Prediction uncertainty of wind-generated noise spectra from wind speed.

The prediction of wind-generated noise spectral levels at one frequency is typically based on a linear regression function, which is defined over the logarithm of the 10-m wind speed. However, despite its widespread success, the linear regression model does not pay attention to its prediction uncertainty because it makes point predictions. The main reasons for the uncertainty in the predicted value of the wind-generated noise level are that it cannot be uniquely determined by 10-m wind speed and its measurements may be corrupted by other sources of ambient noise. To quantify the uncertainty in predictions in this scenario, a Bayesian treatment of linear regression models and its associated predictive distribution are applied, making distribution predictions instead of point predictions. Once the predictive distribution for one frequency has been fixed, its linear variants are used to obtain predictive distributions for other frequencies. The data for the ocean ambient noise and 10-m wind speed are collected from two deep-water experiments, conducted in the South China Sea, and reanalysis data sets of the European Centre For Medium-Range Weather Forecasts, respectively. Empirical expressions for the predictive distribution of noise spectra (0.5-10 kHz) at wind speeds from 3.3 to 14 m/s have been developed. The results indicate decreasing uncertainties with an increasing wind speed.

Probability density function of ocean noise based on a variational Bayesian Gaussian mixture model.

Extensive ocean noise records have kurtoses markedly different from the Gaussian distribution and therefore exhibit non-Gaussianity, which influences the performance of many sonar signal processing methods. To model the amplitude distribution, this paper studies a Bayesian Gaussian mixture model (BGMM) and its associated learning algorithm, which exploits the variational inference method. The most compelling feature of the BGMM is that it automatically selects a suitable number of effective components and then can approximate a sophisticated distribution in practical applications. The probability density functions (PDFs) of three types of noise in different frequency bands collected in the South China Sea-ambient noise, ship noise, and typhoon noise-are modeled and the goodness of fit is examined by applying the one-sample Kolmogorov-Smirnov test. The results demonstrate that: (i) Ambient noise in the low-frequency band may be slightly non-Gaussian, ship noise in each considered band is apparently non-Gaussian, and typhoons affect the noise in the low-frequency band to make it apparently non-Gaussian, while the noise in the high-frequency band is less affected and appears to be Gaussian. (ii) BGMM has higher goodness of fit than the Gaussian or Gaussian mixture model. (iii) In the non-Gaussian case, despite some components having small mixing coefficients, they are of great significance for describing the PDF.

Passive broadband source localization based on a Riemannian distance with a short vertical array in the deep ocean.

A passive broadband source localization approach in the deep ocean is proposed based on the coherent matched-field processing combined with a Riemannian distance. The spatial coherence between hydrophones over frequency bandwidth is utilized for eliminating the unknown source spectrum. Three Riemannian distances are introduced for the measurement of the similarity between the data and replica matrices. The experimental results indicate that the proposed processors with a short array can effectively suppress the sidelobe and improve the localization performance. The source localization influenced by the factors such as the aperture, number of hydrophones, and desired bandwidth is also demonstrated.

Mapping sea surface observations to spectra of underwater ambient noise through self-organizing map method.

This letter presents a model for mapping sea surface observations to the spectra of underwater ambient noise through the self-organizing map method (SOM). The data used to train and test the proposed model include observations of wind speed, atmospheric pressure, and significant wave height from public databases, as well as observations of ambient noise from two deep-water experiments. SOM extracts nonlinear relations from the data and is more suitable for the study of nonlinear dynamics in the ocean than conventional methods. Results indicate the proposed model is reliable with coefficients of determination above 0.9 and root-mean-square errors below 1 dB.

A performance study of acoustic interference structure applications on source depth estimation in deep water.

A recent publication by Duan et al. [J. Acoust. Soc. Am. 142(3), EL245-EL250 (2017)] analyzed the interference pattern observed in the plot of acoustic intensity versus source-receiver range and frequency in deep water. This paper extends this work by applying an equivalent acoustic intensity surface versus the vertical angle of arrivals and frequency to depth estimation of broadband sources. A matched-interference-structure (MIS) method that processes the whole acoustic intensity surface is proposed and formulated as a filtration of the surface where the binominal filter weights ±1 for each surface grid point and presumed source depth are based on model-based interference structures. For comparison, the conventional method of matching multipath delays is reformulated and applied to process the surface. Because the numerical prediction of interference structures can be costly, analytical expressions are derived to improve the efficiency. Monte Carlo simulations are carried out to compare the performance and experimental data of broadband random sequences and pulse sources are used to illustrate the methods. The MIS method has a higher resolution and can work under a lower signal-to-noise ratio in the case of weak source depth fluctuation but is not as robust to the large source depth fluctuation as the other method.

Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean.

Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.

Striation-based source depth estimation with a vertical line array in the deep ocean.

A striation-based method with a vertical line array is proposed for source depth estimation. Broadband striation structures of direct and surface-reflected arrivals after propagating to receivers near the ocean bottom are applied. A tracking algorithm for the striation structures is proposed based on the extended Kalman filter. A cost function for source depth estimation is presented by matching the traces of the measured striations with a library of model-based traces under different source depths. The method is demonstrated on array data collected during an acoustic research experiment in the South China Sea in 2016.

Spatial Vertical Directionality and Correlation of Low-Frequency Ambient Noise in Deep Ocean Direct-Arrival Zones.

Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP) and Volunteer Observation System (VOS) were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line array deployed near the bottom in deep ocean direct-arrival zones.

Moving source localization with a single hydrophone using multipath time delays in the deep ocean.

Localizing a source of radial movement at moderate range using a single hydrophone can be achieved in the reliable acoustic path by tracking the time delays between the direct and surface-reflected arrivals (D-SR time delays). The problem is defined as a joint estimation of the depth, initial range, and speed of the source, which are the state parameters for the extended Kalman filter (EKF). The D-SR time delays extracted from the autocorrelation functions are the measurements for the EKF. Experimental results using pseudorandom signals show that accurate localization results are achieved by offline iteration of the EKF.

Line spectrum extraction based on autoassociative neural networks.

Line spectrum is an important feature for the detection and classification of underwater targets. This letter presents a method for extracting the line spectrum submerged in underwater ambient noise through autoassociative neural networks (AANN). Compared with the traditional methods, the proposed method based on AANN can directly enhance the line spectrum from the raw time-domain noise data without relying on prior information and spectral features. Moreover, the proposed method can suppress the background noise while extracting the line spectrum. Both the numerical simulation and experimental data test results demonstrate that the proposed method provides a good ability to extract the line spectrum from the strong background noise.

[Inhibition of mixture of lucid garoderma and lucid garoderma spore on tumor cell in vitro and in vivo].

BACKGROUND & OBJECTIVE: Garoderma lucidum (lingzhi) is a kind of medicinal fungus with a long history in China as a valuable tonic folk remedy. It has been demonstrated that lingzhi had growth inhibition effect against implanted mice tumors. It is generally regarded that anti-tumor action of lingzhi is induced by promoting function of immune system. Recently, we found that mixture of lucid garoderma and lucid garoderma spore (MLGLGS) could markedly inhibit activities of topoisomerase I and II, so this study was designed to further evaluate the inhibition effects of MLGLGS on tumor cell in vitro and in vivo. METHODS: Inhibition of MLGLGS on tumor cell were detected by MTT or SRB method in vitro study and by human tumor nude mice xenografts as models in vivo study. RESULTS: Among them, the IC50 values were lower than 2 mg/ml in P388, U-937, and HL-60 leukemias, and lower than 4 mg/ml in 2 human lung cancer and 2 human gastric cancer cells, respectively. In vivo study revealed that MLGLGS had certain experimental therapeutic action on human lung tumor xenograft LAX-83 (dose: 1 g/kg, inhibitory rate: 49.47%) and gastric xenograft SGC-7901 (dose: 2 g/kg, inhibitory rate: 43.09%). CONCLUSION: MLGLGS has inhibitory action on tumor cell at high concentration and high dosage.