In all applications and specially in real time applications, image processing and compression plays in modern life a very important part in both storage and transmission over internet for example, but finding orthogonal matrices as a filter or transform in different sizes is very complex and importance to using in different applications like image processing and communications systems, at present, new method to find orthogonal matrices as transform filter then used for Mixed Transforms Generated by using a technique so-called Tensor Product based for Data Processing, these techniques are developed and utilized. Our aims at this paper are to evaluate and analyze this new mixed technique in Image Compression using the Discrete Wavelet Transform and Slantlet Transform both as 2D matrix but mixed by Tensor Product. The performance parameters such as Compression Ratio, Peak Signal to Noise Ratio, and Root Mean Squared Error, are all evaluated for both standard colored and gray images. The simulation result shows that the techniques provide the quality of the images it was normal but acceptable and need more researchers works.
In this work, plasma parameters such as (electron temperature (Te), electron density (ne), plasma frequency (fp) and Debye length (λD)) were studied using spectral analysis techniques. The spectrum of the plasma was recorded with different energy values, SnO2 and ZnO anesthetized at a different ratio (X = 0.2, 0.4 and 0.6) were recorded. Spectral study of this mixing in the air. The results showed electron density and electron temperature increase in zinc oxide: tin oxide alloy targets. It was located that The intensity of the lines increases in different laser peak powers when the laser peak power increases and then decreases when the force continues to increase.
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The aim of the research is to know the level of time management application and its impact on the performance of the job, a survey search in the general company for communication and information technology and provide recommendations that help employees to optimize the use of time and improve performance, which is an important element in controlling the various functions of the company. In order to achieve the objectives of the research, the questionnaire was based on two main variables and distributed to a random sample of (44) employees in the company out of (308) employees, thus the proportion of the sample (14%). After collecting the samples from the sample, there are (6) incomplete forms that have been retri
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The ejector refrigeration system is a desirable choice to reduce energy consumption. A Computational Fluid Dynamics CFD simulation using the ANSYS package was performed to investigate the flow inside the ejector and determine the performance of a small-scale steam ejector. The experimental results showed that at the nozzle throat diameter of 2.6 mm and the evaporator temperature of 10oC, increasing boiler temperature from 110oC to 140oC decreases the entrainment ratio by 66.25%. At the boiler temperature of 120oC, increasing the evaporator temperature from 7.5 to 15 oC increases the entrainment ratio by 65.57%. While at the boiler temperature of 120oC and
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Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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