Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow photodetector. The technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal object. However, the implementation of temporal ghost imaging necessitates ultrafast detectors or modulators for measuring or pre-programming the probing intensity patterns, which are not available in all spectral regions especially in the mid-infrared range. Here, we demonstrate a frequency downconversion temporal ghost imaging scheme that enables to extend the operation regime to arbitrary wavelengths regions where fast modulators and detectors are not available. The approach modulates a signal with temporal intensity patterns in the near-infrared and transfers the patterns to an idler via difference-frequency generation in a nonlinear crystal at a wavelength where the temporal object can be retrieved. As a proof-of-concept, we demonstrate computational temporal ghost imaging in the mid-infrared with operating wavelength that can be tuned from 3.2 to 4.3 μm. The scheme is flexible and can be extended to other regimes. Our results introduce new possibilities for scan-free pump-probe imaging and the study of ultrafast dynamics in spectral regions where ultrafast modulation or detection is challenging such as the mid-infrared and THz regions.

In most optical fiber distributed acoustic sensing (DAS) systems, to obtain the desired outcome, the sensing signal acquired by DAS systems normally needs to be denoised. In some applications, such as the identifiation of the position of fast-moving targets, we need DAS systems to respond with sufficient speed. However, most classical denoising algorithms do not work if the signals are insufficiently collected within a short period (called a short-time signal). To obtain ideal results within a short time window, we propose an attention-based convolutional neural network (CNN) structure with extremely short signal windows to learn and approximate the results of classical denoising methods. To evaluate the effectiveness of the proposed method, the experiment is conducted under a real field highway scenario where the desired signals are overwhelmed with noise. The results show that by using signals collected within extremely short time windows of 100 ms, an insufficient time for the processing of existing denoising algorithms, our structure yields a satisfactory denoising performance.

To maintain pipeline transportation’s optimal efficiency and safety, pipelines must be regularly inspected and cleaned by the pipeline inspection gauge (PIG). Since the PIG passes through the whole pipeline, which is generally tens of kilometers, observing where the PIG is working in real-time is necessary. When the PIG passes through the pipe joint, the vibration signal will generate by the collision between the PIG and the girth weld, which can transmit along the fiber cable besides the pipeline. In this paper, we demonstrate that the optical fiber distributed acoustic sensing (DAS) can acquire the collision vibration in terms of the V pattern in the waterfall diagram, whose vertex is the collision position of PIG. The vertex will not exist when the cable positions are far away from the pipeline, which is relatively common in practice. We proposed an algorithm based on the clustering algorithm and the Hough transform to detect the position of the PIG even when the vertex of the V pattern does not exist. A field experiment shows that the proposed positioning algorithms applied to the DAS can effectively track the PIG online.

Optical fiber distributed acoustic sensing (DAS) instrument enables distributed acoustic measurements, garnering significant attention due to its high sensitivity, superior spatial resolution, and long sensing range. However, as one of the most widely adopted DAS technologies, phase-sensitive optical time-domain reflectometry (φ-OTDR) faces three key challenges in the phase demodulation of Rayleigh backscattering: 1) the original phase unwrapping algorithm cannot recover strong acoustic signals when the phase change between the two adjacent sample time exceeds π rad; 2) when the fading occurs, even if the phase change generated by a strong acoustic wave does not exceed π rad, φ-OTDR still frequently fails to recover stronger acoustic signals with high fidelity; 3) fading prevents φ-OTDR from recovering weak acoustic signals. This paper presents a novel approach to improve both the dynamic range and anti-fading performance of φ-OTDR by utilizing a conventional φ-OTDR structure and a convolutional neural network. We trained the model by establishing a mapping relationship between the wrapped phase with noise and the real phase of multi-channels. The trained model can improve both the dynamic range and anti-fading performance of φ-OTDR significantly, which has been demonstrated in 6.2 km fiber: the maximum measured strain achieved is increased from 81.1 nε to 365.9 nε, the self-noise is suppressed from -64 dB rad ² / Hz to -109 dB rad ² / Hz , and the dynamic range reaches 122.6 dB, which is 42.8 dB greater than that of original phase unwrapping algorithm.

Distributed Acoustic Sensing (DAS) instrument connected to dark fibers that widely deployed near roads can collect quasi-static strain signals generated by vehicles over a large range. This approach addresses the high deployment and maintenance costs and limited coverage of traditional roadside sensing technologies, making DAS a highly promising vehicle detection technology for intelligent transportation systems. However, using existing communication cables rather than specially laid sensing cables as the DAS sensing medium, while offering ultra-low deployment and maintenance cost advantages, poses significant challenges for detecting lightweight, low-speed vehicles that are far from the fiber cable. The quasi-static strain generated by such vehicles are low and easily overwhelmed by environmental noise and DAS fading noise. In this paper, we analyze the causes of weak vehicle quasi-static signals and propose a Unet image segmentation network, trained to recognize these weak signals using a large window with a small step size for data input. In a typical campus test scenario containing numerous lightweight low-speed vehicles, we tested various vehicle quasi-static signals using Unet. The results demonstrated that our method has high recognition accuracy and excellent resistance to DAS fading noise.