Advances in Computational Research

10-13

Authors: Desai C.G., Patil M.B., Mahale V.D., Umrikar B.

Due to the rapid course of urbanization, the haphazard growth of major cities is one of the challenging situations in front of any country. As unorganized urbanization is becoming the major problem, it requires the immediate solution for sustainable development of urban land. In the emerging scenario it is essential to have updated information on urban growth patterns and its impact on the living environment. The growth and development of cities are likely to continue and therefore there will be a need for proper planning and managing or improving the existing infrastructure facilities. In this juncture, the state of the art technologies Remote Sensing (RS) and Geographic Information System (GIS) can play an imperative role. Pune metropolis is one such rapidly developing city in Maharashtra. This paper explores the study of Urbanization growth of Pune city using RS data and GIS.

18-29

Authors: Mahajan P.M., Kolhe S.R., Patil P.M.

Quality inspection is an important aspect of modern industrial manufacturing. In textile industry production, automate fabric inspection is important for maintain the fabric quality. For a long time the fabric defects inspection process is still carried out with human visual inspection, and thus, insufficient and costly. Therefore, automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. The development of fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The detection of local fabric defects is one of the most intriguing problems in computer vision. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. Various approaches for fabric defect detection have been proposed in past and the purpose of this paper is to categorize and describe these algorithms. This paper attempts to present the survey on fabric defect detection techniques, with a comprehensive list of references to some recent works. The aim is to review the state-of-the-art techniques for the purposes of visual inspection and decision making schemes that are able to discriminate the features extracted from normal and defective regions. Therefore, on the basis of nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based.

34-38

Authors: Tayade A.Y., Prabhu-Ajgaonkar S.G.

This paper is a contribution to stylostatistics which has, as a discipline, established itself in the recent years. We have considered probability distribution to the data of sentence-length collected from the book of late Pandit Jawaharlal Nehru.

43-46

Authors: Sunanda Gupta, Garg M.L.

During the last two decades solving combinatorial optimization problems, using genetic algorithms (GA), has attracted the attention of many researchers. The genetic algorithm on which this work is based on uses a special repair operator to prevent the generation of infeasible solutions and to transform each feasible solution into a locally optimal solution. In longer runs it is likely that this algorithm produces candidate solutions that have already been generated and evaluated before. This effect can significantly reduce the algorithm's overall performance. To prevent the reconsideration of already evaluated solutions, a solution based on a Trie is studied. This paper presents the algorithms and data structures for compressing the Binary Trie and incorporates this in the GA implementation of the Multi Dimensional Knapsack Problem.

52-55

Authors: Sangame S.K., Ramteke R.J., Rajkumar Benne

This paper presents unconstrained handwritten Kannada vowels recognition based upon invariant moments. The proposed system extracts Invariant moments feature from zoned images. A Euclidian distance criterion and K-NN classifier is used to classify the handwritten Kannada vowels. A total 1625 image are considered for experimentation and overall accuracy found to be 85.53%. The novelty of the proposed method is independent of size, slant, orientation, and translation in handwritten characters.