In this paper, visible image watermarking algorithm based on biorthogonal wavelet
transform is proposed. The watermark (logo) of type binary image can be embedded in the
host gray image by using coefficients bands of the transformed host image by biorthogonal
transform domain. The logo image can be embedded in the top-left corner or spread over the
whole host image. A scaling value (α) in the frequency domain is introduced to control the
perception of the watermarked image. Experimental results show that this watermark
algorithm gives visible logo with and no losses in the recovery process of the original image,
the calculated PSNR values support that. Good robustness against attempt to remove the
watermark was s

The complexity of multimedia contents is significantly increasing in the current world. This leads to an exigent demand for developing highly effective systems to satisfy human needs. Until today, handwritten signature considered an important means that is used in banks and businesses to evidence identity, so there are many works tried to develop a method for recognition purpose. This paper introduced an efficient technique for offline signature recognition depending on extracting the local feature by utilizing the haar wavelet subbands and energy. Three different sets of features are utilized by partitioning the signature image into non overlapping blocks where different block sizes are used. CEDAR signature database is used as a dataset f

Abstract:

One of the important things provided by fuzzy model is to identify the membership functions. In the fuzzy reliability applications with failure functions of the kind who cares that deals with positive variables .There are many types of membership functions studied by many researchers, including triangular membership function, trapezoidal membership function and bell-shaped membership function. In I research we used beta function. Based on this paper study classical method to obtain estimation fuzzy reliability function for both series and parallel systems.

The research took the spatial autoregressive model: SAR and spatial error model: SEM  in an attempt to provide practical evidence that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial and that includes all of the spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. The spatial analysis had been applied to Iraq Household Socio-Economic Survey: IHS

This research aimed to develop a simulation traffic model for an urban street with heterogeneous traffic capable of analyzing different types of vehicles of static and dynamic characteristics based on trajectory analysis that demonstrated psychophysical driver behavior. The base developed model for urban traffic was performed based on the collected field data for the major urban street in Baghdad city. The parameter; CC1 minimum headway (represented the speed-dependent of the safety distance from stop line that the driver desired) justified in the range from (2.86sec) to (2.17 sec) indicated a good match to reflect the actual traffic behavior for urban traffic streets. A good indication of the convergence between simulat

This paper assesses the impact of changes and fluctuations in bank deposits on the money supply in Iraq. Employing  the research constructs an Error Correction Model (ECM) using  monthly time series data from 2010 to 2015. The analysis begins with  the Phillips-Perron unit root test to ascertain the stationarity of the  time series and the Engle and Granger cointegration test to examine  the existence of a long-term relationship. Nonparametric regression functions are estimated using two methods: Smoothing Spline and M-smoothing. The results indicate that the  M-smoothing approach is the most effective, achieving the shortest adjustment period and the highest adjustment ratio for short-term disturbances, thereby facilitating a return

Cervical Uterine Cancer is a disease that explains the vulnerability in which women are in terms of reproductive health with an impact on occupational health and public health, even when in Mexico the prevalence rate is lower than the other member countries of the OECD, its impact on Human Development and Local Development shows the importance that the disease have in communities more than in cities where prevention policies through check-ups and medical examinations seem to curb the trend, but show the lack of opportunities and capacities of health centers in rural areas.   To establish the reliability, validity, and correlations between the variables reported in the literature with respect to their weighting in a public hospital. A

The research was performed in order to investigate the prevalence of Eimeria spp in buffalo. Coccidiosis, is a common livestock disease include water buffaloes and nothing is known about the most pathogenic species of Eimeria. Since the highest prevalence of oocyst shedding and incidence of disease occurs in buffalo calves less than one year of age. The omnipresent occurrence and negative effects of the infection on health and buffalo growth output are taken into account. Therefore, both farmers and veterinarians should pay greater attention to infections with Eimeria spp. And there is little analysis of data reported in Iraq and the world regarding Eimeria infection in river buffalo spp.

This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances of these estimators compared to other common estimators using the mean squared error as a criterion for the goodness of fit. A numerical example and a simulation study are proposed to explain the performance of the estimators.

The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing.  Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds.  This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by usin