In this paper, introduce a proposed multi-level pseudo-random sequence generator (MLPN). Characterized by its flexibility in changing generated pseudo noise (PN) sequence according to a key between transmitter and receiver. Also, introduce derive of the mathematical model for the MLPN generator. This method is called multi-level because it uses more than PN sequence arranged as levels to generation the pseudo-random sequence. This work introduces a graphical method describe the data processing through MLPN generation. This MLPN sequence can be changed according to changing the key between transmitter and receiver. The MLPN provides different pseudo-random sequence lengths. This work provides the ability to implement MLPN practically

Most of us are tired of the circumstances that surround us because of their deficiency, deprivation, and sullenness, even though troubles and pains are the soil in which the seeds of strong personality  sprout.             

   The content of the research is summed up in the fact that there is no need to frown, so let our face be free, and our word good and tolerant, so that we can be the most loving people of those who give them, and the Messenger of God (may God’s prayers and peace be upon him and his family) was known to be the heaviest  concerns  of people, but he was most smiling of people, by smiling, we buy lives, so we should get used to it, because that gives us hope and finds the world in our hands, an

The industrial sector of the important drivers of economic activity sectors due to having the capabilities and the potential to qualify biggest contribution to the economic development of the country, which requires the availability of a higher degree of safety in industrial organizations, and the preservation of the lives of employees and workers in emergency situations, and that all the work is not without its dangers, which produces in a year of uncertainty in the possible events get in the future, therefore, reduce the dangers that could be exposed to industrial organizations and management are at the heart of the strategic direction which works to stimulate thinking about the future and respond to the changes that occur in theextern

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as com

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a s

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as