Unlike traditional visual cryptography based on a threshold mechanism in which the secret image can be revealed by stacking k,(2≤k≤n) shadows or more. In our suggested (kk,nn) progressive secret sharing scheme with unexpanded pixel, we could obtain until n−1n−1 revealed secret images with progressive enhanced contrast in an increasing way from the lowest quality, in the case of stacking two shares, to a highest quality in the case of stacking all the nn shares. Our scheme not only has the advantage of the unexpanded shares, but also the use of very simple Boolean XOR and OR operations to recover the secret image perfectly. Moreover, the proposed scheme does not need any codebooks to construct shares. The experimental results indicate the privilege of our method compared with the other related work.

Electrocardiogram (ECG) signals are often contaminated with artefacts and noises which can lead to incorrect diagnosis when they are visually inspected by cardiologists. In this paper, the well-known discrete Fourier series (DFS) is re-explored and an efficient DFS-based method is proposed to reduce contribution of both baseline wander (BW) and powerline interference (PLI) noises in ECG records. In the first step, the determination of the exact number of low frequency harmonics contributing in BW is achieved. Next, the baseline drift is estimated by the sum of all associated Fourier sinusoids components. Then, the baseline shift is discarded efficiently by a subtraction of its approximated version from the original biased ECG signal. Concerning the PLI, the subtraction of the contributing harmonics calculated in the same manner reduces efficiently such type of noise. In addition of visual quality results, the proposed algorithm shows superior performance in terms of higher signal-to-noise ratio and smaller mean square error when faced to the DCT-based algorithm.

In this paper, state variables estimation and Fuzzy Sliding Mode Control (FSMC) are presented in order to estimate the state variables and altitude-attitude tracking control in presence of internal and external disturbances for unmanned quadrotor. The main idea of the proposed control strategy is the development of an Extended Kalman Filter (EKF) for the observation of the states. Fuzzy logic systems are used to adapt the unknown switching-gains to eliminate the chattering phenomenon induced by Sliding Mode Control (SMC). The stability of the system is guaranteed in the sense of Lyapunov. The effectiveness and robustness of the proposed controller-observer scheme that takes into account internal and external disturbances are demonstrated on computer simulation using Matlab environment.

A robust nonlinear controller based on an improved feedback linearization technique with state observer in presence of uncertainties and external disturbances is developed for a class of nonlinear systems. First, by combining classical feedback linearization approach with a robust control term and a fuzzy logic system, we design and study an efficient controller for such systems. Second, we propose an optimized extended Kalman filter for the observation of the states. The parameters to be optimized are the covariance matrices Q and G, which play an important role in the extended Kalman filter performances. This optimization is insured by the particle swarm optimization algorithm. The Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is simulated on a nonlinear inverted‐pendulum system. Simulation results demonstrate the robustness and effectiveness of the proposed scheme and exhibit a more superior performance than its conventional counterpart.

In this study, an optimized extended Kalman filter (EKF), and an interval type-2 fuzzy sliding mode control (IT2FSMC) in presence of uncertainties and disturbances are presented for robotic manipulators. The main contribution is the proposal of a novel application of Biogeography-Based Optimization (BBO) to optimize the EKF in order to achieve high performance estimation of states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the performances of EKF. The interval type-2 fuzzy logic system is used to avoid chattering phenomenon in the sliding mode control (SMC). Lyapunov theorem is used to prove the stability of control system. The suggested control approach is demonstrated using a computer simulation of two-link manipulator. Finally, simulations results show the robustness and effectiveness of the proposed scheme, and exhibit a more superior performance than its conventional counterpart.

The main purpose of this paper is to integrate fuzzy logic technique and backstepping synthesis to sliding mode control to develop a Fuzzy Backstepping-Sliding Mode Controller (FBSMC) to resolve the problem of altitude and attitude tracking control of unmanned quadrotor systems under large external disturbances. First, a backstepping-sliding mode control for quadrotor is introduced. Moreover, a fuzzy logic system is employed to adapt the unknown switching gains to eliminate the chattering phenomenon induced by switching control on the conventional Backstepping-Sliding Mode Controller (BSMC). The dynamical motion equations are obtained by EulerNewton formalism. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. Simulation results are carried out using Matlab/Simulink environment to illustrate the effectiveness and robustness of the proposed controller.

Baseline wander (BW) is a common low frequency artifact in electrocardiogram (ECG) signals. The prime cause from which BW arises is the patient's breathing and movement. To facilitate reliable visual interpretation of the ECG and to discern particular patterns in the ECG signal, BW needs to be removed. In this paper, a novel BW removal method is presented. The hypothesis is based on the observation that ECG signal variation covaries with its BW. As such, the P, Q, R, S, and T peaks will follow the baseline drift. On this basis, the following proposition is true: a reliable approximation of the baseline drift can be obtained from the shape derived from the interpolation of one form of the ECG signal peak (peak envelope). The simulation was performed by adding artificial BW to ECG signal recordings. The signal-to-noise ratio, mean squared error, and improvement factor criteria were used to numerically evaluate the performance of the proposed approach. The technique was compared to that of the Hilbert vibration decomposition method, an empirical-mode decomposition technique and mathematical morphology. The results of the simulation indicate that the proposed technique is most effective in situations where there is a considerable distortion in the baseline wandering.

In this investigation a novel type-2 fuzzy model for electrophysiological signals is presented. It is based on interval type-2 fuzzy systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. This approach will significantly reduce the number of type-2 fuzzy rules and simultaneously preserve the fitting quality. The proposed model comprises a parallel interconnection of two type-2 sub-fuzzy models. The first is the primary model, which represents an ordinary model with a low resolution for the electrophysiological signal under consideration, the second is a fuzzy sub-model called the error model, which represents uncertainty in the primary model. Identification is achieved by innovative metaheuristic optimisation algorithms. The method's effectiveness is evaluated through testing on synthetic and real ECG signals. In addition, a detailed comparative study with several benchmark methods will be given. Intensive computer experimentations confirm that the proposed method can significantly improve convergence and resolution.

Reduction of the Total Harmonic Distortion (THD) in multilevel inverters requires resolution of complex nonlinear transcendental equations; in this paper we propose a combination of one of the best existing optimized hardware structures with the recent firefly algorithm, which was used to optimize the THD, through finding the best switching angles and guaranteeing the minimization of harmonics within a user defined bandwidth. The obtained THD through the simulation of the thirteen-level symmetric inverter has been reduced down to 5% (FFT of 60 harmonics). In order to validate the simulation results, a thirteen-level symmetric inverter prototype has been made, and practically experimented and tested with different loads. Consequently, the measured THD with resistive load was 4.7% on a bandwidth of 3 kHz. The main advantage of the achieved work is the reduction of the THD.

A robust nonlinear controller based on an improved feedback linearization technique with state observer is developed for a class of nonlinear systems with uncertainties and external disturbances. First, by combining classical feedback linearization approach with a discontinuous control and a fuzzy logic system, we design and study a robust controller for uncertain nonlinear systems. Second, we propose an optimized extended Kalman filter (EKF) for the observation of the states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the EKF performances. The particle swarm optimization algorithm insures this optimization. Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is applied on a two-link robot system under Matlab environment. Simulation results have confirmed the effectiveness of the proposed approach against uncertainties and external disturbances; and exhibited a more superior performance than the non-improved control actions.

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.