Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.

In this letter, the impact of the non-linearity of the propagation medium is studied in fluid antennas systems (FAS) systems. First and second order statistics are deduced for FAS under α-μ fading channels. Based on this and in order to evaluate the performance of the mentioned systems, expressions are also deduced for the outage probability (OP) and ergodic channel capacity. Furthermore, an exact reduction of the OP due to N-th port for an (N-1)-port is also presented. All the expressions emerging from this study are entirely new, marking a significant contribution to the existing body of knowledge. Several curves are shown under different values for the system parameters and channel non-linearity. This is the first work in which the effect of the channel non-linearity is evidenced in a FAS.

In this article, it is provided a comprehensive survey on the most relevant research in the area of detection, classification and location of partial discharge (PD) sources using the radiometric or ultra high frequency (UHF) method, focusing mainly on the works related to the use of antennas. To the best of the authors knowledge, this is the first review paper that focuses on the application of antennas based on UHF method in the complete diagnosis of PD. Specifically, it is first presented the main contributions to the development and optimization of UHF sensors for application in the detection of PD sources. Second, articles that used the UHF method to identify different types of PD in high voltage equipment are described. Finally, the main contributions regarding the location of sources of defects using the radiometric method are presented. Summary of works in the area of UHF detection sensors, PD classification and PD location, with their respective contributions, are shown. Finally, research trends, challenging and open issues are discussed. The purpose of this article is to provide important information about the complete diagnosis of PD, which could be used to guide the development of more efficient techniques in this research area.

The degradation level of high voltage equipment insulation systems can be estimated from the monitoring of partial discharge (PD) activity. The radiometric method stands out as one of the most promising methods for monitoring these discharges, however, in substations, the PD signals may be susceptible to external interference and, in some cases, hide by background noise. This work presents the application of the stationary discrete wavelet transform (SDWT) over PD signals measured by bio-inspired printed monopole antennas (PMA) operating at the UHF frequency range (300-3000 MHz). In addition to the filtering from the SDWT, the use of the SDWT scaling coefficients energy is evaluated as a support tool for the location of PD. The PD signals are investigated under low signal-to-noise ratio (SNR) and; high SNR with a high pulse repetition rate. These signals are obtained from a laboratory setup composed by a discharge generating oil cell, and acquired through a bio-inspired PMA. After the application of the SDWT over the evaluated signals, together with the energy calculation using different mother wavelets, it is verified that the proposed methodology provides an efficient filtering result, being appropriate for PD detection. From the energy of SDWT scaling coefficients, it is possible to highlight the constant delays between the detection of consecutive pulses, minimally interfering over the time differences of arrival and providing better location results.

In this paper, the recently proposed doubly inverted Gamma-Gamma (IGGG) turbulence channel model is considered to analyze the performance of a free-space optical communication system. To this end, the probability density function (PDF) and cumulative distribution function (CDF) of the irradiance fluctuations of an optical wave propagating through an IGGG turbulence channel under nonzero-boresight pointing errors are derived. Furthermore, the PDF and CDF under zero-boresight pointing errors are obtained as a special case. To study the impact of nonzero-boresight pointing error parameters and turbulence condition on the system performance, exact closed-form expressions for outage probability, bit error rate, and average capacity under intensity modulation/direct detection are derived. Besides, asymptotic analysis is provided which help us to identify the diversity gain of the system. The analytical analyzes are verified through numerical and Monte-Carlo simulation results.