channel Estimation and Equalization Based on Deep L...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Xueting Huang Linshu Chen Xiaoyan Chen Mo Wang Yuxin Liu Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Nowadays, the research on wireless communication is hot, and it has achieved leapfrog development in the past few decades and has made astonishing breakthroughs in emerging applications such as 5G technology, Internet of Things (lOT), and smart cities. Channel estimation and equalization in wireless communication are very important technologies, which are cru-cial for ensuring reliable data transmission and improving system performance. However, with the increasing demand for wireless high-speed data communication, traditional channel estimation and equalization technologies are facing enormous challenges. Nowadays, with the development of artificial intelligence, deep learning has been successfully applied to wireless communication systems. Compared to traditional channel estimation and equalization techniques, deep learning-based channel estimation and equalization techniques have many advantages. For example, traditional channel estimation and equalization methods typically require modeling of channel models and are only applicable to specific channel conditions, making it difficult to achieve optimal performance under different channel conditions. Deep learning technology can improve the performance of channel estimation and equalization by learning a large amount of data and training under different channel conditions, especially in nonlinear and complex channel conditions. Therefore, this article will provide a detailed description of the research on channel estimation and equalization based on deep learning, in order to demonstrate its ultra-high performance and development prospects.

关键词： Wireless communication Deep learning Training Smart cities System performance Channel estimation Data communication

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