Abstract: The aim of this paper is image encryption using Genetic Algorithm (GA). The proposed encryption method consists of two phases. In modification phase, pixels locations are altered to reduce correlation among adjacent pixels. Then, pixels values are changed in the diffusion phase to encrypt the input image. Both phases are performed by GA with binary chromosomes. For modification phase, these binary patterns are generated by Local Binary Pattern (LBP) operator while for diffusion phase binary chromosomes are obtained by Bit Plane Slicing (BPS). Initial population in GA includes rows and columns of the input image. Instead of subjective selection of parents from this initial population, a random generator with predefined key is utilized. It is necessary to decrypt the coded image and reconstruct the initial input image. Fitness function is defined as average of transition from 0 to 1 in LBP image and histogram uniformity in modification and diffusion phases, respectively. Randomness of the encrypted image is measured by entropy, correlation coefficients and histogram analysis. Experimental results show that the proposed method is fast enough and can be used effectively for image encryption.
Abstract: We present a novel scheme to recognize isolated speech
signals using certain statistical parameters derived from those signals.
The determination of the statistical estimates is based on extracted
signal information rather than the original signal information in
order to reduce the computational complexity. Subtle details of
these estimates, after extracting the speech signal from ambience
noise, are first exploited to segregate the polysyllabic words from
the monosyllabic ones. Precise recognition of each distinct word is
then carried out by analyzing the histogram, obtained from these
information.