Automated control of mobile robot navigation is a challenging area in the field of robotics research. In this work, an attempt is made to use a new neural network training algorithm based on gravitational search (GS) and feed forward neural network (FFNN) for automatic robot navigation of wall following mobile robots. The GS strategy is used for setting the optimal weight set of the FFNN so as to increase the performance of the neural network. The algorithm is tested with three large datasets obtained from UCI machine learning repository, containing a sequence of sensor readings where sensors are arranged around the waist of the SCITOS G5 robot. The proposed method shows promising results for all the datasets.
{"title":"Controlling Wall Following Robot Navigation Based on Gravitational Search and Feed Forward Neural Network","authors":"T. Dash, T. Nayak, R. Swain","doi":"10.1145/2708463.2709070","DOIUrl":"https://doi.org/10.1145/2708463.2709070","url":null,"abstract":"Automated control of mobile robot navigation is a challenging area in the field of robotics research. In this work, an attempt is made to use a new neural network training algorithm based on gravitational search (GS) and feed forward neural network (FFNN) for automatic robot navigation of wall following mobile robots. The GS strategy is used for setting the optimal weight set of the FFNN so as to increase the performance of the neural network. The algorithm is tested with three large datasets obtained from UCI machine learning repository, containing a sequence of sensor readings where sensors are arranged around the waist of the SCITOS G5 robot. The proposed method shows promising results for all the datasets.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a novel Content Based Medical Image Retrieval (CBMIR) scheme for color endoscopic images using Multi-scale Geometric Analysis (MGA) of Nonsubsampled Contourlet Transform (NSCT) and the statistical framework based on Generalized Gaussian Density (GGD) model and Kullback-Leibler Distance (KLD). The subband images obtained from the NSCT decomposition are divided into number of blocks and then the coefficients of each block of each subband is modeled with GGD parameters and computing the similarity using the KLD among the model parameters. The retrieval performance of the proposed system is further improved using Least Square-Support Vector Machine (LSSVM) classifier. Extensive experiments were carried out to evaluate the effectiveness of the proposed system on endoscopic image databases consisting of 276 images. Experimental results show that the proposed CBMIR system performs efficiently in image retrieval paradigm.
{"title":"Endoscopic Image Retrieval System Using Multi-scale Image Features","authors":"M. Chowdhury, M. Kundu","doi":"10.1145/2708463.2709046","DOIUrl":"https://doi.org/10.1145/2708463.2709046","url":null,"abstract":"We present a novel Content Based Medical Image Retrieval (CBMIR) scheme for color endoscopic images using Multi-scale Geometric Analysis (MGA) of Nonsubsampled Contourlet Transform (NSCT) and the statistical framework based on Generalized Gaussian Density (GGD) model and Kullback-Leibler Distance (KLD). The subband images obtained from the NSCT decomposition are divided into number of blocks and then the coefficients of each block of each subband is modeled with GGD parameters and computing the similarity using the KLD among the model parameters. The retrieval performance of the proposed system is further improved using Least Square-Support Vector Machine (LSSVM) classifier. Extensive experiments were carried out to evaluate the effectiveness of the proposed system on endoscopic image databases consisting of 276 images. Experimental results show that the proposed CBMIR system performs efficiently in image retrieval paradigm.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128440475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Bej, Amitava Akuli, A. Pal, T. Dey, A. Chaudhuri, S. Alam, Rajendra Khandai, N. Bhattacharyya
This paper proposes a non-destructive technique for silk content estimation in cocoons. The price of a cocoon is determined by the silk content which is determined manually by visual inspection or feeling the toughness of the cocoon shell. The above methods are subjective, non-repeatable and prone to human error. With such non-transparent conventional methods of silk estimation, the buyers and sellers are unhappy over any transaction. Our proposed non-destructive technique uses soft x-ray image analysis technique backed up by soft computing algorithm to estimate silk content. Advance image processing and analysis techniques have been applied to extract morphological features from the x-ray images of the cocoons and features are fed to GRNN to estimate the silk content. Total 594 tasar cocoons have been analyzed with the developed solution and the results have been validated with human experts. Accuracy of the system for silk content estimation has been calculated as more than 85%.
{"title":"X-Ray Imaging and General Regression Neural Network (GRNN) for Estimation of Silk Content in Cocoons","authors":"G. Bej, Amitava Akuli, A. Pal, T. Dey, A. Chaudhuri, S. Alam, Rajendra Khandai, N. Bhattacharyya","doi":"10.1145/2708463.2709048","DOIUrl":"https://doi.org/10.1145/2708463.2709048","url":null,"abstract":"This paper proposes a non-destructive technique for silk content estimation in cocoons. The price of a cocoon is determined by the silk content which is determined manually by visual inspection or feeling the toughness of the cocoon shell. The above methods are subjective, non-repeatable and prone to human error. With such non-transparent conventional methods of silk estimation, the buyers and sellers are unhappy over any transaction. Our proposed non-destructive technique uses soft x-ray image analysis technique backed up by soft computing algorithm to estimate silk content. Advance image processing and analysis techniques have been applied to extract morphological features from the x-ray images of the cocoons and features are fed to GRNN to estimate the silk content. Total 594 tasar cocoons have been analyzed with the developed solution and the results have been validated with human experts. Accuracy of the system for silk content estimation has been calculated as more than 85%.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127908231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Different facial feature extraction schemes are available in face recognition literature where the face is cropped and feature points are extracted using mathematical formulae along with probabilistic distance measure between major feature points. In most of the cases the face cropping is done manually and the formulae for extracting a feature point are highly complicated. In this article a facial feature extraction method is proposed where color face images are auto-cropped and control points are extracted, both using the same segmentation mechanism. The segmentation method is first used to auto crop the image and then again applied on this auto cropped image for the detection of major connected components. The feature points are detected simply using the geometrical measurement of location and size of the component without any a priory knowledge of the probabilistic distance between the feature points or using any feature point extraction formula. A T-shaped face image is formed comprising of major feature points. Recognition rate on the unprocessed face images, using PCA, is recorded. PCA is applied on the T-shaped faces and the improvement in rate of recognition is concluded to be statistically significant.
{"title":"Face Recognition using Face-Autocropping and Facial Feature Points Extraction","authors":"Dhiman Karmakar, C. A. Murthy","doi":"10.1145/2708463.2709056","DOIUrl":"https://doi.org/10.1145/2708463.2709056","url":null,"abstract":"Different facial feature extraction schemes are available in face recognition literature where the face is cropped and feature points are extracted using mathematical formulae along with probabilistic distance measure between major feature points. In most of the cases the face cropping is done manually and the formulae for extracting a feature point are highly complicated. In this article a facial feature extraction method is proposed where color face images are auto-cropped and control points are extracted, both using the same segmentation mechanism. The segmentation method is first used to auto crop the image and then again applied on this auto cropped image for the detection of major connected components. The feature points are detected simply using the geometrical measurement of location and size of the component without any a priory knowledge of the probabilistic distance between the feature points or using any feature point extraction formula. A T-shaped face image is formed comprising of major feature points. Recognition rate on the unprocessed face images, using PCA, is recorded. PCA is applied on the T-shaped faces and the improvement in rate of recognition is concluded to be statistically significant.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128030553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose an approach for sparse representation of dense features for action classification. Sparse representation has already been shown in literature as a good approximation for signals for various computer vision applications. This property is leveraged to represent a dense feature like action bank in the form of sparse dictionaries. These dictionaries are learnt using on-line dictionary learning (ODL) which further facilitates incorporating new training examples into existing dictionaries for more robust representation of various categories of action as and when required. Evaluation of the proposed method on realistic action datasets like UCF50 and HMDB51 shows that considering sparse representation of a dense feature is more suitable for classification than the feature itself.
{"title":"Sparsifying Dense Features for Action Classification","authors":"Debaditya Roy, M. Srinivas, C. Mohan","doi":"10.1145/2708463.2709047","DOIUrl":"https://doi.org/10.1145/2708463.2709047","url":null,"abstract":"We propose an approach for sparse representation of dense features for action classification. Sparse representation has already been shown in literature as a good approximation for signals for various computer vision applications. This property is leveraged to represent a dense feature like action bank in the form of sparse dictionaries. These dictionaries are learnt using on-line dictionary learning (ODL) which further facilitates incorporating new training examples into existing dictionaries for more robust representation of various categories of action as and when required. Evaluation of the proposed method on realistic action datasets like UCF50 and HMDB51 shows that considering sparse representation of a dense feature is more suitable for classification than the feature itself.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124930847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiple occurrences of single object in same page or different pages of same document can generally be handled by a repository of reusable document component (RDCR). The same objects of different size and orientation could also be tagged in the same RDCR when orientation and scale maps are used as pointers. Hence after object collection and flattening in document image processing system, the compression ratio would be improved by employing OS-RDC (Orientation and Scale Mapped Reusable Document Component) which in turn would improve the performance of the system (as printing system). But especially for the scaled down and non-orthogonally rotated objects, jagging effect creates significant imaging artifacts in terms of image quality degradation. The current paper addresses to improve the perceptual image quality through OS-RDC and perception based anti-aliasing (PAA) filter. The combination of OS-RDC (used to achieve better speed) and PAA (used to improve the quality of the geometric transformed images) ensures both compression ratio and IQ (Image Quality) which are generally antagonistic in nature.
{"title":"PAA: Perception based Anti-Aliasing for High Quality Transformed Image Reconstruction from Single Image","authors":"M. Sankaralingam, A. Das","doi":"10.1145/2708463.2709061","DOIUrl":"https://doi.org/10.1145/2708463.2709061","url":null,"abstract":"Multiple occurrences of single object in same page or different pages of same document can generally be handled by a repository of reusable document component (RDCR). The same objects of different size and orientation could also be tagged in the same RDCR when orientation and scale maps are used as pointers. Hence after object collection and flattening in document image processing system, the compression ratio would be improved by employing OS-RDC (Orientation and Scale Mapped Reusable Document Component) which in turn would improve the performance of the system (as printing system). But especially for the scaled down and non-orthogonally rotated objects, jagging effect creates significant imaging artifacts in terms of image quality degradation. The current paper addresses to improve the perceptual image quality through OS-RDC and perception based anti-aliasing (PAA) filter. The combination of OS-RDC (used to achieve better speed) and PAA (used to improve the quality of the geometric transformed images) ensures both compression ratio and IQ (Image Quality) which are generally antagonistic in nature.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123170259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Saha, S. Ghorai, B. Tudu, R. Bandyopadhyay, N. Bhattacharyya
The electronic tongue (ET) system is a multi-electrode system where each electrode generates a specific electronic response in presence of different chemical substances in the sample. The efficiency of an ET system mostly depends on the discriminating power of the electronic signature generated by the electrode array. In this work, a sliding window approach is used to extract discrete cosine transform (DCT) coefficients from the ET response and the corresponding energy for different position of window are used as the features of the ET response. The efficacy of the proposed method is verified on three types of ET data sets to predict four different quality of black tea using two kernel classifiers, namely support vector machine (SVM) and vector valued regularized kernel function approximation (VVRKFA).
{"title":"Sliding Window-based DCT Features for Tea Quality Prediction Using Electronic Tongue","authors":"P. Saha, S. Ghorai, B. Tudu, R. Bandyopadhyay, N. Bhattacharyya","doi":"10.1145/2708463.2709032","DOIUrl":"https://doi.org/10.1145/2708463.2709032","url":null,"abstract":"The electronic tongue (ET) system is a multi-electrode system where each electrode generates a specific electronic response in presence of different chemical substances in the sample. The efficiency of an ET system mostly depends on the discriminating power of the electronic signature generated by the electrode array. In this work, a sliding window approach is used to extract discrete cosine transform (DCT) coefficients from the ET response and the corresponding energy for different position of window are used as the features of the ET response. The efficacy of the proposed method is verified on three types of ET data sets to predict four different quality of black tea using two kernel classifiers, namely support vector machine (SVM) and vector valued regularized kernel function approximation (VVRKFA).","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124235231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accurate speaker identification is difficult due to a number of factors. One of the most prominent factors is environmental noise. In this paper, the effect of two nonlinear compression functions, namely log and cubic root used in the feature extraction process, on the performance of the closed set text-independent speaker identification system under clean- and noisy-speaking environments is addressed. Performance is analyzed with Mel frequency cepstral coefficients (MFCC) and Gammatone frequency cepstral coefficients (GFCC). The Gaussian mixture model approach is used for speaker modeling. Two databases, namely, Marathi and Hindi databases were used for the experimentation. It has been observed that the cubic-root based features outperform the log based features under noisy conditions with SNR < 20 dB.
{"title":"Effect of Nonlinear Compression Function on the Performance of the Speaker Identification System under Noisy Conditions","authors":"N. P. Jawarkar, R. S. Holambe, T. Basu","doi":"10.1145/2708463.2709049","DOIUrl":"https://doi.org/10.1145/2708463.2709049","url":null,"abstract":"The accurate speaker identification is difficult due to a number of factors. One of the most prominent factors is environmental noise. In this paper, the effect of two nonlinear compression functions, namely log and cubic root used in the feature extraction process, on the performance of the closed set text-independent speaker identification system under clean- and noisy-speaking environments is addressed. Performance is analyzed with Mel frequency cepstral coefficients (MFCC) and Gammatone frequency cepstral coefficients (GFCC). The Gaussian mixture model approach is used for speaker modeling. Two databases, namely, Marathi and Hindi databases were used for the experimentation. It has been observed that the cubic-root based features outperform the log based features under noisy conditions with SNR < 20 dB.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114919519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Content Based Image Retrieval (CBIR) involves the process of searching and retrieving relevant images from a database. Most CBIR systems rely on the global content of the image but the desired content in an image is often localized, demanding the need for an object-centric CBIR. We propose a cognitive inspired framework WOR (What Object to Retrieve), to solve the problem of an object-centric CBIR. The key contributions in the proposed approach are: (i) Automatic creation of class dendogram in kernel feature space, for an efficient object recognition task; (ii) Integration of information from "What" and "Where" channels in an iterative feedback mechanism, to filter erroneous contents in the outputs of individual modules. Finally, our system extracts HOG feature descriptor from the output of Where channel, for detecting similarity and rank-order the retrieved samples. Experimentations are done with real-life datasets (including PASCAL VOC) exhibits the superior performance.
{"title":"Cognitive Inspired WOR Framework to Reveal Image Semantics, for Efficient Content Based Image Retrieval","authors":"N. Gupta, Sukhendu Das, Gyanesh Dwivedi","doi":"10.1145/2708463.2709034","DOIUrl":"https://doi.org/10.1145/2708463.2709034","url":null,"abstract":"Content Based Image Retrieval (CBIR) involves the process of searching and retrieving relevant images from a database. Most CBIR systems rely on the global content of the image but the desired content in an image is often localized, demanding the need for an object-centric CBIR. We propose a cognitive inspired framework WOR (What Object to Retrieve), to solve the problem of an object-centric CBIR. The key contributions in the proposed approach are: (i) Automatic creation of class dendogram in kernel feature space, for an efficient object recognition task; (ii) Integration of information from \"What\" and \"Where\" channels in an iterative feedback mechanism, to filter erroneous contents in the outputs of individual modules. Finally, our system extracts HOG feature descriptor from the output of Where channel, for detecting similarity and rank-order the retrieved samples. Experimentations are done with real-life datasets (including PASCAL VOC) exhibits the superior performance.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125095205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arunangshu Ghosh, T. Chatterjee, P. Borah, Dilip Sing, B. Tudu, P. Tamuly, N. Bhattacharyya, R. Bandyopadhyay
In a constant endeavour to improve the quality evaluation methodology of black tea in a rapid and objective manner, electronic tongue has been a promising technology. In this direction, a multi-frequency pulse voltammetric electronic tongue is proposed towards the quantification of specific astringent compounds in tea having direct bearing on tea taste. Twenty tea samples were analysed with a voltammetric electrode array of noble metals, i.e. gold, iridium, palladium, platinum and rhodium. The electronic tongue response has been preprocessed and prediction models have been developed with partial least squares regression (PLSR) technique. The concentrations of simple theaflavin (TF1) and Theaflavin-3'-gallate (TF3) have been estimated from the electronic tongue response. The predictions of electronic tongue are in good agreement with the high performance liquid chromatography (HPLC) based estimations of TF1 and TF3. The electronic tongue predictions have been used to obtain the quality perception of tea in terms of briskness values.
{"title":"Multi-frequency Large Amplitude Pulse Voltammetric Electronic Tongue to Assess Key Compounds Imparting the Taste of Briskness to Finished Black Tea Liquor","authors":"Arunangshu Ghosh, T. Chatterjee, P. Borah, Dilip Sing, B. Tudu, P. Tamuly, N. Bhattacharyya, R. Bandyopadhyay","doi":"10.1145/2708463.2709041","DOIUrl":"https://doi.org/10.1145/2708463.2709041","url":null,"abstract":"In a constant endeavour to improve the quality evaluation methodology of black tea in a rapid and objective manner, electronic tongue has been a promising technology. In this direction, a multi-frequency pulse voltammetric electronic tongue is proposed towards the quantification of specific astringent compounds in tea having direct bearing on tea taste. Twenty tea samples were analysed with a voltammetric electrode array of noble metals, i.e. gold, iridium, palladium, platinum and rhodium. The electronic tongue response has been preprocessed and prediction models have been developed with partial least squares regression (PLSR) technique. The concentrations of simple theaflavin (TF1) and Theaflavin-3'-gallate (TF3) have been estimated from the electronic tongue response. The predictions of electronic tongue are in good agreement with the high performance liquid chromatography (HPLC) based estimations of TF1 and TF3. The electronic tongue predictions have been used to obtain the quality perception of tea in terms of briskness values.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"24 14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115524135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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