Adrien P. Genoud

TL;DR: This paper reports on the remote characterization of flying mosquitoes using a continuous-wave infrared optical remote sensing system and discusses the limitations of using wing beat frequency alone to identify insect species.

TL;DR: In this article, the authors compared different machine learning algorithms for the identification of mosquitoes through optical signals, based on predictor variables derived from the wing beat frequency and optical cross section of mosquitoes, these algorithms were trained to perform different classification tasks.

TL;DR: It is demonstrated that the gravidity of female mosquitoes can be identified from changes in their spectral and polarimetric backscatter cross sections, which allows for the identification of gravid females with a precision and recall of 86% and 87%, respectively.

TL;DR: Backscattering properties and hemispherical reflectance of topographic targets are measured in the visible, near-inf infrared, and mid-infrared spectral ranges and it is shown that these results are useful for optimizing active stand-off detection and hard-target lidars and for increasing their overall efficiency.

TL;DR: This work proposes a methodology to convert transit counts into an absolute insect population density, expressed in number of flying insects per meter cube, and offers an empirical solution to describe the relationship between the population density temporal resolution and its uncertainty.