Underwater Sound Research Articles

Improved LEACH Protocol Based on Underwater Energy Propagation Model, Parallel Transmission, and Replication Computing for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

Multivariate Multiscale Higuchi Fractal Dimension and Its Application to Mechanical Signals

Fractal dimension, as a common nonlinear dynamics metric, is extensively applied in biomedicine, fault diagnosis, underwater acoustics, etc. However, traditional fractal dimension can only analyze the complexity of the time series given a single channel at a particular scale. To characterize the complexity of multichannel time series, multichannel information processing was introduced, and multivariate Higuchi fractal dimension (MvHFD) was proposed. To further analyze the complexity at multiple scales, multivariate multiscale Higuchi fractal dimension (MvmHFD) was proposed by introducing multiscale processing algorithms as a technology that not only improved the use of fractal dimension in the analysis of multichannel information, but also characterized the complexity of the time series at multiple scales in the studied time series data. The effectiveness and feasibility of MvHFD and MvmHFD were verified by simulated signal experiments and real signal experiments, in which the simulation experiments tested the stability, computational efficiency, and signal separation performance of MvHFD and MvmHFD, and the real signal experiments tested the effect of MvmHFD on the recognition of multi-channel mechanical signals. The experimental results show that compared to other indicators, A achieves a recognition rate of 100% for signals in three features, which is at least 17.2% higher than for other metrics.

Towards better differentiation of archaeological objects based on geomorphometric features of a digital elevation model, the case of the Old Oder Canal

AbstractLimited visibility in the underwater environment often restricts opportunities for archaeological prospection. Especially in reservoirs with a high content of suspended solids, methods based on acoustics prove to be extremely useful. This study represents the first high‐resolution acoustic mapping and archaeological prospections of the Old Oder Canal, which has extremely poor visibility. The study site is located near the town of Krosno Odrzańskie in Poland. The town is one of the country's most significant river crossings and settlements of mediaeval origin (including its wooden bridges). The following research objectives were identified: (1) exploration of the Old Oder Canal, including underwater acoustics and archaeological prospections; (2) analysis and interpretation of the study area based on acquired datasets; and (3) evaluation of secondary features of the river bathymetry for identification of archaeological objects. Possible locations of archaeological objects were determined based on analysis and interpretation of multibeam echosounder measurements of the riverbed. Fieldwork allowed structural elements of mediaeval bridges to be found and dendrochronological sampling performed. Feature selection analyses allowed the determination and evaluation of geomorphometric attributes, combining the characteristics of the discovered objects and diagnostics in order better to differentiate archaeological remains. Proposed secondary features may facilitate archaeological explorations in difficult environments.

One-dimensional synthetic waterborne phononic crystals

Underwater acoustics has wide applications in underwater communication, underwater positioning, underwater navigation, and so on. Inspired by the concept of topological physics, the study of topological states in waterborne phononic crystals provides a brand-new way for innovatively controlling underwater waves, which has both basic research value and important application prospects. In this work, we design a one-dimensional bilayer iron grid waterborne phononic crystal to realize a synthetic two-dimensional Dirac point by considering the relative lateral translation between the two layers as a synthetic dimension. Through changing the relative lateral translation, the double degenerate band opens a gap, which is characterized by the valley Chern number. As the band gap opens, closes and reopens, the bulk band undergoes a band inversion, that is, a topological phase transition from one valley topological phase to another. At the interface formed by two phononic crystals with distinct valley topological phases, the valley Chen number ensures the deterministic existence of the interface state. Experimental measurements are in good agreement with numerical simulations, both showing the bulk bands of waterborne phononic crystals at different valley topological phases and the interface state dispersion between them. The waterborne phononic crystal proposed in this work has a simple structure. With the help of the concept of synthetic dimension, it provides an effective way to study the topological properties of high-dimensional systems in low-dimensional real space systems, and gives new ideas for designing topological functional underwater acoustic devices. In addition, we can expand the real space system to two or even three dimensions, and introduce more synthetic dimensions to study the topological states and associated transport characteristics of higher-dimensional systems.

Deep neural network for learning wave scattering and interference of underwater acoustics

It is challenging to construct generalized physical models of underwater wave propagation owing to their complex physics and widely varying environmental parameters and dynamical scales. In this article, we present a deep convolutional recurrent autoencoder network (CRAN) for data-driven learning of complex underwater wave scattering and interference. We specifically consider the dynamics of underwater acoustic scattering from various non-uniform seamount shapes leading to complex wave interference patterns of back-scattered and forward-propagated waves. The CRAN consists of a convolutional autoencoder for learning low-dimensional system representation and a long short-term memory (LSTM)-based recurrent neural network for predicting system evolution in low dimensions. The convolutional autoencoder enables efficient dimension reduction of wave propagation by independently learning global and localized wave features. To improve the time horizon of wave dynamics prediction, we introduce an LSTM architecture with a single-shot learning mechanism and optimal time-delayed data embedding. On training the CRAN over 30 cases containing various seamount geometries and acoustic source frequencies, we can predict wave propagation up to a time horizon of 5 times the initiation sequence length for 15 out-of-training cases with a mean L2 error of approximately 10%. For selected out-of-training cases, the prediction time horizon could be increased to 6 times the initiation sequence length. Importantly, such predictions are obtained with physically consistent wave scattering and wave interference patterns and at 50% lower L2 error compared to routinely use standard LSTMs. These results demonstrate the potential of employing such deep neural networks for learning complex underwater ocean acoustic propagation physics.

3D Dynamic Topology With Energy-Aware Forwarding in Underwater Acoustic Networks (MAEARS)

3D Dynamic Topology With Energy-Aware Forwarding in Underwater Acoustic Networks (MAEARS)

Experimental Results of Underwater Sound Speed Profile Inversion by Few-Shot Multi-Task Learning

Underwater Sound Speed Profile (SSP) distribution is crucial for the propagation mode of acoustic signals, so fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feed-forward neural networks (FNNs), among which the FNN shows better real-time performance while maintaining the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is difficult to satisfy in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different training tasks. By MTL, common features could be extracted, which accelerates the learning process on given tasks, and reduces the demand for reference samples, enhancing the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 in the South China Sea. Results show that MTL outperforms the other mainstream methods in terms of accuracy for SSP inversion, while inheriting the real-time advantage of FNN during the inversion stage.

Self‐Micropatterned Wood Hydrophone for Underwater Detection

AbstractA hydrophone is a core component of sonar and plays a significant role in underwater detection. High sensitivity and wide‐frequency responsiveness are crucial parameters for underwater mechanosensors. This study introduces an innovative wood‐based hydrophone for precise detection of water wave spectra and a wide range of underwater sounds. The wood hydrophone consists of a conductive wood electrode with deposited polypyrrole (PPy) microbranches (polypyrrole wood (PW)) and a cellulosic micropatterned anti‐swelling hydrogel (MPASH) serving as the dielectric layer. As pressure increases within the range of 6.4 Pa to 1.2 kPa, the densely distributed PPy microbranches within the wood fiber cell lumina contact with more tips of the self‐wrinkled micropatterns of MPASH. This interaction results in a four‐order‐of‐magnitude increase in capacitance, leading to an unprecedented sensitivity of 1153 kPa−1 toward microstresses. Due to the presence of multisized configurations in both PW and MPASH, the sensitivity of the wood hydrophone decreases gradually (268 kPa−1 up to 37 kPa). The receiving sensitivity of the wood hydrophone for acoustic waves is greater than ‐174 dB within the frequency range of 250–2300 Hz. This work presents a promising avenue for the development of underwater detection devices utilizing graded wood channels and micropatterned cellulosic hydrogels.

Модель синтезу гідроакустичних сигналів із використанням нейронних мереж

Underwater acoustics (also called hydroacoustics), which is associated with the study of the patterns of propagation of sound waves in water, is a driving force in the research and development of systems of hydroacoustic technologies and means of communication, monitoring and detection of surface and underwater objects of biological or artificial origin, study of marine resources and environments, noise measurement, etc. This kind of research requires the analysis of huge amounts of data, revealing non-obvious patterns and creating models for the mathematical description of physical phenomena, such as sound propagation in a medium with random characteristics and radiation from different sources, as well as radiation from sources with different apertures or sound scattering, etc. That is why, in order to create the latest technologies in this area, it is necessary to solve complex specialized problems of a fundamental and applied nature using machine learning algorithms and artificial intelligence. Neural networks are nonlinear systems that allow you to effectively classify data compared to mathematical and statistical methods, which are currently quite widely used. In this paper, the authors propose to use a pre-trained neural network for the analysis and classification of hydroacoustic signals. This procedure for distinguishing acoustic signals has a number of advantages, in particular, individual objects are divided into groups based on information about one or more characteristics inherent in these objects, and on the basis of a training sample of pre-labeled objects. Thus, the proposed model of signal synthesis using neural networks is characterized by increased informativeness of the characteristics of the propagation of hydroacoustic signals, which will have prospects in further practical implementation.

Annoying noise: effect of anthropogenic underwater noise on the movement and feeding performance in the red cherry shrimp, Neocaridina davidi

Anthropogenic noise in marine and freshwater environments has increased dramatically, with a range of negative impacts and detrimental consequences on many aquatic animals across taxa. Benthic organisms, including many invertebrates, can sense underwater sounds, yet the responses they trigger in these organisms have received little attention. We conducted two laboratory-based experiments to investigate the effect of underwater sound playback on the movement behavior and feeding performance of the red cherry shrimp Neocaridina davidi as a model of freshwater decapod. Movement speed decreased significantly upon opening the divider in both the sound and control treatments. However, there were no significant sound-dependent changes overall between the control and sound treatments. The spatial distribution of shrimp in response to the sound treatment showed significant changes; shrimp spent more time at the farthest one-third position from the sound source. Feeding latency (latency to find food) also increased in the sound treatment compared to the control. Moreover, in terms of the number of successes and failures in finding the food source in the control treatment, significantly more shrimp succeeded in finding the food source. The number of revisits to the food source decreased in the sound treatment compared to control and more shrimp were significantly distracted in the sound treatment. Our study highlights the potential for human-made sound to impact on crustacean activity. Thus, they are prone to the impacts of anthropogenic noise, causing negative impacts on their movement-swimming activities, and feeding behavior. Behavioral changes observed, namely altered feeding and locomotory behavior may have wider-reaching negative effects, including detrimental impacts on animal fitness.

Drivers of morphological evolution in the toothed whale jaw

Toothed whales (odontocetes) emit high-frequency underwater sounds (echolocate)-an extreme and unique innovation allowing them to sense their prey and environment. Their highly specialized mandible (lower jaw) allows high-frequency sounds to be transmitted back to the inner ear. Echolocation is evident in the earliest toothed whales, but little research has focused on the evolution of mandibular form regarding this unique adaptation. Here, we use a high-density, three-dimensional geometric morphometric analysis of 100 living and extinct cetacean species spanning their ∼50-million-year evolutionary history. Our analyses demonstrate that most shape variation is found in the relative length of the jaw and the mandibular symphysis. The greatest morphological diversity was obtained during two periods of rapid evolution: the initial evolution of archaeocetes (stem whales) in the early to mid-Eocene as they adapted to an aquatic lifestyle, representing one of the most extreme adaptive transitions known, and later on in the mid-Oligocene odontocetes as they became increasingly specialized for a range of diets facilitated by increasingly refined echolocation. Low disparity in the posterior mandible suggests the shape of the acoustic window, which receives sound, has remained conservative since the advent of directional hearing in the aquatic archaeocetes, even as the earliest odontocetes began to receive sounds from echolocation. Diet, echolocation, feeding method, and dentition type strongly influence mandible shape. Unlike in the toothed whale cranium, we found no significant asymmetry in the mandible. We suggest that a combination of refined echolocation and associated dietary specializations have driven morphology and disparity in the toothed whale mandible.

Global inventory of species categorized by known underwater sonifery

A working group from the Global Library of Underwater Biological Sounds effort collaborated with the World Register of Marine Species (WoRMS) to create an inventory of species confirmed or expected to produce sound underwater. We used several existing inventories and additional literature searches to compile a dataset categorizing scientific knowledge of sonifery for 33,462 species and subspecies across marine mammals, other tetrapods, fishes, and invertebrates. We found 729 species documented as producing active and/or passive sounds under natural conditions, with another 21,911 species deemed likely to produce sounds based on evaluated taxonomic relationships. The dataset is available on both figshare and WoRMS where it can be regularly updated as new information becomes available. The data can also be integrated with other databases (e.g., SeaLifeBase, Global Biodiversity Information Facility) to advance future research on the distribution, evolution, ecology, management, and conservation of underwater soniferous species worldwide.

Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) has great potential for the implementation of high spectral-efficiency underwater acoustic (UWA) communications. However, general receivers consisting of the optimal maximum likelihood detection suffer from high computational load, which prohibits real-time data transmissions in underwater scenarios. In this paper, we propose a detection based on a vector approximate message passing (VAMP) algorithm for UWA OFDM-IM communications. Firstly, a VAMP framework with a non-loopy factor graph for index detection is formulated. Secondly, by utilizing the sparsity inherently existing in OFDM-IM symbols, a novel shrinkage function is derived based on the minimum mean square error criterion, which guarantees better posterior estimation. To reduce the errors from estimated non-existing indices, one trick is utilized to search the elements from the look-up table with the minimal Euclidean distance for the replacement of erroneously estimated indices. Experiments verify the advantages of the proposed detector in terms of low complexity, robustness and effectiveness compared with the state-of-art benchmarks.

MTSA-Net: A multiscale time self-attention network for ship radiated self-noise reduction

For a surface ship carrying a towed array, its in-band radiated self-noise is one of the near-field strong interference, which will seriously limit the performance of the underwater acoustic (UWA) signal detection system. Typically, the conventional ship noise cancellation methods requires time-synchronized hydrophone arrays, such as the towed linear array (TLA), to suppress its self-noise by using spatial filters. However, the spatial filter based methods fail when the direction of arrival of the desired signal is in the ship noise masking area. In cooperative scenarios, the prior knowledge of the template signal provides additional temporal information, which can be utilized to design a time-domain representations based detection system. In this paper, a multiscale time self-attention network (MTSA-Net) is proposed to mitigate the ship radiated self-noise and enhance the desired signal to improve the performance of signal detection system. Experimental results based on sea trial data indicate the effectiveness of our proposed method.

Advances and Trends in Channel Codes for Underwater Acoustic Communications

Due to the simultaneous existence of severe difficulties caused by multi-path, Doppler, and environmental noise caused by underwater acoustic channels, designing a stable and reliable underwater acoustic communication system (UWACS) is a challenging task. In addition to channel estimation and equalization, the technique of channel codes, which is capable of approaching Shannon’s capacity at a low encoding and decoding complexity, offers an efficient and reliable solution to improve the performance of the UWACS and thus draws extensive attention from the research community. By seeking a balance between communication efficiency and channel tolerance via multiple coding rate tuning, it has been recognized that the rate-compatible codes can achieve smooth rate-adaptive transmission under highly diverse underwater acoustic channel states. While there has been a substantial number of investigations on rate-compatible channel codes for the UWACS, in this paper, the research of rate-compatible channel codes has been summarized, reviewed, and compared, mainly focusing on the low-density parity-check (LDPC) and polar codes, which render flexibility in terms of code length and rate. Furthermore, typical research was evaluated and compared from the practical viewpoint of performance under real noise in a shallow water environment. Finally, problems encountered by the LDPC and polar codes for the UWACS were highlighted, and several future research issues were discussed.

Study of underwater sound effects relation to current velocity and Siganus sp. population in seaweed cultivation area

Underwater sound is one of the interesting phenomena in the ocean. Underwater sounds can impact marine organisms, especially fish and mammals. One of the organisms that can be influenced by underwater sound is the Siganus fish. This study aims to determine sound propagation with a certain level of fish pest repellents about the surface currents velocity and Siganus canaliculatus. population in seaweed cultivation areas. This research was conducted in a seaweed cultivation area in Baubau City, Southeast Sulawesi, Indonesia, in May-July 2022. The tool used is a seaweed pest repellent Ver.04 (Usirikan Ver 03). Underwater sound, at a strength level of 76.19-83.09 dB, can propagate quickly at 60 m in diameter of the sea area. The farthest distance multiplied at a strength of 62.41-69.30 dB can reach 150 m in diameter of the sea area. The fish population (S. canaliculatus) was not found in the seaweed cultivation area, with a sound level of 62.41 dB. The population of Siganus fish is around 2-4 ind/m2 at a sound level of 48.62 dB. Underwater sound with a sound level above 50 dB can impact the population of S. canaliculatus. Seaweed fish repellent can significantly affect behavior. of S. canaliculatus

Designing Acoustic Reconfigurable Intelligent Surface for Underwater Communications

The UnderWater Acoustic (UWA) communication is the foundation for oceanic information applications. However, existing UWA systems suffer from low data rate problem. Although the Multiple Input Multiple Output (MIMO) scheme is proved to be able to increase channel capacity in many terrestrial scenarios, the high cost and complexity of acoustic MIMO significantly limit its usage. Recently, the acoustic reconfigurable intelligent surfaces (acoustic RIS) concept has been proposed to address the aforementioned problem. In this paper, with the real-world constraints taken into consideration, three key components of the acoustic RIS are designed to realize the underwater RIS concept, including the new acoustic RIS hardware, the ultra-wideband (UWB) beamforming, and practical operation protocol. Specifically, a completely new hardware design of acoustic RIS is first provided, since existing electromagnetic RIS designed for terrestrial environments do not work for underwater acoustic waves. Then, the UWB beamforming solution is developed, since underwater acoustic RIS need to control the acoustic signals, whose bandwidth is comparable to its carrier frequency. Finally, the practical operation protocol is developed to realize the acoustic RIS functionalities in complex underwater environment. The acoustic RIS design is validated through both COMSOL multiphysics simulations and end-to-end Bellhop-based simulations.

Enhancing the robustness of ocean sound speed profile representation via interpretable deep matrix decomposition.

Developing an effective and robust representation model for ocean sound speed profiles (SSPs) is crucial for numerous ocean acoustic applications. However, the performance of existing sound speed profile (SSP) representation methods, such as empirical orthogonal function and K-singular value decomposition, heavily relies on the number of selected basis functions. This could lead to overfitting of noise, as these methods are unable to distinguish between signals and noise during the basis function learning process. To overcome these limitations and effectively learn a large number of basis functions with strong representation power from potentially noisy SSP data, we propose a novel algorithm called deep matrix decomposition (deep MD). This algorithm utilizes untrained deep neural networks as priors to reject noise within the interpretable matrix decomposition framework. To achieve optimal performance with deep MD, we propose a stopping strategy based on the rank estimate to determine the termination epoch. Experimental results using real-life datasets demonstrate that deep MD is robust against various types of noise and outperforms traditional SSP representation methods in terms of SSP reconstruction and characterizing the transmission loss in underwater acoustics.

Multi-Segment Verification FrFT Frame Synchronization Detection in Underwater Acoustic Communications

Multi-Segment Verification FrFT Frame Synchronization Detection in Underwater Acoustic Communications

The "4Rs" Teaching Method in Underwater Acoustic Communication Systems Course

According to the training goal of underwater acoustic engineering in Information Engineering University, we find the characteristics of underwater acoustic communication systems, summarize the experience during the years of teaching students practice, and then propose the teaching method of "underwater acoustic communication system" course, which is summarized as "4Rs". This paper firstly describes the meanings of "4Rs", which is suitable for our students. Then, we show the teaching contents and what the students can learn. And finally, we tell the implementation ideas of 4Rs teaching method, and through the consecutive practical teaching training, we can have many favorable teaching results.