Improved Source Localization Research Articles

Improved Source Localization Method of the Small-Aperture Array Based on the Parasitic Fly's Coupled Ears and MUSIC-Like Algorithm

Improved Source Localization Method of the Small-Aperture Array Based on the Parasitic Fly's Coupled Ears and MUSIC-Like Algorithm

Machine learning-based improvement of crack localization in concrete structures

Acoustic emission (AE) method is useful for damage detection in structures. However, processing the massive amount of AE data with traditional analysis methods is time-consuming. Therefore, machine learning (ML) integration provides a more rational and efficient solution by accelerating the data analysis and improving the damage detection accuracy. This study aims to improve source localization, which provides to identify cracking patterns by signal-based analysis, by training the ML model with the AE dataset for accurate damage diagnosis of concrete structures. Analyzes were conducted on the ML model using AE data collected on a concrete member under laboratory conditions. In this way, the ML model that learned signals with multi-variable wave characteristics and parameters was integrated into the source localization algorithm and thus, AE source location results were enhanced. Furthermore, due to its ability to identify damaged areas, this approach is essential for structural safety by providing early damage detection and accurate localization of cracks. This integration of the machine learning model with AE data will be considered as an advanced tool for the effective monitoring of damages in structures.

Multichannel Dynamic Sound Rendering and Echo Suppression in a Room Using Wave Field Synthesis

With multichannel sound rendering systems, an immersive sound can be experienced by a broader audience. Rendering of a stationary source is widely studied in the literature. However, a dynamic sound, where the source’s position is continuously changing, provides improved source localization and a listener experience more immersed in the rendered sound. This paper investigates the multichannel sound rendering of a dynamic sound source using a wave field synthesis method in a reverberant shoe-box room model. Finite difference time domain is employed to visualize the reproduced sound field in the time domain. To address the challenge of room echo-induced degradation in sound quality, a method is presented in which the reflected sound is compensated by rendering the primary source’s images from the auxiliary actuators, enhancing SNR by approximately 2–3.5 dB.

Localizing aeroacoustic sources in complex geometries: A hybrid method coupling 3D microphone array and time-reversal

Flow-induced noise is widely encountered in engineering domains, and noise source localization is the first step to understand the generation mechanisms. A large part of aeroacoustic noise measurements is performed in wind tunnels by using microphone arrays. A widespread method to identify the noise sources based on these measurements is the beamforming technique. This relies on a Green function, which ideally accounts for source type, flow, and reflecting boundaries. This function is generally not known analytically. An alternative approach is the time-reversal technique considered here. This method consists in propagating the sound field from the microphones back to focus points interpreted as sources. The back propagation can be done numerically with an acoustic propagation solver, which replaces the Green function needed in the beamforming. To date time reversal has been used to localize sound sources with flow in two-dimensions, without considering any reflecting/diffracting boundary. The objective of this work is to consider a three-dimensional situation with flow, and to include the presence of such boundaries to see if this can improve source localization. A synthetic sound source enclosed in a streamlined body is placed in a wind tunnel, and its sound field is diffracted by a nearby airfoil. A three-dimensional antenna with 768 microphones is used to measure the sound field, and time-reversal is performed by solving the three-dimensional linearized Euler equations with a discontinuous Galerkin method. The interest of taking into account the airfoil geometry for source localization is discussed, and some comparisons with the beamforming method are performed.

STARTS: A self-adapted spatio-temporal framework for automatic E/MEG source imaging.

To obtain accurate brain source activities, the highly ill-posed source imaging of electro- and magneto-encephalography (E/MEG) requires proficiency in incorporation of biophysiological constraints and signal-processing techniques. Here, we propose a spatio-temporal-constrained E/MEG source imaging framework-STARTS that can reconstruct the source in a fully automatic way. Specifically, a block-diagonal covariance is adopted to reconstruct the source extents while maintain spatial homogeneity. Temporal basis functions (TBFs) of both sources and noise are estimated and updated in a data-driven fashion to alleviate the influence of noises and further improve source localization accuracy. The performance of the proposed STARTS is quantitatively assessed through a series of simulation experiments, wherein superior results are obtained in comparison with the benchmark ESI algorithms (including LORETA, EBI-Convex, SI-STBF, &BESTIES). Additional validations on epileptic and resting-state EEG data further indicate that the STARTS can produce neurophysiologically plausible results. Moreover, a computationally efficient version of STARTS: smooth STARTS is also introduced with an elementary spatial constraint, which exhibited comparable performance and reduced execution cost. In sum, the proposed STARTS, with its advanced spatio-temporal constraints and self-adapted update operation, provides an effective and efficient approach for E/MEG source imaging.

Improved source localization in passive acoustic mapping using delay-multiply-and-sum beamforming with virtually augmented aperture

High-intensity focused ultrasound (HIFU) is a promising non-invasive treatment method whose applications include tissue ablation, hemostasis, thrombolysis and blood–brain barrier opening etc. Its therapeutic effects come from the thermal necrosis and the mechanical destruction associated with acoustic cavitation. Passive acoustic mapping (PAM) is capable of simultaneous monitoring of HIFU-induced cavitation events using only receive beamforming. Nonetheless, conventional time exposure acoustics (TEA) algorithm has poor spatial resolution and suffers from the X-shaped artifacts. These factors lead to difficulties in precise localization of cavitation source. In this study, we proposed a novel adaptive PAM method which combines Delay-Multiply-and-Sum (DMAS) beamforming with virtual augmented aperture (VA) to overcome the problem. In DMAS-VA beamforming, the magnitude of each channel waveform is scaled by p-th root while the phase is multiplied by L. The p and L correspond respectively to the degree of signal coherence in DMAS beamforming and the augmentation factor of aperture size. After channel sum, p-th power is applied to restore the dimensionality of source strength and then the PAM image is reconstructed by accumulating the signal power over the observation time. Based on simulation and experimental results, the proposed DMAS-VA has better image resolution and image contrast compared with the conventional TEA. Moreover, since the VA method may introduce grating lobes into PAM because of the virtually augmented pitch size, DMAS coherent factor (DCF) is further developed to alleviate these image artifacts. Results indicate that, with DCF weighting, the PAM image of DMAS-VA beamforming could be constructed without detectable image artifacts from grating lobes and false main lobes.

Passive Cavitation Imaging Artifact Reduction Using Data-Adaptive Spatial Filtering.

Passive cavitation imaging (PCI) with a clinical diagnostic array results in poor axial localization of bubble activity due to the size of the point spread function (PSF). The objective of this study was to determine if data-adaptive spatial filtering improved PCI beamforming performance relative to standard frequency-domain delay, sum, and integrate (DSI) or robust Capon beamforming (RCB). The overall goal was to improve source localization and image quality without sacrificing computation time. Spatial filtering was achieved by applying a pixel-based mask to DSI- or RCB-beamformed images. The masks were derived from DSI, RCB, or phase or amplitude coherence factors (ACFs) using both receiver operating characteristic (ROC) and precision-recall (PR) curve analyses. Spatially filtered passive cavitation images were formed from cavitation emissions based on two simulated sources densities and four source distribution patterns mimicking cavitation emissions induced by an EkoSonic catheter. Beamforming performance was assessed via binary classifier metrics. The difference in sensitivity, specificity, and area under the ROC curve (AUROC) differed by no more than 11% across all algorithms for both source densities and all source patterns. The computational time required for each of the three spatially filtered DSIs was two orders of magnitude less than that required for time-domain RCB and thus this data-adaptive spatial filtering strategy for PCI beamforming is preferable given the similar binary classification performance.

Effects of head modeling errors on the spatial frequency representation of MEG

Objectives. We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM). Approach. A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.33 S m−1 was defined. The vertices were then randomly perturbed radially up to 2%, 4%, 6%, 8% and 10% of the radius. For each head perturbation case, the forward signal was calculated for dipolar sources located at 2 cm, 4 cm, 6 cm and 8 cm from the origin (center of the sphere), and for a 324 sensor array located at 10 cm to 15 cm from the origin. Equivalent current dipole (ECD) source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was then analyzed in the spatial frequency domain, and the signal and ECD errors were quantified relative to the unperturbed case. Main results. In the noiseless and high signal-to-noise ratio (SNR) case of approximately ≥6 dB, inaccuracies in our spherical BEM head conductor models lead to increased signal and ECD inaccuracies when sensor arrays are placed closer to the head. This is true especially in the case of deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays allows for an improved ECD fit and outweighs the effects of head geometry inaccuracies. Significance. OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential.

Nonlocal Noise Removal Technique to Improve the Electromagnetic Field Pattern in Near-Field Scanning of a Device

A nonlocal noise removal technique, known as block-matching 3-D (BM3D), is introduced to improve the quality of a blurry electromagnetic pattern generated from near field of a device. Noise distribution in electromagnetic pattern is analyzed. Taking the nonlocal self-similarity into account, the BM3D technique removes the noise of the pattern in a 3-D transformation domain based on sparse representation. First, the noise removal technique was validated well on the profile recovery of weak emission of a microstrip line driven by a square wave. Then, the BM3D technique is applied to improve source localization in a field programmable gate array (FPGA) and also to improve pattern clustering in a double data rate (DDR) synchronous dynamic random access memory (SDRAM) integrated circuit (IC). Both the applications demonstrate the effectiveness of the BM3D noise removal technique in the near-field scanning patterns of a device.

Model based spectral feature analysis in reverberant acoustic fields

The spectral features of a reverberant acoustic field that can lead to improved source localization methods are investigated. Of particular interest are identification of deterministic and probabilistic features that characterize the environment and models of the acoustic transfer function that generate such features. For example, models that predict Schroeder’s invariant standard deviation in the pressure response at source-receiver distances where the direct sound approaches the reverberant field energy are proposed. Models examined include non-stationary Gaussian processes for the acoustic reverberant components and all-pass parametric models of the acoustic impulse response.

An intermediate polar candidate toward the Galactic plane

Context. For the past decade, it has been suggested that intermediate polars (IPs), a subclass of magnetic cataclysmic variables (CVs), are one of the main contributors to the hard diffuse X-ray emission from the Galactic center (GC) and Galactic ridge. Aims. In our ongoing XMM-Newton survey of the central region of the Galactic disk (20° ×2°), we detected a persistent IP candidate, 1.7° away from the GC. In this work, we better characterize the behavior of this source by looking at the new and archival XMM-Newton data. Methods. We performed a detailed X-ray spectral modeling of the source. Furthermore, we searched for X-ray pulsations in the light curve as well as its counterpart at other wavelengths. Results. The XMM-Newton spectrum (0.8–10 keV) of the source is described by a partial covering collisionally ionized diffuse gas with plasma temperature kT = 15.7−3.6+20.9 keV. In addition, the spectrum shows the presence of iron lines at E = 6.44, 6.65, and 6.92 keV with equivalent widths of 194−70+89, 115−75+79, and 98−74+93 eV, respectively. The X-ray light curve shows a coherent modulation with a period of P = 432.44 ± 0.36 s, which we infer is the spin period of the white dwarf. The white dwarf mass estimated from fitting a physical model to the spectrum results in MWD = 1.05−0.21+0.16 M⊙. We were able to find a likely optical counterpart in the Gaia catalog with a G magnitude of 19.26, and the distance to the source derived from the measured Gaia parallax is ∼4.3 kpc. Conclusions. We provide an improved source localization with subarcsec accuracy. The spectral modeling of the source indicates the presence of intervening circumstellar gas, which absorbs the soft X-ray photons. The measured equivalent width of the iron lines and the detection of the spin period in the light curve are consistent with those from IPs.

Spatial and Frequency Specific Artifact Reduction in Optically Pumped Magnetometer Recordings.

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) opens up new opportunities for brain research. However, OPM recordings are associated with artifacts. We describe a new artifact reduction method, frequency specific signal space classification (FSSSC), to improve the signal-to-noise ratio of OPM recordings. FSSSC was based on time-frequency analysis and signal space classification (SSC). SSC was accomplished by computing the orthogonality of the brain signal and artifact. A dipole phantom was used to determine if the method could remove artifacts and improve accuracy of source localization. Auditory evoked magnetic fields (AEFs) from human subjects were used to assess the usefulness of artifact reduction in the study of brain function because bilateral AEFs have proven a good benchmark for testing new methods. OPM data from empty room recordings were used to estimate magnetic artifacts. The effectiveness of FSSSC was assessed in waveforms, spectrograms, and covariance domains. MEG recordings from phantom tests show that FSSSC can remove artifacts, normalize waveforms, and significantly improve source localization accuracy. MEG signals from human subjects show that FSSC can reveal auditory evoked magnetic responses overshadowed and distorted by artifacts. The present study demonstrates FSSSC is effective at removing artifacts in OPM recordings. This can facilitate the analyses of waveforms, spectrograms, and covariance. The accuracy of source localization of OPM recordings can be significantly improved by FSSSC. Brain responses distorted by artifacts can be restored. The results of the present study strongly support that artifact reduction is very important in order for OPMs to become a viable alternative to conventional MEG.

Massive black hole binaries and where to find them with dual detector networks

A single space-based gravitational wave detector will push the boundaries of astronomy and fundamental physics. Having a network of two or more detectors would significantly improve source localization. Here we consider how dual networks of space-based detectors would improve parameter estimation of massive black hole binaries (MBHBs). We consider two scenarios: a network composed of the Laser Interferometer Space Antenna (LISA) and an additional LISA-like heliocentric detector (e.g., Taiji), and a network composed of LISA with an additional geocentric detector (e.g., TianQin). We use Markov chain Monte Carlo techniques and Fisher matrix estimates to explore the impact of a two detector network on sky localization and distance determination. The impact on other source parameters is also studied. With the addition of Taiji or TianQin, we find lower uncertainties in the extrinsic parameters of both higher and lower mass MBHBs. Our most significant results are in sky location and luminosity distance. For sky location, we find several orders of magnitude improvements for high mass binaries and an order of magnitude improvement in low mass binaries. For luminosity distance, we find an order of magnitude improvement for high mass binaries and no improvement for low mass binaries.

EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization.

For the analysis of simultaneous EEG-fMRI recordings, it is vital to use effective artifact removal tools. This applies in particular to the ballistocardiogram (BCG) artifact which is difficult to remove without distorting signals of interest related to brain activity. Here, we documented the use of surrogate source models to separate the artifact-related signals from brain signals with minimal distortion of the brain activity of interest. The artifact topographies used for surrogate separation were created automatically using principal components analysis (PCA-S) or by manual selection of artifact components utilizing independent components analysis (ICA-S). Using real resting-state data from 55 subjects superimposed with simulated auditory evoked potentials (AEP), both approaches were compared with three established BCG artifact removal methods: Blind Source Separation (BSS), Optimal Basis Set (OBS), and a mixture of both (OBS-ICA). Each method was evaluated for its applicability for ERP and source analysis using the following criteria: the number of events surviving artifact threshold scans, signal-to-noise ratio (SNR), error of source localization, and signal variance explained by the dipolar model. Using these criteria, PCA-S and ICA-S fared best overall, with highly significant differences to the established methods, especially in source localization. The PCA-S approach was also applied to a single subject Berger experiment performed in the MRI scanner. Overall, the removal of BCG artifacts by the surrogate methods provides a substantial improvement for the analysis of simultaneous EEG-fMRI data compared to the established methods.

Deep Unrolled Recovery in Sparse Biological Imaging: Achieving fast, accurate results

Deep algorithm unrolling has emerged as a powerful, model-based approach to developing deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization. This framework is well suited to applications in biological imaging, where physics-based models exist to describe the measurement process and the information to be recovered is often highly structured. Here we review the method of deep unrolling and show how it improves source localization in several biological imaging settings.

Passive Cavitation Mapping by Cavitation Source Localization From Aperture-Domain Signals-Part I: Theory and Validation Through Simulations.

Passive cavitation mapping (PCM) algorithms for diagnostic ultrasound arrays based on time exposure acoustics (TEA) exhibit poor axial resolution, which is in part due to the diffraction-limited point spread function of the imaging system and poor rejection by the delay-and-sum beamformer. In this article, we adapt a method for speed of sound estimation to be utilized as a cavitation source localization (CSL) approach. This method utilizes a hyperbolic fit to the arrival times of the cavitation signals in the aperture domain, and the coefficients of the fit are related to the position of the cavitation source. Wavefronts exhibiting poor fit to the hyperbolic function are corrected to yield improved source localization. We demonstrate through simulations that this method is capable of accurate estimation of the origin of coherent spherical waves radiating from cavitation/point sources. The average localization error from simulated microbubble sources was 0.12 ± 0.12mm ( 0.15 ± 0.14λ0 for a 1.78-MHz transmit frequency). In simulations of two simultaneous cavitation sources, the proposed technique had an average localization error of 0.2mm ( 0.23λ0 ), whereas conventional TEA had an average localization error of 0.81mm ( 0.97λ0 ). The reconstructed PCM-CSL image showed a significant improvement in resolution compared with the PCM-TEA approach.

Frequency-difference autoproduct cross-term analysis and cancellation for improved ambiguity surface robustness.

Frequency-differencing, or autoproduct processing, techniques are one area of research that has been found to increase the robustness of acoustic array signal processing algorithms to environmental uncertainty. Previous studies have shown that frequency differencing techniques are able to mitigate problems associated with environmental mismatch in source localization techniques. While this method has demonstrated increased robustness compared to conventional methods, many of the metrics, such as ambiguity surface peak values and dynamic range, are lower than would typically be expected for the observed level of robustness. These previous studies have suggested that such metrics are reduced by the inherent nonlinearity of the frequency-differencing method. In this study, simulations of simple multi-path environments are used to analyze this nonlinearity and signal processing techniques are proposed to mitigate the effects of this problem. These methods are used to improve source localization metrics, particularly ambiguity surface peak value and dynamic range, in two experimental environments: a small laboratory water tank and in a deep ocean (Philippine Sea) environment. The performance of these techniques demonstrates that many source localization metrics can be improved for frequency-differencing methods, which suggests that frequency-differencing methods may be as robust as previous studies have shown.

Acoustic source imaging using densely connected convolutional networks

The localization of acoustic sources using acoustic imaging methods such as acoustic beamforming can be limited by the Rayleigh resolution limit at relatively low frequencies and the output of these methods may also produce spatial aliasing images referred to as sidelobes, particularly at high frequencies. To date, there are very few Deep Neural Network (DNN) applications for acoustic imaging to help alleviate some of these issues. In this study, several DNN models were developed with a training strategy specifically designed for an acoustic imaging task. This proposed DNN-based method is examined for various acoustic source conditions and compared with other classic acoustic imaging methods. The DNN method is able to recognize the pattern behind microphone array signals for different source positions, by using the real-component of the cross-spectral matrix of the received pressure vectors at the microphone positions. This input feature allows the DNN model to accurately locate and quantify the source strengths of complicated distributed acoustic sources, even at low frequencies that typically challenge acoustic beamforming deconvolution methods. The loss function of the DNN model is based on the difference between the estimated and true acoustic source maps, that is used to iteratively improve weighting functions within the DNN hidden layers. DNN models with both a fixed and random number of input sources are simulated for a range of specific frequencies. The DNN model with a random number of input sources is tested against conventional beamforming, CLEAN-SC and DAMAS, revealing a far improved source localization and source strength estimation. These DNN models represent a very promising proof-of-concept for the use of DNN models in the field of acoustic imaging.

Simultaneous diffuse optical and bioluminescence tomography to account for signal attenuation to improve source localization

Photonics based pre-clinical imaging is an extensively used technique to allow for the study of biologically relevant activity typically within a small-mouse model. Namely, bioluminescent tomography (BLT) attempts to tomographically reconstruct the 3-dimensional spatial light distribution of luminophores within a small animal given surface light measurements and known underlying optical parameters. Often it is the case where these optical parameters are unknown leading to the use of a ‘best’ guess approach or to direct measurements using either a multi-modal or dedicated system. Using these conventional approaches can lead to both inaccurate results and extending periods of imaging time. This work introduces the development of an algorithm that is used to accurately localize the spatial light distribution from a bioluminescence source within a subject by simultaneously reconstructing both the underlying optical properties and source spatial distribution and intensity from the same set of surface measurements. Through its application in 2- and 3-dimensional, homogeneous and heterogenous numerical models, it is demonstrated that the proposed algorithm is capable of replicating results as compared to ‘gold’ standard where the absolute optical properties are known. Additionally, the algorithm has been applied to experimental data using a tissue mimicking block phantom, recovering a spatial light distribution that has a localization error of ∼1.53 mm, which is better than previously published results without the need of assumptions regarding the underlying optical properties or source distribution.

Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method.

Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eightsets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of thepatients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.