On the Assessment of Stability and Patterning of Speech Movements

Speech requires the control of complex movements of orofacial structures to produce dynamic variations in the vocal tract transfer function. The nature of the underlying motor control processes has traditionally been investigated by employing measures of articulatory movements, including movement amplitude, velocity, and duration, at selected points in time. An alternative approach, first used in the study of limb motion, is to examine the entire movement trajectory over time. A new approach to speech movement trajectory analysis was introduced in earlier work from this laboratory. In this method, trajectories from multiple movement sequences are time- and amplitude-normalized, and the STI (spatiotemporal index) is computed to capture the degree of convergence of a set of trajectories onto a single, underlying movement template. This research note describes the rationale for this analysis and provides a detailed description of the signal processing involved. Alternative interpolation procedures for time-normalization of kinematic data are also considered

A Parametric Model for Multispectral Scanners

Efficient acquisition and utilization of remotely sensed data requires an extensive a priori evaluation of the performance of the basic data collection unit, the multispectral scanner. The objective is the development of a fully parametric technique to theoretically evaluate the systems response in any desired operational environment and provide the necessary information in selecting a set of optimum parameters. In this paper the multispectral scanner spatial characteristics are represented by a linear shift-invariant multiple-port system where the N spectral bands comprise the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial and hence spectral correlation matrices through the systems, is developed. Specific cases for Gaussian point spread functions are examined. The integration of the scanner spatial model and a parameter classification error estimator provides the necessary technique to evaluate the performance of a multispectral scanner. A set of test statistics are specified and the corresponding output quantities computed by the characteristic function. Two sets of classification accuracies, one at the input and one at the output are estimated. The scanner\u27s instantaneous field of view is changed and the variation of the output classification performance monitored

Estimation of Sampling Requirements for Track-Type Remote Sensing Surveys

Many types of remote sensing measurements are made along lines or tracks over the earth\u27s surface which are spaced at distances governed primarily by assumptions about the nature of the phenomena being measured and cost considerations. In geophysical surveys, aircraft-borne magnetic, gravity, gamma ray, electromagnetic and other sensors are flown at low altitude with approximately parallel line spacings ranging from 1/4-mile to several miles. These measurements are commonly sampled and digitized at an arbitrarily high rate along the flight path generating an adequately sampled record with respect to the Nyquist rate which is governed by the bandwidth of the physical phenomenon being observed. The sampling interval in the across track direction is the track spacing and closely spaced samples there would be extremely costly to obtain because of the increased number of flight lines required. The research discussed in this paper addresses the problem of determining the sampling requirements for proper representation of the geophysical fields and is based on study of the power spectral density of the measured quantities. The primary object of the measurement of various geophysical phenomena in exploration for minerals and petroleum deposits is to locate anomalies in these variables which may relate to targets of economic value. The spacing of survey lines has an important impact on the ability to reconstruct the measured surface and subsequent detection of anomalies. A method of selecting line spacing is discussed, in which the along track spectrum is used to predict the across tract frequency content of the scene using certain assumptions on the isotropy of the fields of interest. Comparisons will be shown of spectral estimation using classical windowed periodogram and autoregressive methods. Analysis results using data from analytical models and real data from U.S. Energy Research and Development Administration airborne geophysical surveys will be presented

AIR-TO-GROUND RADAR RANGING

Abstract not availabl

Effects of Spatial Distortion on Image Registration Performance

It is frequently desired to accurately register or overlay different images of the same scene. Under conditions in which the sensor-to-scene geometry is exactly the same for both images, the registration is accomplished by simply determining the relative translation between the images. However, in many instances there are variations in the sensor-to-scene geometry which cause the images to be spatially distorted relative to one another. This condition requires that a spatial warping be applied to one of the images in addition to determining the relative translation between the images in order to achieve registration. For many situations in which the relative spatial distortion is small (e-g., temporally differing LANDSAT images), it is assumed that the spatial differences are negligible for small subimages. Registration is accomplished by overlaying corresponding subimages within each of the images via translation only and then applying a spatial warping to one of the images based upon the subimage registrations. One of the primary parameters that must be determined in such a procedure is the subimage size to be used for registration. The analysis carried out presents a method by which the optimum image size may be chosen based upon a model of the spatial distortion and the premise that the registration processor is designed to overlay spatially congruent images. This is done by determining the effect of the relative spatial distortion upon the output signal-to-noise ratio of the registration processor. It is shown that for spatial distortions which increase with image size (e.g., rotation, scale difference), there is an optimal image size which maximizes the output signal-to-noise ratio. The analysis is also applied to a model of spatial distortions between temporally differing LANDSAT images