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

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

It is often needed to have circuits that can display the decimal representation of a binary number and specifically in this paper on a 7-segment display. In this paper a circuit that can display the decimal equivalent of an n-bit binary number is designed and it’s behavior is described using Verilog Hardware Descriptive Language (HDL). This HDL program is then used to configure an FPGA to implement the designed circuit.

The aims of this study the infection with parasites Entamoeba histolytica and Giardia lamblia and prevalence rates and their relationship with gende , age group , the number of family members and source of drinking water among the patients at attending the Bint Al Huda and Al- Hussin Teaching hospital in Nassriyia city in Thi- Qar province , it was noticed that the percentage of parasitic infection at females was 38.55% and at males was 61.45% and the percentage of parasitic infection with Entamoeba histolytica 97% was higher than the percentage of parasitic infection with Giardia lamblia 3%,and the reduction of percentage of infection is associated with increasing of the number of family member ,the percentage of infection was 49% at the

Background: Antioxidant, sedative, anticancer, and antibacterial properties are among the numerous pharmacological characteristics of Galium verum. Aim: The primary goal of this research was to investigate the therapeutic effects of G. verum extract against folic acid-induced acute kidney injury (AKI). Materials and methods: 18 male rats were assigned into three groups:  Control, AKI, and G. verum. AKI was induced by a dose of folic acid (250 mg/kg, i.p.) while G. verum (250 mg/kg) was administrated for 7 consecutive days. Results: G. verum methanol extract contains flavonoids, anthraquinones, tannins, iridoids, triterpen

Background: Although mammography is a powerful screening tool in detection of early breast cancer, it is imperfect, particularly for women with dense breast, which have a higher risk to develop cancer and decrease the sensitivity of mammogram, Automated breast ultrasound is a recently introduced ultrasonography technique, developed with the purpose to standardize breast ultrasonography and overcome some limitations of handheld ultrasound, this study aims to evaluate the diagnostic efficacy of Automated breast ultrasound and compare it with handheld ultrasound in the detection and characterization of breast lesions in women with dense breasts. Objectives: To evaluate the diagnostic efficacy of Automated breast ultrasound and compare

Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate