It is shown that the location of most of the epicenters of marsquakes, recorded during the SEIS experiment of the InSight mission, is located in the zones of extension and sufficiently large shear stresses resulting from the deviation of the planet from the state of hydrostatic equilibrium

The study discussed in this paper contributes to the extensive interdisciplinary research dedicated to developing a method for controlling robots using human bioelectrical signals. The focus of this research is on investigating the signals obtained through electroencephalography (EEG), assessing their representativeness, and exploring the potential for clustering these signals. A crucial aspect examined is the hypothesis that signals originating from different regions of the brain can be effectively separated and accurately identified. The paper presents the outcomes of an experiment involving the induction of stable visual evoked potentials in human subjects, followed by the creation of a practical database based on these findings. The proposed approach involves extracting significant features from the EEG signal through the application of machine learning methods. The efficiency and practicality of the computational data obtained are thoroughly evaluated. The primary conclusions are formulated, and the hypothesis that each brain channel exhibits distinct waves characteristic of specific brain regions is validated.

The article reviews algorithms used to determine the position and orientation of a wheeled robot based on the odometric data, inertial sensors, and a camera. The authors present mathematical models of robot navigation for a flat surface. The authors consider the problem solution to exemplify the position determining for a TurtleBot3 Burger mobile robot.

Large agro-industrial complexes are interested in deep automation of the yields control processes to reduce costs caused by errors or a shortage of qualified personnel. Existing approaches solve problems such as yield assessment or plant pathologies detection, but they cannot properly quantify the volume of plant biomass or the diseased area. One of the reasons for this limitation is the poor quality of masks of object instances formed in machine vision systems. This occurs because of Mask R-CNN architecture, which is usually used in the computer vision. In this paper, we propose an algorithms composition for obtaining accurate masks of objects in task of segmentation of tomato leaf instances in images collected in difficult conditions of industrial greenhouses. The use of Mask R-CNN combined with CascadePSP neural network algorithm increased the average IoU by 1.194% compared to “pure” Mask R-CNN on images with complex object-like background.

Stabilization of a chain of three integrators subject to the control and phase constraints is discussed. The paper continues our previous work reported at the Optima 2022 conference, where a control law based on two nested saturators was proposed and was proved that it globally stabilizes the system in the lack of control and phase constraints. In this work, to meet the phase and control constraints, we apply an advanced six-parameter feedback law in the form of three nested saturation functions. The number of the parameters reduces to twothe control resource and the desired exponential convergence rate near the originby turning to dimensionless variables and taking three coefficients from a one-parameter family. The selection of the parameters is discussed that optimizes the performance of the controller. We define an optimal controller as that that globally stabilizes the system while ensuring the greatest convergence rate. The phase portrait of the closed-loop system is shown to be very complicated. When the convergence rate exceeds an optimal value, a bifurcation occurs resulting in an infinite number of periodic motions the trajectories of which lie on the surface of a cylinder in the phase space. A lower bound of the optimal convergence rate is obtained analytically, and its approximate value is found numerically.