The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant colony optimization (ACO) that detect edges. We here used ACO for edge detection of noisy ima...

The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian Lévy noise is devised. It works effectively with reasonable c...

Medical ultrasound images are widely used for diagnostic purposes. A major problem regarding these images is in their inherent corruption by speckle noise. The presence of speckle noise severely hampers the interpretation and analysis of medical ultrasound images. The objective of the paper is to propose a method for removal of noise in the medical ultrasound images. The image noise content is ...

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Gaussian filter has been used extensively in signal image processing for many years. Gaussian or Gaussian derivative filtering is in several ways optimal for applications requiring low-pass filters or running averages. In this paper, a highly efficient noise removing technique based on a modified dynamic Gaussian filter is introduced. Called smooth filter, the Gaussian filter is known to be mor...

We consider the additive white Gaussian noise channels. We prove that the error probability of decoding tends to one exponentially for rates above the capacity and derive the optimal exponent function. We shall demonstrate that the information spectrum approach is quite useful for investigating this problem. Keywords—Additive white Gaussian noise channels, Strong converse theorem, Information s...

There are several problems where it is very important to know whether the tested data are distributed according to the Gaussian law. At the detection of the hidden information within the digitized pictures (steganography), one of the key factors is the analysis of the noise contained in the picture. The incorporated noise should show the typically Gaussian distribution. The departure from the G...

A new approach to analyzing and decoding MIMO signaling has been developed for the regular model of non-Gaussion noise, consisting of background and impulse noise named ε-noise. It has been shown that non-Gaussion noise performance is significantly worse than Gaussion noise performance. Simulation results support the suggested theory. Robust in a statistical sense detection rule is suggested fo...

A central problem in sensor array processing is the localization of multiple sources and the reception of the signals emitted by those sources. Many approaches have been studied for this problem when the additive noise in the sensor array data is modeled with a Gaussian distribution. However, the schemes designed for Gaussian noise typically perform very poorly when the noise is non-Gaussian. A...

Abstract Previous works have shown that time asymmetric forcing on one hand, as well as non-Gaussian noises on the other, can separately enhance the efficiency and current of a Brownian motor. Here, we study the result of subjecting a Brownian motor to both effects simultaneously. Our results have been compared with those obtained for the Gaussian white noise regime in the adiabatic limit. We f...