A fuzzy histogram weighting method for efficient image contrast enhancement

Multimedia Tools and Applications - Image contrast enhancement is an important step in digital image processing applications. In this paper, we present an efficient contrast enhancement approach,...     

An efficient contrast enhancement method using repulsive force of edges

Multidimensional Systems and Signal Processing - This paper presents a novel algorithm which employs edge information to automatically improve the quality of low contrast images. This algorithm...     

Two-dimensional strip spectral correlation algorithm to fast estimation of 2D-cyclic spectral function for texture analysis

This paper presents a new affective scheme to estimate the two-dimensional cyclic spectral function of a texture as a two-dimensional signal. Recently, considering textures as cyclostationary signals, several algorithms have been introduced to utilize the more discriminant features of hidden periodicity for texture analysis, such as one-dimensional strip spectral correlation analysis (1D-SSCA), one-dimensional FFT-accumulated method, and direct frequency smoothing method. Although the reported results of these algorithms are proper, all of them suffer a drawback: they sweep texture images row by row and column by column and analyze them as one-dimensional signals, and hence lose the relationships between neighboring pixels. In this paper a new efficient extended algorithm namely two-dimensional SSCA is proposed to estimate the two-dimensional cyclic spectral function for two-dimensional signals. This algorithm is fast respect to other cyclic spectral function estimators and is based on 1D-SSCA algorithm. The effectiveness of the proposed algorithm is evaluated on three well-known databases. The experimental results illustrate that the proposed scheme is computationally efficient, generates flexible features and improves correct classification rate, in comparison with other studies in this field.     

Non-stationary Channel Estimation with Diffusion Adaptation Strategies Over Distributed Networks

In this paper we address non-stationary channel estimation problem with diffusion least mean square algorithm in distributed adaptive wireless sensor networks. Here we estimate channel coefficients or taps that are produced with Rayleigh fading models. All detailed explanations regarding to this fading channel type are presented and it is explained that how we can extend channel estimation with sensor networks to other newly presented channel types. We use the tracking performance analysis of diffusion cooperation over adaptive sensor networks to investigate the reliability of used algorithms and show the link between channel estimation problem and tracking a time varying entity. Theoretical analyzes are performed and the results are compared with simulation performance diagrams. It is proven that there is a reasonable match between these two outcomes. We present our results with the mean square deviation criteria.     

Theoretical Performance Analysis of Sparse System Identification Using Incremental and Diffusion Strategies Over Adaptive Networks

Wireless Personal Communications - This paper is dedicated to the complete theoretical analysis of the distributed sparsity aware algorithms in the sparse system estimation and identification...     

Matrix based cyclic spectral estimator for fast and robust texture classification

Utilization of cyclostationarity is a fresh paradigm in texture classification. This paper employs the Strip Spectral Correlation Analyzer (SSCA) as the new and superior method of such a category. The SSCA has been much more computational efficient than the other spectral correlation estimators, such as the FFT-Accumulated Method (FAM) or Direct Frequency Smoothing (DFS). Further, for comparable efficacy of the cyclostationary based analyzers, two new algorithms for implementation of both SSCA and FAM are proposed. The algorithms are fast, parallel, and linear-algebraic based, which brings many advantages in computational competence, feature generation flexibility, simplicity, and hardware implementation. SSCA as the unused promising texture analyzer and the new FAM implementation are compared with other state of the art methods in the case of classification accuracy, noise resistance and feature efficiency.     

Simulation of two dimensional unilateral contact using a coupled FE/EFG method

In this paper, simulation of two dimensional unilateral contact problems using a coupled finite element/element free Galerkin method is proposed. For the analysis, the element free Galerkin method and Galerkin formulation for two dimensional elasticity problems are considered. Then, the penalty method for imposition of contact constraint is proposed. The finite element shape functions are used in the penalty term of contact constraint. Finally, the accuracy of the presented method is verified through some examples. The numerical results have demonstrated that the presented approach is simple and accurate for frictionless contact analysis of 2D solids.     

Distributed fault detection and isolation in time-varying formation tracking UAV multi-agent systems

The problem of distributed fault detection and isolation (DFDI) in conjunction with time-varying formation control of unmanned aerial vehicle (UAV) multi-agent systems with multiple-leader leader–follower structure is studied, in this paper. It is assumed that the communication data of the agents are noisy, and faults may occur in either leaders or followers. The followers are not aware of the main trajectory and just follow the leaders. The first step in this paper is to reduce the communication noise. Besides, if a fault occurs in one of the leaders causing to exit the leader from the main trajectory, the followers should be able to detect the fault, isolate the leader, use the other leaders' data, and keep the main trajectory. Tracking the trajectory by followers in such a case is the second step of this paper. Preserving the time-varying formation despite a fault occurring in one of the followers is the next step. To reach these goals, a distributed array of Kalman filters is used for noise cancellation and data extraction. Next, the combination of state vector data fusion and some type of $\chi$² test are employed for DFDI, in agents. Besides, a controller is implemented in each agent for formation tracking. In this controller, estimated relative position and velocity vectors of the agents are employed to keep the time varying formation while tracking the leaders. The closed-loop stability is studied via Lyapunov stability theorem, and various simulations are carried out to demonstrate the capability of the proposed strategy.