In this paper, we designed a pint-sized underwater pulsed lidar system for underwater obstacles detection based on a 532nm Nd-YAG pulsed laser as a source and a Hamamatsu photomultiplier tube (PMT) as a detector. In order to acquire the location of the obstacles, an algorithm was devised to handle the echo signal. Through this algorithm, the background noise was suppressed and the accurate range information of the target was obtained. A high-capacity lithium battery was employed to support this lidar system operating as long as eight hours continuously. To ensure our lidar system working steady in the natural underwater environment, a stable waterproof housing was designed for the system which has good water-tightness at 40m depth underwater. This system is small, compact and hand-held. An experiment was conducted in laboratory which proof that the system can achieve target detection within 25m. At last, this lidar system was tested in natural underwater environment of Fuxian Lake in Yunnan Province. There are lots of organic particles and other impurity particles in Fuxian Lake and the attenuation coefficient of the lake is about 0.67m<sup>-1</sup>. The results showed that this small-size lidar system was able to catch sight of the target within 20 meters and perform smoothly in the natural underwater environment.
Small bubbles are widely present in the marine environment, the presence of small bubbles generated by whitecaps, microorganisms, ships sailing will greatly affect the optical properties of seawater. A lot of work has been carried out around the detection of small bubbles, this article will introduce a method of detecting small bubbles underwater with the way of high-speed imaging underwater. The optical mechanisms to measure the parameters of small bubbles are mainly high-speed photography, laser interferometry and holography. The advantages of high-speed photography are intuitive and low cost, the experimenters can real-time monitor the shooting circumstances, and can obtain more detailed parameters concerning the small bubbles. The paper also discusses an experimental method of high-speed imaging for small bubbles in water, that is to get a cooperative target in the back of the bubbles, then shoot the bubbles, and a lot of experiments with the two methods have been done. In order to compare the imaging quality of the two sets of experiments intuitively, the histograms and the results of edge detecting of the pictures have been given. After compared the results, it is found that the images are clearer and higher in contrast in the case of there is a cooperative target behind the bubbles, and with the imaging rate of the high-speed camera increases, the image quality is significantly reduced.
A typical modulated pulse bathymetric lidar system is investigated by simulation using a modulated pulse lidar simulation system. In the simulation, the return signal is generated by Monte Carlo method with modulated pulse propagation model and processed by mathematical tools like cross-correlation and digital filter. Computer simulation results incorporating the modulation detection scheme reveal a significant suppression of the water backscattering signal and corresponding target contrast enhancement. More simulation experiments are performed with various modulation and reception variables to investigate the effect of them on the bathymetric system performance.
Multiangle dynamic light scattering (MDLS) compensates for the low information in a single-angle dynamic light scattering (DLS) measurement by combining the light intensity autocorrelation functions from a number of measurement angles. Reliable estimation of PSD from MDLS measurements requires accurate determination of the weighting coefficients and an appropriate inversion method. We propose the Recursion Nonnegative Phillips-Twomey (RNNPT) algorithm, which is insensitive to the noise of correlation function data, for PSD reconstruction from MDLS measurements. The procedure includes two main steps: 1) the calculation of the weighting coefficients by the recursion method, and 2) the PSD estimation through the RNNPT algorithm. And we obtained suitable regularization parameters for the algorithm by using MR-L-curve since the overall computational cost of this method is sensibly less than that of the L-curve for large problems. Furthermore, convergence behavior of the MR-L-curve method is in general superior to that of the L-curve method and the error of MR-L-curve method is monotone decreasing. First, the method was evaluated on simulated unimodal lognormal PSDs and multimodal lognormal PSDs. For comparison, reconstruction results got by a classical regularization method were included. Then, to further study the stability and sensitivity of the proposed method, all examples were analyzed using correlation function data with different levels of noise. The simulated results proved that RNNPT method yields more accurate results in the determination of PSDs from MDLS than those obtained with the classical regulation method for both unimodal and multimodal PSDs.