The advent of wavelet in itself is a revolution in the field of signal processing. The simultaneous localization of signal in both its time and frequency domain was what attracted the engineers the most. However, most of them still fail to appreciate the contribution of Ingrid Daubechies, whose scaling and wavelet functions have several surprising features. Here, we try to throw light into the astonishing features of the Daubechies scaling and wavelet functions. Understanding of these features appears to be very important for mathematicians for exploring and exploiting new function spaces. The main purpose of this article is to convince ourselves (readers) the exotic properties of scaling and wavelet functions through computational experiments.
{"title":"An Intriguing Property of Scaling Function in Wavelet Theory and its Verification Using Daubechies-Lagarias Algorithm","authors":"T. Arathi, K. Soman","doi":"10.1109/ARTCom.2009.189","DOIUrl":"https://doi.org/10.1109/ARTCom.2009.189","url":null,"abstract":"The advent of wavelet in itself is a revolution in the field of signal processing. The simultaneous localization of signal in both its time and frequency domain was what attracted the engineers the most. However, most of them still fail to appreciate the contribution of Ingrid Daubechies, whose scaling and wavelet functions have several surprising features. Here, we try to throw light into the astonishing features of the Daubechies scaling and wavelet functions. Understanding of these features appears to be very important for mathematicians for exploring and exploiting new function spaces. The main purpose of this article is to convince ourselves (readers) the exotic properties of scaling and wavelet functions through computational experiments.","PeriodicalId":210885,"journal":{"name":"Advances in Recent Technologies in Communication and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030654","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 system to identify Tamil numerals using a two-stage approach, for a subset of Tamil alphabet or numerals. In the first stage, five sets of each Tamil numeral are trained and in the second stage un-trained Tamil numerals are tested against the trained numerals. We use some pre-processing steps to enhance the images. Resizing has been performed on the images for having a standard size for all the images. Gray-scale conversion has been performed on the images for reducing dimensionality of images, which are required for further processing. Binarization has been performed using a threshold technique to change the pixel value to 0 or 1. After binarization feature extractions of the images are performed as rows, columns signal variations. Training and testing processes are carried out to evaluate the feature extraction.
{"title":"Tamil Numerals Identification","authors":"R. Bremananth, A. Prakash","doi":"10.1109/ARTCom.2009.19","DOIUrl":"https://doi.org/10.1109/ARTCom.2009.19","url":null,"abstract":"This paper describes a system to identify Tamil numerals using a two-stage approach, for a subset of Tamil alphabet or numerals. In the first stage, five sets of each Tamil numeral are trained and in the second stage un-trained Tamil numerals are tested against the trained numerals. We use some pre-processing steps to enhance the images. Resizing has been performed on the images for having a standard size for all the images. Gray-scale conversion has been performed on the images for reducing dimensionality of images, which are required for further processing. Binarization has been performed using a threshold technique to change the pixel value to 0 or 1. After binarization feature extractions of the images are performed as rows, columns signal variations. Training and testing processes are carried out to evaluate the feature extraction.","PeriodicalId":210885,"journal":{"name":"Advances in Recent Technologies in Communication and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127691311","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}
Recent work has shown that ontologies are useful to improve the performance of retrieval. In this paper, we present a new distance measure using ontologies. Ontology based correlation analysis is implemented to find the relations between the terms. Combining the ontology based correlation analysis and the traditional vector space model, the document similarity is calculated. Our results show that ontology based distance measure makes better relevance measure. The proposed method has been evaluated on USGS Science directory collection. Preliminary experiments results show that our method may generate relevant document in the top rank.
{"title":"Ontology Based Semantic Measures in Document Similarity Ranking","authors":"U. Sridevi, N. Nagaveni","doi":"10.1109/ARTCom.2009.144","DOIUrl":"https://doi.org/10.1109/ARTCom.2009.144","url":null,"abstract":"Recent work has shown that ontologies are useful to improve the performance of retrieval. In this paper, we present a new distance measure using ontologies. Ontology based correlation analysis is implemented to find the relations between the terms. Combining the ontology based correlation analysis and the traditional vector space model, the document similarity is calculated. Our results show that ontology based distance measure makes better relevance measure. The proposed method has been evaluated on USGS Science directory collection. Preliminary experiments results show that our method may generate relevant document in the top rank.","PeriodicalId":210885,"journal":{"name":"Advances in Recent Technologies in Communication and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114242049","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}
V. Dhanalakshmi, P. Padmavathy, M. A. Kumar, K. Soman, S. Rajendran
This paper presents the chunker for Tamil using Machine learning techniques. Chunking is the task of identifying and segmenting the text into syntactically correlated word groups. The chunking is done by the machine learning techniques, where the linguistical knowledge is automatically extracted from the annotated corpus. We have developed our own tagset for annotating the corpus, which is used for training and testing the POS tagger generator and the chunker. The present tagset consists of thirty tags for POS and nine tags for chunking. A corpus size of two hundred and twenty five thousand words was used for training and testing the accuracy of the Chunker. We found that CRF++ affords the most encouraging result for Tamil chunker.
{"title":"Chunker for Tamil","authors":"V. Dhanalakshmi, P. Padmavathy, M. A. Kumar, K. Soman, S. Rajendran","doi":"10.1109/ARTCom.2009.191","DOIUrl":"https://doi.org/10.1109/ARTCom.2009.191","url":null,"abstract":"This paper presents the chunker for Tamil using Machine learning techniques. Chunking is the task of identifying and segmenting the text into syntactically correlated word groups. The chunking is done by the machine learning techniques, where the linguistical knowledge is automatically extracted from the annotated corpus. We have developed our own tagset for annotating the corpus, which is used for training and testing the POS tagger generator and the chunker. The present tagset consists of thirty tags for POS and nine tags for chunking. A corpus size of two hundred and twenty five thousand words was used for training and testing the accuracy of the Chunker. We found that CRF++ affords the most encouraging result for Tamil chunker.","PeriodicalId":210885,"journal":{"name":"Advances in Recent Technologies in Communication and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128098755","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}
Design of a quality software system is a complex and challenging task. This complexity arises due to negligence of non-functional requirements (NFRs) during development process. Model-Driven Development helps in reducing design complexity in the form of a continuous transformation of models of the desired system by providing abstraction at earlier phases of development and code generation. However, non-functional requirements which are important for building correctness in the design of the system are not adequately considered in current model driven development methods. This paper discusses the challenges in dealing non-functional requirements in model-driven development of systems and provides an approach based on architecture design decisions and technical decisions for the integration of nonfunctional properties in model-driven architecture.
{"title":"Integration of non-functional requirements in model-driven architecture","authors":"Kiran Khatter, A. Kalia","doi":"10.1049/CP.2013.2213","DOIUrl":"https://doi.org/10.1049/CP.2013.2213","url":null,"abstract":"Design of a quality software system is a complex and challenging task. This complexity arises due to negligence of non-functional requirements (NFRs) during development process. Model-Driven Development helps in reducing design complexity in the form of a continuous transformation of models of the desired system by providing abstraction at earlier phases of development and code generation. However, non-functional requirements which are important for building correctness in the design of the system are not adequately considered in current model driven development methods. This paper discusses the challenges in dealing non-functional requirements in model-driven development of systems and provides an approach based on architecture design decisions and technical decisions for the integration of nonfunctional properties in model-driven architecture.","PeriodicalId":210885,"journal":{"name":"Advances in Recent Technologies in Communication and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134591653","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}
{"title":"Event Modeling for Content Based Publish/Subscribe Systems","authors":"S. D. M. Kumar, P. Lisha","doi":"10.1109/ARTCom.2009.110","DOIUrl":"https://doi.org/10.1109/ARTCom.2009.110","url":null,"abstract":"","PeriodicalId":210885,"journal":{"name":"Advances in Recent Technologies in Communication and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124886019","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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