Theft is among the most common crimes committed in all societies. The present study aims at exploring the STATIONARY and non STATIONARY theft rates in the cities of Zanjan. All those who committed theft during 2007 to 2008 in the cities of Zanjan (N=493) were the statistical sample. The required data of this field study were collected via questionnaire, interview, and observation. SPSS was used to analyze the collected data. The sample findings are as follows: 1.There is a significant relationship between "The theft technique and thieves' motivations".2.There are significant relationships between."The theft technique and criminal records", "The theft technique and drug abuse", "The theft technique and the theft type", "The theft technique and residential place", and "The theft technique and the place of birth of thieves".

In this paper, the problem of diagonalization of the autocovariance matrices of STATIONARY processes is discussed. Basically, there is no explicit form for the diagonal matrices. An analytic solution has been provided for STATIONARY ARMA models based on Band matrices. The results have been compared with Dargahi-Noubary and Fuller in a numerical study. It has been demonstrated that the results are more accurate compared to Dargahi-Noubary and Fuller approaches.

In this paper, we study Projected non-STATIONARY Simultaneous It-erative Reconstruction Techniques (P-SIRT). Based on algorithmic op-erators, convergence result are adjusted with Opial’ s Theorem. The advantages of P-SIRT are demonstrated on examples taken from to-mographic imaging.

The Gaussian random field is commonly used to analyze spatial data. One of the important features of this random field is having essential properties of the normal distribution family, such as closure under linear transformations, marginalization and conditioning, which makes the marginal consistency condition of the Kolmogorov extension theorem. Similarly, the skew-Gaussian random field is used to model skewed spatial data. Although the skew-normal distribution has many of the properties of the normal distribution, in some definitions of the skew-Gaussian random field, the marginal consistency property is not satisfied. This paper introduces a stationery skew-Gaussian random field, and its marginal consistency property is investigated. Then, the spatial correlation model of this skew random field is analyzed using an empirical variogram. Also, the likelihood analysis of the introduced random field parameters is expressed with a simulation study, and at the end, a discussion and conclusion are presented.

Spatial aliasing is unavoidable in some seismic data acquisitions and has serious adverse impacts on the performance of multichannel data processing and interpretation. So far, many methods have been used for removing spatial aliasing, such as interpolation methods of seismic traces by various transforms like Radon transform. In this paper, a new method for complete removal of spatial aliasing using STATIONARY wavelet transform is presented. Our method has several advantages over previous ones such as, fixing the number of traces, avoiding transfiguration of wavelet shape that constructs the traces, minimizing the phase shift in seismic events, and reducing the run time.

The separation of unobserved sources from mixed observed data is a fundamental ‎signal processing problem. Most of the proposed techniques for solving this problem ‎rely on independence or at least uncorrelation assumption for source signals. This ‎paper introduces a technique for cases that source signals are correlated with each ‎other. The method uses Wold decomposition principle for extracting desired and ‎proper information from the predictable part of the observed data, and exploits ‎approaches based on second-order statistics to estimate the mixing matrix and source ‎signals. Simulation results are provided to illustrate the effectiveness of the method‏.‏

In this paper, an online signature verification method using extended regression in STATIONARY wavelet domain is presented. To calculate the similarity between two signatures by extended regression, we should equalize the time length of the corresponding signals in two signatures. Using all points of the signals to equalize their time length will decrease the difference between a genuine signature and its forgery. Here a new approach based on the extreme points warping of the signals is presented. This approach equalizes the time length of two signals without degrading the differences between them. Also we calculated the similarity of signatures by using the details of the signals in STATIONARY wavelet transform, SWT, domain, which showed very good results. The proposed system was tested on SVC2004 signature database. The results were compared with the results of participant teams in the first international signature verification competition. We have gained EER=6% for skilled forgery signatures. Comparing the result, it shows that we stand in the second rank between all the participants. This system has no verification error for random forgery signatures and stands in the first rank. Our experimental results show that using SWT domain instead of time domain decreases the verification error rate by 35%.

Deconvolution is one of the significant steps in seismic signal processing for obtaining high resolution images of subsurface. In this paper, a new nonSTATIONARY deconvolution is developed to retrieve the impulse response of the earth and remove the earth attenuation effects from it via a simulated annealing (SA) optimization. The time location of each spike and the corresponding quality factor is estimated by the SA method and its amplitude is calculated using a least-squares method. All three parameters of the reflection coefficient, time lag, and the quality factor for each layer are estimated simultaneously. Available information from well-logs can also be incorporated as a priori information. Numerical examples from simulated and field seismic data show high performance of the proposed algorithm for estimating the reflectivity structure and the model of the quality factor. Numerical examples confirm promising applicability of the new method for processing and interpretation of seismic data. The goal of non-STATIONARY deconvolution is to remove the effects of the source wavelet and the attenuation filter from the data to retrieve the earth impulse response with an enhanced temporal resolution.