Design of a Knowledge-Based Geographic Information System

A GIS has been designed with limited Functionalities; but with a novel approach in Aits design. The spatial data model adopted in the design of KBGIS is the unlinked vector model. Each map entity is encoded separately in vector fonn, without referencing any of its neighbouring entities. Spatial relations, in other words, are not encoded. This approach is adequate for routine analysis of geographic data represented on a planar map, and their display (Pages 105-106). Even though spatial relations are not encoded explicitly, they can be extracted through the specially designed queries. This work was undertaken as an experiment to study the feasibility of developing a GIS using a knowledge base in place of a relational database. The source of input spatial data was accurate sheet maps that were manually digitised. Each identifiable geographic primitive was represented as a distinct object, with its spatial properties and attributes defined. Composite spatial objects, made up of primitive objects, were formulated, based on production rules defining such compositions. The facts and rules were then organised into a production system, using OPS5

Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Isp Knowledge Portal : A Resource Discovery Tool In Optics

The need to structure knowledge is as important now as it ever has been. This paper has tried to study the ISP knowledge portal to explore how knowledge on various resources and topics in photonics and related areas are organized in the knowledge portal of International School of Photonics, CUSAT. The study revealed that ISP knowledge portal is one of the best portals in the filed. It provides a model for building an effective knowledge portal in other fields

Sustainable Development through the Environment Management Tool Ecological Footprint Analysis – A Study in Kochi City, India

In the past, natural resources were plentiful and people were scarce. But the situation is rapidly reversing. Our challenge is to find a way to balance human consumption and nature’s limited productivity in order to ensure that our communities are sustainable locally, regionally and globally. Kochi, the commercial capital of Kerala, South India and the second most important city next to Mumbai on the Western coast is a land having a wide variety of residential environments. Due to rapid population growth, changing lifestyles, food habits and living standards, institutional weaknesses, improper choice of technology and public apathy, the present pattern of the city can be classified as that of haphazard growth with typical problems characteristics of unplanned urban development. Ecological Footprint Analysis (EFA) is physical accounting method, developed by William Rees and M. Wackernagel, focusing on land appropriation using land as its “currency”. It provides a means for measuring and communicating human induced environmental impacts upon the planet. The aim of applying EFA to Kochi city is to quantify the consumption and waste generation of a population and to compare it with the existing biocapacity. By quantifying the ecological footprint we can formulate strategies to reduce the footprint and there by having a sustainable living. In this paper, an attempt is made to explore the tool Ecological Footprint Analysis and calculate and analyse the ecological footprint of the residential areas of Kochi city. The paper also discusses and analyses the waste footprint of the city. An attempt is also made to suggest strategies to reduce the footprint thereby making the city sustainable