Faculty Publications
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Item Reliability Analysis of Exponential Models Based on Skewness and Kurtosis(Springer India, 2015) Roopashri Tantri, B.; Murulidhar, N.N.Every field in modern era is computerized. As the requirements of software increase, competitions among the manufacturers of software also increase. Thus, there is a need for reliable software. Software reliability is defined as the probability of failure-free operation of software in a specified environment for a specific period of time. Thus, if T denotes the failure time of software, then, its reliability, denoted by R(t), is given by R(t) = P(T *gt; t). Various models of software reliability have been developed. One such model is the exponential class model. For such a model, the reliability function is given by R(t) = фe-фt, where ф is the failure rate. Various estimates of reliability have been obtained for this class of models. The most commonly used method is the method of Maximum Likelihood Estimation (MLE). But it is not as efficient as the Minimum Variance Unbiased Estimation (MVUE). In our previous work, we obtained this minimum variance unbiased estimator for the reliability function R(t) and proved its efficiency by comparing it with the Maximum likelihood estimator. We used variance as a measure of comparison. But variance is only a second order measure. In this paper, we are trying to enhance our work further by comparing higher order measures. We are also trying to analyze the same using skewness and kurtosis. © Springer India 2015.Item Phase-edge based approach for pedestrian segmentation using NIR camera and tracking for driver assistance(2013) Kumar, K.S.C.A new approach using phase edge based techniquefor night time pedestrian segmentation and tracking using Near IR camera is proposed in this paper. A Gabor filter is used to extract even weak potential vertical edges that belong to candidate pedestrian blocks even under non-uniform illuminations and poor contrast. Vertical and horizontal projection on Gabor filtered response is carried out to determine the bounding-box of pedestrian-like candidate blobs. Pedestrian like candidate blocks undergo through a series of rule based classifiers. Once such set of pedestrian blocks are identified, candidate pedestrian blocks are tracked in next consecutive frames of a video using density confidence based tracker which uses Gabor filter edge response for feature-space analysis. False positives are greatly removed using spatio-temporal analysis and tracking mechanism. Performance characterization of the algorithm has been carried out in highway roads under nonuniform illuminations and varying contrast. Experimental result shows that algorithm can produce a detection rate of nearly 69% and can be used in real-time. © 2013 IEEE.