This study is focused on numerical simulation analysis and experimental study regarding the influence of backscattering characteristics of objects for long-range laser voice acquisition. Based on theoretical analysis, three parameters, including surface roughness of an object, incident angle, and refractive index, which will influence the performance of remote scattered reflection laser interference voice acquisition, are investigated and analyzed. After analysis and simulation, an experimental system is set up to demonstrate the influence of backscattering characteristics of an object. The results show that the restored amplitude of a voice signal decreases gradually, corresponding to an increase of surface roughness from 0.4 to 12.5 μm; the incident angle of the measured laser shall reside between 57.32 deg (1 rad) and $-57.32\text{deg}$ ($-1\mathrm{rad}$); the optimal incident angle is 0 deg for all kinds of objects; and the metal object is a better choice of material selection. In addition, the restored amplitude of a voice signal rises with the attenuation coefficient of metal increasing. It also increases with the refractive index for a nonmetallic object. Comparing a metal to a nonmetallic object, the amplitude of voice signal varies significantly.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. In this paper, we propose a method for measuring the refractive index of seawater, based on a position-sensitive detector (PSD). A theoretical model was established to depict the correlation between laser spot displacement and refractive index change, utilizing a combination of a position-sensitive detector and laser beam deflection principles. Based on this optical measurement method, a seawater refractive index measurement system was established. To effectively enhance the sensitivity of refractive index detection, a focusing lens was incorporated into the optical path of the measuring system, and simulations were conducted to investigate the impact of focal length on refractive index sensitivity. The calibration experiment of the measuring system was performed based on the relationship between the refractive index of seawater and underwater pressure (depth). By measuring laser spot displacement at different depths, changes in displacement, with respect to both refractive index and depth, were determined. The experimental results demonstrate that the system exhibits a sensitivity of $9.93\times {10}^{-9}\mathrm{R}\mathrm{I}\mathrm{U}$ (refractive index unit), and the refractive index deviation due to stability is calculated as $\pm 7.54\times {10}^{-9}\mathrm{R}\mathrm{I}\mathrm{U}$. Therefore, the feasibility of this highly sensitive measurement of seawater refractive index is verified. Since the sensitivity of the refractive index measurement of this measurement system is higher than the refractive index change caused by the wake of underwater vehicles, it can also be used in various applications for underwater vehicle wake measurement, as well as seawater refractive index measurement, such as the motion state monitoring of underwater navigation targets such as AUVs and ROVs.

Quick response (QR) codes are widely used in many fields. Various QR code recognition approaches have been proposed to improve the accuracy of decoding QR code. However, the recognition of distorted QR codes with one missing position detection pattern (PDP) remains a problem. In this study, based on the vector relationship and the structural features, the authors introduce a new method for decoding distorted QR code with one missing PDP. Three methods, Zxing, Halcon, and the newly proposed method, are used to test the decoding capability. For QR codes with one missing PDP, the experimental results show that the proposed method could meet the recognition angle range as much as 110°, while Zxing fails to recognise, and the angle of decoding for Halcon is 90°. Especially, the proposed method is available at an extremely harsh luminance and contrast environment, e.g. both phases as a 60% discount, when the decoding angle of Halcon is only 35°, while the proposed one better than 2.7 times of it. Besides, the proposed method is more robust to decode the QR codes with a missing PDP under different backgrounds and noisy images.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high precision seawater refractive index measurement. In this paper, we propose a method for measuring the refractive index of seawater based on a Position Sensitive Detector (PSD). Theoretical model is established to depict the correlation between laser spot displacement and refractive index change, utilizing a combination of position sensitive detector and laser beam deflection principle. Based on this optical measurement method, a seawater refractive index measurement system was established. To effectively enhance the sensitivity of refractive index detection, a focusing lens was incorporated into the optical path of the measuring system, and simulations were conducted to investigate the impact of focal length on refractive index sensitivity. The calibration experiment of the measuring system was performed based on the relationship between refractive index of seawater and underwater pressure (depth). By measuring laser spot displacement at different depths, changes in displacement with respect to both refractive index and depth were determined. Experimental results demonstrate that the system exhibits a sensitivity of 9.9277×10−9RIU (refractive index unit), the refractive index deviation due to stability is calculated as ±7.545×10−9RIU. Thereby the feasibility of highly sensitive measurement of seawater refractive index is verified.

The refractive index measurement of seawater has proven significance in oceanography, while an optical heterodyne interferometer is an important, highly accurate, tool used for seawater refractive index measurement. However, for practical seawater refractive index measurement, the refractive index of seawater needs to be monitored for long periods of time, and the influence of drift error on the measurement results for these cases cannot be ignored. This paper proposes a drift error compensation algorithm based on wavelet decomposition, which can adaptively separate the background from the signal, and then calculate the frequency difference to compensate for the drift error. It is suitable for unstable signals, especially signals with large differences between the beginning and the end, which is common in actual seawater refractive index monitoring. The authors identify that the primary cause of drift error is the frequency instability of the acousto-optic frequency shifter (AOFS), and the actual frequency difference was measured through experimentation. The frequency difference was around 0.1 Hz. Simulation experiments were designed to verify the effectiveness of the algorithm, and the standard deviation of the optical length of the results was on the scale of 10⁻⁸ m. Liquid refractive index measurement experiments were carried out in a laboratory, and the measurement error was reduced from 36.942% to 0.592% after algorithm processing. Field experiments were carried out regarding seawater refractive index monitoring, and the algorithm-processing results are able to match the motion of the target vehicle. The experimental data were processed with different algorithms, and, according to the comparison of the results, the proposed algorithm performs better than other existing drift error elimination algorithms.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 1 kg/m³ in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.