Search Results (320)

This paper studies how intense internal waves (IIWs) affect the holographic reconstruction of the sound field generated by a moving source in a shallow-water environment. It is assumed that the IIWs propagate along the acoustic path between the source and the receiver. The presence of IIWs introduces inhomogeneities into the waveguide and causes significant mode coupling, which perturbs the received sound field. This paper proposes the use of holographic signal processing (HSP) to eliminate perturbations in the received signal caused by mode coupling due to IIWs. Within the HSP framework, we examine the interferogram (the received sound intensity distribution in the frequency–time domain) and the hologram (the two-dimensional Fourier transform of the interferogram) of a moving source in the presence of space–time inhomogeneities caused by IIWs. A key finding is that under the influence of IIWs, the hologram is divided into two regions that correspond to the unperturbed and perturbed components of the sound field. This hologram structure enables the extraction and reconstruction of the interferogram corresponding to the unperturbed field as it would appear in a shallow-water waveguide without IIWs. Numerical simulations of HSP application under the realistic conditions of the SWARM’95 experiment were carried out for stationary and moving sources. The results demonstrate the high efficiency of holographic reconstruction of the unperturbed sound field. Unlike matched field processing (MFP), HSP does not require prior knowledge of the propagation environment. These research results advance signal processing methods in underwater acoustics by introducing efficient HSP methods for environments with spatiotemporal inhomogeneities. Full article

Near-field acoustic holography (NAH) provides an effective way to achieve wide-band, high-resolution visualization measurement of the sound insulation performance of building components. However, based on Green’s function, the microphone array’s inherent amplitude and phase mismatch errors will exponentially amplify the sound field inversion process, significantly reducing the measurement accuracy. To systematically evaluate this problem, this study combines numerical simulation with actual measurements in a soundproof room that complies with the ISO 10140 standard, quantitatively analyzes the influence of array system errors on NAH reconstructed sound insulation and acoustic images, and proposes an error correction strategy based on channel transfer function normalization. The research results show that when the array amplitude and phase mismatch mean values are controlled within 5% and 5°, respectively, the deviation of the weighted sound insulation measured by NAH can be controlled within 1 dB, and the error in the key frequency band of building sound insulation (200–1.6k Hz) does not exceed 1.5 dB; when the mismatch mean value increases to 10% and 10°, the deviation of the weighted sound insulation can reach 2 dB, and the error in the high-frequency band (≥1.6k Hz) significantly increases to more than 2.0 dB. The sound image shows noticeable spatial distortion in the frequency band above 250 Hz. After applying the proposed correction method, the NAH measurement results of the domestic microphone array are highly consistent with the weighted sound insulation measured by the standard method, and the measurement difference in the key frequency band is less than 1.0 dB, which significantly improves the reliability and applicability of low-cost equipment in engineering applications. In addition, the study reveals the inherent mechanism of differential amplification of system errors in the propagating wave and evanescent wave channels. It provides quantitative thresholds and operational guidance for instrument selection, array calibration, and error compensation of NAH technology in building sound insulation detection. Full article

This study aims to address the challenges posed by uneven energy amplitude and a low signal-to-noise ratio (SNR) in the total focus imaging of coarse-crystalline elastic anisotropic materials. A novel method for acoustic field correction vector-coherent total focus imaging, based on the materials’ properties, is proposed. To demonstrate the effectiveness of this method, a test specimen, an austenitic stainless steel nozzle weld, was employed. Seven side-drilled hole defects located at varying positions and depths, each with a diameter of 2 mm, were examined. An ultrasound simulation model was developed based on material backscatter diffraction results, and the scattering attenuation compensation factor was optimized. The acoustic field correction function was derived by combining acoustic field directivity with diffusion attenuation compensation. The phase coherence weighting coefficients were calculated, followed by image reconstruction. The results show that the proposed method significantly improves imaging amplitude uniformity and reduces the structural noise caused by the coarse crystal structure of austenitic stainless steel. Compared to conventional total focus imaging, the detection SNR of the seven defects increased by 2.34 dB to 10.95 dB. Additionally, the defect localization error was reduced from 0.1 mm to 0.05 mm, with a range of 0.70 mm to 0.88 mm. Full article

The substantial volume of data acquired through remote-detection acoustic logging poses a remarkable challenge because of the limited real-time upload speed of the cable, which severely impedes its further application. To address this issue, a two-stage data compression method that was implemented at the acquisition node was proposed in this study. This approach includes a field programmable gate array (FPGA)-based hardware system and a two-stage downhole data compression algorithm combining wavelet transform and adaptive differential pulse-code modulation paired with ground decompression software. Finally, the proposed compression method was evaluated using actual logging data. The test results revealed that the overall compression rate of the two-stage compression method was 25.1%. The reconstructed waveforms highly retained the overall shape of the original waveforms, and the severe relative distortion of individual data points did not affect the extraction of the sliding longitudinal, sliding transverse and reflected waveforms. The FPGA compressed 2048 16-bit waveforms in approximately 100 μs with low resource utilization and workload. It considerably outperformed DSP-based pre-transmission compression. Herein, the data compression method at the acquisition node helped in reducing the workload on the master control node and increasing the effective speed of the cable transmission up to 400%, thereby enhancing the remote-detection acoustic logging. Full article

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article

Sound speed profile (SSP) inversion based on remote sensing parameters allows for the acquisition of global quasi-real-time SSPs without the need for on-site measurements, thereby fulfilling the requirements of many acoustic applications. This study makes two enhancements to the single empirical orthogonal function regression (SEOF-R) method. First, the k-means clustering algorithm is utilized to cluster SSP samples, ensuring the consistency of perturbation modes in the physical regression. Second, baroclinic modes are employed to derive a novel SSP basis function, named the ocean mode basis, which accurately characterizes the inversion relationship. Validation experiments using data from the South China Sea yield promising results. Compared with the SEOF-R method, the reconstruction error of the improved approach is reduced by 27%, with an average reconstruction error of 1.73 m/s. The average prediction transmission loss error decreases by 70%, reaching 1.29 dB within 50 km. The grid-free processing and low sample dependence of the proposed method further enhance the applicability and accuracy of remote sensing-based SSP inversion. Full article

The increasing demand for accurate and accessible medical imaging has driven efforts to develop technologies that overcome limitations associated with conventional imaging techniques, such as MRI and CT scans. This study presents the design and implementation of an electronic interface for acquiring signals from a piezoelectric ultrasound sensor with the aim of improving image reconstruction quality by addressing electromagnetic interference and speckle noise, two major factors that degrade image fidelity. The proposed interface is installed between the ultrasound transducer and acquisition system, allowing real-time signal capture without altering the medical equipment’s operation. Using a printed circuit board with 110-pin connectors, signals from individual piezoelectric elements were analyzed using an oscilloscope. Results show that noise amplitudes occasionally exceed those of the acoustic echoes, potentially compromising image quality. By enabling direct observation of these signals, the interface facilitates the future development of analog filtering solutions to mitigate high-frequency noise before digital processing. This approach reduces reliance on computationally expensive digital filtering, offering a low-cost, real-time alternative. The findings underscore the potential of the interface to enhance diagnostic accuracy and support further innovation in medical imaging technologies. Full article

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost. Full article

The application of multiple parametric arays (MPAs) has been increasingly prominent in recent years due to the high directivity of parametric arrays. However, existing beamforming methods for MPAs are limited to special scenarios, such as narrow-edged beamforming, or have high complexity, such as requiring numerous acoustic transfer function (ATF) identifications. This paper proposes a low-complexity versatile beamforming method based on the transitive relationship among ATFs. For N parametric arrays, the number of identified ATFs can be reduced from N² to N through interpolation and flipping. Moreover, by neglecting the less affected part in the acoustic field structure, the number of identified ATFs can be reduced to less than N. On the basis of ATF matrix estimated, the desired acoustic field can be generated by optimizing the emission weight coefficient. The accuracy of ATF estimation is verified through the precise reconstruction of the acoustic field. Even when the number of identified ATFs does not exceed N, the desired acoustic field of different types of beam patterns can be formed correctly. The beamforming effects of MPAs confirm the low-complexity and versatility of the proposed method, offering a highly feasible solution for acoustic field control. Full article

The number of hologram points in near-field acoustical holography (NAH) for a vibro-acoustic system plays a vital role in conditioning the transfer function between the source and measuring points. The requirement for many overdetermined hologram points for extended sources to obtain high accuracy poses a problem for the practical applications of NAH. Furthermore, overdetermination does not generally ensure enhanced accuracy, stability, and convergence, owing to the problem of rank deficiency. To achieve satisfactory reconstruction accuracy with underdetermined hologram data, the best practice for choosing hologram points and regularization methods is determined by comparing cross-linked sets of data-sorting and regularization methods. Three typical data selection and treatment methods are compared: iterative discarding of the most dependent data, monitoring singular value changes during the data reduction process, and zero padding in the patch holography technique. To test the regularization method for inverse conditioning, which is used together with the data selection method, the Tikhonov method, Bayesian regularization, and the data compression method are compared. The inverse equivalent source method is chosen as the holography method, and a numerical test is conducted with a point-excited thin plate. The simulation results show that selecting hologram points using the effective independence method, combined with regularization via compressed sensing, significantly reduces the reconstruction error and enhances the modal assurance criterion value. The experimental results also support the proposed best practice for inverting underdetermined hologram data by integrating the NAH data selection and regularization techniques. Full article

Accurate measurement of atmospheric turbulent fluctuations is critical for understanding environmental dynamics and improving models in applications such as wind energy. Advanced remote sensing technologies are essential for capturing instantaneous velocity and temperature fluctuations. Acoustic tomography (AT) offers a promising approach that utilizes sound travel times between an array of transducers to reconstruct turbulence fields. This study presents a systematic evaluation of the time-dependent stochastic inversion (TDSI) algorithm for AT using synthetic travel-time measurements derived from large-eddy simulation (LES) fields under both neutral and convective atmospheric boundary-layer conditions. Unlike prior work that relied on field observations or idealized fields, the LES framework provides a ground-truth atmospheric state, enabling quantitative assessment of TDSI retrieval reliability, sensitivity to travel-time measurement noise, and dependence on covariance model parameters and temporal data integration. A detailed sensitivity analysis was conducted to determine the best-fit model parameters, identify the tolerance thresholds for parameter mismatch, and establish a maximum spatial resolution. The TDSI algorithm successfully reconstructed large-scale velocity and temperature fluctuations with root mean square errors ($RMSE\mathrm{s}$) below 0.35 m/s and 0.12 K, respectively. Spectral analysis established a maximum spatial resolution of approximately 1.4 m, and reconstructions remained robust for travel-time measurement uncertainties up to 0.002 s. These findings provide critical insights into the operational limits of TDSI and inform future applications of AT for atmospheric turbulence characterization and system design. Full article

Theaters in ancient Pompeii played a vital role in Roman entertainment, shaping the auditory experiences of spectators. This study examines the acoustic properties of the Amphitheater, the Grand Theater, and the Odeon using impulse response (IR) analysis to reconstruct their soundscapes. Next, the study considers the impact of typical musical instruments, vocal performances, and ambient sounds—such as gladiatorial combat—on these spaces’ acoustics. Findings reveal significant differences in reverberation times, sound clarity, and spatial characteristics, shaped by each theater’s design. These sites, preserved after the eruption of Mount Vesuvius, provide a unique opportunity to analyze how architecture influenced sound propagation. The Amphitheater, with its vast open structure, enhanced large-scale events, while the Grand Theater’s semi-enclosed design balanced musical and theatrical performances. The Odeon, the most intimate of the three, prioritized speech clarity. These insights highlight how Roman architects engineered performance spaces to accommodate diverse entertainment forms. By combining acoustic measurements with historical context, this research offers a deeper understanding of ancient Pompeii’s soundscapes and the auditory experiences of its inhabitants and offers insights for composition and soundscape creations that take inspiration from these prominent historical architectural and cultural icons whilst exploring its potential within contemporary immersive listening practices. Full article

Seafloor topography super-resolution reconstruction is critical for marine resource exploration, geological monitoring, and navigation safety. However, sparse acoustic data frequently result in the loss of high-frequency details, and traditional deep learning models exhibit limitations in uncertainty quantification, impeding their practical application. To address these challenges, this study systematically investigates the combined effects of various regularization strategies and uncertainty quantification modules. It proposes a hybrid dropout model that jointly optimizes high-precision reconstruction and uncertainty estimation. The model integrates residual blocks, squeeze-and-excitation (SE) modules, and a multi-scale feature extraction network while employing Monte Carlo Dropout (MC-Dropout) alongside heteroscedastic noise modeling to dynamically gate the uncertainty quantification process. By adaptively modulating the regularization strength based on feature activations, the model preserves high-frequency information and accurately estimates predictive uncertainty. The experimental results demonstrate significant improvements in the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Peak Signal-to-Noise Ratio (PSNR). Compared to conventional dropout architectures, the proposed method achieves a PSNR increase of 46.5% to 60.5% in test regions with a marked reduction in artifacts. Overall, the synergistic effect of employed regularization strategies and uncertainty quantification modules substantially enhances detail recovery and robustness in complex seafloor topography reconstruction, offering valuable theoretical insights and practical guidance for further optimization of deep learning models in challenging applications. Full article

This paper presents hardware-efficient phase demodulation schemes for FPGA-based digital phase-sensitive optical time-domain reflectometry ($\varphi$-OTDR) receivers. We first derive a signal model for the heterodyne $\varphi$-OTDR frontend, then propose and analyze three demodulation methods: (1) a baseband reconstruction approach via zero-IF downconversion, (2) an analytic signal generation technique using the Hilbert transform (HT), and (3) a wavelet transform (WT)-based alternative for analytic signal extraction. Algorithm-hardware co-design implementations are detailed for both RFSoC and conventional FPGA platforms, with resource utilization comparisons. Additionally, we introduce an incremental DC-rejected phase unwrapper (IDRPU) algorithm to jointly address phase unwrapping and DC drift removal, minimizing computational overhead while avoiding numerical overflow. Experiments on simulated and real-world $\varphi$-OTDR data show that the HT method matches the performance of zero-IF demodulation with simpler hardware and lower resource usage, while the WT method offers enhanced robustness against fading noise (3.35–22.47 dB SNR improvement in fading conditions), albeit with slightly ambiguous event boundaries and higher hardware utilization. These findings provide actionable insights for demodulator design in distributed acoustic sensing (DAS) applications and advance the development of single-chip DAS systems. Full article

This paper explores the application of a specialized end-to-end framework, crafted to study historical soundscapes, with a specific focus on 18th-century Naples. The framework combines historical research, natural language processing, architectural acoustics, and virtual acoustic modelling to achieve historically accurate and physically based soundscape reconstructions. Central to this study is the development of a Historically Informed Soundscape (HIS) map, which concentrates on the urban spaces of Largo di Palazzo and Via Toledo in Naples. Using virtual and audio-augmented reality, the HIS map provides 3D spatialized audio, offering an immersive experience of the acoustic environment of 18th-century Naples. This interdisciplinary approach not only contributes to the field of sound studies but also represents a significant methodological innovation in the analysis and interpretation of historical urban soundscapes. By incorporating historical maps as interactive graphical user interfaces, the project fosters a dynamic, multisensory engagement with the past, offering a valuable tool for scholars, educators, and the public to explore and understand historical sensory environments. Full article