In this paper, silicon carbonitried thin films were prepared by the method of photolysis of the silane (SiH4) and ethylene (C2H4) gases, with and without ammonia gas (NH3), which is represented by the ratio between the (PNH3) and (PSiH4 + PC2H4 + PNH3), (which assign by the letter X), X has the values (0, 0.13, 0.33). This method carried out by using TEA-CO2 laser, on glass substrate at (375 oC), deposition rate (0.416-0.833) nm/pulse thin film thickness of (500-1000) nm. The optical properties of the films were studied by using Absorbance and Transmittance spectrums in wavelength range of (400-1100) nm, the results showed that the electronic transitions is indirect and the energy gap for the SiCN films increase with increasing of nitrog

Face Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades. Recently multiwavelet-based neural networks (multiwavenets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification. This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multiwavenet. The proposed multiwavenet has a structure similar to a multilayer perceptron (MLP) neural network with three layers, but the activation function of hidden layer is replaced with multiscaling functions. In experiments performed on the ORL face database it achieved a

Background: This study aimed to determine the gender of a sample of Iraqi adults using the mesio-distal width of mandibular canines, inter-canine width and standard mandibular canine index, and to determine the percentage of dimorphism as an aid in forensic dentistry. Materials and methods: The sample included 200 sets of study models belong to 200 subjects (100 males and 100 females) with an age ranged between 17-23 years. The mesio-distal crown dimension was measured manually, from the contact points for the mandibular canines (both sides), in addition to the inter-canine width using digital vernier. Descriptive statistics were obtained for the measurements for both genders; paired sample t-test was used to evaluate the side difference of

Face Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades. Recently multiwavelet-based neural networks (multiwavenets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification. This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multiwavenet. The proposed multiwavenet has a structure similar to a multilayer perceptron (MLP) neural network with three layers, but the activation function of hidden layer is replaced with multiscaling functions. In experiments performed on the ORL face database it achieved a

Longer follow-up defense , one of basketball skills that require the team collective action involving active part and consistent to acquire bouncing balls even not be a chance for members of the team striker acquisition rebounding from the target area and bring it back again , which reduces the chances of scoring, and it enables team members defender of the performance of fast attack and score points for being the increase your chances of success.In light of the foregoing, reflected the importance of research in achieving the objective basis of skill tests that require a circumstance similar to the circumstances of the game with the standard operating procedures for the registration, and that the validity judged by consistency between tests

High cost of qualifying library standard cells on silicon wafer limits the number of test circuits on the test chip. This paper proposes a technique to share common load circuits among test circuits to reduce the silicon area. By enabling the load sharing, number of transistors for the common load can be reduced significantly. Results show up to 80% reduction in silicon area due to load area reduction.