Today the NOMA has exponential growth in the use of Optical Visible Light Communication (OVLC) due to good features such as high spectral efficiency, low BER, and flexibility. Moreover, it creates a huge demand for electronic devices with high-speed processing and data rates, which leads to more FPGA power consumption. Therefore; it is a big challenge for scientists and researchers today to recover this problem by reducing the FPGA power and size of the devices. The subject matter of this article is producing an algorithm model to reduce the power consumption of (Field Programmable Gate Array) FPGA used in the design of the Non-Orthogonal Multiple Access (NOMA) techniques applied in (OVLC) systems combined with a blue laser. However, The power consumption comes from Complex Digital Signal Processing (DSP) due to mathematical operations such as addition and multiplication which consume more FPGA power when compared with other parts of NOMA. The multiplication operation consumes more FPGA power than the additional operation. The article's goal is to propose low FPGA power consumption algorithms called recursive IFFT/FFT which reduce the FPGA power consumption by more than 45% compared with the model without the proposed algorithm using AMD Xilinx Kintex-7 with high-speed analogue card.
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Abstract
Objectives: This study aimed to evaluate the effectiveness of an educational program on nurses' practices concerning therapeutic communication.
Methodology: A quasi experimental design was carried out at Karbala Center for Cardiac Diseases and Surgery, Imam Hussein Medical City and Al-Handia General Hospital for period 10th June 2017 to the 15th of August 2018.
The program and instruments were constructed and developed by the researcher to measure the purpose of the study. Purposive sample comprised of (57) nurses were divided into two groups, study and control groups. The questionnaire consisted from two parts, first part is related to nurses' demographic characteristics and second part which include practices checkl
The mobile phone has become one of the most important in our days. The effects of waves from mobile base station may cause health effects on human. The aim of this work was to study the effect of radiofrequency (RF) emitted from mobile base station on the hemoglobin (Hb), packed cell (PCV), white blood cells (WBC) and liver enzymes activity including glutamic oxaloacetic transaminase (GOT), glutamic pyruvie transaminase (GPT) and Alkaline phosphatase (ALP). In this study the people divided into control group who living away from mobile base station and experimental group who living near to the mobile base station. The present result found there is no significant differences (P<0.05) in the Hb and PCV, but there was a significant increases (
... Show MoreDespite the great economic and commercial importance given to real estate by virtue of its view of the landscape or public roads, US courts have differed in their position on compensation for damages resulting from blocking that view or vision by public projects. Some courts compensated for such damages, other courts approved such compensation. Hence, this research came to shed light on the extent of the possibility of compensation for blocking the view or vision as a result of public projects, and the research has supported us with many judicial decisions.
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 through our ca
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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
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The purpose of this study is aimed to lay down an arranged platform suited to Iraqi constructional associations which in charge to carry out multi constructional projects, as it fulfilled management requirements and supervising, so that low - cost projects will be controlled in due term and quality. Based on primary info and observed data collected, the study thesis has been formulated in this way: Iraqi constructional sector bodies which are in charge to implement simultaneously multi constructional projects in need to reformulate its organized structure so that it will be more fitted to management and control of these projects. This thesis includes a
theoretical part contained presenting the most important resources locally and int