Search Results (305)

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

In the framework of the MATIM project, an acoustic test campaign was conducted on a platform derived from a commercial-class quadcopter within the CIRA semi-anechoic chamber. A dedicated rotor rig allowed systematic measurements of thrust, torque, and shaft speed together with near- and far-field noise using ten calibrated 1/2-inch precision microphones. Three configurations were examined: an isolated rotor, the same rotor mounted on an aluminium quadcopter plate, and the full four-rotor assembly. The resulting data set, acquired over 3000–8000 rpm, documents the azimuthal directivity and radial decay of tonal and broadband noise while separating motor, propeller, and installation contributions. Analysis shows that a nearby rigid plate scatters part of the sound field towards frontal and oblique observers and produces a shielding effect in the rotor plane. The combined operation of four rotors further redistributes energy and broadens blade-passing frequency harmonics. The database is intended as a benchmark for aeroacoustics codes and for the development of reduced-order models. 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

Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency. Deep learning approaches, based on data-driven principles, enable accurate input–output approximation and batch processing of large-scale datasets, significantly reducing computation time and cost. To establish a rapid prediction model mapping sound speed profiles (SSPs) to acoustic TL through controllable generation, this study proposes a hybrid framework that integrates a variational autoencoder (VAE) and a normalizing flow (Flow) through a two-stage training strategy. The VAE network is employed to learn latent representations of TL data on a low-dimensional manifold, while the Flow network is additionally used to establish a bijective mapping between the latent variables and underwater physical parameters, thereby enhancing the controllability of the generation process. Combining the trained normalizing flow with the VAE decoder could establish an end-to-end mapping from SSPs to TL. The results demonstrated that the VAE–Flow network achieved higher computational efficiency, with a computation time of 4 s for generating 1000 acoustic TL samples, versus the over 500 s required by the KRAKEN model, while preserving accuracy, with median structural similarity index measure (SSIM) values over 0.90. Full article

This paper presents an experimental acoustic noise characterization of a switched reluctance motor (SRM) designed for a wind turbine pitch angle control application. It details the fixture design for holding and positioning the sound intensity probes, along with the essential hardware setup for conducting acoustic noise experiments. Additionally, the software configuration is described to ensure compliance with specific measurement requirements. To study the effect of speed and load variations on the motor’s acoustic noise characteristics, tests are conducted at various operating points. The tests employ pulse-width modulation (PWM) current control, operating at a switching frequency of 12.5 kHz. Sound pressure and sound intensity are measured across different operating conditions to determine the sound power and psychoacoustic metrics. Furthermore, the effect of different factors on the motor’s sound power level, as well as on psychoacoustic metrics such as sharpness, loudness, and roughness, is analyzed and discussed. Full article

Since the beginning of the 21st century, the supercritical carbon dioxide (sCO₂) Brayton cycle has emerged as a hot topic of research in the energy field. Among its key components, the sCO₂ compressor has received significant attention. In particular, axial-flow sCO₂ compressors are increasingly being investigated as power systems advance toward high power scaling. This paper reviews global research progress in this field. As for performance characteristics, currently, sCO₂ axial-flow compressors are mostly designed with large mass flow rates (>100 kg/s), near-critical inlet conditions, multistage configurations with relatively low stage pressure ratios (1.1–1.2), and high isentropic efficiencies (87–93%). As for internal flow characteristics, although similarity laws remain applicable to sCO₂ turbomachinery, the flow dynamics are strongly influenced by abrupt variations in thermophysical properties (e.g., viscosities, sound speeds, and isentropic exponents). High Reynolds numbers reduce frictional losses and enhance flow stability against separation but increase sensitivity to wall roughness. The locally reduced sound speed may induce shock waves and choke, while drastic variation in the isentropic exponent makes the multistage matching difficult and disperses normalized performance curves. Additionally, the quantitative impact of a near-critical phase change remains insufficiently understood. As for the experimental investigation, so far, it has been publicly shown that only the University of Notre Dame has conducted an axial-flow compressor experimental test, for the first stage of a 10 MW sCO₂ multistage axial-flow compressor. Although the measured efficiency is higher than that of all known sCO₂ centrifugal compressors, the inlet conditions evidently deviate from the critical point, limiting the applicability of the results to sCO₂ power cycles. As for design and optimization, conventional design methodologies for axial-flow compressors require adaptations to incorporate real-gas property correction models, re-evaluations of maximum diffusion (e.g., the DF parameter) for sCO₂ applications, and the intensification of structural constraints due to the high pressure and density of sCO₂. In conclusion, further research should focus on two aspects. The first is to carry out more fundamental cascade experiments and numerical simulations to reveal the complex mechanisms for the near-critical, transonic, and two-phase flow within the sCO₂ axial-flow compressor. The second is to develop loss models and design a space suitable for sCO₂ multistage axial-flow compressors, thus improving the design tools for high-efficiency and wide-margin sCO₂ axial-flow compressors. Full article

In the fields of fault diagnosis and structural health monitoring using sound and vibration, there is increasing interest in data compression techniques based on Compressed Sensing (CS). However, conventional CS approaches that use standard bases such as Fourier or wavelets are unable to achieve sparse representations of operational vibrations in rotating machinery with speed variations, leading to significantly reduced compression performance. To overcome this limitation, this study introduces a CS approach that incorporates order analysis, a technique commonly used in the analysis of rotating machinery. The method constructs an order basis using randomly sampled rotational speed data, enabling sparse observation of operational vibrations through CS. This represents a novel approach for efficiently capturing the essential features of vibration signals under rotational speed variations. The proposed method was validated through numerical experiments. The results showed that for rotational vibrations with speed variations of approximately 10% of the average speed, the compression performance was 20 times higher than that of conventional methods using the Fourier basis. Furthermore, evaluations using simulated vibration signals from eccentric faulty gears, as well as experimental data from defective propellers and bearings with outer ring defects, demonstrated that the proposed method could successfully reconstruct signals even under conditions with substantial speed variation—conditions under which conventional Fourier-based methods fail. Due to its superior compression performance and its ability to handle unknown operational vibrations, the proposed method is highly suitable for applications in fault diagnosis, structural health monitoring, and vibration measurement. Full article

With the large-scale construction of rail transit in mainland China, the noise problem caused by passing trains has become increasingly prominent. The vertical sound barrier is currently the most effective noise control measure for rail transit. However, the noise reduction performance of the vertical sound barrier at different train speeds remains unclear. This study focuses on the box-girder cross-sections of an elevated urban rail transit line with and without vertical sound barriers, conducting field tests during train passages. Based on the test results, the influence of train speed on noise levels at both cross-sections was investigated, the sound source characteristics were analyzed, and the noise reduction performance of the vertical sound barriers at different speeds was explored. The findings indicate the following: Regardless of the presence of sound barriers, within the speed range of 20 to 80 km/h, the linear sound pressure levels at the track-side and beam-side measurement points exhibit a strong linear correlation with speed, while the correlation is weaker at the beam-bottom measurement points. As speed increases, the wheel–rail noise increases by approximately 1.5 dB compared to the structural noise at the same speed. Vertical sound barriers significantly reduce mid-to-high-frequency noise, but in the low frequency band between 20 and 63 Hz, the noise increases, likely due to secondary structural noise radiated by the self-vibration of the barriers when trains pass. At speeds of 20, 40, 60, and 80 km/h, the insertion loss at measurement points located 7.5 m from the track centerline ranges from 6.5 to 9.0, 8.5 to 10.5, 7.5 to 9.5, and 7.5 to 10.2 dB, respectively. At 25 m from the track centerline, the insertion loss ranges from 1.5 to 2.5, 6.0 to 6.5, 5.5 to 6.0, and 5.0 to 6.0 dB, respectively. The noise reduction capability of the vertical sound barrier initially increases and then decreases with higher speeds, and the rate of reduction slows as speed increases. This research will provide a reference and basis for determining speed limits in the rail transit sections equipped with sound barriers. Full article

This paper presents an experimental and numerical investigation of a 254 mm resin-printed propeller operating at rotational speeds between 3000 and 9000 RPM. Propeller thrust and torque were measured using a six-degree-of-freedom load cell, while acoustic data were captured with a microphone positioned three times the propeller diameter from the center. To complement the experimental analysis, computational simulations were conducted using ANSYS Fluent with the detached eddy simulation (DES) model, the Ffowcs-Williams and Hawkings (FW-H) model, and a transient flow solver. The figure of merit (FM) results show that the resin propeller slightly outperforms two commercial counterparts with a marginal difference between the wood and resin propellers. Additionally, the resin propeller demonstrates better noise performance, exhibiting the lowest primary tonal noise, broadband noise, and overall sound pressure level (OASPL), with minimal differences between the two commercial counterparts. ANSYS Fluent simulations predict thrust and torque within a 10% error margin, showing particularly accurate results for primary tonal noise. A new trade-off index is proposed to assess the balance between propeller performance and aeroacoustics, revealing distinct trends compared to traditional metrics. Furthermore, aerodynamic phenomena such as flow separation on the leading edge near the tip, flow separation behind the middle trailing edge, and vortex interactions at the root are identified as key contributors to tonal and broadband noise. These findings provide valuable insights into propeller design and aeroacoustic optimization. Full article

Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys. Full article

Acoustic temperature measurement (ATM) and sound source localization (SSL) are two important applications of acoustic sensors. The development of novel acoustic sensors capable of both ATM and SSL is an innovative research topic with great interest. In this work, an acoustic Fabry-Perot resonance detector (AFPRD) and its cross-shaped array were designed and fabricated, and the passive ATM function of the AFPRD and the SSL capability of the AFPRD array were simulated and experimentally verified. The AFPRD consists of an acoustic waveguide and a microphone with its head inserted into the waveguide, which can significantly enhance the microphone’s sensitivity via the FP resonance effect. As a result, the frequency response curve of AFPRD can be easily measured using weak ambient white noise. Based on the measured frequency response curve, the linear relationship between the resonant frequency and the resonant mode order of the AFPRD can be determined, the slope of which can be used to calculate the ambient sound velocity and air temperature. The AFPRD array was prepared by using four bent acoustic waveguides to expand the array aperture, which combined with the multiple signal classification (MUSIC) algorithm can be used for distant multi-target localization. The SSL accuracy can be improved by substituting the sound speed measured in real time into the MUSIC algorithm. The AFPRD’s passive ATM function was verified in an anechoic room with white noise as low as 17 dB, and the ATM accuracy reached 0.4 °C. The SSL function of the AFPRD array was demonstrated in the outdoor environment, and the SSL error of the acoustic target with a sound pressure of 35 mPa was less than 1.2°. The findings open up a new avenue for the development of multifunctional acoustic detection devices and systems. Full article

Based on the theory of uniform finite-length incoherent line source radiation and real vehicle online test data of Shanghai Maglev trains, a prediction model for environmental noise is established using an equivalent segmented line sound source approach. The noise produced by Shanghai high-speed Maglev trains running at speeds of 235, 300, and 430 km/h is tested and analyzed using microphones. The test data are combined with computational fluid dynamics simulations to divide the train’s sound sources equally into five sections. Theoretical calculations are carried out on the noise test data collected as the train passes by, and the source strength of each individual sub-sound source during the train operation is determined using the least-squares method. As a result, a prediction model for the environmental noise of high-speed Maglev trains, represented as a combination of multiple sources, is developed. The predicted results are compared with the measured values to validate the accuracy of the model. The proposed model can be used for environmental assessments before new train lines are launched, allowing for appropriate mitigation measures to be taken in advance to reduce the impact of Maglev noise on the surrounding residential and ecological environments. Full article

In this paper, the thermodynamic properties of terpene mixtures were investigated because they represent a promising group of compounds, usually extracted from biomass, with their most notable application as fuel performance enhancers. The densities, viscosities, refractive indices, and ultrasonic speeds of sound were measured for three binary mixtures, citral + chloroform, limonene + chloroform, and linalool + chloroform, across the full composition range at temperatures between 288.15 K and 323.15 K under atmospheric pressure. Using experimental data, excess molar volumes, viscosity deviations, refractive index deviations, and isentropic compressibility, deviations were calculated. Additionally, properties such as partial molar volumes, excess partial molar volumes, partial molar volumes at infinite dilution, and apparent molar volumes were derived. The excess and deviation properties were analyzed using the Redlich–Kister equation. A single mathematical model, the Heric–Brewer–Jouyban–Acree model, was used to represent densities, viscosities, refractive indices, and ultrasonic speeds of sound. The results obtained in this work suggest that dispersive interactions dominate in the limonene and linalool binary mixtures, while hydrogen bonding plays a significant role in the citral + chloroform system. In summary, dispersive interactions are dominant in nonpolar systems like limonene and linalool, while hydrogen bonding significantly affects the citral-chloroform mixture, where the polar groups in citral interact with chloroform molecules. These differences in intermolecular forces help explain the distinct behavior of each mixture. The modeling outcomes demonstrated that the Heric–Brewer–Jouyban–Acree model accurately correlated the experimental thermodynamic properties, with average percent deviations below 1% for all three systems. Full article

Background and Objectives: Analysing the characteristics of the mandibular bone through panoramic radiographs could be useful as a prescreening tool for detecting individuals with osteoporosis. The aims of this study were to evaluate the possible associations between the mandibular cortical index (MCI) and bone mineral density (BMD) in various bone regions, to investigate whether BMD better identifies moderate–severe mandibular erosion or severe mandibular erosion, and to establish BMD cut-off points to identify individuals with moderate or severe mandibular cortical erosion. Methods: This study analysed 179 Spanish Caucasian women between September 2021 and June 2024. Bone measurements, including amplitude-dependent speed of sound (Ad-SOS), the ultrasound bone profiler index (UBPI), and the bone transmission time (BTT), were obtained via dual energy X-ray absorptiometry (DXA) for the femoral neck, lumbar spine, and trochanter and quantitative bone ultrasound (QUS) for the phalanx. The MCI was calculated via the Klemetti index from panoramic radiographs. Results: According to the Klemetti index classification, lower QUS measurements in the phalanx and DXA measurements in the femoral neck, trochanter, and lumbar spine were found in women with poorer mandibular cortical bone quality. Our results revealed that, compared with moderate cortical erosion, all the BMD measures had better AUCs when identifying severe cortical erosion. Moreover, femoral neck BMD had the largest area under the curve (AUC = 0.719) for detecting severe mandibular cortical erosion, suggesting a cut-off of <0.703 gr/cm². Finally, predictor analysis of osteoporosis revealed that moderate and severe mandibular cortical erosion, compared with an uninjured mandibular cortical area, was independently associated with a diagnosis of osteoporosis. Conclusions: In conclusion, MCI was associated with BMD measurements assessed by QUS and DXA in various bone regions. Our results suggest that the Klemetti index could be used as a predictor of osteoporosis and fracture risk. Full article