Sharif Digital Repository

This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error back-propagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time... 

This paper presents a scheme to enumerate the incident waves impinging on a high dimensional uniform linear array using relatively few samples. The approach is based on Minimum Description Length (MDL) criteria and statistical properties of eigenvalues of the Sample Covariance Matrix (SCM). We assume that several models, with each model representing a certain number of sources, will compete and MDL criterion will select the best model with the minimum model complexity and maximum model decision. Statistics of noise eigenvalue of SCM can be approximated by the distributional properties of the eigenvalues given by Marcenko-Pastur distribution in the signal-free SCM. In this paper we use random... 

This study presents a method based on minimum description length criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the sample covariance matrix (SCM) of Gaussian processes. The authors use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, the authors assume a Marcenko-Pastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces.... 

This study introduces a new scheme for steganalysis of the least significant bit (LSB) embedding, based on the idea of closure of sets (CoS), which is independent of the type of cover signal, applicable to both spatial and transform domains. The CoS is referred to as some special subsets that could be found in a common space whose elements relate to higher-order statistical properties of the signal. The proposed scheme is used for steganalysis of the LSB steganography of greyscale TIFF and JPEG images and audio signals, employing a set of accurate and monotone features that are extracted based on the CoS definition. It is shown that significant improvement to the detection accuracy in... 

This paper studies the statistical properties of the signal-to-noise ratio (SNR) of the dual-hop relaying link in a cooperative wireless communication system in the presence of channel estimation errors for fixed-gain (FG) and variable-gain (VG) relays. The SNR expression is derived and three different analytical approaches with different simplifying assumptions are proposed to obtain the probability distribution function of the SNR and the outage probability in each mode. All but one approach result in closed-form expressions for the outage probability. The simplest approach in each mode has been used to find an optimum power allocation scheme for pilot and data symbols transmission at the... 

In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering. Our conducted experiments show that in the case of constraining Gaussian estimates into a finite-sized windows, the patch clusters are more likely to be derived from the estimated multivariate Gaussian... 

A two-channel time analyser data acquisition system is developed for analysis of stochastic processes of random time interval pulses. The system is implemented on a typical low cost FPGA device. Two stochastic processes of nuclear interactions can be recorded by the system independently without any inter-channel dead time behaviour. The experimental results without any hardware based data reduction are transferred to the computer to perform arbitrary post analysis of the data using powerful software engineering tools to estimate the statistical properties of the processes. The performance of the system is verified experimentally. The maximum time digitization period and the minimum channel... 

In complex systems with fractal properties the scale invariance has an important rule to classify different statistical properties. In two dimensions the Loewner equation can classify all the fractal curves. Using the Weierstrass-Mandelbrot (WM) function as the drift of the Loewner equation we introduce a large class of fractal curves with discrete scale invariance (DSI). We show that the fractal dimension of the curves can be extracted from the diffusion coefficient of the trend of the variance of the WM function. We argue that, up to the fractal dimension calculations, all the WM functions follow the behavior of the corresponding Brownian motion. Our study opens a way to classify all the... 

A new steganalysis system for JPG-based image data hiding is proposed in this paper. We use features extracted from both wavelet and DCT domains that are refined later in the sense of utmost discrimination between the clear and stego images in the classification system. Statistical properties of the SVD of wavelet sub-bands are combined with the extended DCT-Markov features, and the features that are most sensitive to the data embedding are chosen through a SVM-RFE based selection algorithm. Experimental results show significant improvement over baseline methods, especially for steganalysis of Perturbed Quantization (PQ), which is known to be one of most secure JPG-based steganography... 

A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. Although the use of shrinkage is optimal for Gaussian white noise with complete and unitary transforms, it has already been shown that shrinkage has promising results even with redundant transforms. In this paper, we propose using adaptive thresholding of redundant representations of the noisy image for image denoising. In the proposed thresholding scheme, a different threshold is used for each representation coefficient of the noisy image in an overcomplete transform. In this method, each threshold is automatically set based on statistical properties of the... 

This paper presents a new scheme for steganalysis of random LSB embedding, capable of applying to any kind of digital signal in both spatial and transform domains. The proposed scheme is based on defining a space whose elements relate to higher-order statistical properties of the signal and looking for special subsets, which we call Closure of Sets (CoS) in this space. We use this scheme for steganalysis of the LSB steganography in grayscale images, employing a vector of five accurate and monotone features. Experimental results show significantly higher accuracy of the proposed scheme, as compared to those reported in the literature, especially in low embedding rates applications. © 2009... 

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin... 

This paper studies the statistical properties of the signal-to-noise ratio (SNR) of the relay link in a cooperative wireless communication system with fixed gain relay in presence of channel estimation error. The SNR expression is derived and three different analytic approaches with different approximate assumptions are used to obtain the probability distribution function of the SNR and outage probability of the relay link. The first approach is the most accurate one in many cases, however it does not have a closed-form expression. The other two approaches result in closed-form expressions for the outage probability. Therefore, the third approach has been used to find a sub-optimum power... 

In this paper, we propose a novel algorithm that combines color and texture information of skin with cellular learning automata to segment skin-like regions in color images. First, the presence of skin colors in an image is detected, using a committee structure, to make decision from several explicit boundary skin models. Detected skin-color regions are then fed to a color texture extractor that extracts the texture features of skin regions via their color statistical properties and maps them to a skin probability map. Cellular learning automatons use this map to make decision on skin-like regions. The proposed algorithm has demonstrated true positive rate of about 83.4% and false positive... 

H.264/AVC, the latest video coding standard, achieves better video compression rates since it supports new features such as a large number of intra- and inter-prediction candidate modes. H.264/AVC adopts rate-distortion optimization (RDO) technique to obtain the best intra- and inter-prediction, while maximizing visual quality and minimizing the required bit rate. However, the RDO reduces the encoding speed via the exhaustive evaluation of all candidate modes. In this paper, we decrease the encoding time by reducing the computational complexity of the prediction function and the number of candidate modes. First, we improve Pan's method, by eliminating the DC mode from the candidates. Second,...