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

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.

The growing population and the rising standard of living in cities as well as the increased commercial, industrial and agricultural activities around the world led to
massive production of waste containing different materials and one of them is the municipal solid waste (MSW), so there is a major problem facing the cities around the world about the waste, how to collect, transfer it and how to discard it. Because the accumulation of wastes, whether in the city alleys or in its squares and especially in its residential areas affect the health of their populations besides this situation will be a major indication of the deteriorating quality of life in the city, as hygiene considered a fundamental criterion for the city beauty as well

Sulphated zirconia (SZ) is one of the most important solid acid catalysts was synthesize at different operating conditions, different calcination temperature and sulfonating time has been used. The prepared catalyst was distinguished by X-ray Diffraction (XRD), particle size and morphology of catalyst were checked by atomic force microscopy (AFM) and scanning electron microscopy (SEM) respectively, in addition to analysis by (DTA) Differential thermally and Energy Dispersive X-Ray (EDX). Finally, the N2 adsorption-desorption was used to measure the surface area (BET) and pore volume. High degree of tetragonal crystallinity was obtained 90 %, and surface area of 169 m2/g and pore volume of 0.39 cm3g-1 at 600°C calcination temperature for 3

This work examines numerically the effects of particle size, particle thermal conductivity and inlet velocity of forced convection heat transfer in uniformly heated packed duct. Four packing material (Aluminum, Alumina, Glass and Nylon) with range of thermal conductivity (from200 W/m.K for Aluminum to 0.23 W/m.K for Nylon), four particle diameters (1, 3, 5 and 7 cm), inlet velocity ( 0.07, 0.19 and 0.32 m/s) and constant heat flux ( 1000, 2000 and 3000 W/ m ²) were investigated. Results showed that heat transfer (average Nusselt number Nu_av) increased with increasing packing conductivity; inlet velocity and heat flux, but decreased with increasing particle size.Also, Aluminum average Nusselt number is about (0.85,2.