Volume 116, Issue 2, August 2004
Index of content:
- ACOUSTIC SIGNAL PROCESSING 
116(2004); http://dx.doi.org/10.1121/1.1764831View Description Hide Description
Acoustic emission(AE) data from single point turning machining are analyzed in this paper in order to gain a greater insight of the signal statistical properties for tool condition monitoring applications. A statistical analysis of the time series data amplitude and root mean square (rms) value at various tool wear levels are performed, finding that aging features can be revealed in all cases from the observed experimental histograms. In particular, AE data amplitudes are shown to be distributed with a power-law behavior above a crossover value. An analytic model for the rms values probability density function is obtained resorting to the Jaynes’ maximum entropy principle; novel technique of constraining the modeling function under few fractional moments, instead of a greater amount of ordinary moments, leads to well-tailored functions for experimental histograms.
Performance bounds for passive sensor arrays operating in a turbulent medium: Spherical-wave analysis116(2004); http://dx.doi.org/10.1121/1.1760111View Description Hide Description
The Cramer–Rao lower bounds of the angle-of-arrival estimates for a spherical wave incident on a passive acoustic array are investigated for propagation through a turbulent medium with fluctuations described by a von Kármán spectrum. A single monochromatic source and a line-of-sight propagation path are assumed. The propagation distance, turbulence parameters (characteristic length scale and index-of-refraction variance), phase of the source, and signal-to-noise ratio are also included in the unknown parameter set. The Cramer–Rao lower bounds of the angle-of-arrival estimates are affected by the addition of the propagation distance and source phase as unknowns, and are not affected by the addition of the turbulence parameters and signal-to-noise ratio as unknowns.
Iterative algorithms for computing the shape of a hard scattering object: Computing the shape derivative116(2004); http://dx.doi.org/10.1121/1.1771611View Description Hide Description
The problem of determining the shape of an acoustically hard scattering object from remote scattering measurements is considered. An iterative approach is used to find the object shape that minimizes the mean-squared difference between a set of actual and predicted scattering observations. A crucial task in this minimization is the computation of the “shape derivative,” or functional gradient, of the mean-square error with respect to the object’s shape or boundary. The shape derivative tells us how to update the object’s shape to reduce the mean-square error at each iteration. If, for example, the object’s boundary is parameterized with N variables, a brute-force approach to computing the shape derivative using finite-differences would require a minimum of N+1 forward solutions per iteration. We show how the shape derivative can be computed with just two forward solutions: one ordinary forward solution and a suitably constructed adjoint solution. This approach is independent of N and is not only far more efficient, but numerically less error prone, than finite-difference schemes for computing derivatives.
116(2004); http://dx.doi.org/10.1121/1.1707089View Description Hide Description
The performance of a time reversal mirror (TRM) in complex ocean scenarios can be evaluated without invoking spatial reciprocity in the experimental procedure. The experimental implementation requires connectivity between a source array and a receiver array but eliminates the requirement of actually having a probe source collocated with the receiver array. It is shown with data taken in a recent experiment that this streamlined, nonreciprocity-based time reversal procedure yields results potentially better than the classical time reversal method. Further, it provides a more versatile method to study a TRM in a fluctuating medium.