Underwater acoustics is of fundamental importance for marine science and technology. However, acoustic waves transmitted by state‐of‐the‐art underwater acoustic systems are not inherently phase locked, which hinders the development of underwater acoustic technology. For example, the precision of underwater distance measurement can only achieve centimeter level. As a versatile tool, optical frequency combs have enabled revolutionary progress in optical metrology and precision measurement. In parallel with optical frequency combs, here, the generation of fully stabilized, underwater acoustic frequency combs is reported, in which equidistant acoustic modes are produced via a hydroacoustic transducer. The precision of each individual acoustic mode is measured to be 10−9 at 1 s and 10−12 at 1000 s averaging times. Underwater distance measurements are carried out in an anechoic pool using a dual‐comb scheme. Comparison with reference values shows consistency within 50 µm (7 × 10−6 in relative). The relatively long‐duration experiments at 7 m distance yield an Allan deviation of 1.8 µm (2.6 × 10−7 in relative) at 1 s and further 480 nm (6.8 × 10−8 in relative) at 40 s averaging times. The approach to acoustic frequency comb generation offers a promising and powerful platform for future underwater distance measurement, positioning, and navigation.
Ultrasound has been proven to be a valid tool for ranging, especially in water. In this paper, we design a high-resolution ultrasonic ranging system that uses a thin laser beam as an ultrasonic sensor. The laser sensing provides a noncontact method for ultrasound detection based on acousto-optic diffraction. Unlike conventional methods, the ultrasound transmitted from the transducer is recorded as the reference signal when it first passes through the laser. It can be used to improve the accuracy and resolution of the time-of-flight (TOF) by a cross-correlation method. Transducers with a central frequency of 1 MHz and diameters of 20 mm and 28 mm are used in the experiment. Five targets and a test piece are used to evaluate the ranging performance. The sound velocity is measured by the sound velocity profiler (SVP). The repeatability error of TOF is less than 4 ns, and the theoretical resolution of TOF is 0.4 ns. The results show a measurement resolution within one-tenth of the wavelength of ultrasound and an accuracy better than 0.3 mm for targets at a distance up to 0.8 m. The proposed system has potential applications in underwater ranging and thickness detection.
Although shape similarity is one fundamental element in coastline generalization quality, its related research is still inadequate. Consistent with the hierarchical pattern of shape recognition, the Dual-side Bend Forest Shape Representation Model is presented by reorganizing the coastline into bilateral bend forests, which are made of continuous root-bends based on Constrained Delaunay Triangulation and Convex Hull. Subsequently, the shape contribution ratio of each level in the model is expressed by its area distribution in the model. Then, the shape similarity assessment is conducted on the model in a top–down layer by layer pattern. Contrast experiments are conducted among the presented method and the Length Ratio, Hausdorff Distance and Turning Function, showing the improvements of the presented method over the others, including (1) the hierarchical shape representation model can distinguish shape features of different layers on dual-side effectively, which is consistent with shape recognition, (2) its usability and stability among coastlines and scales, and (3) it is sensitive to changes in main shape features caused by coastline generalization.
In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polarimetric images by rescaling the contributions of channels in the network. In addition, we also design the adaptive polarization loss to make the network focus on the polarization information. Experiments show that our method can well restore the details flooded by serious noise and outperforms previous methods. Moreover, the underlying mechanism of channel attention is revealed visually.
As an important marine environmental parameter, sound velocity greatly affects the sound propagation characteristics in the ocean. In marine surveying work, prompt and low-cost acquisition of accurate sound speed profiles (SSP) is of immense significance for improving the measurement and positioning accuracy of marine acoustic equipment and ensuring underwater wireless communication. To address the problem of not being able to glean the accurate SSP in real time, we propose a convolution long short-term memory neural network (Conv-LSTM) which combines the long short-term memory (LSTM) neural network and convolution operation to predict the complete sound speed profile based on historical data. Considering SSP is a typical time series and has strong spatial correlation, Conv-LSTM can grasp not only the temporal relevance of time series, but also the spatial characteristics. The Argo temperature and salinity grid data of the North Pacific from 2004 to 2019 is imported to establish the model’s SSP dataset, and the convolution of input data is performed before going through the neurons in this recurrent neural network to extract the spatial relevance of the data itself. In the meantime, in order to prove the advanced nature of this model, we compare it with the LSTM network under the same parameter settings. The experimental results show that predicting the SSP time series at a single coordinate position under the same parameter conditions, it is best to predict the future SSP next month through the historical data of 24 months, and the prediction effect of Conv-LSTM is much better than that of the LSTM network, and the relative error (RE) is 0.872 m/s, which is 1.817 m/s less than that of LSTM. Predictions in the selected area are also exceedingly accurate relative to the actual data; the prediction error of deep water is less than 0.3 m/s, while RE on the surface layer is larger, exceeding 1.6 m/s.
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