Noises degrade image quality which causes information losing and unsatisfying visual effects. Salt and Pepper noise is one of the most popular noises that affect image quality. In RGB color image Salt and pepper noise changes the number of occurrences of colors combination depending on the noise ratio. Many methods have been proposed to eliminate Salt and Pepper noise from color image with minimum loss of information. In this paper we will investigate the effects of adding salt and pepper noise to RGB color image, the experimental noise ratio will be calculated and the color combination with maximum and minimum numbers of occurrence will be calculated and detected in RGB color image. In addition this paper proposed a methodology of salt and pepper noise elimination for color images using median filter providing the reconstruction of an image in order to accept result with minimum loss of information. The proposed methodology is to be implemented, tested and experimental results will be analyzed using the calculated values of RMSE and PSNR.

The main purpose of mathematical model building while employing statistical data analysis is to obtain high accuracy of approximation within the range of observed data and sufficient predictive properties. One of the methods for creating mathematical models is to use the techniques of regression analysis. Regression analysis usually applies single polynomial functions of higher order as approximating curves. Such an approach provides high accuracy; however, in many cases, it does not match the geometrical structure of the observed data, which results in unsatisfactory predictive properties. Another approach is associated with the use of segmented functions as approximating curves. Such an approach has the problem of estimating the coordinates of the breakpoint between adjacent segments. This article proposes a new method for determining abscissas of the breakpoint for segmented regression, minimizing the standard deviation based on multidimensional paraboloid usage. The proposed method is explained by calculation examples obtained using statistical simulation and real data observation.

Steganography is the technique of hiding secret data (message) within any media such as digital color image. In this paper we will merge steganography process with cryptography process in order to increase the security of the proposed method. The steganography process will based on LSB method, while the cryptography process will based on generating a huge private key and selecting a special function for encryption decryption. The proposed method will be implemented in order to calculate some performance parameters to prove the efficiency of the proposed method.

The growth of society digitalization increases the requirements for data transmission productivity of the information telecommunication networks. Traffic in these networks is self-similar. For its modeling a number of fractal traffic imitation models were developed. However, the modern studies show that the traffic characteristics may change in the wide networks and be dependent of big number of parameters and settings of real networks, protocol and transmitted information’s characteristics, and users’ behavior. So the main aim of the paper was an investigation of modern traffic models to choose the best of them for modern computer networks with different characteristics traffic flow modeling and creation of computer models for generating self-similar traffic in MathCAD. It allows to investigate state of computer networks with different traffic flows conditions, behavior of connected network devices, queue, processing order as good for traditional computer networks and software defined networks with the help of wide-spread MathCAD software. Copyright © 2017 Praise Worthy Prize - All rights reserved.

The current level of development of the cellular services market, qualitative changes in the means and methods of providing services, and the increased volume and diversity of information circulating in cellular networks have evoked the need for service quality assessment system improvement. To maintain competitiveness, the main efforts of operators are aimed at improving quality and increasing the service life of subscribers in the network by ensuring the required level of customer satisfaction in high-quality services through a system of organizational–technical and socioeconomic measures to bring the achieved level of quality-of-service (QoS) provision in accordance with the existing, emerging, or projected needs of subscribers. To achieve improved QoS provision, a functional relationship between network parameters has been established: The impact of key performance indicators on a key quality indicator through the use of cubic Hermitian splines (CHS) has been determined. The use of splines, as a signal model, can significantly improve the quality of signal processing due to the continuity of values and partial derivatives in the joints of spline gluing. CHS are characterized by calculation simplicity, as they provide high-speed computing, which, in turn, is important for real-time work when processing large data sets. Experimentally, it was shown that the use of splines allows for the ability to calculate statistical estimates of the required parameters of spline approximations, but also their confidence intervals, which increases the accuracy and probability of further calculations and is a distinct advantage of this approach. Also, the methods of service quality management through machine learning have been improved, which are effective tools for modeling integrated indicators of communication services quality control, monitoring their condition in terms of final copies of services, determining the causes of degradation, and reporting. It monitors the service performance provided by the cellular operator. Finally, the method checks the readiness and availability of services, detects network nodes through which quality degradation occurs, collects various quality metrics, and compares them with pre-installed quality indicators.