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An Information Quantity-Based Uncertainty Measure to Incomplete Numerical Systems 1

9th International Conference on Fuzzy Information and Engineering, ICFIE 2017

作者： Guo, Xin Xiang, Yingzhuo Shu, Lan School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China

ISBN: (数字)9783030027773

ISBN: (纸本)9783030027766

The uncertainty measure plays an important role in the analysis of data. At present, many uncertain measures for incomplete information systems or incomplete decision systems have been developed. However, these measures are mainly aimed at discrete valued information systems, but they are not suitable for real valued data sets. In this paper, we mainly study the uncertainty measurement method of incomplete numerical information systems. By introducing neighborhood tolerant rough sets model, each concept has a neighborhood tolerant subset called neighborhood tolerance granule. Neighborhood tolerance information quantity uncertainty measure is proposed. We then prove that it satisfies non-negativity and monotonicity, giving maximum and minimum values. On this basis, the concept of neighborhood-tolerance joint quantity and neighborhood-tolerance condition quantity is proposed, and the relation between them and neighborhood tolerance information is discussed. Theoretical analysis and experimental results show that, in incomplete numerical information systems, the uncertainty measures we propose are performed better than the neighborhood-tolerance approximation accuracy measure in some case. © 2019, Springer Nature Switzerland AG.

关键词： Information systems

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A Signal Synchronization and Pilot-Aided DOA Estimation Method in the presence of Interferences

A Signal Synchronization and Pilot-Aided DOA Estimation Meth...

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International Conference on Computer and Communications (ICCC)

作者： Lanxin Wei Siye Wang Wenbo Xu Dong Li Kun Yang Network and Wireless Communication Lab Beijing University of Posts and Telecommunications Beijing China National Key Laboratory of Science and Technology on Blind Signal Processing Shanghai China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Direction of arrival (DOA) estimation is a fundamental problem in signal processing. The super-resolution spatial spectrum estimation algorithm based on feature decomposition, such as multiple signal classification (MUSIC) and estimating signal parameter via rotational invariance techniques (ESPRIT), has a good performance in high signal-to-interference plus noise ratio (SINR) environment and single interference. However, strong interferences will make DOA estimation error. And the number of angles that can be estimated by these methods is limited by the freedom of the antenna. The existence of multiple interferences will affect accuracy of DOA estimation. To address this problem, in this paper, we propose a novel dual-channel synchronization architecture and a Piloted-Aided DOA estimation method. We improve the matching performance by dual channel synchronization. A new matched filter is designed by using the idea of filter truncation, which reduces the computational resource cost. And a new DOA estimation method is proposed, which can estimate the angle of target signal in the environment of low SINR and multiple interferences. Experiments were performed to verify the performance of the proposed design. Results show that this architecture can greatly reduce the computational complexity. And the method successfully gets the direction of target signal in low SINR environment.

关键词： Direction-of-arrival estimation Estimation Signal processing algorithms Interference Computer architecture Synchronization Signal to noise ratio

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Reduced-complexity Transmit Antenna Selection for Spatial Modulation

Reduced-complexity Transmit Antenna Selection for Spatial Mo...

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International Conference on Communication technology (ICCT)

作者： Ruotong Zhu Hao Chen Yue Xiao Xinrong Ji Wenqi Tang Yan Zhao UESTC Chengdu China School of Information and Communication Engineering UESTC Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

In this paper, a novel transmit antenna selection (TAS) scheme is conceived for spatial modulation multiple-input multiple-output (SM-MIMO) systems, in order to reduce the search complexity. Compared to the existing TAS schemes, the proposed scheme shows a similar performance to optimal one with lower complexity, by exploring an efficient pruning algorithm for tree search based on a combined criterion. Simulation result indicates that the proposed scheme is capable of balancing the transmission performance and search complexity.

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Convergence Analysis of Whale Optimization Algorithm

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Wentao Feng National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan 610041 China

The whale optimization algorithm (WOA) has been widely used in different applications. It has simple control parameters and novel population updating mechanism. However, there is few theoretical analysis of WOA and the convergence property of WOA is ambiguity. This paper analyzes the convergence property of WOA by using the Markov chain of the stochastic process theory. The Markov chain model of the WOA algorithm is established. The one step transition probabilities and convergence properties of different population updating mechanisms in WOA are given. It's proved that the convergence property of WOA is determined by its shrinking encircling mechanism. Finally, three algorithms with different population updating mechanisms are tested with thirteen benchmark functions on accuracy and convergence speed. The simulation results on benchmark functions verify the validity of the theoretical analysis of WOA.

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Parallax attention for unsupervised stereo correspondence learning

arXiv

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arXiv 2020年

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Liang, Zhengfa Lin, Zaiping Yang, Jungang An, Wei China The School of Electronics and Communication Engineering Sun Yat-Sen University Guangzhou510275 China The National Key Laboratory of Science and Technology on Blind Signal Processing China

Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities. However, since disparities can vary significantly for stereo cameras with different baselines, focal lengths and resolutions, the fixed maximum disparity used in cost volume techniques hinders them to handle different stereo image pairs with large disparity variations. In this paper, we propose a generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations. Our PAM integrates epipolar constraints with attention mechanism to calculate feature similarities along the epipolar line to capture stereo correspondence. Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks. Moreover, we introduce a new and large-scale dataset named Flickr1024 for stereo image super-resolution. Experimental results show that our PAM is generic and can effectively learn stereo correspondence under large disparity variations in an unsupervised manner. Comparative results show that our PASMnet and PASSRnet achieve the state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Stereo image processing

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A conditional-probability zone transformation coding method for categorical features 19

A conditional-probability zone transformation coding method ...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： He, Liang Shen, Chao Li, Yun National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9781450371582

It has been a key issue for solving problems efficiently by machine learning models with code categorical features. The state-of-the-art one-hot coding is a widely accepted method to convert the categorical features into numerical values. However, it attracts a sparse space and meaningless value after coding. We come up with a novel coding method based on conditional probability after dividing the features into zones, which is called Conditional-probability-based Zone Transformation (CZT) coding. CZT coding calculates the conditional probability of each feature, then divides the features into several zones according to the probability and finally codes the features in each zone. We mathematically prove that compared with the state-of-the-art method, CZT coding reduces the code length by at least the mean of feature space and the issue becomes into an easier one after CZT coding for the following machine learning model. Finally, using the same neuron network as the classifier, we compare the performance of CZT coding and one-hot coding by using the titanic dataset, where most of the features are categorical, and the result is that CZT coding makes the classifier performs better both on the accuracy and steadiness. © 2019 Association for Computing Machinery.

关键词： Probability

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Mobility in China, 2020: a tale of four phases

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national science Review 2021年第11期8卷 110-120页

作者： Suoyi Tan Shengjie Lai Fan Fang Ziqiang Cao Bin Sai Bing Song Bitao Dai Shuhui Guo Chuchu Liu Mengsi Cai Tong Wang Mengning Wang Jiaxu Li Saran Chen Shuo Qin Jessica R.Floyd Zhidong Cao Jing Tan Xin Sun Tao Zhou Wei Zhang Andrew J.Tatem Petter Holme Xiaohong Chen Xin Lu College of Systems Engineering National University of Defense Technology WorldPop School of Geography and Environmental ScienceUniversity of Southampton School of Mathematics and Big Data Foshan University State Key Laboratory on Blind Signal Processing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Chinese Evidence-Based Medicine Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Big Data Research Center University of Electronic Science and Technology of China West China Biomedical Big Data Center West China Hospital Sichuan University Tokyo Tech World Hub Research Initiative Institute of Innovative Research Tokyo Institute of Technology School of Business Central South University Institute of Big Data and Internet Innovations Hunan University of Technology and Business

2020 was an unprecedented year,with rapid and drastic changes in human mobility due to the COVID-19 *** understand the variation in commuting patterns among the Chinese population across stable and unstable periods,we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020,ranging from the Lunar New Year travel season(chunyun), to the exceptional calm of COVID-19 lockdown,and then to the recovery *** observed that cross-city movements,which increased substantially in chunyun and then dropped sharply during the lockdown,are primarily dependent on travel distance and the socio-economic development of *** the Lunar New Year holiday,national mobility remained low until mid-February,and COVID-19 interventions delayed more than 72.89 million people returning to large *** network analysis revealed clusters of highly connected cities,conforming to the social-economic division of urban agglomerations in *** the mass migration back to large cities was delayed,smaller cities connected more densely to form new *** the recovery period after travel restrictions were lifted,the netflows of over 55% city pairs reversed in direction compared to before the *** findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response,transportation planning and regional economic development,among others.

关键词： human mobility travel restrictions COVID-19 mobile phone data behavioral response

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Speech Language Classification Algorithm Based on Multiple-instance Learning with Negative Sample Cleaning

Speech Language Classification Algorithm Based on Multiple-i...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Jianyi Zhang Wei Xu Jiaqi Yao Jikun Yan Xin Huang 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: (数字)9781728152448

ISBN: (纸本)9781728152455

Speech language classification is an important direction in the field of machine learning. In order to further improve the effect of speech classification, this paper propose a new speech language classification algorithm based on multiple-instance learning with negative instance cleaning, namely NUMI-Biased SVM algorithm. Firstly, extract MFCC features from speech files, regarding each file as an instance bag, and every speech frame as an instance. Secondly, adopt PU learning algorithm to clean negative instances in positive bags to reduce their impact on classification. Finally, multiple-instance learning algorithm is used to classify the speech files. Experiments show that, compared with the SVM algorithm and multiple-instance SVM algorithm, NUMI-Biased-SVM has achieved significant improvement in classification performance.

关键词： Support vector machines Machine learning algorithms Feature extraction Telephone sets Cleaning Classification algorithms Mel frequency cepstral coefficient

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Receiver Distortion Nornalization Method for Specific Emitter Identification

Receiver Distortion Nornalization Method for Specific Emitte...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Mentkai Shi Yuanling Huang Guiliang Wang University School 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: (数字)9781728152448

ISBN: (纸本)9781728152455

Specific emitter identification (SEI) is an identification technique that identify the transmitting device with the hardware imperfection. However, the identification features are influenced by the receiver distortions and can be different when the receivers are changed. Previous SEI studies rarely concern the method to normalize the distortions among different receivers. To solve this problem, a SEI receiver normalization method is proposed. In the proposed method, a frequency-domain adaptive equalization system is contained to normalize the distortions in receivers. According to the experiment, this method can effectively eliminate the SEI feature difference caused by receivers. Then the robust of the proposed method is discussed.

关键词： Receivers Adaptive equalizers Distortion Hardware Object recognition Information technology Signal to noise ratio

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Direct Target Tracking Using Laplace Approximation

Direct Target Tracking Using Laplace Approximation

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Chinese Control Conference (CCC)

作者： Ji-An Luo Meng-Chun Guo Xue-Jun Liu Ke-Kang Song Yu-Xiang Yang Key Lab for IOT and Information Fusion Technology of Zhejiang Hangzhou Dianzi University Hangzhou P. R. China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The direct tracking of a moving radio transmitter using spatially distributed receivers is considered based on delay and Doppler-shift induced signal waveforms. One key step for direct tracking is to approximate the generalized likelihood function corresponding to the largest eigenvalue of a Hermitian matrix. Although stochastic approximations can be set arbitrarily close to the optimal solution, these methods generally have high computational cost and tend to be inefficient when applied to high dimensional problems. In this paper, we develop a Laplace approximation approach for direct tracking using Taylor series expansions. The proposed method belongs to the deterministic approximation domain and therefore it is computational efficient. The simulations validate the performance of the proposed method.

关键词： Receivers Target tracking Eigenvalues and eigenfunctions Bayes methods Mathematical model Approximation algorithms Time measurement

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