Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensities. In the third stage, the boundary of the target object is extracted, and in the fourth and fifth stages, respectively, the region of interest (ROI) is highlighted and reconstructed. Our model was tested and evaluated using realistic scenarios which include outdoor and indoor scenes. The results reflect the ability of our approach to detect and remove shadows and reconstruct a shadow free image with a small error of approximately 6%.
The simulation study has been conducted for the harmonics of Nd: YAG laser, namely the second harmonic generation SHG, the third harmonic generation THG, and the fourth harmonic generation FHG. Determination of beam expander's expansion ratio for specific wavelength and given detection range is the key in beam expander design for determining minimum laser spot size at the target. Knowing optimum expansion ratio decreases receiving unit dimensions and increases its performance efficiency. Simulation of the above mentioned parameters is conducted for the two types of refractive beam expander, Keplerian and Galilean. Ideal refractive indices for the lenses are chosen adequately for Nd: YAG laser harmonics wavelengths, so that increasing transm
... Show MoreIn this paper, a single link flexible joint robot is used to evaluate a tracking trajectory control and vibration reduction by a super-twisting integral sliding mode (ST-ISMC). Normally, the system with joint flexibility has inevitably some uncertainties and external disturbances. In conventional sliding mode control, the robustness property is not guaranteed during the reaching phase. This disadvantage is addressed by applying ISMC that eliminates a reaching phase to ensure the robustness from the beginning of a process. To design this controller, the linear quadratic regulator (LQR) controller is first designed as the nominal control to decide a desired performance for both tracking and vibration responses. Subsequently, discontinuous con
... Show MoreA novel ligand, (E)-5-((2-hydroxy-4,6-dimethylphenyl)diazenyl)-2,3-dihydrophthalazine-1,4- dione, was synthesized through the reaction of 3,5-dimethylphenol with the diazonium salt of 5-amino-2,3-dihydrophthalazine-1,4-dione. The ligand underwent characterization through the utilization of diverse spectroscopic methods, including UV-Vis, FT-IR, 13C, and 1H-NMR, alongside Mass spectroscopy and micro elemental analysis (Carbon, Hydrogen, Nitrogen, and Oxygen). Metal chelates of transition metals were prepared and analyzed using elemental analysis, mass spectra, atomic absorption, UV-Vis, FT-IR spectral analysis, as well as conductivity and magnetic measurements. The investigation into the compounds’ nature was conducted by utilizing mole r
... Show MoreBackground: Breast Cancer is the most common malignancy among the Iraqi population; the majority of cases are still diagnosed at advanced stages with poor prospects of cure. Early detection through promoting public awareness is one of the promising tools in its control. Objectives: To evaluate the baseline needs for breast cancer awareness in Iraq through exploring level of knowledge, beliefs and behavior towards the disease and highlighting barriers to screening among a sample of Iraqi women complaining of breast cancer. Methodology: Two-hundred samples were enrolled in this study; gathered from the National
The control of water represents the safe key for fair and optimal use to protect water resources due to human activities, including untreated wastewater, which is considered a carrier of a large number of antibiotic-resistant bacterial species. This study aimed to investigate the prevalence of antibiotic-resistance to E. coli in Tigris River by the presence of resistance genes for aminoglycoside(qepA( ,quinolone (gyrA), and sulfa drugs( dfr1 ,dfr17) due to the frequent use of antibiotics and their release into wastewater of hospitals. Samples were collected from three sites on Tigris River: S1( station wastewater in Adhamiya), S2 (station wastewater in Baghdad Medical city hospital), S3 (station wastew
... Show MoreThe rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which ca
... Show MoreThis research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.
The aim of this research is to determine the most important and main factors that lead to Preeclampsia. It is also about finding suitable solutions to eradicate these factors and avoid them in order to prevent getting Preeclampsia. To achieve this, a case study sample of (40) patients from Medical City - Oncology Teaching Hospital was used to collect data by a questionnaire which contained (17) reasons to be investigated. The statistical package (SPSS) was used to compare the results of the data analysis through two methods (Radial Bases Function Network) and (Factorial Analysis). Important results were obtained, the two methods determined the same factors that could represent the direct reason which causes Preecla
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