Edge detection is a crucial step in various image processing systems like computer vision , pattern recognition and feature extraction.The Canny edge detection algorithm even though exhibits high accuracy, is computationally more complex compared to other edge detection techniques.A block based distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for edge detection depending on block type and the distribution of gradients in each block.A novel method of computation of high threshold has been proposed in this paper.Block-based hysteresis thresholds are computed using a non uniform gradient magnitude histogram.The algorithm exhibits remarkably high edge detection accuracy, scalability and significantly reduced computational time.Pratt's Figure of Merit quantifies the accuracy of the edge detector, which showed better values than that of original Canny and distributed Canny edge detector for benchmark dataset.The method detected all visually prominent edges for diverse block size.

The area of compressed sensing has developed a lot and is of high interest in the last few years because it provides a solid and promising method to exactly recover signals by sampling at very low rate compared to traditional rates. It has been proved that the topic can be applied to almost all signal processing area which deals with sparse signals. But most of the works done in compressed sensing is in digital domain. Very few works have been done in design of compressed sensing hardware based on analog compressed sensing. This paper aims to investigate the scope for designing an analog frontend for sampler which takes samples at sub-Nyquist rate. It is based on analog compressed sensing and is implemented using minimum electronic components.