Magnesium-doped Zinc oxide (ZnO: Mg) nanorods (NRs) films and pure Zinc oxide deposited on the p-silicon substrates were prepared by hydrothermal method. The doping level of the Mg concentration (atoms ratio of Mg to Zn was chosen to be 0.75% and 1.5%. X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDX) were performed to characterize the prepared films. X-ray diffraction analysis showed a decrease in the lattice parameters of the Mg-doped ZnO NRs. Under 10V applied bias voltage, the responsivity of p-n junction UV photodiode based on pure ZnO and Mg: ZnO with doping ratio (0.75% and 1.5%) was 0.06 A/W and (0.15A/W and 0.27A/W) at UV illumination of wavelength 365 nm respectively, 0.071 A/W and (0.084A/W and 0.11A/W) fo

This research discusses the subject of emotional stimulation factors through the stages of product design interaction with the consumer and what they reflect in the formation of a self-consideration of the design in his emotional memory regarding the specific brand through the following question (What is the emotional stimulus in the design of certain products and its reflection on the consumer during the processes of receiving and using it?), The importance of the research comes in the connection of design elements with many sensory factors used and motivating to attract the consumer to the acquisition of a specific design and the demand for its experience, the study aims Detection and access to emotional stimulation in the design of th

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Facial identification is one of the biometrical approaches implemented for identifying any facial image with the use of the basic properties of that face. In this paper we proposes a new improved approach for face detection based on coding eyes by using Open CV's Viola-Jones algorithm which removes the falsely detected faces depending on coding eyes. The Haar training module in Open CV is an implementation of the Viola-Jones framework, the training algorithm takes as input a training group of positive and negative images, and generates strong features in the format of an XML file which is capable of subsequently being utilized for detecting the wanted face and eyes in images, the integral image is used to speed up Haar-like features calc

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

  This paper deals with proposing new lifting scheme (HYBRID Algorithm) that is capable of preventing images and documents which are fraud through decomposing there in to the real colors value arrays (red, blue and green) to create retrieval keys for its properties and store it in the database and then check the document originality by retrieve the query image or document through the decomposition described above and compare the predicted color values (retrieval keys)  of the query document with those stored in the database. The proposed algorithm has been developed from the two known lifting schemes (Haar and D4) by merging them to find out HYBRID lifting scheme. The validity and accuracy of the proposed algorithm have been ev

The nuclear pre-equilibrium emission spectra have been studied and calculated using the exciton model with different reactions and incident energiesfor the target nuclei: . The secondary emissioncomponent has been inserted to the final emission spectrum and its effectshave been studied for only reactions with primary nucleons emission because the restrictions introduced by primary clusters emission reactions. It revealed a big contributioninenhancing the calculated energy spectra atincident energies more than