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1.
Opt Express ; 32(7): 12656-12666, 2024 Mar 25.
Artículo en Inglés | MEDLINE | ID: mdl-38571083

RESUMEN

Ghost imaging (GI) requires each echo from the object being correctly matched with the corresponding illuminiation pattern. We proposed a way for such matching with no physical synchronization towards bistatic configuration. The illumination is dually encoded in spatial and time domain. With aperiodic waveform and progressive correlation, the echoes can be correctly located and images can be obtained. In the experiments, our scheme is verified under different levels of signal to noise ratios, as well as different intensity of crosstalk. Ghost imaging with two transmitters and one receiver is also demonstrated. With our method, it is also possible to improve the imaging speed with multiple sources.

2.
Appl Spectrosc ; 78(1): 76-83, 2024 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-37956465

RESUMEN

A standoff methane (CH4) sensor with actual hard topographic targets (usually called non-cooperative targets) is essential for natural gas pipeline leakage inspection and many other practical applications. To address this requirement, a miniaturized and low-power-consumption gas sensor was developed based on tunable diode laser absorption spectroscopy for standoff CH4 detection with a non-cooperative target. Wavelength modulation spectroscopy with a 1f normalized 2f detection method was employed for calibration-free CH4 measurement. A Kalman filter algorithm was used to improve the precision of the detection. The performance of the standoff CH4 sensor was evaluated comprehensively under various conditions, including different incident angles, different hard topographic targets, and different standoff distances. The results show that the measurement precision is 0.107% and the sensitivity is 4.08 parts per million per meter (ppm·m) with a time resolution of 1 s and a standoff distance of 40 m. The detection limit can achieve 1.24 ppm·m at an optimal integration time of 70 s. This sensor can be easily integrated into mobile platforms, which lays the foundation for intelligent leak inspection.

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