Anu Tharakan, J. Deepthi, S. Divya, J. Gopika, D. D. Krishna
In this work, we have presented a study of various antennas used in mobile phones in terms of their Specific Absorption Rates (SARs). Conventional antennas such as a half wavelength dipole antenna (0.835 GHz), rectangular patch antenna (0.8 GHz) and Planar Inverted F Antenna (PIFA) (0.8 GHz) are designed and simulated. In order to reduce their SAR values, Electromagnetic Band Gap (EBG) structure is placed near the dipole antenna. The various plots such as the S-parameter, radiation patterns and SAR values are generated to investigate results. The human head model with six layers having the appropriate material properties is designed to have a realistic approach in the detailed study on the SAR. All simulations are done in Ansys HFSS.
{"title":"Specific Absorption Rate Reduced (SAR) Mobile Phone Antenna Designs","authors":"Anu Tharakan, J. Deepthi, S. Divya, J. Gopika, D. D. Krishna","doi":"10.1109/ICACC.2015.89","DOIUrl":"https://doi.org/10.1109/ICACC.2015.89","url":null,"abstract":"In this work, we have presented a study of various antennas used in mobile phones in terms of their Specific Absorption Rates (SARs). Conventional antennas such as a half wavelength dipole antenna (0.835 GHz), rectangular patch antenna (0.8 GHz) and Planar Inverted F Antenna (PIFA) (0.8 GHz) are designed and simulated. In order to reduce their SAR values, Electromagnetic Band Gap (EBG) structure is placed near the dipole antenna. The various plots such as the S-parameter, radiation patterns and SAR values are generated to investigate results. The human head model with six layers having the appropriate material properties is designed to have a realistic approach in the detailed study on the SAR. All simulations are done in Ansys HFSS.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133406476","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}
R. Lakshmanan, Shiji T. P, V. Thomas, S. M. Jacob, Thara P
Breast occupies over Pectoral muscle (PM) which is a predominant portion in Medio-Lateral Oblique (MLO) view of mammogram. The similarity in density among PM area and the breast region may generate false positive results which can adversely affect early breast cancer detection. Noise, wedges, opaque markers etc along with labels are unnecessary in mammographic images. The suspicious segments of PM boundary are obtained by extracting contours of homogeneous regions. The geometrical properties of contour segments are analyzed for extracting PM boundary component. An intensity similarity approach extends the detected major PM boundary segment to the two boundaries of mammogram. Experimental analyses were carried out on mammograms obtained from Mammographic Image Analysis database. The proposed methods yields low values for average false positive, average false negative and Hausdorff distance. From the performance analysis of the proposed algorithm, 97% of images have an average error less than 4 mm. Low values of performance measures for the proposed method shows that the extracted PM boundary is close to radiologist drawn PM border.
{"title":"Pectoral Muscle Boundary Detection in Mammograms Using Homogeneous Contours","authors":"R. Lakshmanan, Shiji T. P, V. Thomas, S. M. Jacob, Thara P","doi":"10.1109/ICACC.2015.49","DOIUrl":"https://doi.org/10.1109/ICACC.2015.49","url":null,"abstract":"Breast occupies over Pectoral muscle (PM) which is a predominant portion in Medio-Lateral Oblique (MLO) view of mammogram. The similarity in density among PM area and the breast region may generate false positive results which can adversely affect early breast cancer detection. Noise, wedges, opaque markers etc along with labels are unnecessary in mammographic images. The suspicious segments of PM boundary are obtained by extracting contours of homogeneous regions. The geometrical properties of contour segments are analyzed for extracting PM boundary component. An intensity similarity approach extends the detected major PM boundary segment to the two boundaries of mammogram. Experimental analyses were carried out on mammograms obtained from Mammographic Image Analysis database. The proposed methods yields low values for average false positive, average false negative and Hausdorff distance. From the performance analysis of the proposed algorithm, 97% of images have an average error less than 4 mm. Low values of performance measures for the proposed method shows that the extracted PM boundary is close to radiologist drawn PM border.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134421686","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}
Machine and human interaction is very essential in today's scenario. This interaction would make search engines, social media, artificial intelligence, cognitive computing more interactive and user friendly. Handwriting recognition is the systematic process of identifying the characters, numbers and symbols present in the handwritten document. In the current work, a recognition model for digitizing handwritten Devanagari characters proposed. Auto associative recognition technique for Devanagari characters and numerals proposed in the current work by using classifiers. To solve recognition problem a dynamic model based on Hopfield neural network deployed. The model performs operation in parallel making it faster and optimal in solving recognition problem.
{"title":"Associative Memory Model for Distorted On-Line Devanagari Character Recognition","authors":"Gaurav Pagare, K. Verma","doi":"10.1109/ICACC.2015.42","DOIUrl":"https://doi.org/10.1109/ICACC.2015.42","url":null,"abstract":"Machine and human interaction is very essential in today's scenario. This interaction would make search engines, social media, artificial intelligence, cognitive computing more interactive and user friendly. Handwriting recognition is the systematic process of identifying the characters, numbers and symbols present in the handwritten document. In the current work, a recognition model for digitizing handwritten Devanagari characters proposed. Auto associative recognition technique for Devanagari characters and numerals proposed in the current work by using classifiers. To solve recognition problem a dynamic model based on Hopfield neural network deployed. The model performs operation in parallel making it faster and optimal in solving recognition problem.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121311971","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}
Edge detection plays a vital role in medical imaging applications such as MRI segmentation. Magnetic resonance imaging (MRI) is an imaging technique used in medical science to diagnose tumors of the brain by producing high quality images of the inside of the human body, by using various edge detectors. There exists many edge detector but still, need for research is felt in order to enhance their performance. A very common problem faced by most of the edge detector is the choice of threshold values. This paper presents fuzzy based edge detection using K-means clustering method. The K-means clustering approach is used in generating various groups which are then input to the mamdani fuzzy inference system. This whole process results in the generation of the threshold parameter which is then fed to the classical sobel edge detector which helps in enhancing its edge detection capability using the fuzzy logic. This whole setup is applied on the MR images of the human brain. The retrieved results represents that fuzzy based k-means clustering enhances the performance of classical sobel edge detector and along with retaining much relevant information about the tumors of the brain.
{"title":"The K-means Clustering Based Fuzzy Edge Detection Technique on MRI Images","authors":"N. Mathur, P. Dadheech, M. Gupta","doi":"10.1109/ICACC.2015.103","DOIUrl":"https://doi.org/10.1109/ICACC.2015.103","url":null,"abstract":"Edge detection plays a vital role in medical imaging applications such as MRI segmentation. Magnetic resonance imaging (MRI) is an imaging technique used in medical science to diagnose tumors of the brain by producing high quality images of the inside of the human body, by using various edge detectors. There exists many edge detector but still, need for research is felt in order to enhance their performance. A very common problem faced by most of the edge detector is the choice of threshold values. This paper presents fuzzy based edge detection using K-means clustering method. The K-means clustering approach is used in generating various groups which are then input to the mamdani fuzzy inference system. This whole process results in the generation of the threshold parameter which is then fed to the classical sobel edge detector which helps in enhancing its edge detection capability using the fuzzy logic. This whole setup is applied on the MR images of the human brain. The retrieved results represents that fuzzy based k-means clustering enhances the performance of classical sobel edge detector and along with retaining much relevant information about the tumors of the brain.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117052232","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 discusses about a method adopted to develop a computer-aided diagnostic system to achieve automatic detection and classification of liver lesions. The procedure followed consists of first segmenting the CT scan image so as to accurately extract out the lesion region alone from the rest of the abdominal details. This Region Of Interest(ROI) is now used up for extracting out first order and second order statistical feature values, which aids in the correct classification of lesions. The lesions can be classified into five types: normal liver, cysts, abscesses, benign growth (hemangioma, focal nodular hyperplasia, hepatocellular adenoma etc) and malignant growth (Hepatocellular Carcinoma, metastases etc), and this paper discusses a robust method for correctly identifying and classifying these lesions of the liver.
{"title":"Automatic Detection and Classification of Liver Lesions from CT-scan Images","authors":"Ria Benny, T. Thomas","doi":"10.1109/ICACC.2015.46","DOIUrl":"https://doi.org/10.1109/ICACC.2015.46","url":null,"abstract":"This paper discusses about a method adopted to develop a computer-aided diagnostic system to achieve automatic detection and classification of liver lesions. The procedure followed consists of first segmenting the CT scan image so as to accurately extract out the lesion region alone from the rest of the abdominal details. This Region Of Interest(ROI) is now used up for extracting out first order and second order statistical feature values, which aids in the correct classification of lesions. The lesions can be classified into five types: normal liver, cysts, abscesses, benign growth (hemangioma, focal nodular hyperplasia, hepatocellular adenoma etc) and malignant growth (Hepatocellular Carcinoma, metastases etc), and this paper discusses a robust method for correctly identifying and classifying these lesions of the liver.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131968793","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 work a non linear multiple input multiple output model for binary ethane-ethylene distillation column is derived. Identification is carried out on nonlinear auto regressive with exogenous inputs (NARX) structure based neural network (using both Steepest Descent algorithm and Levenberg-Marquardt algorithm) and NARX based ANFIS. Data used for identification is obtained from Daisy database. Ratio between reboiler duty and feed flow, ratio between reflux rate and feed flow, ratio between distillate and feed flow, input ethane composition and top pressure were used as input variables while top ethane composition, bottom ethylene composition and differential pressure between top and bottom were used as output variables. In this work a new method for identification of distillation column using NARX based ANFIS is proposed. Result showed neural network model and ANFIS model was able to capture nonlinear dynamic behavior of the distillation column. Results were compared with statistical criterion (Correlation Coefficient and Root Mean Square Error) for each of the neural network model and ANFIS model to understand which model performs better. Considering the results it is obvious that NARX based ANFIS model is more accurate with less number of iteration.
{"title":"Identification of Ethane-Ethylene Distillation Column Using Neural Network and ANFIS","authors":"E. Abdul Jaleel, K. Aparna","doi":"10.1109/ICACC.2015.17","DOIUrl":"https://doi.org/10.1109/ICACC.2015.17","url":null,"abstract":"In this work a non linear multiple input multiple output model for binary ethane-ethylene distillation column is derived. Identification is carried out on nonlinear auto regressive with exogenous inputs (NARX) structure based neural network (using both Steepest Descent algorithm and Levenberg-Marquardt algorithm) and NARX based ANFIS. Data used for identification is obtained from Daisy database. Ratio between reboiler duty and feed flow, ratio between reflux rate and feed flow, ratio between distillate and feed flow, input ethane composition and top pressure were used as input variables while top ethane composition, bottom ethylene composition and differential pressure between top and bottom were used as output variables. In this work a new method for identification of distillation column using NARX based ANFIS is proposed. Result showed neural network model and ANFIS model was able to capture nonlinear dynamic behavior of the distillation column. Results were compared with statistical criterion (Correlation Coefficient and Root Mean Square Error) for each of the neural network model and ANFIS model to understand which model performs better. Considering the results it is obvious that NARX based ANFIS model is more accurate with less number of iteration.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130226105","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 mobile ad hoc network (MANET) is a collection of wireless mobile nodes that can communicate without any centralized administration or fixed infrastructure. Because of limited wireless link capabilities, nodes with heavy traffic will reduce their energy rapidly and increases the possibility of congestion and path disconnection. Hence, load balancing and congestion is necessary in MANET. Thus, this paper explores an efficient routing technique called multipath load balancing technique for congestion control (MLBCC) in MANETs to efficiently balance the load among the multiple paths by reducing congestion. The proposed protocol performs two major functions during the data transmission process, firstly, congestion detection by using an arrival rate and an outgoing rate at a particular time interval T. Secondly, selection of gateway node using link cost and the path cost to efficiently distribute the load by selecting the most desirable paths. For an efficient flow of distribution, a node availability degree standard deviation parameter is introduced. The results of our experiments show performance improvements in terms of average end-to-end delay, packet delivery ratio and packet drop ratio in comparison with AOMDV and FMLB.
{"title":"Multipath Load Balancing Technique for Congestion Control in Mobile Ad Hoc Networks","authors":"Sujata V. Mallapur, S. Patil, Jayashree Agarkhed","doi":"10.1109/ICACC.2015.97","DOIUrl":"https://doi.org/10.1109/ICACC.2015.97","url":null,"abstract":"A mobile ad hoc network (MANET) is a collection of wireless mobile nodes that can communicate without any centralized administration or fixed infrastructure. Because of limited wireless link capabilities, nodes with heavy traffic will reduce their energy rapidly and increases the possibility of congestion and path disconnection. Hence, load balancing and congestion is necessary in MANET. Thus, this paper explores an efficient routing technique called multipath load balancing technique for congestion control (MLBCC) in MANETs to efficiently balance the load among the multiple paths by reducing congestion. The proposed protocol performs two major functions during the data transmission process, firstly, congestion detection by using an arrival rate and an outgoing rate at a particular time interval T. Secondly, selection of gateway node using link cost and the path cost to efficiently distribute the load by selecting the most desirable paths. For an efficient flow of distribution, a node availability degree standard deviation parameter is introduced. The results of our experiments show performance improvements in terms of average end-to-end delay, packet delivery ratio and packet drop ratio in comparison with AOMDV and FMLB.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132009372","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}
Even though of its very recent origins, Cloud Computing has matured into a main stream technology over the past few years. Private cloud, where an organization sets up an internal cloud infrastructure, is gaining traction these days because of its perceived security advantages. Two of the major open source cloud middlewares are OpenStack and CloudStack. This paper provides a comparative study of these two cloud middlewares regarding its implementation complexity, overall stability and performance comparison using Unixbench and Bonnie++ benchmarks. Experiment results show that OpenStack perform better in most scenarios in a single node environment. OpenStack exhibits better overall stability but at the cost of increased installation complexity.
{"title":"A Comparative Study of OpenStack and CloudStack","authors":"Jaison Paul Mullerikkal, Yedhu Sastri","doi":"10.1109/ICACC.2015.110","DOIUrl":"https://doi.org/10.1109/ICACC.2015.110","url":null,"abstract":"Even though of its very recent origins, Cloud Computing has matured into a main stream technology over the past few years. Private cloud, where an organization sets up an internal cloud infrastructure, is gaining traction these days because of its perceived security advantages. Two of the major open source cloud middlewares are OpenStack and CloudStack. This paper provides a comparative study of these two cloud middlewares regarding its implementation complexity, overall stability and performance comparison using Unixbench and Bonnie++ benchmarks. Experiment results show that OpenStack perform better in most scenarios in a single node environment. OpenStack exhibits better overall stability but at the cost of increased installation complexity.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121369520","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}
Saliency detection is defined to be a key attention mechanism by enabling organisms to focus their limited perceptual and cognitive resources of available sensory data. In short, saliency detection is nothing but detecting the more attracted regions in an image. Like a loud noise in a quite environment saliency is the contras-tic difference between the visually attracted items and their neighborhood. Detecting those attracted areas in the image is termed to be saliency detection. We proposed a novel salient region detection method by integrating four features namely boundary information, frequency weight map, global contrast and color spatial variance. Finally, the saliency map is defined as being the average of the three feature maps added by color spatial variance. Experimental result shows that the proposed method produce better performance compared to the state-of-the-art of methods. The programming and simulation of the processes as well as the analysis of the results were done using MATLAB.
{"title":"Salient Region Detection Based on Spatial Weight Map","authors":"Shelmy Mathai, Paul P. Mathai","doi":"10.1109/ICACC.2015.100","DOIUrl":"https://doi.org/10.1109/ICACC.2015.100","url":null,"abstract":"Saliency detection is defined to be a key attention mechanism by enabling organisms to focus their limited perceptual and cognitive resources of available sensory data. In short, saliency detection is nothing but detecting the more attracted regions in an image. Like a loud noise in a quite environment saliency is the contras-tic difference between the visually attracted items and their neighborhood. Detecting those attracted areas in the image is termed to be saliency detection. We proposed a novel salient region detection method by integrating four features namely boundary information, frequency weight map, global contrast and color spatial variance. Finally, the saliency map is defined as being the average of the three feature maps added by color spatial variance. Experimental result shows that the proposed method produce better performance compared to the state-of-the-art of methods. The programming and simulation of the processes as well as the analysis of the results were done using MATLAB.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126390691","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 current world, multimedia has a significant role in communicating information. Videos can convey more information. Two drawbacks of the video make it inconvenient in some circumstances. First, it requires more storage. Second, we require watching a video completely to identify the content, which takes too much time. Our proposed work makes the video easy to use by solving these issues. We are trying to reduce the volume of a video by creating its summary. Summarization may either produce a image/video as output. We generate a summary video. Duplicate frame removal and stroboscopic imaging are the main techniques used in the work. For better results, the shots identified at the initial step are further processed to create their own summary clips. Summary clips are clustered together to form the final summary. The result of the proposed work is a summary video with very limited number of frames. Our proposed work can generate summary for any type of videos such as entertainment, game, surveillance and home videos. The summary video keeps the continuity of the video and conveys the meaning too.
{"title":"An Algorithmic Approach for General Video Summarization","authors":"Jina Varghese, K. N. Nair","doi":"10.1109/ICACC.2015.34","DOIUrl":"https://doi.org/10.1109/ICACC.2015.34","url":null,"abstract":"In the current world, multimedia has a significant role in communicating information. Videos can convey more information. Two drawbacks of the video make it inconvenient in some circumstances. First, it requires more storage. Second, we require watching a video completely to identify the content, which takes too much time. Our proposed work makes the video easy to use by solving these issues. We are trying to reduce the volume of a video by creating its summary. Summarization may either produce a image/video as output. We generate a summary video. Duplicate frame removal and stroboscopic imaging are the main techniques used in the work. For better results, the shots identified at the initial step are further processed to create their own summary clips. Summary clips are clustered together to form the final summary. The result of the proposed work is a summary video with very limited number of frames. Our proposed work can generate summary for any type of videos such as entertainment, game, surveillance and home videos. The summary video keeps the continuity of the video and conveys the meaning too.","PeriodicalId":368544,"journal":{"name":"2015 Fifth International Conference on Advances in Computing and Communications (ICACC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126079206","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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