Functional delay and sum beamforming for three-dimensional acoustic source identification with solid spherical arrays

Microphone arrays are commonly used for noise source localization and power estimation in aeroacoustic measurements. The delay-and-sum (DAS) beamformer, which is the most widely used beamforming algorithm in practice, suffers from low resolution and high sidelobe level problems. Therefore, deconvolution approaches, such as the deconvolution approach for the mapping of acoustic sources (DAMAS), are often used for extracting the actual source powers from the contaminated DAS results. However, most deconvolution approaches assume that the sources are uncorrelated. Although deconvolution algorithms that can deal with correlated sources, such as DAMAS for correlated sources, do exist, these algorithms are computationally impractical even for small scanning grid sizes. This paper presents a covariance fitting approach for the mapping of acoustic correlated sources (MACS), which can work with uncorrelated, partially correlated or even coherent sources with a reasonably low computational complexity. MACS minimizes a quadratic cost function in a cyclic manner by making use of convex optimization and sparsity, and is guaranteed to converge at least locally. Simulations and experimental data acquired at the University of Florida Aeroacoustic Flow Facility with a 63-element logarithmic spiral microphone array in the absence of flow are used to demonstrate the performance of MACS.

According to the cross-correlation between the ultrasonic echo signals of ultrasonic imaging system, ultrasonic imaging algorithm based on cross-correlation adaptive weighting is introduced. This method determines the weight on the correlation between the echo signals of scatters at different positions, and then the adaptive weighted imaging is performed. The sidelobe and the noise which has the low correlation are suppressed. Simulations to points object and speckle object pattern using Field Ⅱ show that the method of imaging fast, and it can give a high horizontal and longitudinal resolution. Compared with delay and sum (DAS) algorithm, the contrast of the image to point object is increased by 16 dB. The contrast of the image to sound-absorbing speckle is increased by 0.85 dB. The results of experiments in which the complete data sets are used, show that the resolution is better than that of DAS algorithm and the contrast is increased by 17 dB.

Acoustic emission source localization is the basic function of the application of acoustic emission technology. For complex structures, mathematical analysis positioning algorithms cannot be obtained, and accurate acoustic emission source localization cannot be obtained, which has always been one of the problems in the actual applications of acoustic emission technology. To solve this problem, this paper proposes an acoustic emission source localization method based on deep learning, which can obtain high-precision acoustic emission source positioning without the need for mathematical analysis positioning algorithms. The AI deep learning acoustic emission source localization method adopts the method of meshing, dividing the grid in the measured structure, generating label data at the grid position, using the label data to conduct AI deep learning training to establish a positioning model, and using the trained model to analyze the actual acoustic emission data (non-labeled data) to locate the acoustic emission source. This paper uses a multi-layer perceptron model to train multi-dimensional features (arrival time and amplitude). This method effectively improves the accuracy of acoustic emission source localization. Experimental results show that the positioning accuracy of the designed deep learning model in the test set reaches 99.625%, which is significantly better than the traditional Time Difference of Arrival (TDOA) positioning algorithm. In addition, this paper further verifies the stability and reliability of the model in localization tasks through credibility metrics such as Score, Margin and Entropy. This article provides a new solution for locating acoustic emission sources in complex structures, and lays a theoretical and practical foundation for the future development of non-destructive testing technology.

Toward Real-Time GPU Implementation of Diverging Beam With Synthetic Aperture Technique With Non-linear Beamforming for a Curvilinear Array.

Ultrasound imaging is a real-time diagnostic modality that reconstructs acoustic signals into visual representations of internal body structures. A key component in this process is beamforming, with the Delay and Sum (DAS) algorithm being a standard due to its balance between simplicity and effectiveness. However, the computational cost of DAS can be a limiting factor, especially in real-time scenarios where fast frame reconstruction is essential. In this work, we introduce a time-invariant approximation of the DAS algorithm (tiDAS), designed to accelerate the reconstruction process without compromising image quality. By adopting a one-dimensional, row-wise convolutional formulation, tiDAS significantly reduces computational complexity while preserving the core properties of the original model. This approach not only enables faster image reconstruction but also provides a structured foundation for the application of deconvolution methods aimed at enhancing resolution. Synthetic experiments demonstrate that tiDAS achieves a favorable trade-off between speed and accuracy, making it a promising tool for improving the efficiency of real-time ultrasound imaging.

Endoscope size is a major design constraint that must be managed with the clinical demand for high-quality illumination and imaging. Existing commercial endoscopes most often use an arc lamp to produce bright, incoherent white light, requiring large-area fiber bundles to deliver sufficient illumination power to the sample. Moreover, the power instability of these light sources creates challenges for computer vision applications. We demonstrate an alternative illumination technique using red-green-blue laser light and a data-driven approach to combat the speckle noise that is a byproduct of coherent illumination. We frame the speckle artifact problem as an image-to-image translation task solved using conditional Generative Adversarial Networks (cGANs). To train the network, we acquire images illuminated with a coherent laser diode, with a laser diode source made partially- coherent using a laser speckle reducer, and with an incoherent LED light source as the target domain. We train networks using laser-illuminated endoscopic images of ex-vivo, porcine gastrointestinal tissues, augmented by images of laser-illuminated household and laboratory objects. The network is then benchmarked against state of-the-art optical and image processing speckle reduction methods, achieving an increased peak signal-to-noise ratio (PSNR) of 4.1 db, compared to 0.7 dB using optical speckle reduction, 0.6 dB using median filtering, and 0.5 dB using non-local means. This approach not only allows for endoscopes with smaller, more efficient light sources with extremely short triggering times, but it also enables imaging modalities that require both coherent and incoherent sources, such as combined widefield and speckle ow contrast imaging in a single image frame.

Comparison of deconvolution methods for the visualization of acoustic sources based on cross-spectral imaging function beamforming

Beamforming is an essential step in the ultrasound image formation pipeline and has recently attracted growing interest. An important goal of beamforming is to increase the image spatial resolution, or in other words to narrow down the system Point Spread Function (PSF). In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF. Unfortunately, these two steps have only been considered separately in a sequential approach. Herein, a novel framework for unifying beamforming and deconvolution in ultrasound image reconstruction is introduced. More specifically, the proposed formulation is a regularized inverse problem including two linear models for beamforming and deconvolution plus additional sparsity constraint. We take advantage of the alternating direction method of multipliers algorithm to find the solution of the joint optimization problem. The performance evaluation is presented on a set of publicly available simulations, real phantoms, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> data. As compared to Delay-And-Sum (DAS) beamforming, simulation results indicate improvements of 45% and 44% in terms of axial and lateral resolution, respectively. Moreover, the proposed method improves the contrast of simulation data by 6.7% in comparison to DAS. The superiority of the proposed approach in comparison with the sequential approach as well as the state-of-the-art beamforming and deconvolution approaches is also shown.

Deep reconstruction of high-quality ultrasound images from raw plane-wave data: A simulation and in vivo study

Adjustable spatial coherence systems allow the possibility to make different intensity distributions using one source. Most common adjustable sources are based on the Collet-Wolf system. However, it is also possible to adjust the spatial coherence of the illumination field from white light sources by spatially filtering the source mutual intensity spectrum. We implement the Collet-Wolf source and the LED-based system to experimentally contrast a variety of partially coherent optical vortices that can be generated with spatial light modulation. We experimentally study the effects of changing the transverse coherence in partially coherent optical vortices, using a proposed metric of vortex contrast depth that quantifies the change of the vortex hollowness. To expand the analysis, we use a Michelson interferometer to reconstruct the spiral wavefronts using phase shifting. We found that the LED system at lower spatially correlated light produces truncated triangular distributions (a 50µm pinhole is used), and with higher correlated light, it produces partially coherent optical vortices (a 10µm pinhole is used). The Collet-Wolf system generates partially coherent optical vortices up to 0.5mm of focal shift in the diffuser. Our results provide an experimental understanding and instrumental methodology capable of steering the optical transverse coherence, producing adjustable partially coherent optical vortices that can be obtained using incoherent and coherent sources.

Accurate acoustic source localisation (ASL) has significant potential to improve diagnostic ultrasound imaging of small vessel structures. Recently, super-resolution imaging techniques for both active and passive source mapping have been developed; however, these approaches assume the presence of a single source within the point spread function (PSF) of the system. In reality, multiple sources may be present, for example, when high concentrations of contrast agent are used. We use a two-step approach to localise multiple sources within the PSF of a clinically relevant passive cavitation detection system. First, we apply a Blind Source Separation (BSS) technique known as Independent Component Analysis, which relies on higher-order statistics to separate and reconstruct signals originating from independent sources. Second, we determine the time-difference-of-arrival of the separated signals on each receiver, and perform ASL by fitting a polynomial using least squares regression. Simulation and experimental results demonstrate that with the BSS-ASL combination, multiple sources aligned axially within the PSF of a single array located 70 mm from the focus can be localised with sub-millimetre resolution. We verify sources as distinct using Passive Acoustic Maps generated from a perpendicular linear array. Further work is necessary to ascertain performance limits and to validate the technique in vivo.

Improved performance in many optical instruments has been achieved by considering coherence effects in the design of the optical instrument. In this paper such improvements are reviewed by heuristically describing the fundamental principles of partial coherence which are required in the design of such instruments. Furthermore, these coherence effects, which arise from both coherent and incoherent sources of radiation, are illustrated by discussing a selected set of instruments in which improved performance has been achieved. These examples include high-resolution recording and analyzing instruments which use incoherent sources, as well as imaging and mensuration instruments which use coherent sources. A set of guidelines for determining when coherence effects influence system performance with respect to linearity, resolution and noise is also presented.

Merging two coherent and incoherent optical sources: continuous wave light amplification by stimulated emission of radiations (CW LASER) and light emitting diode (LED) despite the difference in their physical nature from each other could be an efficient approach to expand a highly occupied network while trading-off the cost. In this paper, an efficient SAC–OCDMA system based on coherent and incoherent optical sources (CCIS) is designed. To evaluate CCIS design, fixed right shifting (FRS) code is adopted. FRS is built by utilizing specific type of Jordan matrices with direct algebraic procedures with minimum cross correlation (MCC). Additionally, an MCC elimination process would be more efficiency and can be seen in supporting large number of users. A quantitative comparison between SAC–OCDMA system using LED, CW laser sources and CCIS design is presented. Compared to LED sources, a CCIS design increases the number of supported users by almost three time while a reduction in number of optical sources is obtained by ≈ 50%. As compared to CW Laser sources, a CCIS design shows a reduction in number of optical sources by almost 50% while a lessening in number of supported users by ≈ 21%. A 22.6% cost saving per user is achieved for CCIS design over CW laser for nearly similar performance. It concludes that, CCIS design can provide a good solution for optical communication system.

The noise problem of substations has become increasingly prominent in recent years, and the accurate identification of noise sources has been the prerequisite for substation noise control. By virtue of the advantages of long measurement distance and panoramic acoustic imaging, far-field beamforming based on solid spherical microphone arrays has broad application prospects in large substation noise source identification. Spherical harmonics beamforming (SHB) is a classical algorithm matched with spherical microphone arrays. However, its sound source identification results suffer from problems such as wide mainlobes at low frequencies and abundant sidelobes at high frequencies, which lead to limited acoustic imaging clarity. In order to achieve clear acoustic imaging of noise sources in large substations, the output of far-field SHB is reconstructed and the method of clear SHB (C-SHB) is proposed. The simulations and experiment show that C-SHB can significantly reduce the mainlobe width and suppress the sidelobe contamination, thus effectively improving the imaging clarity of SHB and enhancing its weak source identification capability.

Improving the efficiency of DAMAS for sound source localization via wavelet compression computational grid