The Influence of Temporal Disturbances in EKF Calculations on the Achieved Parameters of Flight Control and Stabilization of UAVs.

This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone's attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system's behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances.

A General Scheme of a Branch-and-Bound Approach for the Sensor Selection Problem in Near-Field Broadband Beamforming.

This paper is devoted to the sensor selection problem. A broadband receiver beamforming working in a near-field is considered. The system response should be as close as possible to the desired one, which is optimized in the sense of L2 norm. The problem considered is at least NP-hard. Therefore, the branch-and-bound algorithm is developed to solve the problem. The proposed approach is universal and can be applied not only to microphone arrays but also to antenna arrays; that is, the methodology for the generation of consecutive solutions can be applied to different types of sensor selection problems. Next, for a larger microphone array, an efficient metaheuristic algorithm is constructed. The algorithm implemented is a hybrid genetic algorithm based on the ITÖ process. Numerical experiments show that the proposed approach can be successfully applied to the sensor selection problem.

Chemometric Evaluation of THz Spectral Similarity for the Selection of Early Drug Candidates.

In this paper we discuss the link between the domain of physical parameters - molecular descriptors of a drug, and terahertz (THz) spectra. We measured the derivatives of the well-known anti-inflammatory drug Piroxicam using THz spectroscopy and employed Principal Component Analysis to build similarity maps in the molecular descriptor and spectral domains. We observed, that the spatial neighborhood on the molecular descriptors map is highly correlated with the spectral neighbourhood within a group of structurally-similar molecules. We built a Partial Least Squares (PLS) predictive model to quantify the relationship between the spectra and the melting point, which can guide the selection of early drug candidates.