Remote chemical detection in the atmosphere or some specific space has always been of great interest in many applications for environmental protection and safety. Laser absorption spectroscopy (LAS) is a highly desirable technology, benefiting from high measurement sensitivity, improved spectral selectivity or resolution, fast response and capability of good spatial resolution, multi-species and standoff detection with a non-cooperative target. Numerous LAS-based standoff detection techniques have seen rapid development recently and are reviewed herein, including differential absorption LiDAR, tunable laser absorption spectroscopy, laser photoacoustic spectroscopy, dual comb spectroscopy, laser heterodyne radiometry and active coherent laser absorption spectroscopy. An update of the current status of these various methods is presented, covering their principles, system compositions, features, developments and applications for standoff chemical detection over the last decade. In addition, a performance comparison together with the challenges and opportunities analysis is presented that describes the broad LAS-based techniques within the framework of remote sensing research and their directions of development for meeting potential practical use.

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton’s principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.

This study explores the flow characteristics of liquid water and liquid metals in closed channels with different roughness by using a modified roughness–viscosity model (RVM). The RVM model explains the influence of surface roughness on laminar flow by incorporating roughness and viscosity. This study proposes that the local Reynolds number should be used instead of the overall Reynolds number. This modified model in the flow of liquid water with different rough enclosures is studied by numerical simulation and then verified by experiments. Moreover, the verification model is used to explore the flow of conductive liquid metal in closed channels with different roughness under a magnetic field. According to the results of the study: (1) In the radial direction of the closed channel, the larger the radius was, the greater the change in the flow rate due to surface roughness. (2) As the flow direction through a closed channel changed, the larger the relative surface roughness was, the smaller the frictional resistance. (3) In a vertical magnetic field, the effect of surface roughness first weakened and then strengthened as the strength of the magnetic field increased. As the latter increased in a horizontal magnetic field, the effect of surface roughness decreased. This verified modified RVM model provides ideas and methods for research on laminar roughness in micro-closed channels in the future.

UAV image restoration technology is widely used for rescue work in extreme vibration environments. In this paper, an image restoration method based on an inertial measurement unit (IMU) is proposed to effectively overcome the image blurring problem caused by the specific motion of the UAV. The technique utilizes IMU data to analyze the attitude and vibration information of the UAV, calculates the point spread function of the blurred image, and combines it with the all-variable regularization method to correct the image blurring problem. Experiments show that this method improves 7.32% over the blind source recovery results in recovering two-axis rotational blurred images. It also avoids the image misfit problem due to specific motion and improves computational efficiency. In addition, we discuss the application of this method in vibration environments and the potential of dynamic carrier image processing.