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Real-time image processing applications requires processing on large data of pixels in a given timing constraints.Reconfigurable device like FPGAs have emerged as promising solutions for reducing execution times by deploying parallelism techniques in image processing algorithms. Implementation of highly parallel system architecture, parallel access of large internal memory banks and optimization of processing element for applications makes FPGA an ideal device for image processing system. Edge detection is basic tool used in many image processing applications for extracting information from image. Sobel edge detection is gradient based edge detection method used to find edge pixels in image. This paper presents a design of a Sobel edge detection algorithm to find edge pixels in gray scale image. Xilinx ISE Design Suite-14 software platforms is used to design a algorithm using VHDL language. MATLAB software platform is used for obtaining pixel data matrix from gray scale image and vice versa. Xilinx FPGAs of family Vertex-5 are more suitable for image processing work than Spartan-3 and Spartan-6.

Network coverage is a measure of efficiency that signifies the extent to which the deployed nodes collectively cover the network area. It is a fundamental and critical quality of service (QoS) parameter for designing wireless sensor networks (WSNs). Various sensing models are reported which can be used to predict the coverage fraction for a given number of nodes in a predetermined network area. However, each of these reported models consider a subset of parameters. In this study, a novel formulation and hence a new model, composite probabilistic sensing model (CPSM) is proposed which combines the cumulative effects of all the possible factors, thus resulting in a realistic study. Further, the model is revisited to estimate the optimal density of randomly deployed nodes required to attain the desired network area coverage. An exhaustive parametric study is carried out and the results obtained are used to empirically derive a formula based on regression analysis using least square polynomial curve fitting technique. The formulation can be readily and accurately used to design any practical WSN system.

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