Search Results (1,320)

Currently, acoustic sensing technology is widely applied in military defense, non-destructive testing (NDT), and biomedical imaging, and it is increasingly penetrating various aspects of daily life. However, traditional piezoelectric acoustic sensors are highly susceptible to performance degradation when operated in harsh environments. In contrast, optical microcavities-a class of optical resonant cavities with characteristic dimensions on the micrometer scale—offer distinct advantages, including compact footprints, immunity to electromagnetic interference (EMI), and ultra-high sensitivity. Leveraging these exceptional properties, researchers have extensively explored acoustic sensing technologies based on optical microcavity platforms. This paper reviews recent research progress in optical microcavity-based acoustic sensing, categorized by the structural configurations of the microcavities. First, we introduce the key performance specifications of different optical microcavities in acoustic sensing, such as sensitivity and frequency response bandwidth. Second, we categorically discuss the structural designs of various optical microcavities alongside corresponding optimization methods to improve sensing performance. Finally, we summarize the current applications of optical microcavity-based acoustic sensing across multiple fields and outline future development trends in this research area. Full article

There is an increasing demand for ultrasound imaging technologies, particularly wearable and portable systems, for continuous physiological monitoring applications. Although some recent flexible ultrasound devices have adopted polymer encapsulations, typical rigid transducer designs still include metal housings and costly electrodes, contributing to increased device weight and fabrication cost. To address these limitations, we developed an aluminum-electrode/3D-printed polymer-housing ultrasound transducer (APUT) utilizing a polyvinylidene fluoride piezoelectric film. Compared to a gold-electrode/metal-housing ultrasound transducer, the APUT material costs and total weight were approximately 66% and 86% lower, respectively. Acoustic evaluation revealed a center frequency of 24.5 MHz and a fractional bandwidth of 60.9%, with axial and lateral resolutions of 51 and 152 μm, respectively. Furthermore, during a 3-h pulsed operation test, the APUT exhibited an initial increase in capacitance followed by a relatively stable response, with no progressive surface-temperature increase detected within the accuracy of the measurement method. Finally, successful ex vivo imaging of chicken breast tissue confirms the APUT’s biomedical applicability, highlighting its potential as a wearable, portable, and disposable ultrasound platform. Full article

The search and discovery of underwater shipwreck sites represent the most arduous and critical phases of underwater archaeology. Wooden shipwrecks, in particular, are characterized by low acoustic impedance contrast and weak magnetic anomalies, coupled with their limited physical dimensions. Consequently, they predominantly exist as shallow-buried, discontinuous small targets scattered within confined areas, making their detection exceptionally challenging. Furthermore, the complexity of the submarine environment—including rugged topography, turbid water columns, and strong currents—poses formidable obstacles to the effective detection of these archaeological remains. Single geophysical methods are often limited by insufficient imaging resolution, interpretation ambiguity, and geological noise, making precise localization and characterization difficult. Focusing on the Nan’ao I Ming Dynasty shipwreck located in waters approximately 24 m deep off the coast of Nan’ao, Guangdong Province, China, this study proposes and validates an “acoustic-magnetic” multi-source data integration detection method. This approach systematically integrates high-resolution multibeam echo sounding (MBES), side-scan sonar (SSS), sub-bottom profiling (SBP), and marine magnetic data to establish a comprehensive framework for identification and integration analysis. The results indicate that the MBES bathymetric data reveal a regular, elongated structure oriented north–south (approximately 34 m × 12 m), closely matching the main hull and deck configuration. The SSS imagery exhibited high backscatter intensity and parallel linear textures, effectively delineating the hard shipwreck structure and the associated rigid protective frame employed for in situ preservation. SBP data confirmed the semi-buried state of the shipwreck (burial depth of approximately 0.6 m). Spatial variations in sediment thickness around the site suggested ongoing modification by strong hydrodynamic processes. Marine magnetic surveys identified localized negative anomalies (−210 nT relative to the ambient magnetic field), contrasting sharply with the positive anomalies of the surrounding natural reefs, thereby indicating an artificial ferromagnetic source. The spatial registration and feature superposition of multi-source data facilitated the characterization of the shipwreck, demonstrating its potential to mitigate environmental interference and enhance detection reliability in this complex environment. Using the Nan’ao I shipwreck site as a case study, this study provides a detailed characterization of the site’s 3D morphology, burial state, and physical properties. The proposed methodology offers a practical and robust technical solution for underwater shipwreck archaeology in complex nearshore environments, providing significant implications for proactive discovery, efficient investigation, and protection of underwater cultural heritage (UCH). Full article

Seed quality profoundly affects productivity, marketability, and food security, yet conventional evaluation methods are destructive, slow, and unsuited to high-throughput screening. Non-destructive techniques, being rapid, non-invasive, and capable of measuring multiple indicators, have therefore gained substantial momentum. This review critically surveys the principles, applications, and limitations of major non-destructive techniques for seed quality assessment. Near-infrared spectroscopy (NIRS) enables fast, simultaneous multi-component analysis in portable formats, but its shallow penetration and poor sensitivity to subtle chemical shifts restrict single-seed vigor tests. Hyperspectral imaging (HSI) uniquely merges spectral with spatial data to map composition and surface defects, though large data volumes, high cost, and limited portability hinder practical use. Machine vision offers low-cost, high-throughput external sorting but captures only surface traits and is illumination-sensitive. X-ray/CT imaging visualizes internal cracks and insect damage, yet radiation safety and bulky hardware preclude field deployment. Complementary tools (NMR, electronic nose, Raman, dielectric, fluorescence, acoustic) address niche needs but face stability, sensitivity, or dimensionality trade-offs. Future breakthroughs demand multi-sensor data fusion, deep learning optimization, and ruggedized low-cost hardware. Bridging laboratory innovation and industrial reality requires concurrent algorithmic, optical, and engineering advances, ultimately transforming seed testing into a reliable, intelligent, and deployable ecosystem. Full article

Optical coherence tomography (OCT) and photoacoustic imaging (PAI) provide complementary structural and absorption contrasts but require different coupling conditions: 1310 nm swept-source OCT is attenuated by water, whereas PAI requires acoustic coupling. We developed a large-field dual-modal imaging system combining temporal medium separation with hardware-based coordinate locking. The OCT head, linear-array ultrasound transducer, and photoacoustic excitation fiber bundle were mounted on a rigid common platform, and a one-time calibration established a two-dimensional affine transformation between the modality coordinate systems. OCT was acquired in air and PAI in deionized water within a common large-field coordinate range. In five paired air–water measurements with an approximately 23 mm water path, the displayed OCT peak level decreased from 98.4 ± 1.5 dB in air to 79.4 ± 1.8 dB in water, corresponding to a mean reduction of 19.0 ± 1.4 dB. Quantitative registration was evaluated using a 5 × 5 dual-modal landmark phantom, with nine landmarks used for affine calibration and 16 excluded landmarks reserved for independent validation. The mean two-dimensional validation error was 0.235 ± 0.128 mm, with an RMSE of 0.266 mm and a maximum error of 0.446 mm. Five additional medium-switching cycles performed without recalibration yielded an overall registration error of 0.369 ± 0.163 mm across 80 validation measurements. PA spatial resolution was further characterized using six thin hair targets, yielding lateral and axial FWHM values of 0.342 ± 0.069 mm and 0.394 ± 0.073 mm, respectively. These results demonstrate reproducible two-dimensional en face OCT–PA coordinate mapping under modality-specific coupling conditions and support the proposed workflow as a phantom-based technical validation for large-field multimodal imaging. Full article

Micromachined ultrasonic transducers (MUTs) represent a notable advance in miniaturized sensing, enabling compact, low-power, and complementary metal-oxide-semiconductor (CMOS)-integrated platforms that extend ultrasonic capabilities into wearable, implantable, and edge-computing domains. The integration of artificial intelligence (AI) has introduced new approaches for signal interpretation, adaptive control, and data-driven optimization, enhancing performance in specific areas such as compressed sensing, neural beamforming, and learned image enhancement that complement conventional signal processing. Meanwhile, sensor fusion strategies that combine ultrasonic data with complementary modalities have improved robustness, contextual awareness, and diagnostic accuracy across applications ranging from industrial monitoring to clinical diagnostics. This review provides a comprehensive analysis of this active research area, systematically covering transducer hardware platforms, design methodologies, and intelligent signal processing frameworks. While traditional bulk piezoelectric transducers remain the benchmark for high-power applications, capacitive and piezoelectric micromachined variants offer superior acoustic impedance matching and monolithic CMOS compatibility essential for portable systems. We examine the evolution from deterministic analytical and numerical modeling toward AI-powered inverse design, which enables the discovery of non-intuitive, high-performance geometries beyond human intuition. Furthermore, the integration of machine learning (ML) for signal recovery, image enhancement, and multi-modal sensor fusion is discussed as a pathway to compensate for hardware constraints such as limited aperture, sparse sampling, and low signal-to-noise ratio (SNR), while pointing out that AI technology cannot overcome fundamental physical limits including acoustic attenuation, thermal noise floors, and transduction efficiency boundaries. By synthesizing recent advancements, this review demonstrates how the convergence of classical acoustic physics and data-driven intelligence is guiding the development of of intelligent ultrasonic systems. Full article

This study addresses target-to-ROV relative localization in an unmanned surface platform-assisted UAV–ROV search-and-recovery system. Because the submerged ROV is not assumed to be visible from the air, the UAV observes the floating target and a GNSS-equipped ROV-associated surface buoy in the same image. The buoy position and target-to-buoy image displacement are combined to construct a world-frame target-position measurement, whose covariance accounts for buoy GNSS uncertainty and correlated image-projection errors. An upward-looking ROV imaging sonar provides range–bearing measurements. A delay-aware extended Kalman filter fuses the asynchronous observations using sea-state- and confidence-dependent covariance adaptation and normalized-innovation gating. ROV acoustic/inertial navigation uncertainty is propagated into the sonar measurement covariance and the reported relative-state covariance, avoiding duplication of the same navigation error in the aerial channel. The method is evaluated using a JONSWAP-based temporal disturbance model, Monte Carlo simulations, and single-factor and joint sea-state–occlusion–delay sensitivity tests. Under the nominal sea-state-5 condition, the proposed method achieves a mean ROV-frame relative RMSE of 0.992 m, compared with 1.083 m for ROV-only localization and 1.054 m for fixed-covariance fusion, with no run exceeding the 5 m divergence threshold. The results demonstrate improved relative-localization robustness within the simulated environment. Full article

The detection of microleaks in subsea gas pipelines remains challenging in shallow-water environments because weak bubble-plume echoes are often obscured by seabed reverberation, ambient noise, and platform-induced interference. This study develops a custom multibeam forward-looking sonar system and a dual-domain framework for acoustic detection and localization of underwater gas microleakage. Local statistical enhancement suppresses stable background interference and strengthens anomalous bubble echoes. Blind deconvolution, adaptive grid-based thresholding, and spatial clustering then improve target sharpness, extract candidate bubble-plume regions, and reduce localized false detections. This unsupervised framework requires no pre-collected training data. It was evaluated in 30 independent sea-trial groups conducted in Bohai Bay using air to simulate leakage. Six orifice diameters from 0.5 to 3.0 mm were tested under different compressor-indicated pressures. The plume-observation distances (defined as the sonar-to-leak-device distance at the first confirmed plume response in the sonar image) ranged from 32 to 66 m across the tested conditions. Under the minimum tested condition of a 0.5 mm orifice and a compressor-indicated pressure of 0.5 MPa, the plume was observed at a distance of 32 m. The results demonstrate the feasibility of the proposed system for ROV-assisted detection and localization of subsea gas microleakage in low-visibility shallow-water environments. Full article

Lightweight materials capable of simultaneously providing noise and mass reduction are increasingly required in aerospace applications. This paper investigates the acoustic performance of a novel hierarchical bimodal architecture in open-cell AlSi porous structures, integrating μm-sized macro-pores (200–400 μm) and mm-sized macro-pores (2–2.5 mm) at 60% porosity. The bimodal specimen was produced through replication casting, and its porosity was evaluated using gravimetric measurements and image-based surface analysis, while acoustic performance was experimentally characterized using the two-microphone impedance tube method in accordance with ISO 10534-2:2023, covering a frequency range from 500 to 6500 Hz. The resulting frequency-averaged sound absorption coefficients (α_FA) were 0.518, 0.72 and 0.844 for the μm-sized macro-porous, mm-sized macro-porous and bimodal structures, respectively. Furthermore, the proposed architecture improved the mass-to-acoustic efficiency ratio η by 125% compared to the μm-sized macro-pores structure and approximately 28.5% relative to the mm-sized macro-pore sample. Based on these values, bimodal architecture is considered to be the most acoustic efficient lightweight solution, among all specimens studied, demonstrating that hierarchical pore architectures can effectively enhance acoustic absorption, while maintaining low structural weight, highlighting the potential of bimodal open-cell AlSi structures for lightweight aerospace noise-control applications. Full article

Osteoarthritis (OA) causes irreversible cartilage damage, highlighting the need for early and sensitive assessment. Current imaging modalities are limited in detecting early-stage changes. Ultrasound (US) provides a non-invasive and accessible alternative, but its clinical adoption is limited by the lack of standardized protocols and reliable cartilage assessment. Simulations can be used to address these challenges by enabling system design, acquisition optimization and validation by providing ground truth when in vivo ground truth is unavailable. The aim of this study is to develop an in silico framework for realistic US imaging of healthy and OA cartilage by combining accurate acoustic wave modeling with a 2D microstructural cartilage phantom. The model was calibrated to healthy cartilage using first-order speckle statistics and extended to simulate degeneration through changes in structural and acoustic properties. As a proof-of-concept study, simulations were evaluated against limited ex vivo US data from healthy and OA cartilage and compared with literature data. The simulations reproduced key OA-related features and trends, including changes in reflection coefficient (R), integrated reflection coefficient (IRC), apparent integrated backscatter (AIB), and gray level distributions. These findings demonstrate the feasibility of using microstructure-based tissue phantoms to model healthy and OA cartilage. The framework provides a platform for systematic investigation of cartilage microstructure and US-derived features and may support future generation of synthetic datasets for data-driven and AI-based OA assessment. Full article

Hybrid structural materials (HSMs), such as nanoparticle-treated fabrics, have been shown to enhance acoustic and ballistic performance in multifunctional protective structures. They offer a promising means for low-frequency (<~1000 Hz) noise mitigation, which remains a critical challenge in aerospace and defense applications. The measurement and modeling of their sound transmission loss (TL) behavior using a coupled acoustic–poroelastic approach is explored in this study. A colloid-based soaking and drying process is used to impregnate nanoparticles into the fabric. Previous studies using SEM imaging have established that at low (<~20 wt.%) treatment levels, the nanoparticles agglomerate in the interstitial spaces between yarn crossover points, whereas at higher levels, they begin to coat the yarn bundle tops. TL was measured experimentally using normal-incidence impedance tube tests. Further, parameters such as static flow resistivity, porosity, flexural modulus, and density required to model the neat and treat samples as fluid-filled porous solids using the Biot–Allard model were obtained from experiments for a limited set of neat and treated cases. Static flow resistivity was measured using an air permeability tester as per ISO 9237, and a modified version of the Peirce’s cantilever beam test was used to obtain the flexural modulus for neat and treated samples. Porosity was estimated using digital image analytics. The poroelastic fabric model was implemented in finite element simulations, and the predicted TL was compared with experiments including those for uncalibrated treated cases. The model shows close alignment with measured TL at low frequencies (<~600 Hz) for all cases but deviates closer towards the theoretical mass law at higher frequencies, where flanking effects and the influence of the hierarchy of pores are expected to be dominant in experiments. Further studies are underway to incorporate such higher-order effects to improve predictions at higher frequencies. The development of this model provides a means to capture the influence of nanoparticle addition on the acoustic performance of Kevlar, enabling fast and efficient virtual design iterations. The approach helps optimize HSMs for noise mitigation in multifunctional applications for the aerospace, defense, and infrastructural sectors. Full article

Optical cameras and imaging sonars are widely used sensors in autonomous underwater vehicles. However, their different imaging mechanisms introduce substantial cross-modal discrepancies in the acquired data. In addition, underwater optical images are often degraded by low illumination, scattering, and turbidity, whereas sonar images commonly suffer from speckle noise and low spatial resolution. As a result, object detection based on a single optical or acoustic modality is often insufficient in challenging underwater environments. To address this problem, this paper proposes an acoustic–optical fusion network for underwater object detection, termed an Acoustic–Optical Proposal Query Network (AOPQ-Net). First, a Sonar Position Encoding (SPE) module is designed to explicitly encode the geometric priors in sonar images. Second, a Bi-directional Discrepancy-aware Spatial Alignment (BDSA) module is introduced to alleviate spatial misalignment between the two modalities at the feature level. Third, a Proposal Query Transformer (PQT) module performs target-oriented cross-modal interaction at the proposal level. Furthermore, this study constructs a dedicated dataset for underwater acoustic–optical fusion object detection, named Haiqin Underwater Fusion (HUF), and conducts systematic experiments on this dataset. The experimental results show that AOPQ-Net outperforms single-modality baselines and representative multimodal fusion methods in both optical and acoustic image spaces, which demonstrate the effectiveness of the proposed method. Full article

Abnormally overpressured helium-rich natural gas occurs in dolomite reservoirs beneath gypsum-bearing strata of the Middle Triassic Zhouchongcun Formation in WWY1 Well, Wuwei Depression. Resolving the vertical arrangement of dolomite, gypsum, and shale is therefore critical for evaluating reservoir–seal coupling in this structurally complex setting. Here, we develop a support vector machine (SVM) workflow using acoustic transit time (AC), bulk density (DEN), compensated neutron log (CNL), gamma ray (GR), and spontaneous potential (SP) to refine lithology classification and support lithology-based screening of potential favorable intervals. The reference lithology column integrates mud-logging descriptions, conventional log responses, and stratigraphic information. An RBF-SVM was optimized by grid-search cross-validation, and formation-specific constraints were introduced to suppress lithologies incompatible with the local stratigraphic association. The unconstrained and formation-constrained models yielded matching rates of 89.16% and 90.23%, respectively. Although the numerical increase is modest, the constrained model reduces cross-formation confusion, improves boundary continuity, and produces a more geologically coherent representation of the gypsum–dolomite–shale succession. The model also delineates several thin sublayers supported by synchronous multiparameter anomalies. Independently, epsilon-SVR reconstruction of U, Th, and K logs achieved R² values of 0.979–0.998 and restored continuous element-specific radioactivity information across the interval lacking spectral gamma-ray measurements. Together, these results establish a high-resolution lithological framework for characterizing reservoir–seal architecture and screening dolomite-dominant intervals beneath effective evaporite seals as potential exploration targets. The workflow is designed for single-well geological refinement rather than universal model benchmarking; final evaluation of reservoir quality requires integration with porosity, fracture, core, image-log, pressure, and production-test data. Full article

The preservation of submerged cultural heritage depends on the ability to locate, document, and monitor sites before they are degraded or lost. Although the North American Great Lakes contain thousands of exceptionally well-preserved shipwrecks, their large geographic extent and diverse operating environments present significant challenges for efficient archeological survey. This study presents a multi-platform autonomous survey framework developed and implemented during 2021–2022 field campaigns in Lake Michigan and Lake Ontario. The framework integrates autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), crewed vessels, side-scan sonar, multibeam bathymetry, magnetometry, optical imaging, and field-based data review within a hierarchical workflow comprising wide-area assessment (WAA) reconnaissance, high-resolution geophysical (HRG) mapping, adaptive mission refinement, and visual confirmation. The surveys produced 19.72 km² of geophysical coverage, including side-scan sonar mosaics, bathymetric surfaces, magnetic anomaly maps, and optical imagery that supported archeological interpretation. A case study from Lake Ontario demonstrates the framework’s effectiveness through the confirmation of a previously undocumented wooden shipwreck using complementary acoustic, magnetic, and visual datasets. Beyond the individual discoveries, the results demonstrate how integrated autonomous systems improve survey efficiency, support adaptive decision-making, and provide scalable methods for digital documentation, baseline site characterization, long-term monitoring, and preservation of submerged cultural heritage in freshwater and marine environments. Full article

This study presents an audio–visual speech recognition (AVSR) model for Kazakh that jointly exploits audio and visual channels. The study introduces QazAVSR, a 57 h dataset collected from 271 speakers, and extracts synchronized audio signals and lip-region video sequences using FFmpeg 7.0, Dlib 19.24, and OpenCV 4.9.0. The proposed architecture uses the self-supervised HuBERT_BASE model in the audio branch and an ImageNet-pretrained ViT-B/16 model in the visual branch. Audio and visual representations are fused by a three-layer BiModalHformer block, where intra- and cross-attention operations are performed at each level. Extensive experimental validation, supplemented by rigorous paired bootstrap resampling significance tests, demonstrates that the full multimodal BiModalHformer model achieves a highly robust average character error rate (CER) of 31.2% and a Word Error Rate (WER) of 43.1%. These results significantly outperform traditional audio-only, video-only, and standard representation-level fusion baselines. Furthermore, comparisons against powerful external baseline architectures—including Whisper-Small and AV-HuBERT configurations rigorously adapted for the Kazakh language—statistically validate the architectural efficacy of the BiModalHformer framework. Additional systematic evaluations utilizing extended metrics such as the Match Error Rate (MER), word information preserved (WIP), and the Multimodal Synergy Index (MSI) confirm that the full audio–visual configuration preserves lexical information significantly more effectively. Finally, extensive noise perturbation experiments confirm that the multimodal architecture exhibits superior structural robustness to complex acoustic distortions, including environmental noise, synthetic room reverberation, and overlapping speech topologies. Full article