Performing the Fourier analysis of the harmonic composition of oscillations is an effective approach to the study of vibration processes in machines and mechanisms. This method of express control remains relevant and highly demanded in the acoustic diagnostics of machines due to its relatively high informativeness. The expansion of its analytical capabilities is implemented by introducing additional means for digital processing of vibration signals, including smart systems. The objective of this work consists in creating a digital method for spectral analysis of vibration signals, the implementation of which would be possible with the use of smart devices. This work presents a method of polyharmonic approximation of the parameters of the signals reflecting oscillatory processes in machines based on the additive combinations of sinusoidal functions. The technology of truncation of the time discretised decomposition of the above signals into a Fourier series is implemented using the Rayleigh theorem and the Lyapunov – Parseval equality. The distortion of the frequency spectrum of oscillations is eliminated by suppressing its side lobes using window filters. To compare the efficiency of different windows, the following special parameters are used: width of the main lobe of the frequency spectrum; coefficient of relative expansion of the main lobes; maximum relative level of side lobes; coefficient of relative attenuation of signal energy. In so doing, the windows of Hanning, Nuttall, Hamming, Barlett were considered, and the parameters of a rectangular window were taken as basic values. The limitations of the digit capacity of analogto-digital signal converters depending on the required accuracy of maintaining the energy balance when truncating the discrete Fourier expansions have been determined. An example of implementation of the digital technology of spectral analysis of the parameters of oscillatory processes is given.

An algorithm for detecting lakes in a point cloud of a lidar image based on a three-dimensional convolutional neural network is proposed. The contours of the lakes were extracted from the point clouds of the lidar image and their geometric characteristics were determined using the chain code algorithm. The accuracy of the proposed algorithm for identifying lakes from clouds of laser scanning points was 96.34%. The proposed algorithm can calculate and analyze information about the shape of lakes.

This article is devoted to the development of a method for detecting forgery of handwritten signatures. The signature still remains one of the most common methods of identification. The signature on financial and other documents can be forged, so detecting forgery is an urgent task. This is the task of binary classification: to determine whether the signature is genuine or fake.
The article describes the results of recognition of handwritten signatures made on paper. A database of handwritten signatures of 10 people was used for experiments. For each person, 10 genuine and 10 forgery signatures made by other people were collected. The signatures were digitized as color images with a resolution of 850×550 pixels. Then a binary representation of each signature was formed. Three variants of reducing signatures to sizes were used for classification: 128×128, 256×256 and 512×512 pixels. These images served as the source data for the convolutional neural network.
As a result of testing the proposed approach, the average accuracy of the correct classification was achieved on medium-sized images and is equal to 93.33%.

This paper presents a technique for obtaining of the main statistical characteristics of the distance estimation between the flying object and a hyperbolic navigation system using two Doppler frequency shifts of the navigation signal. This problem arises when the guidance of the object at a target point along a hyperbola using a bistatic hyperbolic navigation system is considered. Despite the fact that the object guidance loop is closed according to the time difference of arrival information, it is necessary to enter the distance between the object and the center of the navigation system into the algorithm for generating control commands in order to stabilize the frequency response of the control loop and eliminate dynamic errors. This article discusses the statistical characteristics of the estimated range based on the measurements of three values of the object’s speed: its own speed and two projections of this speed on the lines connecting the object with the navigation positions.

improved combined (aerodynamic and gas-dynamic) method of creating control forces and moments.

The objectives of the study are to analyze the basic model of the combined control method and the possibility of using an improved model of the combined control method, at which the invariability of the mass and dimensional characteristics of the upgraded control object will be achieved with a simultaneous increase in the target quality (reduction of the final miss).

Research is based on the use of an improved method for creating control forces and moments, where the optimization of mass characteristics is made, for using of a smaller number of micropulse motors (only at the stages of acceleration and braking), and when setting the balancing position (maintaining a steady angle of attack), instead of impulse engines, a steering drive is used standard onboard stabilization system of an unmanned aerial vehicle aerodynamic control channel. Which ultimately made it possible to preserve the invariability of the mass characteristics of the aircraft being upgraded and to increase the accuracy of its guidance.

The results of experimental mathematical modeling confirm the effectiveness of the proposed synthesis.

The subject of research is the development and implementation of Internet of Things (IoT) network structures for monitoring and analyzing audio information based on Raspberry microprocessor (MP) and an Arduino controller. The purpose of the article is to detail the process of developing an IoT based audio information monitoring network and evaluate the results. The authors have developed two variants of IoT structures for monitoring and analyzing audio and voice information. The IoT network includes a sound sensor (microphone), a unit for analyzing the information received from it and a decision-making module. A diagram of the first IoT structure for assessing the sound level based on the MP and controller is given.

The algorithm of IoT network functioning for the analysis of voice information is detailed. It includes receiving information from the microphone, transmitting this information to the MP, processing it according to certain rules, forming a solution by the controller and issuing recommendations to a user. The algorithm is implemented in the IoT network, which includes a microphone, Raspberry MP, Arduino controller, software, applications for the operator.

A prototype of the IoT network was created for the analysis of voice information and experiments were conducted to test its functioning. The audio recognizer was trained using various audio samples. The voice sound analysis system was tested in four scenarios: with a large and small amount of background noise, loud and quiet voice. Analysis of the results of the experiment showed that the voice sound analysis system works better when the voice is loud, as well as in a place where the situation is with minimal background noise.

The allocation of experts to programmer teams, which meet constraints on professional competences related to programming technologies, languages and tools an IT project specifies is a hard combinatorial problem. This paper solves the problem of forming the maximum number of teams whose experts meet all the constraints within each team. It develops and compares two algorithms: a heuristic greedy and exact optimal. The greedy algorithm iteratively solves the set cover problem on a matrix of expert competences until can create the next workable team of remaining experts. The paper proves that the allocation greedy algorithm is not accurate even if the set cover algorithm is exact. We call the allocation algorithm as double greedy if the set cover algorithm is greedy. The exact algorithm we propose finds optimal solution in three steps: generating a set of all non-redundant teams, producing a graph of team’s independency, and searching for a maximum clique in the graph. The algorithm of generating the non-redundant teams traverses a search tree constructed in such a way as to guarantee the creation of all non-redundant teams and absorbing all redundant teams. The edges of the non-redundant team independency graph connect teams that have no common expert. The maximum clique search algorithm we propose accounts for the problem and graph features. Experimental results show that the exact algorithm is a reference one, and the double-greedy algorithm is very fast and can yield suboptimal solutions for large-size allocation problems.

A mathematical learning model based on control theory in the form of an inhomogeneous linear differential equation is proposed. Analytical formulas and graphs for optimal program control and optimal trajectory are obtained from the principle of the minimum of the Hamiltonian for autonomous systems.