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.
Unmanned aerial vehicles (UAVs) play important role in human life. Today there is a high rate of technology development in the field of unmanned aerial vehicles production. Along with the growing popularity of the private UAVs, the threat of using drones for terrorist attacks and other illegal purposes is also significantly increasing. In this case the UAVs detection and tracking in city conditions are very important. In this paper we consider the possibility of detecting drones from a video image. The work compares the effectiveness of fast neural networks YOLO v.3, YOLO v.3-SPP and YOLO v.4. The experimental tests showed the effectiveness of using the YOLO v.4 neural network for real-time UAVs detection without significant quality losses. To estimate the detection range, a calculation of the projection target points in different ranges was performed. The experimental tests showed possibility to detect UAVs size of 0.3 m at a distance about 1 km with Precision more than 90 %.
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