In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.

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MANETs (Mobile Ad hoc Networks) had become the most important next generation wireless network technologies. It is made up of self-configurable mobile nodes, Intruders mieght decrease MANET functionality due to the dispersed and wireless nature of MANETs, and therefore they were vulnerable to numerous attacks at different levels. The important challenges for MANET were the security and routing protocols. This paper examined the impact of Jammer which it was a kind of DoS attack which interfere with the normal operation of network and show how the Jammer increased the delay and data dropped and decreased the throughput which they were the important parameters for the measurements of network performance. This performance could be improved using routing protocols (AODV, DSR, OLSR and GRP). Riverbed Modeler Academic Edition (17.5) was utilized for this study in number of modeled scenarios for video applications. The results address the impact of Jammers and show that the MANET's Routing Protocols could improve the throughput and data dropped of the network but on the expense of increasing the delay.

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Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.