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In this paper, We demonstrate biomimetic neural circuits (CMOS circuits) responsible for touch induced-locomotion in the nematode Caenorhabditis elegans (C. elegans). Our circuits model the neural network responsible for touch-induced locomotion of C. elegans worm (Chalfie and Sulston, 1985 [1]). Most animals use action potentials (spikes) for information(More)
—Reconstruction error bounds in compressed sensing under Gaussian or uniform bounded noise do not translate easily to the case of Poisson noise. Reasons for this include the signal dependent nature of Poisson noise, and also the fact that the negative log likelihood in case of a Poisson distribution (which is directly related to the generalized(More)
Imaging techniques involve counting of photons striking a detector. Due to fluctuations in the counting process, the measured photon counts are known to be corrupted by Poisson noise. In this paper, we propose a blind dictionary learning framework for the reconstruction of photographic image data from Poisson corrupted measurements acquired by a compressive(More)
With increasing resolution of the sensors in camera detector arrays, acquired images are ever more susceptible to perturbations that appear as grainy artifacts called ‘noise’. In real acquisitions, the dominant noise model has been shown to follow the Poisson distribution, which is signal dependent. Most color image cameras today acquire only one out of the(More)
Most color image cameras today acquire only one out of the R, G, B values per pixel by means of a color filter array (CFA) in the hardware producing the so called ‘CFA image’. In-built software routines are required to undertake the task of obtaining the rest of the color information at each pixel through a process termed demosaicing. The most common CFA(More)
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