Since the dawn of computing, the sorting problem has attracted a great deal of research. In past, many researchers have attempted to optimize it properly using empirical analysis. We have investigated the complexity values researchers have obtained and observed that there is scope for fine tuning in present context. Strong evidence to that effect is also presented. We aim to provide a useful and comprehensive note to researcher about how complexity aspects of sorting algorithms can be best analyzed. It is also intended current researchers to think about whether their own work might be improved by a suggestive fine tuning. Our work is based on the knowledge learned after literature review of experimentation, survey paper analysis being carried out for the performance improvements of sorting algorithms. Although written from the perspective of a theoretical computer scientist, it is intended to be of use to researchers from all fields who want to study sorting algorithms rigorously.

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.

Assignment Problem (A.P.) originates from the classical problems where the objective is to find the optimum assignment of a number of jobs (tasks) to an equal number of machines (or workers) at a minimum cost or minimum time. The multi-objective assignment problem refers to a special class of vector minimum linear programming problems. In this paper, we use a special type of linear and nonlinear membership functions to solve the multi-objective assignment problem. It gives an optimal compromise solution. The result obtained by using a linear membership function has been compared with the solution obtained by using non-linear membership functions. Numerical example has been provided to illustrate the solution procedure.

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.

This paper presents reliability analysis for components using fuzzy operations. Probability assumption and Fuzzy State assumption (PROFUST) reliability theory is used to find out the reliability of each system component. Here, the reliability of each system component is presented by trapezoidal fuzzy number. The proposed method is used to simplify fuzzy arithmetic operations of fuzzy numbers. Finally, a numerical example is illustrated to verify the efficiency of used operations in the functions.

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.

Given a connected, weighted, undirected graph G and a bound D, the bounded diameter minimum spanning tree (BDMST) problem seeks a spanning tree on G of minimum weight among the trees in which no path between two vertices contains more than D edges. This problem is NP-hard for 4 _ D _ |v| -1. In present paper a new randomized greedy heuristic algorithm for solving BDMST is proposed. An evolutionary algorithm encodes spanning trees as lists of their edges, augmented with their center vertices. It applies operators that maintain the diameter bound and always generate valid offspring trees. These operators are efficient, so the algorithm scales well to larger problem instances. On 25 Euclidean instancesof up to 1000 vertices, the EA improved substantially on solutions found by the randomized greedy heuristic.

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.

In this paper an automatic recognition system for isolated handwritten numerals recognition for three popular south Indian scripts. Kannada, Devanagari, and Telugu numeral sets are used for their recognition. The proposed method is thinning free and without size normalization. The structural features viz. directional density of pixels, water reservoirs, maximum profile distancess, and fill hole density are used for handwritten numerals recognition. A Eclidian distance criterion and K-nearest neighbor classifier is used to classify the handwritten numerals. A total of 5250 numeral images are considered for experimentation, and the overall accuracy of 95.40%, 90.20%, and 98.40% for Kannada, Devanagari and Telugu numerals respectively are achived. The novelty of the proposed method is thinning free, fast, and without size normalization.

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.