Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

Critical role of model organism selection in assessing weak urban electromagnetic field effects: Implications for human health

The impact of electromagnetic fields on human health has been investigated in recent years using various model organisms, yet the findings remain unclear. In our work, we examined the effect of less-explored, weak electromagnetic fields commonly found in the urban environments we inhabit. We studied different impacts of electromagnetic fields with a frequency of 50 Hz and a combination of 50 Hz and 150 Hz, on both yeasts (Saccharomyces cerevisiae) and human macrophages. We determined growth, survival, and protein composition (SDS-PAGE) (Saccharomyces cerevisiae) and morphology of macrophages (human monocytic cell line). In yeast, the sole observed change after 24 h of exposure was the extension of the exponential growth phase by 17 h. Conversely, macrophages exhibited morphological transformations from the anti-inflammatory to the pro-inflammatory type within just 2 h of exposure to the electromagnetic field. Our results suggest that effects of electromagnetic field largely depend on the model organism. The selection of an appropriate model organism proves essential for the study of the specific impacts of electromagnetic fields. The potential risk associated with the presence of pro-inflammatory M1 macrophages in everyday urban environments primarily arises from the continual promotion of inflammatory reactions within a healthy organism and deserves further investigation.

Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

This study presents a comprehensive approach to mapping local magnetic field anomalies with robustness to magnetic noise from an unmanned aerial vehicle (UAV). The UAV collects magnetic field measurements, which are used to generate a local magnetic field map through Gaussian process regression (GPR). The research identifies two categories of magnetic noise originating from the UAV's electronics, adversely affecting map precision. First, this paper delineates a zero-mean noise arising from high-frequency motor commands issued by the UAV's flight controller. To mitigate this noise, the study proposes adjusting a specific gain in the vehicle's PID controller. Next, our research reveals that the UAV generates a time-varying magnetic bias that fluctuates throughout experimental trials. To address this issue, a novel compromise mapping technique is introduced, enabling the map to learn these time-varying biases with data collected from multiple flights. The compromise map circumvents excessive computational demands without sacrificing mapping accuracy by constraining the number of prediction points used for regression. A comparative analysis of the magnetic field maps' accuracy and the spatial density of observations employed in map construction is then conducted. This examination serves as a guideline for best practices when designing trajectories for local magnetic field mapping. Furthermore, the study presents a novel consistency metric intended to determine whether predictions from a GPR magnetic field map should be retained or discarded during state estimation. Empirical evidence from over 120 flight tests substantiates the efficacy of the proposed methodologies. The data are made publicly accessible to facilitate future research endeavors.

Towards Fully Autonomous UAVs: A Survey

Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.