Vocal tract length normalization (VTLN) is a process of reducing the effects of VTL differences among speakers. In this paper, frequency warping based on Bark scale is presented to obtain VTL normalized features and Bark scale based features (BSBF) have been proposed. BSBFs are found to improve the accuracy of vowel recognition task by 1.05 % over MFCCs. A feature-level fusion of BSBFs with MFCCs performs better than MFCCs alone under matched and mismatched conditions in phoneme recognition.
{"title":"Combining Evidences from Bark Scale and Mel Scale Warped Features for VTLN","authors":"Shubham Sharma, H. Patil","doi":"10.1145/2708463.2709042","DOIUrl":"https://doi.org/10.1145/2708463.2709042","url":null,"abstract":"Vocal tract length normalization (VTLN) is a process of reducing the effects of VTL differences among speakers. In this paper, frequency warping based on Bark scale is presented to obtain VTL normalized features and Bark scale based features (BSBF) have been proposed. BSBFs are found to improve the accuracy of vowel recognition task by 1.05 % over MFCCs. A feature-level fusion of BSBFs with MFCCs performs better than MFCCs alone under matched and mismatched conditions in phoneme recognition.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"72 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":"116729627","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}
Dhruv Vora, S. Ramesh, Sundararam Rathod, Keshav Parthasarathy, Susan Elias
Accurate predictions of traffic plays an important role in the development of an Intelligent Transport System (ITS). This paper focuses on the combination of several neural experts forming a Committee of Experts that can be used to predict traffic. Since different mechanisms have unique properties that depend on the formulae used for construction and the training data, combining the predictions of multiple experts would inevitably result in a more accurate prediction than using a single expert. The input for these experts is historic speed data, derived from vehicle-specific sensors like GPS. Furthermore, each expert within the committee is assigned a different granularity of data as its training set based on its fundamental properties, such that the expert would be most suited to making accurate predictions using that granularity of data. The different granularities of data used by experts to create models include weekwise, daywise and timewise. Each expert further recognizes the degree of influence of certain factors such as weather, holidays etc. on the flow of traffic. Eventually each expert in the committee machine makes an independent prediction of traffic and these predictions are combined using a weighted average in order to obtain the final prediction.
{"title":"Traffic Analysis and Prediction using a Committee of Experts","authors":"Dhruv Vora, S. Ramesh, Sundararam Rathod, Keshav Parthasarathy, Susan Elias","doi":"10.1145/2708463.2709067","DOIUrl":"https://doi.org/10.1145/2708463.2709067","url":null,"abstract":"Accurate predictions of traffic plays an important role in the development of an Intelligent Transport System (ITS). This paper focuses on the combination of several neural experts forming a Committee of Experts that can be used to predict traffic. Since different mechanisms have unique properties that depend on the formulae used for construction and the training data, combining the predictions of multiple experts would inevitably result in a more accurate prediction than using a single expert. The input for these experts is historic speed data, derived from vehicle-specific sensors like GPS.\u0000 Furthermore, each expert within the committee is assigned a different granularity of data as its training set based on its fundamental properties, such that the expert would be most suited to making accurate predictions using that granularity of data. The different granularities of data used by experts to create models include weekwise, daywise and timewise. Each expert further recognizes the degree of influence of certain factors such as weather, holidays etc. on the flow of traffic. Eventually each expert in the committee machine makes an independent prediction of traffic and these predictions are combined using a weighted average in order to obtain the final prediction.","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":"130561496","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}
A novel improved Non-local Means (NLM) filtering scheme for MR images is proposed based on a simplified pulse-coupled neural network (PCNN) which follows mammal's visual/perceptual response characteristics. The proposed scheme is a two-stage filtering technique. In the first stage, the noisy MR image is passed through a PCNN and the firing time output (time signature) of the PCNN is recorded. The time signature is used to pre-select a subset of pixels in non-local vicinities, depending on their similarities with the pixel of interest. In the second stage, improved unbiased NLM (IUNLM) is applied to denoise the pixel of interest only considering the contributions of the pre-selected subset of pixels in the non-local vicinities. Extensive experiments and comparisons with state-of-the-art MRI denoising techniques were carried out to evaluate the performance of the proposed scheme. The results suggest supporting evidence of the effectiveness of the proposed scheme in MR image denoising.
{"title":"MRI Denoising using Visual System Response Model Simulating PCNN","authors":"Sudeb Das, M. Kundu","doi":"10.1145/2708463.2709031","DOIUrl":"https://doi.org/10.1145/2708463.2709031","url":null,"abstract":"A novel improved Non-local Means (NLM) filtering scheme for MR images is proposed based on a simplified pulse-coupled neural network (PCNN) which follows mammal's visual/perceptual response characteristics. The proposed scheme is a two-stage filtering technique. In the first stage, the noisy MR image is passed through a PCNN and the firing time output (time signature) of the PCNN is recorded. The time signature is used to pre-select a subset of pixels in non-local vicinities, depending on their similarities with the pixel of interest. In the second stage, improved unbiased NLM (IUNLM) is applied to denoise the pixel of interest only considering the contributions of the pre-selected subset of pixels in the non-local vicinities. Extensive experiments and comparisons with state-of-the-art MRI denoising techniques were carried out to evaluate the performance of the proposed scheme. The results suggest supporting evidence of the effectiveness of the proposed scheme in MR image denoising.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"12 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":"115228982","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}
A voltammetric electronic tongue has been developed for black tea assessment. While attempting to standardize the voltammetric electronic tongue a basic taste recognition test has been conducted. This electronic tongue works on cyclic voltammetric principle and has a three electrode configuration (working, reference, and counter electrode). The voltage is applied across the working and reference electrode and the response current is obtained from the counter and reference electrode. Now the huge amount of data, obtained from these response current pulses, was compressed by discrete wavelet transform (DWT). In this paper, in order to determine the optimum level of compression mean square error, separation index and principal component analysis have been employed.
{"title":"Selection of Optimum Level of Data Compression for Voltam metric Electronic Tongue Signal Using DWT","authors":"Ashmita De, Arunangshu Ghosh, B. Tudu","doi":"10.1145/2708463.2709039","DOIUrl":"https://doi.org/10.1145/2708463.2709039","url":null,"abstract":"A voltammetric electronic tongue has been developed for black tea assessment. While attempting to standardize the voltammetric electronic tongue a basic taste recognition test has been conducted. This electronic tongue works on cyclic voltammetric principle and has a three electrode configuration (working, reference, and counter electrode). The voltage is applied across the working and reference electrode and the response current is obtained from the counter and reference electrode. Now the huge amount of data, obtained from these response current pulses, was compressed by discrete wavelet transform (DWT). In this paper, in order to determine the optimum level of compression mean square error, separation index and principal component analysis have been employed.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"48 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":"127233364","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}
Path-finding is a fundamental problem, in mobile robotics which involves finding an optimal collision-free path from the source node to the destination node. Before the robot traces out the required path, the first step to be carried out is, exploring the environment. Localization plays a major role in this case. This paper adopts an RFID (Radio Frequency Identification) based localization technique. A set of RFID(IC) tags arranged in a grid structure in an equidistant manner are used for the purpose of tracing the current co-ordinate/location of the robot. After exploring the environment one virtual map is generated which contains the location of source, destination, obstacles and landmarks. From the map one graph is generated which is composed of a set of vertices that indicates the cells of the grid and a set of edges which indicates the free path in the environment reachable from the source. After analyzing different searching algorithms we found Breadth First Search (BFS) algorithm to be more effective, because it finds the shortest path from the source node to each node in the graph. We improved the BFS algorithm so that the optimal collision free path from source to destination node is generated. We have implemented our techniques in a simulated environment. We have used MATLAB and JAVA for the simulation purpose. Finally we have demonstrated the result of the implementation through examples.
{"title":"Low Complexicity Graph Based Navigation and Path Finding of Mobile Robot Using BFS","authors":"P. Panigrahi, H. K. Tripathy","doi":"10.1145/2708463.2709068","DOIUrl":"https://doi.org/10.1145/2708463.2709068","url":null,"abstract":"Path-finding is a fundamental problem, in mobile robotics which involves finding an optimal collision-free path from the source node to the destination node. Before the robot traces out the required path, the first step to be carried out is, exploring the environment. Localization plays a major role in this case. This paper adopts an RFID (Radio Frequency Identification) based localization technique. A set of RFID(IC) tags arranged in a grid structure in an equidistant manner are used for the purpose of tracing the current co-ordinate/location of the robot. After exploring the environment one virtual map is generated which contains the location of source, destination, obstacles and landmarks. From the map one graph is generated which is composed of a set of vertices that indicates the cells of the grid and a set of edges which indicates the free path in the environment reachable from the source. After analyzing different searching algorithms we found Breadth First Search (BFS) algorithm to be more effective, because it finds the shortest path from the source node to each node in the graph. We improved the BFS algorithm so that the optimal collision free path from source to destination node is generated. We have implemented our techniques in a simulated environment. We have used MATLAB and JAVA for the simulation purpose. Finally we have demonstrated the result of the implementation through examples.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"8 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":"126961030","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}
This paper describes a method to learn Bag-Of-Words (BOW) descriptor for image representation which is robust to domain shift. Domain shift is necessary when a classifier trained on one dataset (source) is applied for classification on a different dataset (target). Datasets acquired with different conditions, have dissimilar feature distributions among them. Traditional method for representing each image by a BOW descriptor with the vocabulary learnt on a reference dataset does not work well for such cross-dataset tasks. We propose a new method to learn an amended dictionary composed of class specific atoms. The proposed Domain-Invariant BOW (DI-BOW) descriptor built from this dictionary has much better class discriminability and inherently attenuates domain-specific characteristics, making it more suitable to cross-domain tasks. Results based on DI-BOW descriptor reveal its efficiency, by outperforming state-of-the-art domain adaptation techniques for object recognition.
{"title":"DI-BOW: Domain Invariant Feature Descriptor Using Bag of Words","authors":"T. Selvan, Suranjana Samanta, Sukhendu Das","doi":"10.1145/2708463.2709044","DOIUrl":"https://doi.org/10.1145/2708463.2709044","url":null,"abstract":"This paper describes a method to learn Bag-Of-Words (BOW) descriptor for image representation which is robust to domain shift. Domain shift is necessary when a classifier trained on one dataset (source) is applied for classification on a different dataset (target). Datasets acquired with different conditions, have dissimilar feature distributions among them. Traditional method for representing each image by a BOW descriptor with the vocabulary learnt on a reference dataset does not work well for such cross-dataset tasks. We propose a new method to learn an amended dictionary composed of class specific atoms. The proposed Domain-Invariant BOW (DI-BOW) descriptor built from this dictionary has much better class discriminability and inherently attenuates domain-specific characteristics, making it more suitable to cross-domain tasks. Results based on DI-BOW descriptor reveal its efficiency, by outperforming state-of-the-art domain adaptation techniques for object recognition.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"16 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":"116640719","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}
Biswajit Biswas, Ritamshirsa Choudhuri, K. Dey, A. Chakrabarti
The multi-focus image fusion used to produce a single image where the entire view is focused by combining multiple images taken with different focus distances. Here we present a concept for multi-focus image fusion using Principal component analysis (PCA) method on shearlet domain. Our proposed concept works on two folds, i) transform the source image into shearlet-image by using shearlet transform (ST), ii) use of PCA model in low-pass sub-band by which the best pixels in smooth parts are selected according to their arrangement. The composition of different high-pass sub-band coefficients achieved by the ST decomposition are realized. Then, the resultant fusion image is reconstructed by performing the inverse shearlet transform (IST). The experimental results, show that our proposed technique can tender enhanced fusion results than some existing methods in the state-of-the-art. This comparative assessment done in the lights of qualitative and quantitative measurements in terms of mutual information and fusion matrix QAB/F.
{"title":"A New Multi-focus Image Fusion Method Using Principal Component Analysis in Shearlet Domain","authors":"Biswajit Biswas, Ritamshirsa Choudhuri, K. Dey, A. Chakrabarti","doi":"10.1145/2708463.2709064","DOIUrl":"https://doi.org/10.1145/2708463.2709064","url":null,"abstract":"The multi-focus image fusion used to produce a single image where the entire view is focused by combining multiple images taken with different focus distances. Here we present a concept for multi-focus image fusion using Principal component analysis (PCA) method on shearlet domain. Our proposed concept works on two folds, i) transform the source image into shearlet-image by using shearlet transform (ST), ii) use of PCA model in low-pass sub-band by which the best pixels in smooth parts are selected according to their arrangement. The composition of different high-pass sub-band coefficients achieved by the ST decomposition are realized. Then, the resultant fusion image is reconstructed by performing the inverse shearlet transform (IST). The experimental results, show that our proposed technique can tender enhanced fusion results than some existing methods in the state-of-the-art. This comparative assessment done in the lights of qualitative and quantitative measurements in terms of mutual information and fusion matrix QAB/F.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"66 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":"125055433","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}
In this paper, we describe a novel procedure to automatically classify the ischemic beats of an ECG signal using multilayer feedforward neural network (MLFFNN) and principal component analysis. A framework for the proposed method is also suggested. ECG recordings are given as input to the system and ischemic beats are detected from the recordings. The efficacy of the framework is tested using recordings from the European ST database. The ECG recording goes through preprocessing steps and feature vector formation. The feature vector is further reduced using principal component analysis. This reduced feature vector is given as input to the 3 layer MLFFNN which uses scaled conjugate gradient backpropagation algorithm for training. The network was further validated and tested to check its efficacy. The efficacy of the procedure was measured using sensitivity (91%) and positive predictive accuracy (94%). Also the proposed method is compared with some other existing work and it is seen that the proposed method outperforms others.
{"title":"A Framework for Ischemic Beat Detection using Multi-Layer Feedforward Neural Network and Principal Component Analysis (IBD-MLFFNN-PCA)","authors":"Sounak Mohanta, K. Dasgupta","doi":"10.1145/2708463.2709050","DOIUrl":"https://doi.org/10.1145/2708463.2709050","url":null,"abstract":"In this paper, we describe a novel procedure to automatically classify the ischemic beats of an ECG signal using multilayer feedforward neural network (MLFFNN) and principal component analysis. A framework for the proposed method is also suggested. ECG recordings are given as input to the system and ischemic beats are detected from the recordings. The efficacy of the framework is tested using recordings from the European ST database. The ECG recording goes through preprocessing steps and feature vector formation. The feature vector is further reduced using principal component analysis. This reduced feature vector is given as input to the 3 layer MLFFNN which uses scaled conjugate gradient backpropagation algorithm for training. The network was further validated and tested to check its efficacy. The efficacy of the procedure was measured using sensitivity (91%) and positive predictive accuracy (94%). Also the proposed method is compared with some other existing work and it is seen that the proposed method outperforms others.","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":"115025566","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}
In the last few years, there has been significant work on using temporal features of speech excitation source, viz., Linear Prediction (LP) residual and its analytic or instantaneous phase, group delay method, glottal glow derivative, etc. for speaker recognition task. In this paper, score-level fusion of Teager Energy Operator (TEO) phase with Mel Frequency Cepstral Coefficients (MFCC) features for text-independent Speaker Verification (SV) task has been proposed. Experiments have been performed on SV system based on Gaussian Mixture Model-Universal Background Model (GMM-UBM). Proposed SV system with fusion of TEO phase with MFCC is found to improve the accuracy of SV system by 3.10% over state-of-the-art MFCC features. This score-level fusion of TEO phase with MFCC performs better than MFCCs alone under matched and mismatched conditions during testing in SV system on 2002 NIST Speaker Recognition Evaluation (SRE) database. This indicates that TEO phase contains information that is complementary to the MFCC features.
{"title":"Fusion of TEO Phase with MFCC Features for Speaker Verification","authors":"Purvi Agrawal, H. Patil","doi":"10.1145/2708463.2709057","DOIUrl":"https://doi.org/10.1145/2708463.2709057","url":null,"abstract":"In the last few years, there has been significant work on using temporal features of speech excitation source, viz., Linear Prediction (LP) residual and its analytic or instantaneous phase, group delay method, glottal glow derivative, etc. for speaker recognition task. In this paper, score-level fusion of Teager Energy Operator (TEO) phase with Mel Frequency Cepstral Coefficients (MFCC) features for text-independent Speaker Verification (SV) task has been proposed. Experiments have been performed on SV system based on Gaussian Mixture Model-Universal Background Model (GMM-UBM). Proposed SV system with fusion of TEO phase with MFCC is found to improve the accuracy of SV system by 3.10% over state-of-the-art MFCC features. This score-level fusion of TEO phase with MFCC performs better than MFCCs alone under matched and mismatched conditions during testing in SV system on 2002 NIST Speaker Recognition Evaluation (SRE) database. This indicates that TEO phase contains information that is complementary to the MFCC features.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"277 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":"121314254","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}
This paper presents an effective scheme to identify the abnormal mammograms in order to detect the breast cancer. The scheme utilizes the segmentation-based fractal texture analysis (SFTA) method to extract the textural features from the mammograms for the classification of normal and abnormal mammograms. A fractal analysis has been applied to collect the qualitative information of textural features. A fast correlation-based filter (FCBF) method has been used to select feature subsets containing significant features, which are used for classification purpose. The scheme was tested on the mammogram images of MIAS database. In this paper, support vector machine (SVM) has been utilized for classification of mammograms. Simulation results show an optimal classification performance index as the area under the curve (AUC) of 0.9831 in the ROC analysis.
{"title":"Automatic Characterization of Mammograms using Fractal Texture Analysis and Fast Correlation Based Filter Method","authors":"S. Beura, B. Majhi, Ratnakar Dash","doi":"10.1145/2708463.2709058","DOIUrl":"https://doi.org/10.1145/2708463.2709058","url":null,"abstract":"This paper presents an effective scheme to identify the abnormal mammograms in order to detect the breast cancer. The scheme utilizes the segmentation-based fractal texture analysis (SFTA) method to extract the textural features from the mammograms for the classification of normal and abnormal mammograms. A fractal analysis has been applied to collect the qualitative information of textural features. A fast correlation-based filter (FCBF) method has been used to select feature subsets containing significant features, which are used for classification purpose. The scheme was tested on the mammogram images of MIAS database. In this paper, support vector machine (SVM) has been utilized for classification of mammograms. Simulation results show an optimal classification performance index as the area under the curve (AUC) of 0.9831 in the ROC analysis.","PeriodicalId":289647,"journal":{"name":"Indo-Japan Conference on Perception and Machine Intelligence","volume":"42 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":"128724448","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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