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检索条件"机构=Jiangsu Key Lab of Image Processing and Image Communications"

共 154 条记录，以下是111-120 订阅

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Attention Electroencephalogram Analysis based on Jensen-Shannon Divergence Analysis

Attention Electroencephalogram Analysis based on Jensen-Shan...

引用

2013 International Conference on Bio-Medical Materials and Engineering(ICBME 2013)

作者： Zhengzhong Zheng Junchang Zhao Xiaolin Huang Jun Wang Image Processing and Image Communications Key Lab College of Geo & Bio InformationNanjing Univ of Posts & Telecomm Institute of Biomedical Electronic Engineering School of Electronic Science and EngineeringNanjing University

In this paper, the complexity analysis method based on Jensen-Shannon Divergence was used to calculate the complexity of the Idle states, the close eyes states and the count numbers states electroencephalogram The study found that the JSD value of close eyes states EEG was highest, followed by that of the Idle states EEG, and that of the count numbers states EEG was minimum The result can be used to assisted clinical diagnosis

关键词： Complexity electroencephalogram Jensen-Shannon Divergence

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Beta Wave of Sleep Electroencephalogram Analysis Based on Multiscale Sign Series Entropy

Beta Wave of Sleep Electroencephalogram Analysis Based on Mu...

引用

2015 International Conference on Artificial Intelligence and Industrial Engineering(AIIE 2015)

作者： Jianhui Jiang Shitong Wang Fengzhen Hou Jun Wang Jin Li Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm School of Science China Pharmaceutical University College of Physics and Information Technology Shaanxi Normal University

ISBN: (纸本)9781510806450

Sleep Electroencephalogram(Sleep EEG) detection and treatment can provide the basis for clinical diagnosis and treatment. According to the non-stationary random character of EEG itself, the paper proposed multiscale sign series entropy(MSSE) method and applied it to the state of sleep EEG analysis. Numerical results showed that, MSSE method can effectively differentiate awake period β wave and sleep stage β wave even if under the influence of the noise. The results show that the algorithm can aid in clinical diagnosis of sleep EEG.

关键词： sleep electroencephalogram multiscale sign series entropy clinical diagnosis

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Coupling analysis of epileptic EEG signals based on the multiscale mutual model entropy

Coupling analysis of epileptic EEG signals based on the mult...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Ning Ji Jia-Fei Dai Jun Wang Feng-Zhen Hou Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

The Multiscale Mutual Model Entropy algorithm is presented to quantify the coupling degree between two EEG time series collected at the same time on different *** extracted the characteristics of EEG signals from the healthy and epileptics based on the *** results show that the entropy value of healthy people is higher than that of *** with the increase of scale,the difference in entropy value between them is more *** indicates that Multiscale Mutual Entropy can distinguish the coupling difference between normal samples and case samples,which is significant for the clinical pathological assessment and brain disease diagnosis.

关键词： multiscale epileptic coupling mutual model entropy

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Beta wave of sleep electroencephalogram analysis based on sign series entropy

Beta wave of sleep electroencephalogram analysis based on si...

引用

2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Min Zhao Yuting Li Lu Yang Jia-Fei Dai Jun Wang Feng-Zhen Hou Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

Sleep and wake EEG have some *** studying their brain waves and calculating the sign series entropy,we use the T test for the detection of sleep and wake EEG data to figure out whether they are *** beta waves being filtered out by the filter,we calculate the entropy of the sign *** results of T test show that the beta waves in the state of sleep and wake are different.

关键词： sleep EEG sign series entropy

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Sleep EEG analysis Based on the Multiscale Jenson-Shannon Divergence

Sleep EEG analysis Based on the Multiscale Jenson-Shannon Di...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Zhengxia Zhang Jia-Fei Dai JunWang Feng-Zhen Hou Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

Sleep EEG signals analysis is a hotspot of research recently,this paper,by using nonlinear dynamics theory knowledge,JSD algorithm and multi-scale JSD algorithm is proposed for some individual conscious period and NREM sleep stage I analyzed the research of EEG signals,and the use of SPSS statistical software to verify the veracity and reliability of the experiment,at the same time,with the error bar graph method to analysis the two different states of sleep EEG signals,the results show that both the JSD algorithm and the multi-scale JSD algorithm can effectively distinguish between awake and NREM sleep stage I of EEG signals,these two conditions' EEG signals exist significant differences,The algorithm we proposed can be further used in the study of sleep EEG in installment,which can also provide all kinds of disease diagnosis and treatment of sleep with effective auxiliary function,the research has important practical significance in the future.

关键词： multi-scale JSD sleep EEG signals

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Fetal Electrocardiogram Extraction Based On Modified Robust Independent Component Analysis

Fetal Electrocardiogram Extraction Based On Modified Robust ...

引用

2013 International Conference on Bio-Medical Materials and Engineering(ICBME 2013)

作者： Wenpo Yao Junchang Zhao Zhengzhong Zheng Tiebing Liu Hongxing Liu Jun Wang Image Processing and Image Communications Key Lab. Nanjing Univ. of Posts & Telecomm Nanjing General Hospital of Nanjing Military Command School of Electronic Science and Engineering Nanjing University

Fetal electrocardiogram(FECG) separation gets widely attention due to its clinical significance In the paper, we proposed an improved robust independent component analysis for fetal ECG separation Firstly, wavelet decomposition was applied to fetal ECG to get the relevant parameters Then, the Robust ICA was used to separate the mixed signals Compared to robust independent component analysis, computing speed of the improved algorithm increased by an average of 15 percent while minimum mean square error fluctuations 00008, which indicated that this algorithm could be effectively used in clinical fetal ECG monitoring

关键词： Robust ICA Wavelet Transform FECG Kurtosis

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Alpha Wave Attention EEG Analysis Based on the Multiscale Jensen-Shannon Divergence

Alpha Wave Attention EEG Analysis Based on the Multiscale Je...

引用

2014 2nd International Conference on Materials Science and Mechanical Engineering(2nd ICMSME 2014)

作者： Zhengxia Zhang Siqiu Xu Erning Zhou Xiaolin Huang Jun Wang Image Processing and Image Communications Key Lab College of Geo & Bio InformationNanjing Univ of Posts & Telecomm Institute of Biomedical Electronic Engineering School of Electronic Science and EngineeringNanjing University

The article adopted the multiscale Jensen- Shannon Divergence method for EEG complexity analysis. Then the study found that the method can distinguish between three different status(eyes closed, count, in a daze) acquisition of Alpha EEG time series, which shows three different states of Alpha EEG time series having significant differences. In each state of the three different states(eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series proxy data, finding that there are large amounts of nonlinear time series in the Alpha EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze EEG signals. Attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

关键词： Multiscale JSD Attention Alpha EEG signals

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Attributes and Semantic Constrained GAN for Face Sketch-Photo Synthesis

Attributes and Semantic Constrained GAN for Face Sketch-Phot...

引用

International Conference on Wireless communications and Signal processing (WCSP)

作者： Jieying Zheng Haoxian Li Feng Liu Jiangsu Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China School of Geographic and Biologic Information Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781665450867

Face sketch-photo synthesis aims at generating a facial photo conditioned on a given photo. It has attracted wide attention in computer vision and has been widely applied in law enforcement and entertainment. However, precisely synthesizing high-quality face photos is still challenging due to the missing color information in face sketches. To alleviate the problem, we propose an attribute and Semantic constrained Generative Adversarial Networks (ASGAN), which introduces face attribute and semantic constraints to improve the quality and accuracy of synthesized photos. Specifically, We first annotate the key attributes in the dataset and form a 14-dimensional attribute vector for each face. Then, the attribute features and semantic features obtained by face parsing are fused. Finally, the generator takes the fused constrain features and sketches as input and constructs two-stream encoders to synthesize high-quality photos. Extensive experimental results demonstrate that our method can significantly outperform state-of-the-art methods. It can synthe-size higher-quality face photos while maintaining the identity, attributes, and structure. Meanwhile, it can alleviate the problem of background and skin color synthesis errors.

关键词： Wireless communication Law enforcement Semantics Entertainment industry Color Signal processing Generative adversarial networks

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THz Spectrum Deconvolution with Total Variation Regularization

THz Spectrum Deconvolution with Total Variation Regularizati...

引用

International Congress on image and Signal processing

作者： Lizhen Deng Hu Zhu Guanming Lu State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan 430074 China Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

ISBN: (纸本)9781479927654

Deconvolution has become one of the most used methods for improving spectral resolution, and blind deconvolution as a typical method has been researched widely. However, the predefined point spread function (PSF) used in blind deconvolution method is not known exactly in practice. In general, the PSF is estimated simultaneously from the observed spectrum, but it becomes difficult when the spectroscopic data are polluted by strong noise. In this paper, we present a deconvolution method used to improve the resolution of THz spectrum. In the method, the energy function is constructed, which includes the likelihood term, Total variation of spectrum term and L2 norm of the PSF term. The PSF is modeled as a parametric function combination with the a priori knowledge about the characteristics of the instrumental response. The spectrum and the parameter of PSF are obtained by minimizing the energy functional using alternate minimization approach. Experimental results are shown to demonstrate the efficiency of the proposed method used for THz spectrum.

关键词： THz spectrum Total variation deconvolution alternate minimization approach

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Electroencephalogram Signal Analysis Based on the Improved k-nearest Neighbor Network

Electroencephalogram Signal Analysis Based on the Improved k...

引用

International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Xian Yu Chengcheng Liu Jun Wang Jiafei Dai Jin Li Fengzhen Hou Image Processing and Image Communications Key Lab Nanjing University of Posts and Telecommunications Nanjing General Hospital of Nanjing Military Command College of Physics and Information Technology Shaanxi Normal University School of Science China Pharmaceutical University

ISBN: (纸本)9781509037117

In this paper, we propose a method to analyze epileptic electroencephalogram based on time series that is transformed from improved k-nearest neighbor network. The study of complex networks has become a hot research of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so researching the time series from the network can also achieve the purpose of studying epileptic electroencephalogram. The results of this experiment show that studying power spectrum of time series from the network is more easily than the power spectrum of time series directly generated from brain data to distinguish between normal and epileptic patients. In addition, studying the clustering coefficient of improved k-nearest neighbor network is also able to distinguish between normal and patients with epilepsy. This study can provide an important reference for the study of epilepsy and clinical diagnosis.

关键词： analysis of electroencephalogram signals improved k-nearest neighbor network time series epilepsy

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