Search Results (14)

Underwater acoustics is the optimal method for long-distance information transmission in aquatic environments. Hydrophones, as the core component of sonar systems, have found widespread application across multiple fields. However, existing types of hydrophones exhibit limited detection capabilities under low-signal conditions. To enhance low-frequency long-range detection performance, the development of new hydrophones featuring low power consumption, low frequency, high sensitivity, and miniaturization has become a research priority, with breakthroughs sought in the principle of electroacoustic conversion. Therefore, this study designed a frustum-cone triboelectric hydrophone (FCTH) based on friction layer materials, utilizing an indium-tin oxide (ITO) flexible conductive film on a polyethylene terephthalate (PET) substrate and a Polytetrafluoroethylene (PTFE) film. The sensor consists of a waterproof, sound-transparent polyurethane flow guide, silicone oil, and a frustum-cone triboelectric sensing unit based on a coupled membrane–cavity structure. The frustum-cone triboelectric sensing unit, based on a thin-film-perforated-tube resonance structure, enables omnidirectional detection of low-frequency hydroacoustic signals. The miniaturized design significantly reduces the volume of the FCTH. The acoustic–electric conversion relationship of the FCTH was derived using acoustic theory, thin-film vibration theory, and Maxwell’s displacement current theory. Furthermore, the low-frequency response characteristics of the frustum-cone triboelectric sensing unit were analyzed. The FCTH achieves a wide-frequency response ranging from 50 Hz to 12,000 Hz, with omnidirectional sensitivity and a maximum sensitivity of −174.6 dB. The FCTH achieves a wide-frequency response capability of 50 Hz to 12,000 Hz, with omnidirectional sensitivity and a maximum sensitivity of −174.6 dB. Additionally, through acoustic signal acquisition experiments in air, indoor, and outdoor water environments, the FCTH has been validated to possess excellent underwater acoustic detection performance and application potential across multiple scenarios. Full article

To address the demand of equivalent self-noise suppression in a distributed hydroacoustic sensing system, this study proposes a method to enhance the acoustic sensitivity and signal-to-noise ratio (SNR) using space division multiplexed (SDM) technology based on multi-core fiber (MCF). Specifically, a dual-channel demodulation system for distributed acoustic sensing is designed using MCF. The responses of different cores in MCF are almost consistent under external acoustic pressure, while their self-noise is inconsistent. Accordingly, the acoustic pressure phase sensitivity (APPS) and SNR gain based on the accumulation of dual-channel signals are analyzed, which are verified by experiments. It is shown that the self-noise correlation coefficient between the two cores is 0.11, increasing the noise power by 3.46 dB. The APPS is increased by 5.97 dB re 1 rad/μPa after the accumulation of two-core signals, which is close to the theoretical value (6 dB). The equivalent self-noise is reduced by 2.54 dB. The experimental results reveal that the enhancement of acoustic pressure phase shift sensitivity and SNR can be achieved by the space division multiplexing (SDM) of multi-core signals, which is of great significance for suppressing the equivalent self-noise of the system and realizing the acoustic pressure detection of weak underwater signals. Full article

Coastal areas have been the target of interdisciplinary research aiming to support studies related to their socio-economic and ecological value and their role in protecting backshore ecosystems and assets from coastal erosion and flooding. Some of these studies focus on either onshore or inshore areas using sensors and collecting valuable information that remains unknown and untapped by other researchers. This research demonstrates how satellite, aerial, terrestrial and marine remote sensing techniques can be integrated and inter-validated to produce accurate information, bridging methodologies with different scope. High-resolution data from Unmanned Aerial Vehicle (UAV) data and multispectral satellite imagery, capturing the onshore environment, were utilized to extract underwater information in Coral Bay (Cyprus). These data were systematically integrated with hydroacoustic including bathymetric and side scan sonar measurements as well as ground-truthing methods such as drop camera surveys and sample collection. Onshore, digital elevation models derived from UAV observations revealed significant elevation and shoreline changes over a one-year period, demonstrating clear evidence of beach modifications and highlighting coastal zone dynamics. Temporal comparisons and cross-section analyses displayed elevation variations reaching up to 0.60 m. Terrestrial laser scanning along a restricted sea cliff at the edge of the beach captured fine-scale geomorphological changes that arise considerations for the stability of residential properties at the top of the cliff. Bathymetric estimations derived from PlanetScope and Sentinel 2 imagery returned accuracies ranging from 0.92 to 1.52 m, whilst UAV reached 1.02 m. Habitat classification revealed diverse substrates, providing detailed geoinformation on the existing sediment type distribution. UAV data achieved 89% accuracy in habitat mapping, outperforming the 83% accuracy of satellite imagery and underscoring the value of high-resolution remote sensing for fine-scale assessments. This study emphasizes the necessity of extracting and integrating information from all available sensors for a complete geomorphological and marine habitat mapping that would support sustainable coastal management strategies. Full article

Optical interferometers are the main elements of interferometric sensing and measurement systems. Measuring their optical path difference (OPD) in real time and evaluating the measurement uncertainty are key to optimizing system noise and ensuring system consistency. With the continuous sinusoidal wavelength modulation of the laser, real-time OPD measurement of the main interferometer is achieved through phase comparison of the main and auxiliary interferometers. The measurement uncertainty of the main interferometer OPD is evaluated. It is the first evaluation of the impact of different auxiliary interferometer calibration methods on OPD measurements. A homodyne quadrature laser interferometer (HQLI) is used as the main interferometer, and a 3 × 3 interferometer is used as the auxiliary interferometer. The calibration of the auxiliary interferometer using optical spectrum analyzer scanning and ruler measurement is compared. The evaluation shows that the auxiliary interferometer is the most significant source of uncertainty and causes the total uncertainty to increase linearly with increasing OPD. It is proven that a high-precision calibration and large-OPD auxiliary interferometer can improve the real-time accuracy of OPD measurements based on the auxiliary interferometer. The scheme can determine the minimum uncertainty to optimize the system noise and consistency for vibration, hydroacoustic, and magnetic field measurements with OPDs of the ~m level. Full article

Spectrum-sensing technology is crucial for the development of underwater acoustic communication networks and plays a key role in detecting spectrum holes and channel occupancy. Energy detection technology, as the fundamental spectrum sensing technology in cognitive radio, has reached a mature level of development. Its application in hydroacoustic communications can significantly enhance the utilization of the hydroacoustic spectrum. However, due to the complexity of the hydroacoustic channel compared with that of the radio channel, the traditional double-threshold energy detection technique faces challenges such as fixed threshold values and limited flexibility. To address this, we propose a model for the hydroacoustic channel that incorporates a weight factor based on the signal-to-noise ratio in the algorithm. This allows for adaptive threshold values based on the user’s signal-to-noise environment, reducing false detection rates and improving overall detection performance. Through simulation experiments and comparisons, our proposed signal-to-noise weighted collaborative spectrum-sensing technique demonstrates superior detection performance compared with other spectrum-sensing techniques. Full article

Fiber-optic hydrophone (FOH) has significant potential in many applications of hydroacoustic sensing and underwater communication. A novel path-matched differential interferometer fiber optic hydrophone (PMDI-FOH) approach incorporating an integrated-optic component (IOC) is presented in this paper. It is presented to meet the demands for high-quality dynamic measurements, which solves the problems with the conventional homodyne detection system’s low modulation frequency. The IOC functions as a phase-generated carrier (PGC) component. The scheme is investigated both in theory and experiments. The theoretical and experimental results verify the effectiveness of the proposed scheme. It achieves a high SNR of up to 20.29 dB demodulations. The proposed system is cost-effective and has excellent potential in building next-generation underwater sensing and communication networks. Full article

Yangjiang coastal waters provide vital spawning grounds, feeding grounds, and nursery areas for many commercial fish species. It is important to understand the spatial distribution of fish for the management, development, and protection of fishery resources. In this study, an acoustic survey was conducted from 29 July to 5 June 2021. Meanwhile, remote sensing data were collected, including sea surface temperature (SST), chlorophyll concentration (Chla), sea surface salinity (SSS), and sea surface temperature anomaly (SSTA). The spatial distribution of density and biomass of fish was analyzed based on acoustic survey data using the geostatistical method. Combining with remote sensing data, we explored the relation between fish density and the environment based on the GAMs model. The results showed that fish are mainly small individuals. The horizontal distri-bution of fish density had a characteristic of high nearshore and low offshore. In the vertical direc-tion, fish are mainly distributed in surface-middle layers in shallow waters (<10 m) and in middle-bottom layers in deeper waters (>10 m), respectively. The deviance explained in the optimal GAM model was 59.2%. SST, Chla, SSS, and longitude were significant factors influencing fish density distribu-tion with a contribution of 35.3%, 11.8%, 6.5%, and 5.6%, respectively. This study can pro-vide a scientific foundation and data support for rational developing and protecting fishery re-sources in Yangjiang coastal waters. Full article

The Shantou-Taiwan shoal fishing ground in southeastern China supports a significant population of pelagic fish, which play a key role in the marine ecosystem. An acoustic survey was carried out using a digital scientific echosounder in June 2019. In this paper, the spatial distribution of pelagic fish is analyzed based on acoustic data using geostatistical analysis tools. Meanwhile, the relationship between fish density from acoustic data and sea surface environment factors were evaluated by using generalized additive models (GAMs) based on the satellite-based oceanographic data of sea surface temperature, sea surface chlorophyll-a concentration, sea surface height and sea surface wind. The results showed the following: (1) Fish density and acoustic biomass have strong spatial correlation; the optimal model for acoustic biomass is exponential and the optimal model for fish density is gaussian; based on optimal model, spatial interpolation analysis of fish density and acoustic biomass was performed using the ordinary kriging method, and the higher values of density and acoustic biomass were located in the central and eastern parts of the study area. The total fish density and acoustic biomass is 2.56 × 10¹⁰ ind. and 1908.99 m²/m, respectively. (2) In vertical distribution, fish gradually move to the middle and lower layers of water during daytime, and gather in the middle and upper layers of water at night. (3) The variance explanation rate of GAM was 88.2% which indicates that the model has an excellent fitting degree, and the results of GAM showed that longitude, sea surface temperature (SST), sea surface wind (SSW), and sea surface height (SSH) had significant effects on fish density. Results of this study were meaningful for understanding the distribution of fishery resources, and as a guide for fish management in the Shantou offshore water. Full article

Recent advances in deep learning, including the development of AlexNet, Residual Network (ResNet), and transfer learning, offer unprecedented classification accuracy in the field of machine vision. A developing application of deep learning is the automated identification and management of aquatic invasive plants. Classification of submersed aquatic vegetation (SAV) presents a unique challenge, namely, the lack of a single source of sensor data that can produce robust, interpretable images across a variable range of depth, turbidity, and lighting conditions. This paper focuses on the development of a multi-sensor (RGB and hydroacoustic) classification system for SAV that is robust to environmental conditions and combines the strengths of each sensing modality. The detection of invasive Hydrilla verticillata (hydrilla) is the primary goal. Over 5000 aerial RGB and hydroacoustic images were generated from two Florida lakes via an unmanned aerial vehicle and boat-mounted sonar unit, and tagged for neural network training and evaluation. Classes included “HYDR”, containing hydrilla; “NONE”, lacking SAV, and “OTHER”, containing SAV other than hydrilla. Using a transfer learning approach, deep neural networks with the ResNet architecture were individually trained on the RGB and hydroacoustic datasets. Multiple data fusion methodologies were evaluated to ensemble the outputs of these neural networks for optimal classification accuracy. A method incorporating logic and a Monte Carlo dropout approach yielded the best overall classification accuracy (84%), with recall and precision of 84.5% and 77.5%, respectively, for the hydrilla class. The training and ensembling approaches were repeated for a DenseNet model with identical training and testing datasets. The overall classification accuracy was similar between the ResNet and DenseNet models when averaged across all approaches (1.9% higher accuracy for the ResNet vs. the DenseNet). Full article

Hydrographic surveys enable the acquisition and processing of bathymetric data, which after being plotted onto nautical charts, can help to ensure safety of navigation, monitor changes in the coastal zone, and assess hydro-engineering structure conditions. This study involves the measurement of waterbody depth, identification of the seabed shape and geomorphology, the coastline course, and the location of underwater obstacles. Hydroacoustic systems mounted on vessels are commonly used in bathymetric measurements. However, there is also an increasing use of Unmanned Aerial Vehicles (UAV) that can employ sensors such as LiDAR (Light Detection And Ranging) or cameras previously not applied in hydrography. Current systems based on photogrammetric and remote sensing methods enable the determination of shallow waterbody depth with no human intervention and, thus, significantly reduce the duration of measurements, especially when surveying large waterbodies. The aim of this publication is to present and compare methods for determining shallow waterbody depths based on an analysis of images taken by UAVs. The perspective demonstrates that photogrammetric techniques based on the SfM (Structure-from-Motion) and MVS (Multi-View Stereo) method allow high accuracies of depth measurements to be obtained. Errors due to the phenomenon of water-wave refraction remain the main limitation of these techniques. It was also proven that image processing based on the SfM-MVS method can be effectively combined with other measurement methods that enable the experimental determination of the parameters of signal propagation in water. The publication also points out that the Lyzenga, Satellite-Derived Bathymetry (SDB), and Stumpf methods allow satisfactory depth measurement results to be obtained. However, they require further testing, as do methods using the optical wave propagation properties. Full article

The amount of sediments transported by a river is difficult to estimate, while this parameter could influence channel geometry. It is possible to derive the bedload transport rate per unit width of the river channel by measuring the migration distance of bedform profiles over time and thickness of bedload layer in motion. Other possible methods include instrumental measurements using bedload traps and empirical formulas. It is possible to use remote-sensing techniques to measure the dynamics of bedform movements and geometries. Landsat images and aerial photographs have been used for this. A new source of remote-sensing information is radar satellite images. Sentinel-1 images have a temporal resolution of 2–3 days and spatial resolution of 25 m at middle latitudes, which make them usable on large rivers. The research area is the 814–820 km reach of the Lower Vistula River, where seven alternate sandbars were selected. The bank lines of the sandbars were delineated on Sentinel-1 images sensed during two low-flow periods of 4 August–26 September 2018 and 1 July–31 August 2019, when discharges at low flow were similar. From water stage observations at gauges, water elevations were assigned to every bank line of the alternate sandbars. The following morphometric parameters were calculated: alternate sandbar centers, volumes and longitudinal profile. Average daily movement of the sandbars in the period 4 August 2018–1 July 2019 was calculated as 0.97 m·day⁻¹. A similar alternate sandbar movement velocity was obtained from a study of Sentinel-2 optical satellite images and hydro-acoustic measurements on the Lower Vistula River. Having depth of bedload in motion and alternate sandbar shift velocities, it was possible to calculate the rate of bedload transport according to the Exner approach formula. Rate of bedload transport was estimated as q_b = 0.027 kg·s⁻¹·m⁻¹. This study shows a novel use of Sentinel-1 images to study the 3D geometry and movement rate of sandbars. Full article

In this study, a remote sensing-based method of mapping and predicting fish spatial distribution in inland waters is developed. A combination of Earth Observation data, in-situ measurements, and hydroacoustics is used to relate fish biomass distribution and water-quality parameters along the longitudinal transect of the Římov Reservoir (Czech Republic) using statistical and machine learning techniques. Parameter variations and biomass distribution are estimated and validated, and apparent trends are explored and discussed, together with potential limitations and weaknesses. Water-quality parameters exhibit longitudinal gradients along the reservoir, while calculations reveal a distinct fish assemblage pattern observed as a patchy overall biomass distribution. Although the proposed methodology has a great potential for sustainable water management, careful planning is needed to ensure the simultaneous acquisition of remote sensing and in-situ data to maximize calibration accuracy. Full article

Quantitative acoustic marine habitat mapping needs to consider the impact of macrobenthic organisms on backscatter data. However, the sensitivity of hydroacoustic systems to epibenthic life is poorly constrained. This study explores the impact of a benthic community with sparse abundance on seafloor microroughness and acoustic backscatter at a sandy seafloor in the German North Sea. A multibeam echo sounder survey was ground-truthed by lander measurements combining a laser line scanner with sub-mm resolution and broad-band acoustic transducers. Biotic and abiotic features and spatial roughness parameters were determined by the laser line scanner. At the same locations, acoustic backscatter was measured and compared with an acoustic scatter model utilizing the small-roughness perturbation approximation. Results of the lander experiments show that a coverage with epibenthic features of 1.6% increases seafloor roughness at spatial wavelengths between 0.005–0.03 m, increasing both spectral slope and intercept. Despite the fact that a strong impact on backscatter was predicted by the acoustic model based on measured roughness parameters, only a minor (1.1 dB) change of backscatter was actually observed during both the lander experiments and the ship-based acoustic survey. The results of this study indicate that benthic coverage of less than 1.6% is insufficient to be detected by current acoustic remote sensing. Full article

Invasive aquatic plant species can expand rapidly throughout water bodies and cause severely adverse economic and ecological impacts. While mechanical, chemical, and biological methods exist for the identification and treatment of these invasive species, they are manually intensive, inefficient, costly, and can cause collateral ecological damage. To address current deficiencies in aquatic weed management, this paper details the development of a small fleet of fully autonomous boats capable of subsurface hydroacoustic imaging (to scan aquatic vegetation), machine learning (for automated weed identification), and herbicide deployment (for vegetation control). These capabilities aim to minimize manual labor and provide more efficient, safe (reduced chemical exposure to personnel), and timely weed management. Geotagged hydroacoustic imagery of three aquatic plant varieties (Hydrilla, Cabomba, and Coontail) was collected and used to create a software pipeline for subsurface aquatic weed classification and distribution mapping. Employing deep learning, the novel software achieved a classification accuracy of 99.06% after training. Full article