Source Localization Of Activity Research Articles

Active source localization in wave guides based on machine learning

In the present work, an active source localization strategy is proposed. The presence of active sources in a waveguide can have several reasons, such as crack initiation or internal friction. In this study, the active source is represented by an impact event. A steel ball is dropped on an aluminum plate at different positions. Elastic waves are excited and will propagate through the plate. The wave response is acquired by a piezoelectric sensor network, which is attached to the plate. After performing numerical and physical experiments, enough data are collected in order to train an artificial neural network and a support vector machine. Those machine learning algorithms will predict the impact position based on the wave response of each sensor, while only numerical data from the finite element simulations are used to train both methods. After the training process is completed, the algorithms are applied to experimental data. A good agreement between reference and predicted results proves that the wave responses at the piezoelectric transducers contain sufficient information in order to localize the impact position precisely.

Spatiotemporal Signatures of Lexical–Semantic Prediction

Although there is broad agreement that top-down expectations can facilitate lexical-semantic processing, the mechanisms driving these effects are still unclear. In particular, while previous electroencephalography (EEG) research has demonstrated a reduction in the N400 response to words in a supportive context, it is often challenging to dissociate facilitation due to bottom-up spreading activation from facilitation due to top-down expectations. The goal of the current study was to specifically determine the cortical areas associated with facilitation due to top-down prediction, using magnetoencephalography (MEG) recordings supplemented by EEG and functional magnetic resonance imaging (fMRI) in a semantic priming paradigm. In order to modulate expectation processes while holding context constant, we manipulated the proportion of related pairs across 2 blocks (10 and 50% related). Event-related potential results demonstrated a larger N400 reduction when a related word was predicted, and MEG source localization of activity in this time-window (350-450 ms) localized the differential responses to left anterior temporal cortex. fMRI data from the same participants support the MEG localization, showing contextual facilitation in left anterior superior temporal gyrus for the high expectation block only. Together, these results provide strong evidence that facilitatory effects of lexical-semantic prediction on the electrophysiological response 350-450 ms postonset reflect modulation of activity in left anterior temporal cortex.

Large-Scale Source Localization with a Wireless Sensor Network Application

AbstractThis paper concerns the problem of large-scale source localization arising when many potential sources must be classified as either active or inactive such that the probability of missing an active source is bounded. A new iterative heuristic called the Iterative Source Localization Procedure (ISLoP) is introduced that reduces the complexity of a source localization problem with J potential sources from 2J to J per iteration, while also providing a local bounds on the maximum probability of a missed source. The ISLoP separates the source localization problem into a likelihood maximization problem followed by an active source localization problem. A diffusion example is used to demonstrate the performance of the ISLoP when compared to an estimation-based approach, where the heuristic is shown to have increasingly better performance as the bound on the maximum probability of a missed source is decreased. An experimental evaluation of the heuristic with respect to common wireless sensor networking errors is provided using a test bed implementation for a CO2 sequestration site monitoring problem.

An introduction to time-delay estimation, localization, and tracking in Acoustics

Acoustic source localization and tracking has a long, productive history in both passive and active sonar signal processing. More recently, microphone arrays have been used to localize and track air acoustic noise sources. An overview of basic localization and tracking techniques of acoustic radiating sources and scatters is presented. For a wide range of applications, many ad-hoc passive and active source localization techniques have been investigated. Concepts from the statistical estimation theory, such as maximum likelihood estimators and Cramér-Rao lower bounds (CRLB), are useful benchmarks for evaluation of these practical ad hoc systems. In both air and underwater media, bearing and range estimates are determined by time-delay estimates; hence, properties of the time-delay estimates are of critical importance. In the case the of narrow-band active sonar, localization and tracking is based on time delay, Doppler, and direction of arrival estimation. In the case of wideband sonar and tracking of high-speed air acoustic sources, tracking and range estimates are determined from time-scale dilation estimates. CRLBs for active sonar are determined by signal-to-noise ratio, signal properties of signal ambiguity functions such as effective duration and mean-square bandwidth of the transmitted signal, and array parameters. [This work has been supported by ONR.]

Scattering from an object in a stratified medium. I. Frequency dispersion and active localization

A signal scattered from an extended object is often very different from a point source signal emitted from the object. Hence active localization based on matched-field/mode processing may not be feasible, depending on how the signal is modified by scattering. In a stratified medium in which propagation of a signal can be expressed in terms of normal modes, the effect of scattering is to induce mode coupling—an incident normal mode is converted to a series of outgoing normal modes by the scatterer. This mode coupling is examined by ensonifying the object with a broadband pulse at some distance from the object. The time-frequency dispersion of a scattered signal is compared with the dispersion of a passive signal traveling the same path (with the scatterer absent). One finds that the coupled normal modes (created by scattering) arrive at a different time than the uncoupled (original) normal modes and can be identified using time-frequency analysis. For active source localization broadband matched-mode processing is applied to the scattered signal. It should be noted that in many problems, the often unknown scattering function (of an unidentified object) prevents one from calculating the replica field for a scattered signal that is required for matched-field processing. For some problems, the relative phase distortion between the modes due to scattering is minimal even though mode coupling is significant. For these cases, matched-mode processing successfully localizes the target; matched-mode processing can tolerate the lack of specific knowledge about the scattering function. Both incoherent and coherent broadband active processing are investigated. In general, one can expect two peaks in the active range and depth ambiguity surface corresponding to the locations of the target and the active source.