Editorial

3 pages, 161 KiB

Open AccessEditorial

Entropy and Information Theory in Acoustics

by Yuxing Li

Entropy 2022, 24(12), 1760; https://0-doi-org.brum.beds.ac.uk/10.3390/e24121760 - 01 Dec 2022

Viewed by 1024

Acoustics is one of the most studied fields in the 21st century, encompassing underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychological acoustics, signal processing in acoustics, and so on [...] Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

Research

Jump to: Editorial

19 pages, 7014 KiB

Open AccessArticle

Bionic Covert Underwater Acoustic Communication Based on Time–Frequency Contour of Bottlenose Dolphin Whistle

by Lei Xie, Jiahui Zhu, Yuqing Jia and Huifang Chen

Entropy 2022, 24(5), 720; https://0-doi-org.brum.beds.ac.uk/10.3390/e24050720 - 18 May 2022

Cited by 4 | Viewed by 2402

Abstract

In order to meet the requirements of communication security and concealment, as well as to protect marine life, bionic covert communication has become a hot research topic for underwater acoustic communication (UAC). In this paper, we propose a bionic covert UAC (BC-UAC) method [...] Read more.

In order to meet the requirements of communication security and concealment, as well as to protect marine life, bionic covert communication has become a hot research topic for underwater acoustic communication (UAC). In this paper, we propose a bionic covert UAC (BC-UAC) method based on the time–frequency contour (TFC) of the bottlenose dolphin whistle, which can overcome the safety problem of traditional low signal–noise ratio (SNR) covert communication and make the detected communication signal be excluded as marine biological noise. In the proposed BC-UAC method, the TFC of the bottlenose dolphin whistle is segmented to improve the transmission rate. Two BC-UAC schemes based on the segmented TFC of the whistle, the BC-UAC scheme using the whistle signal with time-delay (BC-UAC-TD) and the BC-UAC scheme using the whistle signal with frequency-shift (BC-UAC-FS), are addressed. The original whistle signal is used as a synchronization signal. Moreover, the virtual time reversal mirror (VTRM) technique is adopted to equalize the channel for mitigating the multipath effect. The performance of the proposed BC-UAC method, in terms of the Pearson correlation coefficient (PCC) and bit error rate (BER), is evaluated under simulated and measured underwater channels. Numerical results show that the proposed BC-UAC method performs well on covertness and reliability. Furthermore, the covertness of the bionic modulated signal in BC-UAC-TD is better than that of BC-UAC-FS, although the reliability of BC-UAC-FS is better than that of BC-UAC-TD. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

14 pages, 4247 KiB

Open AccessArticle

Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

by Yuxing Li, Peiyuan Gao, Bingzhao Tang, Yingmin Yi and Jianjun Zhang

Entropy 2022, 24(1), 22; https://0-doi-org.brum.beds.ac.uk/10.3390/e24010022 - 23 Dec 2021

Cited by 47 | Viewed by 3377

Abstract

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used [...] Read more.

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

18 pages, 8935 KiB

Open AccessArticle

Multi-Source Coupling Based Analysis of the Acoustic Radiation Characteristics of the Wheel–Rail Region of High-Speed Railways

by Bowen Hou, Jiajing Li, Liang Gao and Di Wang

Entropy 2021, 23(10), 1328; https://0-doi-org.brum.beds.ac.uk/10.3390/e23101328 - 12 Oct 2021

Cited by 3 | Viewed by 1843

Abstract

Based on elastic mechanics, the fluid–structure coupling theory and the finite element method, a high-speed railway wheel-rail rolling-aerodynamic noise model is established to realize the combined simulation and prediction of the vibrations, rolling noise and aerodynamic noise in wheel-rail systems. The field test [...] Read more.

Based on elastic mechanics, the fluid–structure coupling theory and the finite element method, a high-speed railway wheel-rail rolling-aerodynamic noise model is established to realize the combined simulation and prediction of the vibrations, rolling noise and aerodynamic noise in wheel-rail systems. The field test data of the Beijing–Shenyang line are considered to verify the model reliability. In addition, the directivity of each sound source at different frequencies is analyzed. Based on this analysis, noise reduction measures are proposed. At a low frequency of 300 Hz, the wheel-rail area mainly contributes to the aerodynamic noise, and as the frequency increases, the wheel-rail rolling noise becomes dominant. When the frequency is less than 1000 Hz, the radiated noise fluctuates around the cylindrical surface, and the directivity of the sound is ambiguous. When the frequency is in the middle- and high-frequency bands, exceeding 1000 Hz, both the rolling and total noise exhibit a notable directivity in the directions of 20–30° and 70–90°, and thus, noise reduction measures can be implemented in these directions. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

19 pages, 10363 KiB

Open AccessArticle

A High-Efficiency Spectral Method for Two-Dimensional Ocean Acoustic Propagation Calculations

by Xian Ma, Yongxian Wang, Xiaoqian Zhu, Wei Liu, Wenbin Xiao and Qiang Lan

Entropy 2021, 23(9), 1227; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091227 - 18 Sep 2021

Cited by 5 | Viewed by 1610

Abstract

The accuracy and efficiency of sound field calculations highly concern issues of hydroacoustics. Recently, one-dimensional spectral methods have shown high-precision characteristics when solving the sound field but can solve only simplified models of underwater acoustic propagation, thus their application range is small. Therefore, [...] Read more.

The accuracy and efficiency of sound field calculations highly concern issues of hydroacoustics. Recently, one-dimensional spectral methods have shown high-precision characteristics when solving the sound field but can solve only simplified models of underwater acoustic propagation, thus their application range is small. Therefore, it is necessary to directly calculate the two-dimensional Helmholtz equation of ocean acoustic propagation. Here, we use the Chebyshev–Galerkin and Chebyshev collocation methods to solve the two-dimensional Helmholtz model equation. Then, the Chebyshev collocation method is used to model ocean acoustic propagation because, unlike the Galerkin method, the collocation method does not need stringent boundary conditions. Compared with the mature Kraken program, the Chebyshev collocation method exhibits a higher numerical accuracy. However, the shortcoming of the collocation method is that the computational efficiency cannot satisfy the requirements of real-time applications due to the large number of calculations. Then, we implemented the parallel code of the collocation method, which could effectively improve calculation effectiveness. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

27 pages, 2038 KiB

Open AccessArticle

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

by Wasiq Ali, Yaan Li, Muhammad Asif Zahoor Raja, Wasim Ullah Khan and Yigang He

Entropy 2021, 23(9), 1124; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091124 - 29 Aug 2021

Cited by 6 | Viewed by 2132

Abstract

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which [...] Read more.

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

16 pages, 1303 KiB

Open AccessArticle

Two Chebyshev Spectral Methods for Solving Normal Modes in Atmospheric Acoustics

by Yongxian Wang, Houwang Tu, Wei Liu, Wenbin Xiao and Qiang Lan

Entropy 2021, 23(6), 705; https://0-doi-org.brum.beds.ac.uk/10.3390/e23060705 - 02 Jun 2021

Cited by 9 | Viewed by 2082

Abstract

The normal mode model is important in computational atmospheric acoustics. It is often used to compute the atmospheric acoustic field under a time-independent single-frequency sound source. Its solution consists of a set of discrete modes radiating into the upper atmosphere, usually related to [...] Read more.

The normal mode model is important in computational atmospheric acoustics. It is often used to compute the atmospheric acoustic field under a time-independent single-frequency sound source. Its solution consists of a set of discrete modes radiating into the upper atmosphere, usually related to the continuous spectrum. In this article, we present two spectral methods, the Chebyshev-Tau and Chebyshev-Collocation methods, to solve for the atmospheric acoustic normal modes, and corresponding programs are developed. The two spectral methods successfully transform the problem of searching for the modal wavenumbers in the complex plane into a simple dense matrix eigenvalue problem by projecting the governing equation onto a set of orthogonal bases, which can be easily solved through linear algebra methods. After the eigenvalues and eigenvectors are obtained, the horizontal wavenumbers and their corresponding modes can be obtained with simple processing. Numerical experiments were examined for both downwind and upwind conditions to verify the effectiveness of the methods. The running time data indicated that both spectral methods proposed in this article are faster than the Legendre-Galerkin spectral method proposed previously. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

26 pages, 1759 KiB

Open AccessArticle

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

by Wasiq Ali, Wasim Ullah Khan, Muhammad Asif Zahoor Raja, Yigang He and Yaan Li

Entropy 2021, 23(5), 550; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050550 - 29 Apr 2021

Cited by 12 | Viewed by 1964

Abstract

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive [...] Read more.

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

18 pages, 3389 KiB

Open AccessArticle

Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

by Dongri Xie, Shaohua Hong and Chaojun Yao

Entropy 2021, 23(5), 503; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050503 - 22 Apr 2021

Cited by 23 | Viewed by 2356

Abstract

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts [...] Read more.

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics II)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Entropy and Information Theory in Acoustics II

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Related Special Issue

Published Papers (9 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI