In this paper, we consider an integrated optical system designed to analyze the composition of ambient air. The optical system consists of a dynamic Michelson interferometer and a multi-pass White gas cell. We use steady state IR radiation source, that is integrated in the system. IR Radiation is modulated by passing through the interferometer, and then enters a multi-pass gas cell. We use two MCT TE cooled photodetector as reference and signal detectors. To digitize registered signal, we use a 24-bit ADC. For the described system, the sensitivity limit is about ppb levels, that allows detecting volatile substances at the maximum permissible concentrations. Described FTIR spectroscopy systems can be used for ambient air analysis and for breathomics applications.
The paper presents a method for optical detection drones using the YOLO v4 neural network. Recognition performs simultaneously in the visible (Vis) and long-wave infrared (LWIR) ranges. The results of UAV detection on various types of urban background environment at day and night conditions, as well as at different distances from cameras, are presented. An algorithm for detecting of unmanned vehicles in the video cameras field of view of the Vis and LWIR ranges is described. This algorithm takes as input the outputs of two neural networks that recognize the drone in two ranges and estimates the probability of detection. Its shown that the YOLO v4 neural network recognizes unmanned objects on various background substrates with a minimum temperature difference of 0.4 degrees on the NEC 2640 thermal imager.
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