The real and imaginary part of complex dielectric constant for InAs(001) by adsorption of oxsagen atoms has been calculated, using numerical analysis method (non-linear least square fitting). As a result a mathematical model built-up and the final result show a fairly good agreement with other genuine published works.

In this study, method for experimentally determining the electron density (ne) and the electron temperature (Te) in the atmospheric Argon plasma jet is used; it is based on optical emission spectroscopy (OES). Boltzmann plot method used to calculate these parameters measured for different values of gas flow rate. The results show that the electron temperature decreasing with the increase of gas flow rate also indicates an increasing in the electron density of plasma jet with increasing of gas flow rate.

In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. These matrices can be pre-calculated, stored and used repeatedly since they are independent of the image itself. We validated experimentally th

A fully automatic electrothermal atomic emission spectrometry (ETA-AES) is described. This system is based on an echelle monochromator modified for wave¬length modulation which is completely controlled by microcomputer . The advantages of the system in atomic spectrometry have been discussed . Aspects of the analytical performances such as calibration ? dection limit, precision , and recovery for copper are considered . This system is applied for routine determination of copper in commercial powdered mill? by slurr>' atomization versus aqueous atomization techniques.

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.