Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496443
S. Baboo, K. Tajudin
The main aim of the research focuses the clustering centroid value for spatio-temporal data mining. Using k-means, advanced k-means algorithm and Avg Centroid (AC) clustering. The real time data of the hurricane Indian Ocean 2001 to 2010 maximum wind details are focused in this paper. The clustering is taking as selection window method, the first window form the basis of the pixel coordinate value of the screen, the second clustering window one half of the centre point value. The data mining retrieves clustering data form basis of the selection window. Here to discuss k-means algorithmic steps are very few and same iteration is continuing till the same to get the centroid point. The enhanced k-means algorithm taken more steps but result is accurate algorithmic finishing stage; iteration also repeated very minimum times. The final discussion of this paper collects average centroid clustering for all previously selected values and current selected clustering data. The result of this paper gave the comparative study of the k-means, enhanced k-means algorithms and AC clustering values.
{"title":"Clustering centroid finding algorithm (CCFA) using spatial temporal data mining concept","authors":"S. Baboo, K. Tajudin","doi":"10.1109/ICPRIME.2013.6496443","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496443","url":null,"abstract":"The main aim of the research focuses the clustering centroid value for spatio-temporal data mining. Using k-means, advanced k-means algorithm and Avg Centroid (AC) clustering. The real time data of the hurricane Indian Ocean 2001 to 2010 maximum wind details are focused in this paper. The clustering is taking as selection window method, the first window form the basis of the pixel coordinate value of the screen, the second clustering window one half of the centre point value. The data mining retrieves clustering data form basis of the selection window. Here to discuss k-means algorithmic steps are very few and same iteration is continuing till the same to get the centroid point. The enhanced k-means algorithm taken more steps but result is accurate algorithmic finishing stage; iteration also repeated very minimum times. The final discussion of this paper collects average centroid clustering for all previously selected values and current selected clustering data. The result of this paper gave the comparative study of the k-means, enhanced k-means algorithms and AC clustering values.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115003171","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496508
M. Shobana, S. Karthik
Mobile Ad hoc networks (MANET) are characterized by wireless connectivity, continuous changing topology, distributed operation and ease of deployment. The data is being transmitted from source node to destination through multiple intermediate nodes ie., in a multi-hop fashion. Each node has a particular range in which the transmission takes places. When a packet is being transmitted they move from one range to the other range in the network where this may lead to packet loss due to link failure and dynamic changing nature. There are many traditional routing protocols which may prevent from this data loss, but they all are susceptible to the node mobility. Here the traditional protocols are being compared with the geographic routing protocols in terms of packet delivery ratio and transmission delay.
{"title":"A performance analysis and comparison of various routing protocols in MANET","authors":"M. Shobana, S. Karthik","doi":"10.1109/ICPRIME.2013.6496508","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496508","url":null,"abstract":"Mobile Ad hoc networks (MANET) are characterized by wireless connectivity, continuous changing topology, distributed operation and ease of deployment. The data is being transmitted from source node to destination through multiple intermediate nodes ie., in a multi-hop fashion. Each node has a particular range in which the transmission takes places. When a packet is being transmitted they move from one range to the other range in the network where this may lead to packet loss due to link failure and dynamic changing nature. There are many traditional routing protocols which may prevent from this data loss, but they all are susceptible to the node mobility. Here the traditional protocols are being compared with the geographic routing protocols in terms of packet delivery ratio and transmission delay.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126754519","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496494
P. Selvi, Selvikrish. selvi
In this paper, the authors propose a method to recognize Arabic numerals using back propagation neural network. Arabic numerals are the ten digits that were descended from the Indian numeral system. Although the pattern of 0-9 is the same as in Indian numeral system, the glyphs vary for each numeral. The proposed method includes preprocessing of digitized handwritten image, training of BPNN and recognition phases. As a first step, the number of digits to be recognized is selected. The selected numerals are preprocessed for removal of noise and binarization. Separation process separates the numerals. Labelling, segmentation and normalization operations are performed for each of the separated numerals. The recognition phase recognizes the numerals accurately. The proposed method is implemented with Matlab coding. Sample handwritten images are tested with the proposed method and the results are plotted. With this method, the training performance rate was 99.4%. The accuracy value is calculated based on receiver operating characteristics and the confusion matrix. The value is calculated for each node in the network. The final result shows that the proposed method provides an recognition accuracy of more than 96%.
{"title":"Recognition of Arabic numerals with grouping and ungrouping using back propagation neural network","authors":"P. Selvi, Selvikrish. selvi","doi":"10.1109/ICPRIME.2013.6496494","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496494","url":null,"abstract":"In this paper, the authors propose a method to recognize Arabic numerals using back propagation neural network. Arabic numerals are the ten digits that were descended from the Indian numeral system. Although the pattern of 0-9 is the same as in Indian numeral system, the glyphs vary for each numeral. The proposed method includes preprocessing of digitized handwritten image, training of BPNN and recognition phases. As a first step, the number of digits to be recognized is selected. The selected numerals are preprocessed for removal of noise and binarization. Separation process separates the numerals. Labelling, segmentation and normalization operations are performed for each of the separated numerals. The recognition phase recognizes the numerals accurately. The proposed method is implemented with Matlab coding. Sample handwritten images are tested with the proposed method and the results are plotted. With this method, the training performance rate was 99.4%. The accuracy value is calculated based on receiver operating characteristics and the confusion matrix. The value is calculated for each node in the network. The final result shows that the proposed method provides an recognition accuracy of more than 96%.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056640","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496479
P. Babu, K. Prasad
Super Resolution (SR) image reconstruction is the process of producing a high resolution (HR) image from many low resolution (LR) images. SR image reconstruction can be considered as the second generation restoration technique. In this paper we propose SR image reconstruction from clean, noisy and blurred images using binary plane technique (BPT) encoding and Integer wavelet Transform (IWT). Integer wavelet transform maps an integer data set into another integer data set. Objective and subjective analysis of the reconstructed image has a better super resolution factor and a higher qualitative metrics.
{"title":"Binary plane technique for super resolution image reconstruction using integer wavelet transform","authors":"P. Babu, K. Prasad","doi":"10.1109/ICPRIME.2013.6496479","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496479","url":null,"abstract":"Super Resolution (SR) image reconstruction is the process of producing a high resolution (HR) image from many low resolution (LR) images. SR image reconstruction can be considered as the second generation restoration technique. In this paper we propose SR image reconstruction from clean, noisy and blurred images using binary plane technique (BPT) encoding and Integer wavelet Transform (IWT). Integer wavelet transform maps an integer data set into another integer data set. Objective and subjective analysis of the reconstructed image has a better super resolution factor and a higher qualitative metrics.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123854567","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496499
A. N. Kumar, C. Jothilakshmi, M. Ilamathi, S. Kalaiselvi
A new loom of outdoor scene image segmentation algorithm is based on the region amalgamation. Here we are going to identify both structured (e.g. buildings, persons, car, etc.) and unstructured background objects (sky, road, grass, etc.) which are containing the some characteristic based on color, intensity, and texture in sequence. Our main aim is to solve the over segmented objects and strong reflection of objects. These problems are solved by using SRM (Statistical Region Merging) algorithm. In pre-processing the input image is converted into CIE (Commission Internationalde Eclairage) color space technique. Then bottom-up segmentation process is used to capture the structured and unstructured image characteristics. Another process is the Ada boost classifier which is used to classify the background objects in outdoor environment scenes. Ada boost is focused on difficult patterns. Then the contour maps are used to detect the boundary energy. Boundary detection test is the grouping of objects with a pair of connected neighboring regions. In this paper we have used an experimental result of two databases (Gould data set and Berkeley segmentation data set) and provide accurate segmentation using region merging. Finally the statistical region merging provides the groupings of images to identify the computer vision.
{"title":"Outdoor scene image segmentgation using statistical region merging","authors":"A. N. Kumar, C. Jothilakshmi, M. Ilamathi, S. Kalaiselvi","doi":"10.1109/ICPRIME.2013.6496499","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496499","url":null,"abstract":"A new loom of outdoor scene image segmentation algorithm is based on the region amalgamation. Here we are going to identify both structured (e.g. buildings, persons, car, etc.) and unstructured background objects (sky, road, grass, etc.) which are containing the some characteristic based on color, intensity, and texture in sequence. Our main aim is to solve the over segmented objects and strong reflection of objects. These problems are solved by using SRM (Statistical Region Merging) algorithm. In pre-processing the input image is converted into CIE (Commission Internationalde Eclairage) color space technique. Then bottom-up segmentation process is used to capture the structured and unstructured image characteristics. Another process is the Ada boost classifier which is used to classify the background objects in outdoor environment scenes. Ada boost is focused on difficult patterns. Then the contour maps are used to detect the boundary energy. Boundary detection test is the grouping of objects with a pair of connected neighboring regions. In this paper we have used an experimental result of two databases (Gould data set and Berkeley segmentation data set) and provide accurate segmentation using region merging. Finally the statistical region merging provides the groupings of images to identify the computer vision.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125621654","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496445
Dr. V. Karthikeyani, Mr. T. Thiruvenkadam
The current utilization of the spectrum is quite inefficient; consequently, if properly used, there is no shortage of the spectrum that is presently available. Therefore, it is anticipated that more flexible use of spectrum and spectrum sharing between radio systems will be key enablers to facilitate the successful implementation of future systems. Cognitive radio, however, is known as the most intelligent and promising technique in solving the problem of spectrum sharing. In this paper, we consider the technique of spectrum sharing among users of service providers to share the licensed spectrum of licensed service providers. It is shown that the proposed technique reduces the call blocking rate and improves the spectrum utilization.
{"title":"Packet size based performance analysis of IEEE 802.11 WLAN comprising virtual server arrays","authors":"Dr. V. Karthikeyani, Mr. T. Thiruvenkadam","doi":"10.1109/ICPRIME.2013.6496445","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496445","url":null,"abstract":"The current utilization of the spectrum is quite inefficient; consequently, if properly used, there is no shortage of the spectrum that is presently available. Therefore, it is anticipated that more flexible use of spectrum and spectrum sharing between radio systems will be key enablers to facilitate the successful implementation of future systems. Cognitive radio, however, is known as the most intelligent and promising technique in solving the problem of spectrum sharing. In this paper, we consider the technique of spectrum sharing among users of service providers to share the licensed spectrum of licensed service providers. It is shown that the proposed technique reduces the call blocking rate and improves the spectrum utilization.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131563238","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496482
G. Prabakaran, R. Bhavani, M. Ramesh
Nowadays, Digital video is one of the popular multimedia data exchanged in the internet. Commercial activity on the internet and media require protection to enhance security. The 2D Barcode with a digital watermark is a widely interesting research in the security field. In this paper propose a video watermarking with text data (verification message) by using the Quick Response (QR) Code technique. The QR Code is prepared to be watermarked via a robust video watermarking scheme based on the (singular value decomposition)SVD and (Discrete Wavelet Transform)DWT. In addition to that logo (or) watermark gives the authorized ownership of video document. SVD is an attractive algebraic transform for watermarking applications. SVD is applied to the cover I-frame. The extracted diagonal value is fused with logo (or) watermark. DWT is applied on SVD cover image and QR code image. The inverse transform on watermarked image and add the frame into video this watermarked (include logo and QR code image) the video file sends to authorized customers. In the reverse process check the logo and QR code for authorized ownership. These experimental results can achieved acceptable imperceptibility and certain robustness in video processing.
{"title":"A robust QR-Code video watermarking scheme based on SVD and DWT composite domain","authors":"G. Prabakaran, R. Bhavani, M. Ramesh","doi":"10.1109/ICPRIME.2013.6496482","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496482","url":null,"abstract":"Nowadays, Digital video is one of the popular multimedia data exchanged in the internet. Commercial activity on the internet and media require protection to enhance security. The 2D Barcode with a digital watermark is a widely interesting research in the security field. In this paper propose a video watermarking with text data (verification message) by using the Quick Response (QR) Code technique. The QR Code is prepared to be watermarked via a robust video watermarking scheme based on the (singular value decomposition)SVD and (Discrete Wavelet Transform)DWT. In addition to that logo (or) watermark gives the authorized ownership of video document. SVD is an attractive algebraic transform for watermarking applications. SVD is applied to the cover I-frame. The extracted diagonal value is fused with logo (or) watermark. DWT is applied on SVD cover image and QR code image. The inverse transform on watermarked image and add the frame into video this watermarked (include logo and QR code image) the video file sends to authorized customers. In the reverse process check the logo and QR code for authorized ownership. These experimental results can achieved acceptable imperceptibility and certain robustness in video processing.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133699090","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496513
S. Jayalakshmi, M. Sundaresan
In this paper, we have studied various well known Iris Segmentation algorithms which are used for the purpose of Iris recognition. We have gone through many algorithms based on Fourier spectral density, Limbic boundary localization, Gradient-Based edge detection and linking, Dempster-Shafer theory, Pupil detection, Fourier spectral density which will help us for accurate and efficient iris segmentation. In this paper we made a comparison of the results obtained from the implementation of existing algorithms, which will produce better result for segmentation with improved accuracy rate using the CASIA, WVU and UBIRIS databases.
{"title":"A survey on Iris Segmentation methods","authors":"S. Jayalakshmi, M. Sundaresan","doi":"10.1109/ICPRIME.2013.6496513","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496513","url":null,"abstract":"In this paper, we have studied various well known Iris Segmentation algorithms which are used for the purpose of Iris recognition. We have gone through many algorithms based on Fourier spectral density, Limbic boundary localization, Gradient-Based edge detection and linking, Dempster-Shafer theory, Pupil detection, Fourier spectral density which will help us for accurate and efficient iris segmentation. In this paper we made a comparison of the results obtained from the implementation of existing algorithms, which will produce better result for segmentation with improved accuracy rate using the CASIA, WVU and UBIRIS databases.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"49 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133274182","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496721
P. Vigneshvaran, E. Jayabalan, K. Vijaya
Semantic similarity is the processes of identifying similar words. It relates to computing the similarity between documents which are not lexicographically similar. This paper proposed an empirical method to estimate the semantic similarity using HBase. Specifically this paper defines various word co-occurrence in the document measured and its synonyms are also identified using WordNet. By using the statistical approaches such as MSE and MSD, similarity has been measured. This research focuses on evaluating the similarity between the key document and source documents in the document corpus. In this paper, the developed predominant tool using statistical approach has been tested by checking the similarity of the assignments submitted by the students to check the integrity of a student. This tool may also be used to identify Plagiarism of documents and to eliminate duplicates in a text repository.
{"title":"A predominant statistical approach to identify semantic similarity of textual documents","authors":"P. Vigneshvaran, E. Jayabalan, K. Vijaya","doi":"10.1109/ICPRIME.2013.6496721","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496721","url":null,"abstract":"Semantic similarity is the processes of identifying similar words. It relates to computing the similarity between documents which are not lexicographically similar. This paper proposed an empirical method to estimate the semantic similarity using HBase. Specifically this paper defines various word co-occurrence in the document measured and its synonyms are also identified using WordNet. By using the statistical approaches such as MSE and MSD, similarity has been measured. This research focuses on evaluating the similarity between the key document and source documents in the document corpus. In this paper, the developed predominant tool using statistical approach has been tested by checking the similarity of the assignments submitted by the students to check the integrity of a student. This tool may also be used to identify Plagiarism of documents and to eliminate duplicates in a text repository.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133408396","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}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496460
J. Thomas, C. Rajasekaran
Intelligent data acquisition with real time data processing require an efficient algorithm to reduce the amount of redundant data collected during the acquisition process. Changing the sampling rate in accordance with acquired signal bandwidth will reduce the supererogatory information collected. In these case self-adaptive sampling rate is used that will continuously adapts the sample rate during the acquisition. Data are acquired continuously at fixed sample rate then the rest of the process is based on bandwidth estimation algorithm. Decimation factor for acquired signal was found out with the help of bandwidth estimation algorithm. The system optimizes the amount of data collected while retaining the same information.
{"title":"Real time data acquisition system with self adaptive sampling rate using GPU","authors":"J. Thomas, C. Rajasekaran","doi":"10.1109/ICPRIME.2013.6496460","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496460","url":null,"abstract":"Intelligent data acquisition with real time data processing require an efficient algorithm to reduce the amount of redundant data collected during the acquisition process. Changing the sampling rate in accordance with acquired signal bandwidth will reduce the supererogatory information collected. In these case self-adaptive sampling rate is used that will continuously adapts the sample rate during the acquisition. Data are acquired continuously at fixed sample rate then the rest of the process is based on bandwidth estimation algorithm. Decimation factor for acquired signal was found out with the help of bandwidth estimation algorithm. The system optimizes the amount of data collected while retaining the same information.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130904291","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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