Feature Selection in Ischemic Heart Disease Identification using Feed Forward Neural Networks

AbstractFeature Selection in Data Mining refers to an art of minimizing the number of inputs under evaluation. An artificial neural network is the simulation of a human brain which learns with experience. Efficiency of a model or a system in terms of cost, time and accuracy will greatly improve if proper features of a system are selected. This proposed method uses Artificial Neural Network for selecting the interesting or important features from the input layer of the network. A Multi Layer Perceptron Neural Network is used for selection of interesting features from a Ischemic Heart Disease (IHD) data base with 712 patients. Initially the number of attributes was 17 and after feature selection the number of attributes was reduced to 12. All combination of features are attempted as inputs of a Neural Network. When the input features is 12 the predicted accuracy during training is high as 89.4% and during testing is high as 82.2%. Further removal of features lowers the accuracy and hence the interesting features selected for prediction is concluded to be as 12 for this IHD data set

A Singular Perturbation Based Midcourse Guidance Law for Realistic Air-to-Air Engagement

In this study, a singular perturbation based technique is used for synthesis and analysis of a near optimal midcourse guidance law for realistic air-to-air engagement. After designing the proposed midcourse guidance law using three dimensional point mass formulation it has been validated through detailed realistic six degrees of freedom simulation. During terminal phase only proportional navigation guidance have been used. The calculation of optimal altitude in present guidance law has been carried out using Newton’s method, which needs generally one iteration for convergence and suitable for real-time implementation. Extended Kalman filter based estimator has been used for obtaining evader kinetic information from both radar and seeker noisy measurements available during midcourse and terminal guidance. The data link look angle constraint due to hardware limitation which affects the performance of midcourse guidance has also been incorporated in guidance law design. Robustness of complete simulation has been carried out through Monte Carlo studies. Extension of launch boundary due to singular perturbation over proportional navigation guidance at a given altitude for a typical engagement has also been reported

Trajectory Optimisation of Long Range and Air-to-Air Tactical Flight Vehicles

This paper presents formulation and solution of long range flight vehicle and tactical air-to-air flight vehicle trajectory optimisation. The first case study is of a long range flight vehicle. Here an optimum steering program during powered phase has been evolved as control input for achieving maximum range with available propulsions in the presence of path and terminal constraints. The second case study is of a tactical flight vehicle for air-to-air application. Here a minimum flight time trajectory has been generated for covering a specified range pertaining to a specified air-to-air engagement by evolving pitch lateral acceleration as control input. Here also, there are many path and terminal constraints consisting of launch aircraft, pursuer, and evader. The studies have been carried out as part of system design activity of both flight vehicles. Both are real-life optimisation problems under several constraints. Through it is very difficult to solve such practical problems in flight dynamics using classical optimal control theory, it has been solved successfully using direct transcription method based on nonlinear programming. Rapid convergence has been achieved in four passes with minimum grids in first pass, to start with, and increasing the grids in subsequent passes. Solving such a real-life problem with proper convergence subjected to many constraints is claimed as novelty of present research.Defence Science Journal, Vol. 65, No. 2, March 2015, pp.107-118, DOI:http://dx.doi.org/10.14429/dsj.65.823

A Predictive Explicit Guidance Scheme for Ballistic Missiles

A new approach to the design of ballistic missile guidance is presented in this paper. The proposed method uses the missile model to predict the likely impact point at every guidance cycle and apply course corrections based on the predicted impact point (PIP) deviations. The algorithm also estimates the in-flight thrust variation from nominal and accordingly updates the model to reduce the uncertainty in the prediction of the impact point. The performance of the algorithm is tested through 6-DOF simulation. The simulation results show excellent performance of the proposed guidance scheme in nominal & off nominal cases.Defence Science Journal, 2013, 63(5), pp.456-461, DOI:http://dx.doi.org/10.14429/dsj.63.257

Enhanced Singular Value Decomposition based Fusion for Super Resolution Image Reconstruction

The singular value decomposition (SVD) plays a very important role in the field of image processing for applications such as feature extraction, image compression, etc. The main objective is to enhance the resolution of the image based on Singular Value Decomposition. The original image and the subsequent sub-pixel shifted image, subjected to image registration is transferred to SVD domain. An enhanced method of choosing the singular values from the SVD domain images to reconstruct a high resolution image using fusion techniques is proposesed. This technique is called as enhanced SVD based fusion. Significant improvement in the performance is observed by applying enhanced SVD method preceding the various interpolation methods which are incorporated. The technique has high advantage and computationally fast which is most needed for satellite imaging, high definition television broadcasting, medical imaging diagnosis, military surveillance, remote sensing etc

Review on Lossless Image Compression Techniques for Welding Radiographic Images

Abstract: Recent development in image processing allows us to apply it in different domains. Radiography image of weld joint is one area where image processing techniques can be applied. It can be used to identify the quality of the weld joint. For this the image has to be stored and processed later in the labs. In order to optimize the use of disk space compression is required. The aim of this study is to find a suitable and efficient lossless compression technique for radiographic weld images. Image compression is a technique by which the amount of data required to represent information is reduced. Hence image compression is effectively carried out by removing the redundant data. This study compares different ways of compressing the radiography images using combinations of different lossless compression techniques like RLE, Huffman

Design and Analysis of Fusion Algorithm for Multi-Frame Super-Resolution Image Reconstruction using Framelet

A enhanced fusion algorithm for generating a super resolution image from a sequence of low-resolution images captured from identical scene apparently a video, based on framelet have been designed and analyzed. In this paper an improved analytical method of image registration is used which integrates nearest neighbor method and gradient method. Comparing to Discrete Wavelet Transform (DWT) the Framelet Transform (FrT) have tight frame filter bank that offers symmetry and permits shift in invariance. Therefore using framelet this paper also present a framelet based enhanced fusion for choosing the fused framelet co-efficient that provides detailed edges and good spatial information with adequate de-noising. The proposed algorithm also has high advantage and computationally fast which are most needed for satellite imaging, medical imaging diagnosis, military surveillance, remote sensing etc.Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 292-299, DOI: http://dx.doi.org/10.14429/dsj.65.826

Not Available

Not AvailableA method was standardized to isolate quality DNA from cattle and buffalo fat for species identification using QIAamp DNA stool mini kit. The quality of the DNA was sufficient enough to amplify universal primers viz., mt 12S rRNA and mt 16S rRNA, and species specific D loop primers for cattle and buffalo. The sensitivity of the PCR assay in the species specific D loop primer amplification was with a detection level of 0. 47 ng cattle DNA and 0.23 ng buffalo DNA in simplex and, 0. 47 ng cattle DNA and 0.12 ng buffalo DNA in duplex PCR. It is a potentially reliable method for DNA detection to authenticate animal fat.Not Availabl