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

The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to resul

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.

Semiconductor-based metal oxide gas detector of five mixed from zinc chloride Z and tin chloride S salts Z:S ratio 0, 25, 50, 75 and 100% were fabricated on glass substrate by a spray pyrolysis technique. With thickness were about 0.2 ±0.05 μm using water soluble as precursors at a glass substrate temperature 500 ºC±5, 0.05 M, and their gas sensing properties toward CH4, LPG and H2S gas at different concentration (10, 100, 1000 ppm) in air were investigated at room temperature which related with the petroleum refining industry.
Furthermore structural and morphology properties were scrutinize. Results shows that the mixing ratio affect the composition of formative oxides were (ZnO, Zn2SnO4, Zn2SnO4+ZnSnO3, ZnSnO3, SnO2) ratios ment

The structural, optical properties of copper oxide thin films ( CuO) thin films which have been prepared by thermal oxidation with exist air once and oxygen another have been studied. Structural analysis results of Cu thin films demonstrate that the single phase of Cu with high a crystalline structure with a preferred orientation (111). X-ray diffraction results confirm the formation of pure (CuO) phase in both methods of preparation. The optical constant are investigated and calculated such as absorption coefficient, refractive index, extinction coefficient and the dielectric constants for the wavelengths in the range (300-1100) nm.