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.

The four-level integrative model methodology of structural and parametric synthesis of system objects

Abstract: The aim of the article is to present the methodology of four-level integrative models of system objects structural and parametric synthesis. Integrative models are based on the methods of knowledge engineering, morphological analysis and synthesis, optimization theory and decision-making. The proposed models are the optimization models with the structural and parametric control. They contain mechanisms for formal submission requirements, axiomatically given set of structural and parametric solutions, the methods of calculating the characteristics of the object, algorithms for finding rational solutions on a given structure-parameter set. Four-level integrative model can be the basis for distributed multi-agent computer-aided design system of structural and parametric synthesis.

VANET/ITS cybersecurity threats: Analysis, categorization and forecasting

Abstract: The authors analyze and systematize VANET/ITS cybersecurity threats and suggest new classification characteristics for them. Cybersecurity synergy for “hybrid communication mix”, which includes 802.11p and LTE, is considered and its high efficiency in long term is predicted.

Blockchain Models to Improve the Service Security on Board Communications

Abstract: The present article introduces a definition of Blockchain, describes services implemented by this technology and the field of its application. The authors identify the main technical characteristics and their features associated with data transmission through the network. This article provides the operation principles of Blockchain technology and a block structure. The authors determine the network scheme working with blockchain transactions and the dependence of network characteristics on application parameters. The security aspects of data transmission within Blockchain technology are analyzed as well. The types of network attacks and Blockchain devices are listed in the article. In addition, the authors propose a Blockchain traffic identification model that allows discrediting the existing security mechanisms of this technology and makes services vulnerable to these attacks. Furthermore, the article offers recommendations on hiding the Blockchain traffic profile, which will significantly complicate its identification on the data network.

Effect of Magnetic Field on Resonant Tunneling in 3D Waveguides of Variable Cross-Section

Abstract: Chapter 8 contains an asymptotic theory of resonant tunneling in a three-dimensional waveguide in magnetic field. The resonator is formed by two waveguide narrows. The electron energy is between the first and the second thresholds.

Implementation of Universal Neural Network Approximator on a ULP Microcontroller for Wavelet Synthesis in Electroencephalography

Abstract: The paper describes the implementation of universal neural network approximator on microcontroller MSP430G2553 with ultralow power consumption. A multilayer perceptron is used as an approximating artificial neural network. Approximation is one of the stages of building wavelet neural network models. The approximation of electroencephalogram fragments allows obtaining wavelets adapted for its continuous wavelet transform. Implementation of neural network approximator on this microcontroller is one of the stages to create a portable system for automated electroencephalogram analysis. Two ways of implementation a neural network approximator are presented in the paper. The first way is based on the experiment with the neural network approximator without its training. The second way involves the experiment based on the interaction with the neural network approximator with training algorithm. The studies of the approximation rate depending on the number of iterations and the core clock frequency of the microcontroller have been conducted.