LOSSY IMAGE COMPRESSION USING A THREE STEP NONLINEAR WAVELET

The wavelet transform is a powerful and complex tool in the context of data compression. The discovery of the lifting schemes structure make à wavelet filters simple, rapid and reversible. The compression field is an open research area. In recent years, a significant development has experienced leading to the emergence of a large number of applications. This work aims to study some adaptive nonlinear wavelet -developed recently- based on three nonlinear steps. These transforms are applied in lossy image compression; in our work, we used a bit allocation algorithm and scalar quantization

Color Space for Face Authentication Using Enhanced Fisher linear discriminant Model (EFM)

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Improvements for face authentication using color information

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Random Pulling Model (RPM) for Face Authentication

International audienceIn this study, a new model named: Random Pulling Model (RPM) is presented as an alternative feature extraction algorithm for use in automatic face recognition/authentication tasks. We show that the promising RPM algorithm extracts from faces features that are relevant and efficient for authentication. The feasibility of the RPM method has been successfully tested on face authentication using 2360 XM2VTS frontal face images corresponding to 295 subjects, which were acquired under variable illumination and facial expressions

Using Enhanced Fisher linear discriminant Model (EFM) for frontal face authentication

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Fault-Tolerant Lyapunov-Gain-Scheduled PID Control of a Quadrotor UAV

Abstract: The work has done in this paper concern the passive fault tolerant control. Based on Gain-Adaptive Proportional-Integral-Derivative (PID) using the approach from the theory of Lyapunov and their application to the model vertical flying drone Quadrotor type, the PID controller with fixed parameters may fail to provide acceptable control performance. To improve the PID control effect, new designs of the Lyapunov gain Scheduled PID controller (LGSPID) were presented in this paper. The proposed techniques were applied to the Quadrotor, where adaptive PID controllers were proposed for fault-tolerant control system in the presence of actuator faults. The parameters of PID controller were adjusted by an adaptation algorithm gradient type, used to tune in real-time the controller gain, the proposed adaptive PID controller was compared with the conventional PID. The obtained results confirm the effectiveness of the proposed method

Face Authentication Using Enhanced Fisher linear discriminant Model (EFM)

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Novel semi-blind estimation for turbo decoding in impulsive noise channel

International audienceIn order to calculate the branches metric in the maximum a posteriori algorithm of turbo decoder, it is mandatory to know the values of parameters of the noise contaminating the transmitted signal. In the case of a generalized Gaussian distribution impulsive noise, it is very difficult to estimate the shape parameter, because the noise is inseparable from transmitted signal at turbo decoder reception. Until now, few researches about shape parameter estimation for an impulsive noise on turbo codes have been presented, and existing estimation methods use only the high order statistics (HOS). In this paper, we propose a novel semi-blind method, that does not use the HOS, to estimate the shape parameter from only the received signal in the turbo decoder. This method is based on fractional lower order statistics and the probability that the received signal is the same sign as the transmitted signal modulated with BPSK. The results, in terms of root mean square error, show the advantage of our method over other methods using HOS in the case of impulsive noise