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International Congress on Image and signal processing, BioMedical Engineering and Informatics

作者： Jiaxu Yu Zhengbo Sun Qing He Huafeng Peng Science and Technology On Blind Signal Processing Laboratory Chengdu China 610041

ISBN: (纸本)9781509037117

A new collaborative detection method of TDOA (Time Difference of Arrival) and DOA ( Direction of arrival) has been proposed for weak signals. The fuzzy evidence theory is used to fuse the peaks of TDOA correlation and the spatial spectrum of MUSIC (Multiple signal Classification). In the paper, the TODA/DOA collaborative detector is designed, the basic probability assignment fuction (mass fuction or BPA fuction) and the membership fuction are used to express the uncertainty of the TDOA and DOA information. Then the information is fused by the D-S combination formula and the rule of game probability distribution is used to get the detection result. At last, the simulation results show that the performance of the detector is better than the traditional TDOA correlation detector.

关键词： fuzzy evidence theory correlation detection TDOA DOA MUSIC collaborative detection

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A Time-Aware Dynamic Service Quality Prediction Approach for Services

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Tsinghua science and technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of Service(QoS)prediction time-aware service recommendation

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A Snapshot Cluster Segmentation Method Based on Dynamic Network

A Snapshot Cluster Segmentation Method Based on Dynamic Netw...

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2022 International Conference on Statistics, Data science, and Computational Intelligence, CSDSCI 2022

作者： Zhang, Tongxin Lu, Luxi College of Information System Engineering PLA Strategic Support Force Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781510661325

Dynamic graph neural networks are a research hotspot in the field of deep learning, especially in social networks, recommendation systems, traffic networks and other fields with very wide applications. At present, most of the commonly used deep learning based network analysis methods are focused on discrete dynamic graph neural networks. However, the effectiveness of discrete graph neural network methods is based on the degree of good or bad snapshot segmentation of dynamic networks, so this paper proposes a snapshot clustering segmentation method (SCSM) for dynamic networks, and researchers can effectively segment the snapshots of dynamic networks by the SCSM method in this paper to make the downstream tasks can get good results. © 2023 SPIE.

关键词： Graph neural networks

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A Multiple Instance Learning Algorithm Using Graph Convolutional Network for Speech Content Classification 5

A Multiple Instance Learning Algorithm Using Graph Convoluti...

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5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Zhang, Jianyi Yao, Jiaqi Chu, Yanjie Yan, Jikun PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781665415989

Recently, speech content classification begins to be formulated as a multiple instance learning (MIL) problem by some researchers. Under the MIL framework, a speech signal is considered as a bag with labels, and speech fragments are regarded as instances without corresponding labels. On this basis, we propose a new algorithm, named MI-CNN-G, for speech content classification using Graph Convolutional Network (GCN) under the MIL framework. First, we only train the MIL classifier, whose architecture is based on Convolutional Neural Network, for bag classification. Second, we utilize GCN to handle graphs composed of instance features that are the outputs of the MIL classifier for correcting the predicted probabilities of the classifier and then updated the classifier and GCN together. Finally, we employ the MI-CNN-G model to predict the labels of unknown bags. The experimental results show that GCN is effective in improving the performance of the MIL classifier on speech content classification and the MI-CNN-G model outperforms the baseline approaches in the Average Precision metric. © 2021 IEEE.

关键词： Convolutional neural networks

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Federated Client-tailored Adapter for Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Hu, Guyue Song, Siyuan Kang, Yukun Yin, Zhu Zhao, Gangming Li, Chenglong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Internet Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Department of Computer Science The University of Hong Kong Hong Kong

Medical image segmentation in X-ray images is beneficial for computer-aided diagnosis and lesion localization. Existing methods mainly fall into a centralized learning paradigm, which is inapplicable in the practical medical scenario that only has access to distributed data islands. Federated Learning has the potential to offer a distributed solution but struggles with heavy training instability due to client-wise domain heterogeneity (including distribution diversity and class imbalance). In this paper, we propose a novel Federated Client-tailored Adapter (FCA) framework for medical image segmentation, which achieves stable and client-tailored adaptive segmentation without sharing sensitive local data. Specifically, the federated adapter stirs universal knowledge in off-the-shelf medical foundation models to stabilize the federated training process. In addition, we develop two client-tailored federated updating strategies that adaptively decompose the adapter into common and individual components, then globally and independently update the parameter groups associated with common client-invariant and individual client-specific units, respectively. They further stabilize the heterogeneous federated learning process and realize optimal client-tailored instead of sub-optimal global-compromised segmentation models. Extensive experiments on three large-scale datasets demonstrate the effectiveness and superiority of the proposed FCA framework for federated medical segmentation. © 2025, CC BY.

关键词： Federated learning

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A new recognition and classification algorithm of underwater acoustic signals based on multi-domain features combination

A new recognition and classification algorithm of underwater...

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IEEE/OES China Ocean Acoustics (COA)

作者： Huan Shi Jinyu Xiong Chenyang Zhou Su Yang National Key Laboratory of Science and Technology on Blind Signal Processing China

A new algorithm is proposed in this paper to recognize and classify underwater acoustic signals including underwater acoustic communication signals, active sonar signals, ship-radiating noise and burst ocean ambient noise. The paper firstly analyzed the features of underwater acoustic signals in time domain, frequency domain and cyclostationarity domain. At the same time, the way of extracting features about these signals is researched. This algorithm combines the features above and chooses a decision tree classifier to realize the recognition and classification of different underwater acoustic signals. Finally, the effectiveness in different multipath channels is evaluated by simulation. The results have shown that this algorithm is capable of recognizing and classifying different underwater acoustic signals with better performance than algorithms using features in only one domain; and, its performance is robust in a multipath fading environment.

关键词： Analytical models Read only memory

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A method of Robust low-angle target height and compound reflection coefficient joint estimation

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Journal of Systems Engineering and Electronics 2022年第2期33卷 322-329页

作者： WANG Shenghua CAO Yunhe LIU Yutao School of Communications and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710061China Science and Technology on Communication Networks Laboratory Shijiazhuang 050081China National Laboratory of Radar Signal Processing Xidian UniversityXi’an 710071China

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation *** highly deterministic maximum likelihood estimator has a high accuracy,but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the *** this paper,a robust es-timation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle *** compound reflection coefficient is es-timated from the received data of the array and then a one-di-mension generalized steering vector is constructed to estimate the target *** algorithm is robust to the reflecting sur-face height error and the ground reflection coefficient ***-nally,the experiment and simulation results demonstrate the validity of the proposed method.

关键词： low-angle target robust joint estimation compound reflection coefficient multipath direction of arrival(DOA)

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Persona analysis with text topic modelling

Persona analysis with text topic modelling

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International Conference on Automatic Control and Artificial Intelligence, ACAI 2012

作者： Yang, Dong-Mei Zheng, Hui Yan, Ji-Kun Jin, Ye Science and Technology on Blind Signal Processing Laboratory Mail Box No.666 Chengdu 610041 China Southwest Electronics and Telecommunication Technology Research Institute Mail Box No.666 Chengdu 610041 China

ISBN: (纸本)9781849195379

We present a new way called Persona Analysis with Text Topic Modelling (PATTM), which tries to learn the role of personae according to the literal descriptions. It is similar to Latent Dirichlet Allocation (LDA) and Author Topic (AT) model, with the attribute of allowing all text to join in the topic modelling process, even when there is no persona in the text. We experiment on the Libya Event data set which contains more than 4,000 texts collected from the Internet. The PATTM gives lower perplexity than LDA and AT model on the data set.

关键词： Statistics

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Semi-supervised LDA by labelling words

Semi-supervised LDA by labelling words

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International Conference on Automatic Control and Artificial Intelligence, ACAI 2012

ISBN: (纸本)9781849195379

We propose a new semi-supervised learning technique, which is called Words labelled Semi-Supervised Latent Dirichlet Allocation (wssLDA) by labelling words for large text collections analysis. The model incorporates supervision with Latent Dirichlet Allocation by adjusting weights of topic words chosen by users. Results with perplexity for documents and F-measure for clustering show the improvements for the topic learning and document analysis tasks.

关键词： Statistics

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DMFVAE:miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of Computer science 2024年第6期18卷 259-270页

作者： Pijing WEI Qianqian WANG Zhen GAO Ruifen CAO Chunhou ZHENG Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China

MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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