Scientific Publications
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Research Categories
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Computer Science & Engineering
Core computing paradigms, algorithm design, software architectures, advanced computing theory, and hardware engineering.
Computer Networks
Network architectures, wireless ad-hoc protocols, vehicular networks (VANET), buffering, and communications.
Electronics Department
Circuits, semiconductor devices, flexible electronics, solar array modeling, signal processing, and photonics.
Civil Engg
Structural analysis, thermal stability of fluid layers, environmental infrastructure, and applied materials engineering.
Recent Publications (4)
Statistical Analysis and Model Validation of Gompertz Model on different Real Data Sets for Reliability Modelling
A very basic problem in reliability modeling is to obtain information about the form of the population from which the sample is drawn. Goodness of fit test is employed on different real data sets to determine how well the observed sample data "fits" on proposed model for reliability analysis. In this paper, we obtain the tables and graphs of critical values of Kolmogorov-Smirnov (KS) test, and Q-Q test for Gompertz model with two unknown parameters. These plots are used to investigate whether an assumed model adequately fits a set of data and we present power comparison between Computation of MLE using Newton‐Raphson method and p-value with its corresponding D-value obtaining by KS-Test Q-Q test for model validation to obtain feasible real data sets which are most suitable for parameter estimation of Gompertz model. For this analysis, we used different tools which are developed in R language and environment for model analysis, model validation and estimation of parameters using method of maximum likelihood.
Efficient Local Resource management for Wireless Devices integration
Grid computing has emerged as an important field, distinguished from conventional distributed computing by its focus on modern applications, maximize resource sharing. Grid computing is outlined as “flexible, secure, coordinated resource sharing among dynamic collections of people, establishments, and resources”. In the road of making mobile devices as an entity in grid computing technology, and to bridge the gap between mobile world and grid computing world we are using Layered Architecture, which divides the complexities existed in integration, among different layered components. We give our contribution details, in the implementation of the proposed Layered Architecture. In the implementation we considered JAVA enabled mobile devices, GT (Globus Toolkit) based grid services. In Layer-2 of the architecture we need to have an efficient scheduling algorithm which addresses the problem of local scheduling. We are using ABC algorithm to address local resource scheduling problem. With this we try to use efficient use of resources using mobile devices
Comparative Study of Different Techniques for License Plate Recognition
License Plate detection and recognition is a key technique in most of the traffic related applications such as searching of stolen vehicles, road traffic monitoring, airport gate monitoring, speed monitoring and Automatic parking access control. It is simply the ability to automatically extract and recognition of the vehicle license number plate’s character from a captured image. The License plate recognition systems have established a lot of interest from the research community because there is a certain limitation in particular territory, city or countries and lack of equivalence among different license plates. In This paper, we try to give an enhance and comprehensive view of the research work made in the area of LP detection and recognition and the techniques employed in developing a License Plate Recognition (LPR) system. In this analysis we not only study and also compare the License Plate Recognition Techniques based on different criteria like plate region extraction, character segmentation and character recognition rate using ANN, RBFNN and Template Matching along with its pros and cons.
Analysis and Implementation Image Segmentation Through kmean Algorithm with Different Window Sizes Different
Image segmentation is the first step in image analysis and pattern recognition. It is a critical and essential component of image analysis system, is one of the most difficult tasks in image processing, and determines the quality of the final result of analysis The Segmented image require feature vector on the source of which pixel classification can be performed. If we depend only intensity values of image. Then it will not give the better segmentation result. Therefore, it requires some other features which are inherent in the image. The moment is an invariant feature used in the object recognition. The main aim is to classify each pixel of an image by novel feature. The idea of novel feature is to compute each pixel using window based method and then assign class labels using k-means classification technique. If we want to get colour map then colour map is obtained by performing two steps. First quantize the image colors second is labelling the pixels in image. Objective of color quantization is to obtain the image without significant perceptual degradation in restricted color range. Now label the pixels belonging to same quantized range with same label. In this way a color-map is obtained for all pixels in image. Aim is to Comparison between different window sizes with different image size. The basic difference between window images is 2*2 Gives not better result the other one like 3*3 and 4*4 but it is also depend on image size.It is varied with size of image each image of different window size give. As window size increases, smoothness of area considered by that window increases, the noise tolerance of proposed algorithm increases, the computational time of proposed algorithm increases and sharpness of area decreases. It also affects the values of moment computed for feature vector due to averaging of pixels of a window. All the experiments have been performed on both gray and color scale images in MATLAB version 7.10.