Facial recognition has been an active field of imaging science. With the recent progresses in computer vision development, it is extensively applied in various areas, especially in law enforcement and security. Human face is a viable biometric that could be effectively used in both identification and verification. Thus far, regardless of a facial model and relevant metrics employed, its main shortcoming is that it requires a facial image, against which comparison is made. Therefore, closed circuit televisions and a facial database are always needed in an operational system. For the last few decades, unfortunately, we have experienced an emergence of asymmetric warfare, where acts of terrorism are often committed in secluded area with no camera installed and possibly by persons whose photos have never been kept in any official database prior to the event. During subsequent investigations, the authorities thus had to rely on traumatized and frustrated witnesses, whose testimonial accounts regarding suspect’s appearance are dubious and often misleading. To address this issue, this paper presents an application of a statistical appearance model of human face in assisting suspect identification based on witness’s visual recollection. An online prototype system was implemented to demonstrate its core functionalities. Both visual and numerical assessments reported herein evidentially indicated potential benefits of the system for the intended purpose.
DBNRSK Sayed, Journal of Strategic Research in Social Science (JoSReSS), 2020
In this research (100* 40* 4 cm) solar cell panel was used in Baghdad at autumn season (2010), to get best solar cell panel angles experimentally, and then a mirror (40*50 cm) is use to concentrate incident sunlight intensity on a panel. At first case we get (Tilt angle ?P =60°and Surface Azimuth angle ?P =36°E) is the best angles and other case, we add a mirror at angle = 120° at bottom of panel, then we get output power (27.48watt) is bigger than without using a mirror (25.16watt). We can benefit from these cases in variety applications.
This paper aims to evaluate large-scale water treatment plants’ performance and demonstrate that it can produce high-level effluent water. Raw water and treated water parameters of a large monitoring databank from 2016 to 2019, from eight water treatment plants located at different parts in Baghdad city, were analyzed using nonparametric and multivariate statistical tools such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). The plants are Al-Karkh, Sharq-Dijlah, Al-Wathba, Al-Qadisiya Al-Karama, Al-Dora, Al-Rasheed, Al-Wehda. PCA extracted six factors as the most significant water quality parameters that can be used to evaluate the variation in drinkin
Porosity is important because it reflects the presence of oil reserves. Hence, the number of underground reserves and a direct influence on the essential petrophysical parameters, such as permeability and saturation, are related to connected pores. Also, the selection of perforation interval and recommended drilling additional infill wells. For the estimation two distinct methods are used to obtain the results: the first method is based on conventional equations that utilize porosity logs. In contrast, the second approach relies on statistical methods based on making matrices dependent on rock and fluid composition and solving the equations (matrices) instantaneously. In which records have entered as equations, and the matrix is sol
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