In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

In this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.

Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essentia

This research entitled: "Artistic processing of Emotional Scenes in the Narrative Film" deals with how to process and embody those emotional scenes. As there are certain filmic elements that play an effective role in deepening the viewer's sense of the importance of those scenes, and that their presence in the film is necessary and inevitable, and cannot be dispensed because it forms an interconnected connection with the rest of the film's scenes, in addition to its dramatic and aesthetic value in the film in crystallizing the viewer's feelings and integrating him/her into the scene.
The research was divided into four chapters, the first chapter includes: the methodological framework, which represented the research problem, and brin

The core idea of this study revolves around the news coverage by Iraqi satellite channels regarding corruption issues and their implications on the public's perception of the political process. The researcher designed a content analysis form encompassing both primary and sub-categories of news bulletins from the channels, Dijlah and Al-Itijah, spanning from 01/06/2021 to 31/08/2021, using a comprehensive enumeration method. The chosen timeframe preceded the parliamentary elections held in October 2021. Employing a descriptive-analytical approach coupled with observation, the researcher derived results that met the study's objectives. Among these findings, news items enhanced with video content topped the categorie

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as

In this research, porous silicon (PS) prepared by anodization etching on surface of single crystalline p-type Si wafer, then Gold nanoparticle (AuNPs) prepared by pulsed laser ablation in liquid. NPs deposited on PS layer by drop casting. The morphology of PS, AuNPs and AuNPs/PS samples were examined by AFM. The crystallization of this sample was characterized by X-ray diffraction (XRD). The electrical properties and sensitivity to CO2 gas were investigated to Al/AuNPs/PS/c-Si/Al, we found that AuNPs plays crucial role to enhance this properties.

In this work, an optical fiber biomedical sensor for detecting the ratio of the hemoglobin in the blood is presented. A surface plasmon resonance (SPR)-based coreless optical fiber was developed and implemented using single- and multi-mode optical fibers. The sensor is also utilized to evaluate refractive indices and concentrations of hemoglobin in blood samples, with 40 nm thickness of (20 nm Au and 20 nm Ag) to increase the sensitivity. It is found in practice that when the sensitive refractive index increases, the resonant wavelength increases due to the decrease in energy.

The performance of H2S sensor based on poly methyl methacrylate (PMMA)-CdS nanocomposite fabricated by spray pyrolysis technique has been reported. XRD pattern diffraction peaks of nano CdS has been indexed to the hexagonally wurtzite structured The nanocomposite exhibits semiconducting behavior with optical energy gap of4.06eV.SEM morphology appears almost tubes like with CdS/PMMA network. That means the addition of CdS to polymer increases the roughness in the film and provides high surface to volume ratio, which helps gas molecule to adsorb on these tubes. The resistance of PMMA-CdS nanocomposite showed a considerable change when exposed to H2S gas. Fast response time to detect H2S gas was achieved by using PMMA-CdS thin film sensor. The

      In this research, the nonparametric technique has been presented to estimate the time-varying coefficients functions for the longitudinal balanced data that characterized by observations obtained through (n) from the independent subjects, each one of them is measured repeatedly by group of  specific time points (m). Although the measurements are independent among the different subjects; they are mostly connected within each subject and the applied techniques is the Local Linear kernel LLPK technique. To avoid the problems of dimensionality, and thick computation, the two-steps method has been used to estimate the coefficients functions by using the two former technique. Since, the two-