Search Results (227)

For a bottom-moored vertical line array in deep ocean, the underwater maneuvering source will produce interference patterns in both grazing angle–distance (vertical-time record, VTR) and frequency–grazing angle (wideband beamforming output) domains, respectively, and the interference period is modulated by the source depth. Based on these characteristics, an interference feature fusion (IFF) method is proposed in the space–time–frequency domain for source depth estimation, in which the principal interference mode of the VTR is extracted adaptively and the depth ambiguity function is constructed by fusing the ambiguity sequence, mapped by wideband beamforming intensity, and the principal interference mode, which can achieve the long-term depth estimation and recognition of underwater sources without requiring environmental information. Theoretical analysis and simulation results indicate that the IFF can suppress the false peaks generated by the generalized Fourier transform (GFT) method, and the depth estimation error of the IFF for a single source is reduced by at least 47% compared to GFT. In addition, the IFF is proven to be effective at separating the depth of multiple adjacent sources (with the average estimation error reduced by 28%) and exhibits a high degree of robustness within the fluctuating acoustic channel (with the average estimation error reduced by 12%). Full article

The escalating conflict between cetaceans and fisheries underscores the need for efficient mitigation strategies that balance conservation priorities with economic viability. This study presents a TinyML-driven approach deploying an optimized Convolutional Neural Network (CNN) on a Raspberry Pi Zero 2 W for real-time detection of bottlenose dolphin whistles, leveraging spectrogram analysis to address acoustic monitoring challenges. Specifically, a CNN model previously developed for classifying dolphins’ vocalizations and originally implemented with TensorFlow was converted to TensorFlow Lite (TFLite) with architectural optimizations, reducing the model size by 76%. Both TensorFlow and TFLite models were trained on 22 h of underwater recordings taken in controlled environments and processed into 0.8 s spectrogram segments (300 × 150 pixels). Despite reducing model size, TFLite models maintained the same accuracy as the original TensorFlow model (87.8% vs. 87.0%). Throughput and latency were evaluated by varying the thread allocation (1–8 threads), revealing the best performance at 4 threads (quad-core alignment), achieving an inference latency of 120 ms and sustained throughput of 8 spectrograms/second. The system demonstrated robustness in 120 h of continuous stress tests without failure, underscoring its reliability in marine environments. This work achieved a critical balance between computational efficiency and detection fidelity (F1-score: 86.9%) by leveraging quantized, multithreaded inference. These advancements enable low-cost devices for real-time cetacean presence detection, offering transformative potential for bycatch reduction and adaptive deterrence systems. This study bridges artificial intelligence innovation with ecological stewardship, providing a scalable framework for deploying machine learning in resource-constrained settings while addressing urgent conservation challenges. Full article

The most hydrodynamic swimming position occurs with the head submerged, highlighting the benefit of reduced breathing frequency for efficiency. This study aimed to characterize and compare kinematics between two breaststroke breathing patterns—breathing every cycle and breathing every two cycles—while also analyzing intra-cyclic velocity variation (dv) and fractal dimension. In the breathing every cycle pattern, each cycle included a breath. In the breathing every cycle pattern, swimmers breathed once per cycle. In the breathing every two cycles pattern, breathing occurred every second cycle, resulting in three types of cycles: breathing, non-breathing, and the breathing cycle following a non-breathing cycle. To ensure familiarity with the new breathing pattern, swimmers underwent a six-week intervention program. They then performed three maximal 25 m bouts in each breathing pattern. Kinematic data were collected using a dual-media optoelectronic system (Qualisys AB, Sweden), integrating underwater and dry-land camera recordings. The results showed minimal differences between the three cycle types. The non-breathing cycle had the shallowest and deepest head positions, the lowest horizontal head amplitude out of water, and the smallest vertical head amplitude. It also had the fastest maximum vertical velocity of the feet and maximum center of mass velocity in the swimming direction. Full article

Urban noise pollution extends into aquatic environments, influencing underwater ecosystems. This study examines the effectiveness of acoustic indicators in characterizing urban underwater soundscapes using hydrophone recordings. Three indices, the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), and Normalized Difference Soundscape Index (NDSI), were analyzed to assess their ability to distinguish anthropogenic and natural acoustic sources. The results indicate that the ACI tracks urban noise fluctuations, particularly from vehicles and trams, while the ADI primarily reflects transient environmental interferences. The NDSI, while designed to differentiate biophony from anthropogenic noise, proves unreliable in urban underwater settings, often misclassifying noise sources. These findings highlight the limitations of traditional acoustic indices in urban aquatic environments and emphasize the need for refined methods to improve hydrophone data interpretation. Thus, this study aims to understand the acoustic indicators’ interactions with underwater urban noise, which is crucial for enhancing environmental monitoring and noise mitigation strategies. Full article

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (Tursiops truncatus) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries. Full article

Distributed Acoustic Sensing (DAS) technology presents an innovative method for marine monitoring by adapting existing underwater optical fiber networks. This paper examines the use of DAS with the Istituto Nazionale di Fisica Nucleare–Laboratori Nazionali del Sud (INFN-LNS) optical fiber infrastructure in the Gulf of Catania, Eastern Sicily, Italy. This region in the Western Ionian Sea provides a unique natural laboratory due to its tectonic and volcanic activity, proximity to Mount Etna, diverse marine ecosystems and significant human influence through maritime traffic. By connecting a 28 km long optical cable to an Alcatel Submarine Network OptoDAS interrogator, DAS successfully detected a range of natural and human–made signals, including a magnitude 3.5 ML earthquake recorded on 14 November 2023, and acoustic signatures from vessel noise. The earthquake–induced Power Spectral Density (PSD) increased to up to 30 dB above background levels in the 1–15 Hz frequency range, while vessel noise exhibited PSD peaks between 30 and 60 Hz with increases of up to 5 dB. These observations offered a detailed spatial and temporal resolution for monitoring seismic wave propagation and vessel acoustic noise. The results underscore DAS’s capability as a robust tool for the continuous monitoring of the rich underwater environments in the Gulf of Catania. Full article

This study investigated the impact of optically active substances on light attenuation in a tropical eutrophic urban reservoir under different seasonal conditions. Diffuse attenuation coefficients for photosynthetically active radiation (Kd_PAR) and ultraviolet radiation (Kd_UVA and Kd_UVB) were measured at three representative sites and correlated with water quality parameters (chlorophyll-a, total suspended solids [TSS], dissolved organic carbon, and colored dissolved organic matter [CDOM]). The results revealed significant spatial and seasonal differences, with the highest attenuation observed during the rainy season. The Ilha site exhibited the greatest coefficients (Kd_PAR = 6.0 m⁻¹, Kd_UVA = 17.9 m⁻¹, Kd_UVB = 19.0 m⁻¹), while lower values were recorded at Barragem (Kd_PAR = 2.4 m⁻¹, Kd_UVA = 9.1 m⁻¹, Kd_UVB = 12.0 m⁻¹) and Igrejinha (Kd_PAR = 3.1 m⁻¹, Kd_UVA = 10.8 m⁻¹, Kd_UVB = 11.9 m⁻¹). Statistical analyses showed strong correlations between TSS and Kd_PAR (r = 0.66) and between CDOM and both Kd_UVA (r = 0.66) and Kd_UVB (r = 0.59), with regression models confirming TSS and CDOM as key predictors of light attenuation. These findings underscore the pivotal role of particulate and dissolved organic matter in underwater light dynamics, emphasizing the need to reduce their input during periods of heavy rainfall. Full article

Various glutenite reservoirs, developed by fans, can be found in the Junggar Basin. Among these, there are different interpretations of the glutenite reservoirs formed by shallow-water fan deltas in the Triassic system in the northwestern margin of the basin. The characteristics of these deltas and their reservoir architecture have not been understood clearly. It seriously restricts the advancement of the subsequent development of the oilfield. Therefore, it is of great significance to carry out the fine reservoir architecture characterization of the shallow-water fan delta in this area. In this study, the upper member of the Triassic Karamay Formation in the Bai 21 area was selected as the study site. Through core analysis, nine types of sedimentary microfacies, including mudflow deposit, braided river, flood plain, underwater main channel, underwater distributary channel, overbank channel, interchannel deposition, estuary bar, and sheet sand, are found. Through mixed-phase wavelet frequency extension, the main frequency of seismic data is moderately increased and the frequency band is broadened, which makes it possible to identify the thin layer of about 10 m. Through continuous stratal slicing, the thin-layer sedimentary bodies that are difficult to be distinguished in the vertical direction are depicted, and the distribution of sedimentary bodies at different vertical positions is obtained by browsing the slices. Through color fusion based on seismic frequency decomposition, the fusion results contain information reflecting the thickness, and the characterization effect of the fan boundary is significantly improved. In summary, this study depicts the distribution of single-stage fans and recognizes the sand body development characteristics of the two-stage fans. Research suggests that two large shallow-water fan-delta complexes were discovered in the S3 sand group within the study area. Each fan possesses a multilevel branching distributary channel system, resulting in multiple horizontally oriented lobes. Within the fan-delta complex in S3, which is the third sand group in the Upper Triassic Karamay Formation, the fan complex can be divided into two single-stage fans recorded in the sublayer S31 and S32 upward. The two-stage fan deltas show inherited development characteristics in sedimentary characteristics and form in a regression sequence. The sand bodies formed during the low-water-level stage in S31 are thick, with few interlayers developed. Most sand bodies intersect each other vertically. In the shallow fan delta, a widespread estuary bar is deposited, which develops along the underwater distributary channel. This research enhances the understanding of shallow-water fan-delta reservoirs in the study area, and it provides a precise target for oilfield development and solves the key problem of unclear understanding of sand body distribution and combination relationships, which restricts development. Full article

Background/Objectives: This study aimed to evaluate the effects of artificial intelligence (AI)-assisted virtual reality (VR) applications on preoperative anxiety levels and vital signs in children undergoing endoscopy. Methods: A randomized controlled trial design was employed, including a total of 80 children aged 8–17 years (40 in the intervention group and 40 in the control group). Children in the intervention group were exposed to VR applications featuring space and underwater themes, while the control group received standard procedures. Anxiety levels were assessed using the “State-Trait Anxiety Inventory for Children (STAIC)”, and vital signs were evaluated through measurements of systolic and diastolic blood pressure, heart rate, temperature, and SpO₂. Results: VR applications significantly reduced anxiety scores in the intervention group (36.3 ± 1.9), while no significant changes were observed in the control group (45.4 ± 2.74) (p < 0.001). Regarding vital signs, more favorable outcomes were observed in the intervention group. Systolic blood pressure was measured as 89 ± 6.7 mmHg in the intervention group and 96.5 ± 10.5 mmHg in the control group (p < 0.001). Diastolic blood pressure was 60.8 ± 4.7 mmHg in the intervention group and 63.8 ± 6 mmHg in the control group (p < 0.05). Heart rate was recorded as 88.7 ± 10.1 bpm in the intervention group and 94.5 ± 14.8 bpm in the control group (p < 0.05). SpO₂ levels were 98 ± 1 in the intervention group and 96.2 ± 1.3 in the control group (p < 0.001). Conclusions: AI-assisted VR applications emerge as an effective non-pharmacological method for reducing preoperative anxiety and promoting physiological stability in children. This approach holds the potential to enhance pediatric experiences during invasive procedures such as endoscopy. Full article

The time–frequency characteristics of dolphin whistle signals under diverse ecological conditions and during environmental changes are key research topics that focus on the adaptive and response mechanisms of dolphins to the marine environment. To enhance the quality and utilization of passive acoustic monitoring (PAM) recorded dolphin whistles, the challenges faced by current wavelet thresholding methods in achieving precise threshold denoising under low signal-to-noise ratio (SNR) are confronted. This paper presents a thresholding denoising method based on stationary wavelet transform (SWT), utilizing suppression impulsive and autocorrelation function (SI-ACF) to select precise thresholds. This method introduces a denoising metric $\rho$, based on the correlation of whistle signals, which facilitates precise threshold estimation under low SNR without requiring prior information. Additionally, it exploits the high amplitude and broadband characteristics of impulsive noise, and utilizes the multi-resolution information of the wavelet domain to remove impulsive noise through a multi-level sliding window approach. The SI-ACF method was validated using both simulated and real whistle datasets. Simulated signals were employed to evaluate the method’s denoising performance under three types of typical underwater noise. Real whistles were used to confirm its applicability in real scenarios. The test results show the SI-ACF method effectively eliminates noise, improves whistle signal spectrogram visualization, and enhances the accuracy of automated whistle detection, highlighting its potential for whistle signal preprocessing under low SNR. Full article

Low-frequency ocean noise (50–500 Hz) was recorded by a single omnidirectional hydrophone in the open waters of the Chukchi Plateau from 31 August 2021 to 6 September 2021 (local time). After other non-wind interference was filtered out, wind-generated noise source levels (NSLs) were extracted from the wind-generated noise. The correlation coefficients between the one-third octave wind-generated NSLs and sea surface wind speed exceed 0.84, an improvement of approximately 10% compared to those between the raw data and the wind speed. For 200–500 Hz, the wind-generated NSLs are highly consistent with Wilson’s (1983) estimated curve. The 50–300 Hz results closely match those of Chapman and Cornish (1993) from vertical line array (VLA) measurements. Both demonstrate the feasibility of extracting wind-generated NSLs by utilizing a single omnidirectional hydrophone in the Chukchi Plateau’s open waters. Furthermore, the research results of wind speed dependence and frequency dependence can be applied to calculate wind-generated NSLs in the Chukchi Plateau. Wind-derived ocean ambient noise data are useful for background correction in underwater target detection, recognition, tracking, and positioning. Full article

This study investigates underwater debris in a freshwater lake using remotely operated vehicles (ROVs) during two distinct survey periods: 2019 and 2024. The primary objective was to count and document visible debris (metal and plastic) on the lakebed based on ROV video recordings. A total of 356 debris items were observed in 2019, while only 39 items were recorded in 2024. The notable decrease in debris visibility in 2024 is likely attributed to dense algal growth during the survey months, which hindered the visual identification of objects on the lakebed. The study highlights the challenges of monitoring underwater debris in freshwater systems, particularly during periods of high algal activity, which can significantly impact visibility and detection efforts. While ROVs have proven effective in identifying submerged debris in clear water, this research underscores their limitations under reduced visibility conditions caused by algal blooms, turbidity diminishing the video quality. The results provide valuable insights into the temporal variation in debris visibility and contribute to ongoing efforts to improve freshwater debris monitoring techniques. Full article

This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations. Full article

The Trabocchi Coast in the Chieti district of the mid-Adriatic (Italy) is one of the few rocky areas within the General Fisheries Commission GSA 17, alongside Mount Conero (Ancona 43°00′01″ N 13°52′13″ E) and the small San Nicola Rock (Ascoli Piceno; 43°32′0″ N 13°36′0″ E). This coastline is known for its biodiversity-rich bays, inlets, and submerged cliffs. Since 2015, annual biodiversity surveys have been conducted in the area, focusing on marine species richness and the identification of non-native species. In September 2024, a juvenile ornate wrasse (Thalassoma pavo) was documented for the first time in the middle Adriatic during an underwater visual survey at Trabocco Punta Torre, a key site along the Trabocchi Coast near artificial and biogenic reefs. This record extends the known distribution of T. pavo, a thermophilic species previously reported only along the southern Adriatic coast of Puglia. This is the first confirmed sighting on the middle and northern Adriatic coast of Italy. The discovery highlights the importance of ongoing biodiversity monitoring to track changes in marine ecosystems, particularly as the Adriatic Sea faces environmental shifts linked to climate warming. The presence of T. pavo in this area suggests the potential for the species to establish populations in previously uninhabited northern regions. Further research is needed to explore the role of biotic and abiotic factors—such as water temperature, current patterns, and habitat availability—in the survival and potential reproduction of T. pavo in the middle Adriatic. The observation contributes to the broader understanding of the meridionalization process in the Adriatic Sea, where rising water temperatures are facilitating the northward expansion of thermophilic species. Continuous monitoring is recommended to assess the long-term viability of T. pavo populations in the Adriatic Sea and better predict the impacts of ongoing climate change on marine biodiversity. Full article

Underwater drag reduction using superhydrophobic surfaces is a promising method due to its simplicity and low energy consumption. However, most attempts to obtain drag reduction using superhydrophobic surfaces have failed. Explanations such as air layer or air bubble vanishment and surface roughness are proposed in the existing works. In this work, the drag increase caused by spherical air bubbles adhering to the superhydrophobic surface is reported, and the drag increase mechanism is revealed by numerical simulation. In the water tunnel and towing tank experiment, we found that the experimental samples exhibited drag increase around a specific velocity, and the recorded optical images showed that the superhydrophobic surfaces were adhered by spherical air bubbles. Through numerical simulation, we found that the spherical air bubbles not only reduced the frictional drag but also introduced pressure drag. The drag increase was produced when the introduced pressure drag exceeded the reduced frictional drag. This work might be helpful for the drag reduction application of the superhydrophobic surface. Full article