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The last decade has seen increasing interest in techniques for the enhancement of digital speech signals. Significant gains have been made in terms of signal-to-noise ratio (SNR) and quality, but few techniques have produced improvements in intelligibility. A method for speech enhancement based on nonlinear expansion of the spectral envelope is presented. The expansion is consistent with both the long-term spectrum of the speech and with the probability that speech is present in a given sample. Objective SNR measures are used to compare this algorithm with the well-known spectral subtraction method, with an alternative expansion scheme, and with limiting SNRs resulting from perfect recovery of the amplitude spectrum. For the purpose of intelligibility assessments, a simplified version of the algorithm has been implemented on a Texas Instruments TMS320-C25 system. Listening trials with this real-time system, conducted using a modified rhyme test, have produced small, but consistent, improvements in articulation scores.

Abstract-Biometric authentication technology identifies people by their unique biological information. An account holder's body characteristics or behaviors are registered in a database and then compared with others who may try to access that account to see if the attempt is legitimate. Since veins are internal to the human body, its information is hard to duplicate. Compared with a finger or the back of a hand, a palm has a broader and more complicated vascular pattern and thus contains a wealth of differentiating features for personal identification. However, a single biometric is not sufficient to meet the variety of requirements, including matching performance imposed by several large-scale authentication systems. Multi-modal biometric systems seek to alleviate some of the drawbacks encountered by uni-modal biometric systems by consolidating the evidence presented by multiple biometric traits/sources. This paper proposes a multi-modal authentication technique based on Palm Veins as a personal identifying factor, augmented by face features to increase the accuracy of security recognition. The obtained results point at an increased authentication accuracy.

Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. Most of the classical methods for edge detection are based on the first and second order derivatives of gray levels of the pixels of the original image. These processes give rise to the exponential increment of computational time. This paper shows the new algorithm based on both the Tsallis entropy and the Shannon entropy together for edge detection using split and merge technique. The objective is to find the best edge representation and minimize the computation time. A set of experiments in the domain of edge detection are presented. An edge detection performance compared to the previous classic methods, such as Canny, LOG, and Sobel. Analysis show that the effect of the proposed method is better than those methods in execution time and also is considered as easy implementation

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