Structural prediction of protein-protein complexes has important application in such domains as modeling of biological processes and drug design. Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. Experimental techniques to identify the structure of protein complex require enormous amount of time and resources, and each method has its own limitations. Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. Building on the idea of this approach, we developed an algorithm to model the 3D structure of homodimer based on deep learning. It consists of two major steps: at the first step a protein complex contact map is predicted with the deep convolutional neural network, and the second stage is used to predict 3D structure of homodimer based on obtained contact map and optimization procedure. The use of the neural network in combination with optimization procedure based on gradient descent method allowed to predict structures for protein homodimers. The suggested approach was tested and validated on a dataset of protein homodimers from Protein Data Bank (PDB). The developed procedure could be also used for evaluating protein homodimer models as one of the stages in drug compounds developing.

The relevance of testing of memory devices of modern computing systems is shown. The methods and algorithms for implementing test procedures based on classical March tests are analyzed. Multiple March tests are highlighted to detect complex pattern-sensitive memory faults. To detect them, the necessary condition that test procedures must satisfy to deal complex faults, is substantiated. This condition is in the formation of a pseudo-exhaustive test for a given number of arbitrary memory cells. We study the effectiveness of single and double application of tests like MATS ++, March C– and March A, and also give its analytical estimates for a different number of k ≤ 10 memory cells participating in a malfunction. The applicability of the mathematical model of the combinatorial problem of the coupon collector for describing multiple memory testing is substantiated. The values of the average, minimum, and maximum multiplicity of multiple tests are presented to provide an exhaustive set of binary combinations for a given number of arbitrary memory cells. The validity of analytical estimates is experimentally shown and the high efficiency of the formation of a pseudo-exhaustive coverage by tests of the March A type is confirmed.

Simpler than known structure of the system with error correction in calculations is proposed based on duplication and triplication of blocks with majority principle of choosing the values of signals. It is advisable to use the new fault-tolerant structure for automation devices with combinational logic. In fault-tolerant structure synthesis, the parity method is used to establish the fact of a fault in the main logic unit and the logical complement method is used determine incorrectly calculated output functions and to generate signals for their correction. The method also allows to adjust the values of incorrectly calculated functions. Structural diagram and description of error correction system are given. The synthesis algorithm of control equipment is described with minimization of the technical implementation complexity. The experiment results with control combinational circuits are given, confirming the high efficiency of proposed system structure with error correction.

The possibility of control of a guided supersonic aviation unmanned interceptor on a moving target in an autonomous airborne multi-position radar station has been investigated. To obtain the coordinate information, the algorithm of difference-range finding and difference range-Doppler spatial measurements is selected using a limited number of transmitting positions that do not provide an unambiguous determination of the coordinates and speed of the object with high accuracy. The paper proposes various approaches to eliminate a priori uncertainty regarding the estimated coordinates in a limited set of measuring radio engineering positions.

An analytical method is considered for constructing an optimal state control device for a nonlinear multidimensional and multiply connected dynamic object, taking into account the constraints, which allows one to obtain finite computational relations in a closed algebraic form. The control device was synthesized using measurements in the Cartesian and spherical coordinate systems, several varieties of the combined method of pointing a controlled object to an instant meeting point were obtained. The given guidance algorithms are a generalization of the method of proportional navigation widely used in practice and its modifications. A distinctive feature of the synthesized algorithms is the natural accounting for the nonlinear, multidimensional, and multiply connected structure of the control object, as well as the parameters of unsteady perturbations (acceleration of gravity, projections of the longitudinal acceleration of the object and acceleration of the target’s maneuver) that act in the guidance process.

The unified kinematic differential equations that describe the dynamics of the control object, are obtained, and they can be used to synthesize a controller that operates according to switched or smoothly matched non-stationary optimality criteria (target functions, target integral manifolds). The developed algorithms can be used in the design of autonomous homing and telecontrol systems, implemented in hardware and software both on board an unmanned aerial or artillery interceptor, and as a part of autonomous multiposition airborne radar stations.

Time-management refers to planning the available time in correspondence with personal goals and preferences of the employee. The criterion for efficient time-management lies in achieving definite goals in minimum possible time. This paper presents a short review of the literature on time-management. It is shown how scheduling theory may be used for optimal planning in time-management. The problem of minimizing the total (average) weighted completion time of the planned jobs by an employee is considered provided that only lower and upper bounds of the possible processing time of each job are known before scheduling. Software for constructing a permutation of the given jobs with the largest relative semi-perimeter of the optimality parallelepiped has been developed. Computational experiment on the computer showed the effectiveness of the developed algorithms for constructing schedules for time-management.

The method for solving the boundary-value problem of penetration of monochromatic electromagnetic fields with axial symmetry through the plane screen made from the permalloy is developed. The boundary-value problem is based on the use of differential Maxwell equations and complementary nonlinear differential equation for the field of magnetization, characterizing the permalloy. Classical boundary conditions of continuity of the tangential components of the fields and complementary boundary conditions for the field of magnetization on the face surfaces of the shield are used. For solution simplification of the boundary-value problem as a result of exclusion value the entering in nonlinear equation second-order infinitesimal, nonlinear task is transformed into linear task. Roots (wave numbers) of a dispersion algebraic equations of four order, which characterizing electromagnetic fields with axial symmetry in layer made from the permalloy, is constructed. The sequences of four forward and four backward counter-propagating electromagnetic waves with axial symmetry in the layer of permalloy is formed. Two-sided boundary conditions connecting electromagnetic fields with axial symmetry on both sides of the screen is constructed. The amplitudes of reflected and transient through the shield electromagnetic fields are calculated.

In the paper the algorithm for object detection in high resolution images is proposed. The approach uses multiscale image representation followed by block processing with the overlapping value. For each block the object detection with convolutional neural network was performed. Number of pyramid layers is limited by the Convolutional Neural Network layer size and input image resolution. Overlapping blocks splitting to improve the classification and detection accuracy is performed on each layer of pyramid except the highest one. Detected areas are merged into one if they have high overlapping value and the same class. Experimental results for the algorithm are presented in the paper.

An autonomous visual navigation algorithm is considered, designed for “home“ return of unmanned aerial vehicle (UAV) equipped with on-board video camera and on-board computer, out of GPS and GLONASS navigation signals. The proposed algorithm is similar to the well-known visual navigation algorithms such as V-SLAM (simultaneous localization and mapping) and visual odometry, however, it differs in separate implementation of mapping and localization processes. It calculates the geographical coordinates of the features on the frames taken by on-board video camera during the flight from the start point until the moment of GPS and GLONASS signals loss. After the loss of the signal the return mission is launched, which provides estimation of the position of UAV relatively the map created by previously found features. Proposed approach does not require such complex calculations as V-SLAM and does not accumulate errors over time, in contrast to visual odometry and traditional methods of inertial navigation. The algorithm was implemented and tested with use of DJI Phantom 3 Pro quadcopter.

This paper is focused on the field of the skeletonization of the binary image. Skeletonization makes it possible to represent a binary image in the form of many thin lines, the relative position, sizes and shape of which adequately describe the size, shape and orientation in space of the corresponding image areas. Skeletonization has many variety methods. Iterative parallel algorithms provide high quality skeletons. They can be implemented using one or more sub-iterations. In each iteration, redundant pixels, the neighborhoods of which meet certain conditions, are removed layer by layer along the contour and finally they leave only the skeleton. Many one-sub-iterations algorithms are characterized by a breakdown in connectivity and the formation of excess skeleton fragments. The highest-quality skeletons are formed by the well-known single-iteration OPTA algorithm, which based on 18 binary masks, but it is sensitive to contour noise and has a high computational complexity. The Zhang and Suen two-iteration algorithm (ZS), which is based on 6 logical conditions, is widely used due to its relative simplicity. But it suffers from the problem of the blurs of the diagonal lines with a thickness of 2 pixels and the lost of the square which size is 2×2 pixels. Besides, both algorithms mentioned above do not achieve the unit pixel thickness of the skeleton lines (many non-node pixels have more than two neighbors). Mathematical model and OPCA (One-Pass Combination Algorithm) algorithm which is based on a combination and simplification of single-iterative OPTA and two-iterative ZS are proposed for constructing extremely thin bound skeletons of binary images with low computational complexity. These model and algorithm also made it possible to accelerate the speed of skeletonization, to enhance recoverability of the original image on the skeleton and to reduce the redundancy of the bonds of the skeleton elements.

The paper investigates the problem of voice activity detection from a noisy sound signal. An extremely compact convolutional neural network is proposed. The model has only 385 trainable parameters. Proposed model doesn’t require a lot of computational resources that allows to use it as part of the “internet of things” concept for compact low power devices. At the same time the model provides state of the art results in voice activity detection in terms of detection accuracy. The properties of the model are achieved by using a special convolutional layer that considers the harmonic structure of vocal speech. This layer also eliminates redundancy of the model because it has invariance to changes of fundamental frequency. The model performance is evaluated in various noise conditions with different signal-to-noise ratios. The results show that the proposed model provides higher accuracy compared to voice activity detection model from the WebRTC framework by Google.