As a result of the significance of image compression in reducing the volume of data, the requirement for this compression permanently necessary; therefore, will be transferred more quickly using the communication channels and kept in less space in memory. In this study, an efficient compression system is suggested; it depends on using transform coding (Discrete Cosine Transform or bi-orthogonal (tap-9/7) wavelet transform) and LZW compression technique. The suggested scheme was applied to color and gray models then the transform coding is applied to decompose each color and gray sub-band individually. The quantization process is performed followed by LZW coding to compress the images. The suggested system was applied on a set of seven stand

Due to wind wave actions, ships impacts, high-speed vehicles and others resources of loading, structures such as high buildings rise bridge and electric transmission towers undergo significant coupled moment loads. In this study, the effect of increasing the value of coupled moment and increasing the rigidity of raft footing on the horizontal deflection by using 3-D finite element using ABAQUS program. The results showed that the increasing the coupled moment value leads to an increase in lateral deflection and increase in the rotational angle (α◦). The rotational angle increases from (0.014, 0.15 to 0.19) at coupled moment (120 kN.m), (0.29, 0.31 and 0.49) at coupled moment (240 kN.m) and (0.57, 0.63 and 1.03) at cou

In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

The results of theoretical and experimental investigations carried out to study the effect of load and relative sliding speed on the abrasive wear behavior in drilling bit teeth surfaces of an insert tungsten carbide bit have been presented. Experimentally, an apparatus for abrasive wear tests conducted on the modified ASTM-G65 was modified and fabricated to facilitate loading and measurement of wear rate for the sand/ steel wheel abrasion test, which involves two cases of contact; first is at dry sand and second is under wet condition. These tests have been carried under varied operating parameters of normal load and sliding speed. A theoretical model based upon the Archard equation has been developed for predicting wear simulation by u

In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. These matrices can be pre-calculated, stored and used repeatedly since they are independent of the image itself. We validated experimentally th