In various applications the problem of separation of the original signal and the noise arises. For example, in the identification problem for discrete linear and causal systems, the original signal consists of the values of transfer function at some points in the unit disk. In this paper we discuss the problem of choosing the points in the unite disk, for which it is possible to remove the addi...

In this article, we consider the problem of inverting exponential Radon transform a function in presence noise. We propose kernel estimator to estimate true function. Such an is closely related filtered backprojection type inversion formulas noise-less setting. For proposed then show that convergence at minimax optimal rate.

noise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. a common problem in imaging systems by using cmos or ccd sensors is appearance of  the salt and pepper noise. this paper presents cellular automata (ca) framework for noise removal of distorted image by the salt an...

Over the years ElectroCardioGram (ECG) signal has been used to assess the cardiovascular condition of humans. In practice, real time acquisition and transmission of the ECG may contain noise signals superimposed on it. In general, the signal processing algorithms employed for denoising provide optimal performance and eliminate the high frequency noise between any two beats contained in a contin...

where α1 and α2 are positive regularization parameters. The main advantage of using the new data fitting term ∑n2 i=1 ( [z]i + [g]iei ) is that its second derivative with respect to [z]i is equal to [g]i e−[z]i , therefore it implies that the new data fitting term is strictly convex for all z. Here we add a fitting term ||z −w||2 in the new minimization method. We can interpret the total variat...

Tensor completion techniques (including tensor denoising) can be used to solve the ubiquitous multidimensional data reconstruction problem. We present a robust tensor reconstruction method that can tolerate the presence of erratic noise. The method is derived by minimizing a robust cost function with the addition of low rank constraints. Our presentation is based on the Parallel Matrix Factoriz...

We show that the capacity Cn,FB(P ) of the Gaussian channel with feedback is a concave function of P , and give two types of inequalities: both 1 α Cn,FB(αP ) and Cn,FB(αP ) + 1 2 ln 1 α are decreasing functions of α > 0. As their application we can obtain two upper bounds on the capacity of discrete time blockwise white Gaussian channel with feedback.

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

background and aim: it will be discussed about five mechanisms in relation to speech in noise perception; including neural encoding and decoding, centrifugal pathways, pitch perception, asymmetric sampling in time and cognitive skills. these mechanisms are related to each other and each is important to recognize speech in noise. in this article, we have tried to rely on the latest studies to de...