Unnamed Aerial Vehicles (UAVs) are becoming increasingly popular and widely used for surveillance and reconnaissance. There are some recent studies regarding moving object detection, tracking, and classification from UAV videos. A unifying study, which also extends the application scope of such previous works and provides real-time results, is absent from the literature. This paper aims to fill this gap by presenting a framework that can robustly detect, track and classify multiple moving objects in real-time, using commercially available UAV systems and a common laptop computer. The framework can additionally deliver practical information about the detected objects, such as their coordinates and velocities. The performance of the proposed framework, which surpasses human capabilities for moving object detection, is reported and discussed.

We experimentally demonstrate quantum channel monitoring by wavelength-time multiplexing of classical wrapper bits with quantum payloads. Bit-error-rate measurements of 5 Gb/s classical bits infer the coincidence-to-accidental ratio of the quantum channel up to 13.3 dB.

We study self-phase modulation of sub-picosecond telecom wavelength pulses in partially periodically poled thin-film lithium niobate waveguides. We experimentally and computationally investigate the effect of phase mismatching on spectral broadening.

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