Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

A Review of Radio Frequency Fingerprinting Techniques

Abstract: Radio frequency (RF) fingerprinting techniques have been used as an extra security layer for wireless devices. Unique fingerprints are used to identify wireless devices in order to avoid spoofing or impersonating attacks. These unique features can be extracted from imperfections of analog components during the manufacturing. This paper presents a general review of recent progress on RF fingerprinting techniques. Several studies are investigated for RF fingerprinting using different parts of a signal. The majority of these studies have been focused on the transient part of the signal. For this purpose, the transient signal must be extracted precisely. A number of common techniques of transient extraction are theoretically analyzed in this review. Then, some other approaches using the modulated part of the signal are also discussed. For all these approaches, the applied methodologies, the classification algorithms and a taxonomy of features are described. A comprehensive overview of the methods in RF fingerprinting is presented to demonstrate the state-of-the-art works.

Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors

Abstract: Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free component has some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum (AMAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intrascale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the state-of-the-art techniques qualitatively and quantitatively.