Search Results (961)

Distributed passive acoustic tracking is an important technique for detecting and localizing a non-cooperative underwater target, in which the frequency difference of arrival (FDOA) is a widely used measurement. FDOA reflects the differences in the target’s radial velocity relative to spatially distributed receiving nodes through Doppler-induced variations in the instantaneous frequencies of line-spectrum components. However, conventional FDOA-based methods rely heavily on the stable and accurate estimation of instantaneous line-spectrum frequencies, and their performance degrades when the line spectrum is affected by frequency fluctuations caused by target operating variations and external disturbances. To address this issue, this paper proposes a new measurement, the cross-correlation frequency difference of arrival (CFDOA), which exploits the temporal correlation of line-spectrum to characterize inter-node radial-velocity differences and reduces the reliance on accurate instantaneous-frequency estimation. To evaluate the effect of the proposed CFDOA measurement on positioning performance, a unified FDOA/CFDOA measurement equation is established within the same target position estimation framework. In addition, for scenarios with a limited number of receiving nodes, a recursive estimation scheme combining constrained initial-state search and particle filtering is developed. The simulation and sea-trial results demonstrate that, in the presence of line-spectrum frequency fluctuations, the proposed CFDOA measurement yields more accurate position estimates than conventional FDOA. Full article

Underwater object detection using forward-looking sonar presents fundamental challenges absent from terrestrial imagery: low-contrast single-channel inputs, multi-scale acoustic shadows, and object classes spanning a wide range of acoustic scattering characteristics. Three coordinated modifications to the YOLOv8 framework are proposed to address structural limitations of standard bottleneck chains for this domain. A fractional-order feature propagation mechanism based on Grunwald–Letnikov discretization enables each bottleneck to access a decaying-weighted history of all prior intra-chain feature states via a single learnable scalar per block. A boundary-aware gating module with joint spatial-channel attention selectively suppresses fractional correction at geometric boundary locations. A parameter-free energy-based attention module applied in the detection neck exploits the local statistical distinctiveness of genuine acoustic features during multi-scale fusion. Evaluated on the Underwater Acoustic Target Detection dataset, the proposed system achieves mAP50 of 0.8635 and mAP50-95 of 0.3964, improvements of 0.0188 and 0.0136 respectively over the YOLOv8n baseline at less than 2.0% parameter overhead, surpassing larger generic YOLOv8 variants on mAP50. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

In underwater target recognition, the scattering characteristics of small targets are weak and highly sensitive to observation angles, posing significant challenges to achieving stable and robust recognition in complex environments. Existing methods are mainly data-driven and rely on closed-set classifiers, which often lack physical interpretability and show limited generalization under different observation conditions. To address these issues, a physics-guided deep embedding framework for underwater target recognition is proposed. Firstly, an encoder–decoder network is designed to learn representative and physically consistent scattering features from measured echo frequency spectra. The encoder is then extracted to construct a Triplet-based embedding model, which maps high-dimensional scattering spectra into a discriminative low-dimensional feature space. In the embedding space, a similarity-based decision strategy is further adopted to replace the traditional classifier, and recognition is achieved by evaluating the relationships among embedded features. Experimental results show that the proposed method achieves robust recognition performance under varying observation angles and establishes an interpretable connection between scattering characteristics and recognition results. The proposed framework provides an effective way to combine physics-guided feature learning with deep embedding methods for underwater target recognition. Full article

Honeybees are indispensable pollinators for agricultural ecosystems, and a colony’s stability and reproductive capacity depend critically on the presence of a healthy queen. Acoustic monitoring has emerged as a promising non-invasive, lighting-independent approach for long-term colony observation. However, existing studies have largely been confined to single-apiary datasets or merged datasets from multiple similar apiaries for model training. Moreover, model evaluation has relied primarily on overall performance metrics, with insufficient attention to cross-region generalization and the detection of queen loss, a rare but critical condition. This study systematically investigates three complementary strategies: noise-augmented data diversification, lightweight convolutional neural network (CNN) architecture optimization via comprehensive ablation experiments, and transfer learning with fine-tuning to bridge the domain gap between source and target apiaries. Under cross-apiary evaluation, the proposed approach achieves an accuracy of 92.79%, a negative-class F1-score of 0.7900, and a negative-class recall of 0.7834 when only limited target-domain training samples are available. With full target-domain training data, the same strategy further attains an accuracy of 95.05%, a negative-class F1-score of 0.8596, and a negative-class recall of 0.8733. t-distributed Stochastic Neighbor Embedding (t-SNE) visualization demonstrates that noise augmentation effectively expands sample diversity in the feature space, while Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps confirm the successful transfer of source-domain acoustic features to the target domain. This work provides a practical approach for deploying acoustic-based queen status monitoring across diverse apiaries with minimal local data collection. Full article

As critical air-dropped acoustic sensors for underwater target detection, sonobuoys are frequently compromised by severe hydrodynamic self-noise induced by sea-surface wave excitation, which masks target signals and degrades detection performance. While structural optimizations have traditionally been employed, effective signal-processing-based noise suppression remains challenging because the noise is non-stationary and physically coupled with buoy motion. To address the limited physical interpretability of conventional decomposition methods, this study proposes a physically guided self-noise suppression framework: VMD Constrained by DCCA Correlation (VMD-DCCA). The main contribution is the incorporation of the Detrended Cross-Correlation Analysis (DCCA) coefficient between the sonobuoy’s vertical velocity and the acoustic data as a correlation-dependent constraint within the Variational Mode Decomposition (VMD) optimization process. This motion prior allows more targeted isolation of motion-induced components than standard data-driven decomposition. Simulation and controlled water-tank results show that VMD-DCCA outperforms EEMD and standard VMD, achieving an SNR improvement of approximately 15 dB at an input SNR of −9 dB. The reconstructed signal also preserves visible narrowband spectral lines in the time-frequency representation. These results demonstrate the potential of the proposed method for controlled or post-processing sonobuoy self-noise reduction, while validation under irregular open-ocean conditions remains necessary. Full article

Underwater target detection via side-scan sonar is vital for defense and economy but hindered by sparse targets, high data costs, and feature extraction difficulties due to textureless acoustic data and limited samples. To overcome these limitations, particularly for few-shot, small-object detection, we propose a Spatial Distribution Probability-Guided Detection Framework to aid Unmanned Underwater Vehicles (UUVs) in precise localization and clustering. The framework features a novel module that leverages a pre-trained Vision Foundation Model (DINOv3) to generate spatial distribution probability maps, guiding a Transformer-based network for accurate detection with scarce data. Additionally, it incorporates a Target Position Calculation Module and a DBSCAN-based post-processing module to determine global geographic coordinates and cluster discrete points, respectively. Experiments were conducted on both a Public Mine Detection Dataset and a self-collected dataset containing simulated mines and buoys. Ablation studies and comparison experiments demonstrated that the proposed guidance mechanism significantly improves detection performance. Furthermore, two comb-search missions verified that the system could accurately locate and cluster targets, distinguishing real targets from false detections (noise). These results confirm the framework’s efficacy in enabling high-precision perception and autonomous operations for complex underwater inspection tasks. Full article

Manual arc welding is highly dependent on operator skill, leading to variability in weld quality and an increased risk of defects; therefore, reliable monitoring methods for Shielded Metal Arc Welding (SMAW) are required, particularly in manual environments where process variability and environmental noise are inherent. This study proposes a monitoring approach for classifying SMAW process parameters using airborne acoustic signals generated by the welding arc. Welding experiments were conducted on carbon steel plates of different thicknesses (3, 6, and 12 mm) using E6010, E6011, E6013, and E7018 electrodes under Alternating Current (AC) and Direct Current (DC) configurations; acoustic signals were recorded in real time and processed using Mel-Frequency Cepstral Coefficients (MFCCs) and deep audio embeddings from pre-trained VGGish and YAMNet models as inputs to artificial neural network classifiers for multi-output classification of welding process parameters. Model performance was evaluated using per-target metrics (accuracy and macro F1-score) and joint multi-output metrics (Exact Match and Hamming Accuracy). MFCC-based models significantly outperformed embedding-based approaches, achieving up to 94.51% Exact Match and 97.88% Hamming Accuracy, while reducing computational costs. These results demonstrate the feasibility of SMAW monitoring using arc sound, suggesting that spectral features are an effective solution for welding-process monitoring and a promising foundation for future weld-quality monitoring systems. Full article

Laser beam welding (LBW) of aluminium busbar-to-terminal connections for electric-vehicle battery packs requires precise in-process monitoring. Membrane-free optical microphones provide a high-bandwidth (DC–MHz) acoustic channel that captures keyhole, melt-pool, and plume dynamics. This study proposes Acoustic Signal Intelligence (ASI), a deep learning framework for LBW state classification from a single optical microphone, evaluated on an open dataset (183 AA1050 welds, $f_s$ = 2.5 MHz) under a five-class taxonomy: lack of fusion, lack of connection, sound, marginal, and piercing. The contributions are: (i) a compact 1-D CNN encoder on a mel-scale STFT spectrogram, reaching the highest macro-F1 (0.72 mean across three-fold replicate-out cross-validation) and 100% piercing recall in every fold—a multi-representation fusion variant adding a wavelet-packet decomposition and a 24-feature library targeting the 8, 63 and 110 kHz keyhole-resonance peaks was evaluated as an ablation arm and did not survive cross-validation, so the proposed model is mel-only; (ii) a systematic benchmark against six classical-ML and four deep learning baselines in which Transformer-hybrid ablations and ACGAN-style augmentation underperform compared to the compact CNN on the 122-sample training set, with the Transformer underperformance confirmed by a 30-configuration grid search over learning rate, weight decay, and dropout (best tuned macro-F1 = 0.441 vs. CNN 0.724); and (iii) a Grad-CAM analysis that recovers the keyhole-resonance bands without prior knowledge. A single optical microphone is thus a viable real-time alternative to multi-sensor stacks for battery-pack laser welding. Full article

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BrCa), owing to its lack of targetable receptors and resistance to chemical and molecularly targeted therapeutic approaches. While chemotherapy and surgical resection remain the standard of care, these interventions have significant side effects and varying patient outcomes. Thermally ablative focused ultrasound (T-FUS)—a non-invasive and non-ionizing therapy that utilizes targeted acoustic energy to debulk tumors—has displayed immunomodulatory effects in BrCa. However, T-FUS as a monotherapy has had limited clinical efficacy in TNBC due to the presence of anti-inflammatory immunosuppressive myeloid cells (isMCs). We hypothesized that the elimination of isMCs or initiating tumoricidal activity from them would lead to augmented activity of T-FUS. Thus, we interrogated the ability of myeloablative chemotherapies and antibodies; myeloid recruiting chemokine receptor blockade; and TLR agonists to remodel the tumor myeloid populations. Consistent with our previous studies, we found that while myeloablative chemotherapies decreased circulating isMCs, they had little impact on intratumoral isMCs. In contrast, antibodies targeting Ly6C and Ly6G ablated intratumoral isMCs and systemic isMCs, yet their effect was transient and was accompanied by a surprising depletion of T cells. While targeting CCR2, the dominant chemokine receptor for intratumoral isMC diminished a large subset of immunosuppressive cells within the TME; it also depleted T cells and dendritic cells. Contrary to previous studies, TLR stimulation failed to repolarize myeloid cells into a pro-inflammatory, tumoricidal phenotype but did lead to their depletion from the tumor microenvironment (TME) and mobilization of conventional dendritic cells to the draining lymph nodes. We therefore hypothesized that combining isMC depletion and TLR-driven immune activation would enhance FUS efficacy; however, this combinatorial regimen did not enhance overall survival or control tumor volume after T-FUS treatment. Thus, the BrCa TME is highly resistant to approaches intended to remodel the myeloid cell component which fail to synergize with T-FUS-mediated tumor ablation. Full article

Low-frequency traffic noise below 500 Hz is difficult to mitigate because its long wavelengths require impractically large conventional resonators. Here, we report an active-learning-guided inverse-design approach for scalable phononic-crystal-based acoustic metamaterial resonators that simultaneously suppress low-frequency noise transmission and harvest acoustic energy. The approach combines Gaussian process regression surrogate modeling with genetic algorithm optimization to efficiently explore high-dimensional cavity geometries. By iteratively retraining the surrogate with FEM-validated designs, the active-learning process guides the search toward high-performance structures while reducing costly FEM evaluations compared with conventional GA optimization. After geometric scaling, the 2.5D prototype derived from the nine-point optimized cavity achieved a pressure amplification factor of approximately 20 near 490 Hz, while the revolved 3D cavity exhibited amplification exceeding 30 and a transmission loss of approximately 14 dB near the target frequency. Integrated with a mass-loaded five-PZT stack, the device generated 5.5 V_pp and 0.25 mW under 100 dB SPL, corresponding to a normalized power density of 0.58 μW Pa⁻² cm⁻³. These results demonstrate a route toward multifunctional piezoelectric acoustic devices for noise mitigation, localized energy harvesting, and self-powered sensing. Full article

High-precision identification of acoustic convergence zones (CZs) and acoustic shadow zones (SZs) is a core prerequisite for deep-sea sonar performance prediction and long-range underwater target detection. However, in data-scarce marine environments, traditional acoustic identification methods suffer from high environmental sensitivity and significant computational costs, while pure data-driven deep learning methods face dilemmas such as a lack of physical consistency and poor generalization on small samples. To address these issues, a three-level cascaded recognition framework based on physics-prior-augmented deep learning is proposed in this paper, enabling accurate segmentation of CZs and intelligent classification of sound field types under data-scarce scenarios. In this framework, physical acoustic principles are incorporated exclusively as priors through a training dataset generated by a Gaussian beam acoustic propagation code (Bellhop) and through hand-crafted geometric features derived post hoc from the initial segmentation outputs. Taking a typical deep-sea area in the Northwest Pacific Ocean as the research object, a hybrid dataset comprising 5000 simulated transmission loss images and 500 simulated images from a geographically distinct sea area is constructed. The sound field is categorized into four types: strong convergence, usable convergence, weak convergence, and shadow zone. In the first stage, the ResNet-34 backbone is improved by integrating deformable convolution and a global statistical feature module, which, combined with a joint loss function, achieves high-precision pixel-level segmentation of CZs and SZs, with the regional gray contrast reaching 86.9%. In the second stage, a customized dual-channel VGG16 architecture is designed to fuse the extracted geometric priors and visual features, achieving a sound field classification accuracy of 89.91%. In the third stage, a hybrid data augmentation technique combining Mixup and convolutional autoencoder is adopted alongside a transfer learning strategy to mitigate the data scarcity under cross-domain conditions, boosting the small-sample classification accuracy to 84.45%. The experimental results demonstrate that the models in each stage of the proposed framework significantly outperform traditional methods and baseline networks. This study provides a novel methodology and technical support for intelligent sound field identification in data-scarce marine environments. Finally, the core contributions and current limitations are summarized, and future research directions, such as constructing a dynamic hydrological parameter feedback mechanism and identifying three-dimensional complex sound fields, are prospected. Full article

This study investigated Sitka spruce (Picea sitchensis (Bong.) Carr.) specimens from the same batch over a moisture-content range of 2%–12%. Under free–free boundary conditions with non-contact magnetic excitation, the first- to fifth-order resonance frequencies and loss factors were obtained, and the responses of resonance frequency, loss factor, dynamic modulus of elasticity, specific dynamic modulus of elasticity, acoustic radiation quality constant, and acoustic conversion efficiency to moisture content were analyzed. moisture-content offset correction models were established for the first- to fifth-order resonance frequencies and loss factors. The results showed that, with increasing moisture content, resonance frequencies decreased, loss factors increased, and the dynamic modulus of elasticity, specific dynamic modulus of elasticity, acoustic radiation quality constant, and acoustic conversion efficiency decreased. The interval decreases in the mean first-order resonance frequency decreased from 1.61 Hz in the 2%–4% interval to 0.84 Hz in the 10%–12% interval, while the loss factor increment declined from 1.11 × 10⁻³ to 5.80 × 10⁻⁴. The mean differences between measured and model-calculated values were −0.011 to 0.021 Hz for the first-order resonance frequency and −1.1 × 10⁻⁴ to 5.0 × 10⁻⁵ for the loss factor, indicating accurate conversion to a unified target moisture content. Full article

Subsea Christmas trees are often deployed in turbid coastal waters or seabed environments. During manipulator operations on Christmas tree panels, conventional optical servoing is severely limited by rapid electromagnetic attenuation and strong scattering from suspended particles, resulting in reduced visibility. Forward-looking sonar (FLS) provides stable imaging, but its unique imaging geometry and low resolution make direct 6D pose estimation challenging. To address this issue, this paper proposes a 6D object pose estimation method for FLS images, in which conventional optical control-point-based pose estimation is restructured to resolve the mismatch between optical-centric network assumptions and acoustic imaging characteristics, and is further integrated with acoustic projection-based pose inversion. First, to address the limited diversity of target appearances and the scarcity of training data, we construct an FLS imaging model based on primary truncation for image simulation, providing data for model pretraining. Second, a multi-task acoustic control-point detection network, Acoustic-Yolo6D, is designed to mitigate localization degradation caused by heavy speckle noise, low boundary contrast, and resolution variations associated with polar-coordinate imaging, through heatmap regression, auxiliary object segmentation, and explicit range-bearing positional encoding. An Acoustic-n-Point (AnP) model is then used to recover the target 6D pose. Finally, simulation and water-tank experiments on the socket target verify the feasibility and robustness of the proposed method under limited-data conditions. The method achieves a 3.1 cm mean translation error, a 10.88° mean orientation error, and 52 FPS in real underwater acoustic environments. Full article

Background/Objectives: In human language, speech sound units, referred to as segments, are rigidly ordered. Certain orderings are typologically common, while others are typologically rare. This pattern has led linguists to posit a scalar segment-intrinsic feature, referred to as “sonority,” and a hierarchy relating to ordering referred to as the “sonority hierarchy.” In generative phonology, it has been proposed that the sonority hierarchy is present in the innate underlying grammar as the Sonority Sequencing Principle (SSP) and variations thereof. In grammar-based approaches it is common for speech sounds to be abstractly classified based on manner with obstruents being the least sonorous and vowels being most sonorous and with three levels of adherence to the SSP: 1 strict adherence with a sonority rise as in the sequence /pla/, 2 slight adherence/slight violation with a sonority plateau as in /pta/ or /mna/, and 3 violation with a sonority fall before the vowel as in /lpa/. Some linguists have proposed that segmental ordering is the result of generalized pressures relating to speech perception, cognitive processing, and gestural coordination, and therefore not part of the underlying grammar. This study examines the relative contribution of perceptual salience (defined as loudness and segmental recoverability) to segmental ordering. Methods: We used eye-tracking in the visual world paradigm to track real-time processing of perception of novel consonant clusters. We compared whether salience or sonority had a stronger association with how participants simplify clusters in perception, where salience and sonority had different predictions. Results: We found that participants looked more towards salience-predicted competitors than sonority-predicted ones prior to focusing on the target. Conclusions: This finding is in line with theories based on acoustic and auditory salience being the strongest predictors of perceptual ease. Full article