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Image separation using wavelet-complex shearlet dictionary

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Journal of Systems Engineering and Electronics 2014年第2期25卷 314-321页

作者： Shuaiqi Liu Shaohai Hu Yang Xiao Institute of information science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology

This paper presents a new method for image separation through employing a combined dictionary consisting of wavelets and complex shearlets. Because the combined dictionary sparsely represents points and curvilinear singularities respectively, the image can be decomposed into pointlike and curvelike parts as accurate as possible. The proposed method based on the geo- metric separation theory introduced by Donoho in 2005 shows that accurate geometric separation of the morphologically distinct fea- tures of points and curves can be achieved by l1 minimization. The experimental results show that the proposed method can not only be effective but also greatly reduce the computing time.

关键词： geometric separation l1 minimization sparse approx-imation complex shearlet.

A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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science China(information sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU Institute of information science School of Computer and Information TechnologyBeijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last decades.However, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： Φ-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM

Cells grouping scheme against pilot contamination in large scale antennas system

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Journal of Systems Engineering and Electronics 2016年第3期27卷 531-540页

作者： Zhichao Zhou Yang Xiao Dong Wang Institute of information science Beijing Jiaotong UniversityBeijing 100044China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044China

The influence of cells groupings factor to the performance of the cells groupings time-shift pilot scheme is researched for the multiple cells large scale antennas systems（LSAS）. The former researches have confirmed that the cells groupings time-shift pilots scheme is effective to reduce inter-cell interference, especially pilot contamination, which results from the pilot reuse in adjacent cells. However, they have not specified reasonable cells groupings factor, which plays a critical role in the general performance of the LSAS. Therefore, this problem is researched in details. The time for reverse-link data transmission will be compressed, when the groupings factor surpasses a certain range. Thus it is not always beneficial to increase the cells groupings factor without limitation. Furthermore,a reasonable cells groupings factor is deduced from the perspective of optimization to enhance the system performance. Simulations verify the proposed cell grouping factor.

关键词： large scale antenna system multiple cells inter-cell interference time-shift pilots scheme cells grouping factor system performance

Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China beijing laboratory of advanced information networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer science and technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

Unleashing potentials with deep learning: decoding the complex events for distributed fiber optic sensing applications

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science China(information sciences) 2024年第5期67卷 337-338页

作者： Yujiao LI Liqin HU Kuanglu YU Institute of information science School of Computer and Information Technology Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University

After decades of research, distributed fiber optic sensors have become essential tools for precise long-distance measurements. They are applied across various sectors such as railway transportation, gas and petroleum, and smart grids. Signal processing, particularly in phase-sensitive optical time domain reflectometry(Φ-OTDR) event recognition and classification, is a primary research focus.

关键词： Deep learning

Effects of ZnO,FeO and Fe_(2)O_(3)on the spinel formation,microstructure and physicochemical properties of augite-based glass ceramics

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International Journal of Minerals,Metallurgy and Materials 2023年第6期30卷 1207-1216页

作者： Shuai Zhang Yanling Zhang Shaowen Wu State key laboratory of advanced Metallurgy University of Science and Technology BeijingBeijing 100083China

Augite-based glass ceramics were synthesised using ZnO,FeO,and Fe_(2)O_(3)as additives,and the spinel formation,matrix structure,crystallisation thermodynamics,and physicochemical properties were investigated.The results showed that oxides resulted in numerous preliminary spinels in the glass matrix.FeO,ZnO,and Fe_(2)O_(3)influenced the formation of spinel,while FeO simplified the glass network.FeO and ZnO promoted bulk crystallisation of the parent glass.After adding oxides,the grains of augite phase were refined,and the relative quantities of augite crystal planes were also influenced.All samples displayed good mechanical properties and chemical stability.The 2wt%ZnO-doping sample displayed the maximum flexural strength(170.3 MPa).Chromium leaching amount values of all the samples were less than the national standard(1.5 mg/L),confirming the safety of the materials.In conclusion,an appropriate amount of zinc-containing raw material is beneficial for the preparation of augite-based glass ceramics.

关键词： spinel network structure thermodynamics microstructure glass ceramics

5G Ultra-Dense network Fingerprint Positioning Method Based on Matrix Completion

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China Communications 2023年第3期20卷 105-118页

作者： Yuexia Zhang Chong Liu key laboratory of information and Communication Systems Ministry of Information IndustryBeijing Information Science and Technology UniversityBeijing 100101China key laboratory of Modern Measurement and Control technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100101China

The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching algorithms.First,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint database.Second,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint databases.Finally,a classification weighted K-nearest neighbor fingerprint matching algorithm is proposed.The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database.The simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.

关键词： indoor positioning fingerprint matching matrix completion 5G UDN RSSI

Research on Loophole with Second Distribution of Real Value Detectors

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Chinese Journal of Electronics 2016年第6期25卷 1141-1150页

作者： WANG Tianbo ZHANG Fengbin XIA Chunhe beijing key laboratory of network technology Beihang University network and information Security laboratory Harbin University of Science and Technology

Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. There still remain many problems such as the randomness of detector generation, incompleteness of selfset and the generalization ability of detectors, which would cause a lot of loopholes in non-self space. A heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self space in the first distribution and the mutation regions of self space is proposed. The algorithm can distribute real value detectors through omission data based on the methods of partition and movement. A method is proposed to solve the problem on how to get the optimal solutions to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that our algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.

关键词： Artificial immune Anomaly detection Loophole Parameter estimation Detector

Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

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science China(information sciences) 2023年第6期66卷 258-266页

作者： Junjiang XIANG Qing PAN Zhengrong ZHANG Songnian FU Yuwen QIN advanced Institute of Photonics technology School of Information EngineeringGuangdong University of Technology Guangdong Provincial key laboratory of Photonics information technology Guangdong University of Technology Guangxi key laboratory of Multimedia Communications and network technology School of Computer Electronics and InformationGuangxi University

To meet the challenge of camouflaged object detection (COD),which has a high degree of intrinsic similarity between the object and background,this paper proposes a double-branch fusion network(DBFN)with a parallel attention selection mechanism (PASM).In detail,a schismatic receptive field block(SRF)combined with an attention mechanism for low-level information is performed to learn texture features in one branch,and an integration of the SRF,a hybrid attention mechanism (HAM),and a depth feature polymerization module (DFPM)is employed for high-level information to extract detection features in the other branch.Then,both texture features and detection features are input into the PASM to acquire selective expression matrices.Eventually,the final result is obtained after further selective matrix optimization with atrous spatial pyramid pooling (ASPP)and a residual channel attention block (RCAB)being applied serially.Experimental results on three public datasets verify that our method outperforms the state-of-the-art methods in terms of four evaluation metrics,i.e.,mean absolute error (MAE),weighted F βmeasure (Fβω),structural measure (Sα),and E-measure (Eφ)

关键词： camouflaged object detection attention mechanism feature extraction feature aggregation texture information fuzzy boundary