<p>In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:</p><p>Eigenfaces → createEigenFaceRecognizer()</p><p>Fisherfaces → createFisherFaceRecognizer()</p><p>Local Binary Patterns Histograms → createLBPHFaceRecognizer()</p>Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.<p> </p>

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In the paper the application of uncertainty analysis performed for microelectromechanical resonator is presented. Main objective of undertaken analysis is to assess the propagation of considered uncertainties in the variation of chosen dynamic characteristics of Finite Element model of microresonator. Many different model parameters have been assumed to be uncertain: geometry and material properties. Apart from total uncertainty propagation, sensitivity analysis has been carried out to study separate influences of all input uncertain characteristics. Uncertainty analysis has been performed by means of fuzzy arithmetics in which alpha-cut strategy has been applied to assemble output fuzzy number. Monte Carlo Simulation and Genetic Algorithms have been employed to calculate intervals connected with each alpha-cut of searched fuzzy number. Elaborated model of microresonator has taken into account in a simplified way the presence of surrounding air and constant electrostatic field.

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The geometric layout is the key factor for enhancing the efficiency of the fluid mixing in passive micromixers. Therefore, by adjusting the geometric design and by controlling the geometric parameters, one can enhance the mixing process. However, through any fabrication process, the geometric parameters present slight, inherent variation from the designed values than might affect the performance of the micromixer. This paper proposes a numerical study on the influence of the unavoidable geometric tolerances on the mixing efficiency in passive micromixers. A probabilistic simulation model, based on the Monte Carlo method, is developed and implemented for this purpose. An uncertainty simulation model shows that significant deviations from the deterministic design can appear due to small variations in the geometric parameters values and demonstrates how a more realistic mixing performance can be estimated.