St. Petersburg State University of Telecommunications

About: St. Petersburg State University of Telecommunications is a education organization based out in Saint Petersburg, Russia. It is known for research contribution in the topics: Computer science & The Internet. The organization has 408 authors who have published 465 publications receiving 2160 citations.

Higher-order 3D Parabolic Equation for Radio Wave Propagation Modeling in a Street Canyon

Abstract: This paper addresses the radio wave propagation modeling in urban environment. Higher order rational approximations of the three-dimensional Helmholtz equation are applied. Finite-difference marching algorithm for propagation in unbounded street canyon is presented. Rational Pade approximations of the propagation operator provide a sufficiently efficient numerical solution and can be considered as a sequence of the Crank-Nikolson schemes. Utilizing the finite-difference scheme gives an opportunity to take various types of boundary conditions into account. Numerical examples are given.

Modeling of digital AGC with multi-signal impact and adaptation of the reference level

Abstract: The increasing role of digital radio receiving techniques and various adaptation techniques in modern radio systems has led to the appearance of variety of automatic gain control (AGC) systems used as systems of adaptation. AGC types classification and its development tendencies are considered. The paper investigates the influence of the value of the reference level of wideband digital radio receiver digital AGC on its behavior under multi-signal impact. The amplitude of the input signal effect on the value of the signal constant component is considered. The variant of AGC system design is suggested. Conclusions about the quantitative parameters and permissible changes of AGC reference level with an aim to increase performance speed are made.

Selective image authentication using shearlet coefficients tolerant to JPEG compression

Abstract: A novel selective image authentication system based on the robust digital watermarking is proposed The discrete shearlet transform is performed in order to extract the feature vector from the image The cone-adapted version of the transform is used to calculate the shearlet coefficients more precisely and to avoid the biased treatment The proposed approach allows to use conventional cryptographic digital signature for the image feature vector verification and makes the authentication scheme more secure In order to embed watermark (WM) into the image the areas HL3 and LH3 of the Haar wavelet transform coefficients are used Experimental results show that the proposed selective image authentication system is effective in terms of tolerance to JPEG compression, malicious image tampering detection and visual image quality just after embedding

Application of slope-intercept diagram to determine the parameters of the nanocomposite field emitters in-situ

Abstract: The method of the field emission data online processing of materials, perspective for a nanoelectronics, is developed. The method is based on the slope-intercept diagram analysis of the current voltage characteristics registered during the experiment. It allows to build calibration grids of the microscopic emission parameters, such as work function, effective emission area and field enhancement factor, and and to estimate changes in the sample structure online.

IoT Traffic Prediction with Neural Networks Learning Based on SDN Infrastructure

Abstract: In recent time, there are more achievements in technologies for 5G/MT-2020 networks the international research area have. Any way, one of the main important task in this area is the IoT traffic recognition and prediction. Currently, researchers face a new challenge to their experience, talents and desire to reach new heights in information and communication technologies. The new challenge is IMT-2030 networking technologies and services. In this case, the question with effective IoT traffic prediction methods is still relevant during transition to the next IMT-2030 network and services. IoT it is the ubiquitous conception, on which the new IMT-2030 services also based. For example, Tactile Internet, part of the solutions in digital avatars, and others. There more, these algorithms have to be more efficient and fastly in work with huge data capacity, which characterized the different services of Internet of Things. Recently, there are Machine Learning and Big Data algorithms are took this place of new algorithms for efficient and complex algorithms. In this paper, we implement IoT traffic prediction approaches using single step ahead and multi-step ahead prediction with NARX neural network. As a data we used the metadata of flows which were received through the northern interface. The prediction accuracy has been evaluated using three neural network traing algorithms: Traincgf, Traincgp, Trainlm, with MSE as performance function in term of using mean absolute percent of error (MAPE) as prediction accuracy measure IoT.