Compounds were prepared from In2O3 doped SnO2 with different doping ratio by mixing and sintering at 1000oC. Pulsed Laser Deposition PLD was used to deposit thin films of different doping ratio In2O3: SnO2 (0, 1, 3, 5, 7 and 9 % wt.) on glass and p-type wafer Si(111) substrates at ambient temperature under vacuum of 10-3 bar thickness of ~100nm. X-ray diffraction and atomic force microscopy were used to examine the structural type, grain size and morphology of the prepared thin films. The results show the structures of thin films was also polycrystalline, and the predominate peaks are identical with standard cards ITO. On the other side the prepared thin films declared a reduction of degree of crystallinity with the increase of doping ra

In this study, tin oxide (SnO2) and mixed with cadmium oxide (CdO) with concentration ratio of (5, 10, 15, 20)% films were deposited by spray pyrolysis technique onto glass substrates at 300ºC temperature. The structure of the SnO2:CdO mixed films have polycrystalline structure with (110) and (101) preferential orientations. Atomic force microscopy (AFM) show the films are displayed granular structure. It was found that the grain size increases with increasing of mixed concentration ratio. The transmittance in visible and NIR region was estimated for SnO2:CdO mixed films. Direct optical band gap was estimated for SnO2 and SnO2 mixed CdO and show a decrease in the energy gap with increasing mixing ratio. From Hall measurement, it was fou

The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages

Cloud computing is the new technological trend for future generations. It represents a new way to use IT resources more efficiently. Cloud computing is one of the most technological models for developing and exploiting infrastructure resources in the world. Under the cloud, the user no longer needs to look for major financing to purchase infrastructure equipment as companies, especially small and medium-sized ones, can get the equipment as a service, rather than buying it as a product. The idea of ​​cloud computing dates back to the sixties of the last century, but this idea did not come into actual application until the beginning of the third millennium, at the hands of technology companies such as Apple, Hp, IBM, which had

   The research aims to identify the importance of using the style of the cost on the basis of activity -oriented in time TDABC and its role in determining the cost of products more equitably and thus its impact on the policy of allocation of resources through the reverse of the changes that occur on an ongoing basis in the specification of the products and thus the change in the nature and type of operations . The research was conducted at the General Company for Textile Industries Wasit / knitting socks factory was based on research into the hypothesis main of that ( possible to calculate the cost of activities that cause the production through the time it takes to run these activities can then be re- distributed product cost

          The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed