Distorted Acquisition of Dynamic Events Sensed by Frequency-Scanning Fiber-Optic Interrogators and a Mitigation Strategy.

Fiber-optic dynamic interrogators, which use periodic frequency scanning, actually sample a time-varying measurand on a non-uniform time grid. Commonly, however, the sampled values are reported on a uniform time grid, synchronized with the periodic scanning. It is the novel and noteworthy message of this paper that this artificial assignment may give rise to significant distortions in the recovered signal. These distortions increase with both the signal frequency and measurand dynamic range for a given sampling rate and frequency scanning span of the interrogator. They may reach disturbing values in dynamic interrogators, which trade-off scanning speed with scanning span. The paper also calls for manufacturers of such interrogators to report the sampled values along with their instants of acquisition, allowing interpolation algorithms to substantially reduce the distortion. Experimental verification of a simulative analysis includes: (i) a commercial dynamic interrogator of 'continuous' FBG fibers that attributes the measurand values to a uniform time grid; as well as (ii) a dynamic Brillouin Optical time Domain (BOTDA) laboratory setup, which provides the sampled measurand values together with the sampling instants. Here, using the available measurand-dependent sampling instants, we demonstrate a significantly cleaner signal recovery using spline interpolation.

Gaussian bandwidth selection for manifold learning and classification.

Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel's scale parameter, also referred to as the kernel's bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold's intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.

On the question of whether lubricants fluidize in stick-slip friction.

Intermittent sliding (stick-slip motion) between solids is commonplace (e.g., squeaking hinges), even in the presence of lubricants, and is believed to occur by shear-induced fluidization of the lubricant film (slip), followed by its resolidification (stick). Using a surface force balance, we measure how the thickness of molecularly thin, model lubricant films (octamethylcyclotetrasiloxane) varies in stick-slip sliding between atomically smooth surfaces during the fleeting (ca. 20 ms) individual slip events. Shear fluidization of a film of five to six molecular layers during an individual slip event should result in film dilation of 0.4-0.5 nm, but our results show that, within our resolution of ca. 0.1 nm, slip of the surfaces is not correlated with any dilation of the intersurface gap. This reveals that, unlike what is commonly supposed, slip does not occur by such shear melting, and indicates that other mechanisms, such as intralayer slip within the lubricant film, or at its interface with the confining surfaces, may be the dominant dissipation modes.

An estimation method for improved extraction of the decay curve signal from CPMG-like measurements with a unilateral scanner.

Unilateral NMR devices are valuable tools used to study non-invasively arbitrarily-sized objects. They have been utilized in various applications, including non-destructive testing and well logging. However, measurements with such scanners are characterized by a low sensitivity, which is mainly the result of the low and inhomogeneous magnetic field B0. The resulting poor signal to noise ratio (SNR) is a prominent limitation, as it deteriorates the accuracy of data analysis. Improving the SNR is typically done by the use of averaging repetitions that result in too long scan times. This work presents a statistical signal-processing method that can improve the sensitivity of a Carr-Purcell-Meiboom-Gill (CPMG)-like sequence for measurements of transverse-relaxation with unilateral scanners. The method improves the extraction of the decay curve from the noisy data. This is done by exploiting the redundancy in the acquired signal and by the use of the noise characteristics, which are both incorporated into a weighted least-squares estimation approach. This technique is especially effective in applications where RF shielding is not in use, and the measurements are corrupted by dominant non-white noise. The method performance was evaluated with a series of CPMG-like measurements applied on two samples. Decay curves were extracted from each measurement with the proposed method and were compared to a conventional extraction of the decay curve. All measurements showed a significant improvement in the accuracy of estimation of the decaying signal. Thus, the improvement in the sensitivity can be translated into a reduction in the acquisition times (by reducing the need in averaging repetitions) or to a more accurate fitting process of the traverse relaxation distribution.

An estimation method of MR signal parameters for improved image reconstruction in unilateral scanner.

Unilateral NMR devices are used in various applications including non-destructive testing and well logging, but are not used routinely for imaging. This is mainly due to the inhomogeneous magnetic field (B0) in these scanners. This inhomogeneity results in low sensitivity and further forces the use of the slow single point imaging scan scheme. Improving the measurement sensitivity is therefore an important factor as it can improve image quality and reduce imaging times. Short imaging times can facilitate the use of this affordable and portable technology for various imaging applications. This work presents a statistical signal-processing method, designed to fit the unique characteristics of imaging with a unilateral device. The method improves the imaging capabilities by improving the extraction of image information from the noisy data. This is done by the use of redundancy in the acquired MR signal and by the use of the noise characteristics. Both types of data were incorporated into a Weighted Least Squares estimation approach. The method performance was evaluated with a series of imaging acquisitions applied on phantoms. Images were extracted from each measurement with the proposed method and were compared to the conventional image reconstruction. All measurements showed a significant improvement in image quality based on the MSE criterion - with respect to gold standard reference images. An integration of this method with further improvements may lead to a prominent reduction in imaging times aiding the use of such scanners in imaging application.

A hybrid technique for blind separation of non-gaussian and time-correlated sources using a multicomponent approach.

Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.

Blind separation of superimposed shifted images using parameterized joint diagonalization.

We consider the blind separation of source images from linear mixtures thereof, involving different relative spatial shifts of the sources in each mixture. Such mixtures can be caused, e.g., by the presence of a semi-reflective medium (such as a window glass) across a photographed scene, due to slight movements of the medium (or of the sources) between snapshots. Classical separation approaches assume either a static mixture model or a fully convolutive mixture model, which are, respectively, either under- or over-parameterized for this problem. In this paper, we develop a specially parameterized scheme for approximate joint diagonalization of estimated spectrum matrices, aimed at estimating the succinct set of mixture parameters: the static (gain) coefficients and the shift values. The estimated parameters are, in turn, used for convenient frequency-domain separation. As we demonstrate using both synthetic mixtures and real-life photographs, the advantage of the ability to incorporate spatial shifts is twofold: Not only does it enable separation when such shifts are present, but it also warrants deliberate introduction of such shifts as a simple source of added diversity whenever the static mixing coefficients form a singular matrix-thereby enabling separation in otherwise inseparable scenes.