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NASA NTRS2003

Parallel Subconvolution Filtering Architectures

These architectures are based on methods of vector processing and the discrete-Fourier-transform/inverse-discrete- Fourier-transform (DFT-IDFT) overlap-and-save method, combined with time-block separation of digital filters into frequency-domain subfilters implemented by use of sub-convolutions. The parallel-processing method implemented in these architectures enables the use of relatively small DFT-IDFT pairs, while filter tap lengths are theoretically unlimited. The size of a DFT-IDFT pair is determined by the desired reduction in processing rate, rather than on the order of the filter that one seeks to implement. The emphasis in this report is on those aspects of the underlying theory and design rules that promote computational efficiency, parallel processing at reduced data rates, and simplification of the designs of very-large-scale integrated (VLSI) circuits needed to implement high-order filters and correlators.

Gray, Andrew A.↗

NASA NTRS1992

A low-cost vector processor boosting compute-intensive image processing operations

Low-cost vector processing (VP) is within reach of everyone seriously engaged in scientific computing. The advent of affordable add-on VP-boards for standard workstations complemented by mathematical/statistical libraries is beginning to impact compute-intensive tasks such as image processing. A case in point in the restoration of distorted images from the Hubble Space Telescope. A low-cost implementation is presented of the standard Tarasko-Richardson-Lucy restoration algorithm on an Intel i860-based VP-board which is seamlessly interfaced to a commercial, interactive image processing system. First experience is reported (including some benchmarks for standalone FFT's) and some conclusions are drawn.

Adorf, Hans-Martin↗

NASA NTRS1973

Iterative techniques to estimate signature vectors for mixture processing of multispectral data

Two methods for obtaining the required spectral signatures for a particular mixture model are considered. For the model considered, the spectral signatures become signature vectors. The first method is based upon determination of the signature vectors in such a way that a measure of the inconsistency between the mixture model and the observed data is minimized. The second method is based upon determination of the signature vectors in such a way that the estimated mean percentage coverage of individual species matches apriori or ground truth estimates. The two methods proposed are applied to actual multispectral data in order to verify the concepts presented.

Salvato, P., Jr.↗

NASA NTRS1996

Mapping Pixel Windows To Vectors For Parallel Processing

Mapping performed by matrices of transistor switches. Arrays of transistor switches devised for use in forming simultaneous connections from square subarray (window) of n x n pixels within electronic imaging device containing np x np array of pixels to linear array of n(sup2) input terminals of electronic neural network or other parallel-processing circuit. Method helps to realize potential for rapidity in parallel processing for such applications as enhancement of images and recognition of patterns. In providing simultaneous connections, overcomes timing bottleneck or older multiplexing, serial-switching, and sample-and-hold methods.

Duong, Tuan A.↗

NASA NTRS1983

User alternatives in post-processing for raster-to-vector conversion

A number of Landsat-based coniferous forest stratum maps have been created of the Eldorado National Forest in California. These maps were produced in raster image format which is not directly usable by the U.S. Forest Service's vector-based Wildland Resource Information System (WRIS). As a solution, raster-to-vector conversion software has been developed for processing classified images into polygonal data structures. Before conversion, however, the digital classification images must be simplified to remove high spatial variance ('noise', 'speckle') and meet a USFS ten acre minimum requirement. A post-processing (simplification) strategy different from those commonly used in raster image processing may be desired for preparing maps for conversion to vector format, because simplification routines typically permit diagonal connections in the process of reclassifying pixels and forming new polygons. Diagonal connections are often undesirable when converting to vector format because they permit polygons to effectively cross over each other and occupy a common location. Three alternative methodologies are discussed for simplifying raster data for conversion to vector format.

Logan, T. L.↗

NASA NTRS1978

A single user efficiency measure for evaluation of parallel or pipeline computer architectures

A precise statement of the relationship between sequential computation at one rate, parallel or pipeline computation at a much higher rate, the data movement rate between levels of memory, the fraction of inherently sequential operations or data that must be processed sequentially, the fraction of data to be moved that cannot be overlapped with computation, and the relative computational complexity of the algorithms for the two processes, scalar and vector, was developed. The relationship should be applied to the multirate processes that obtain in the employment of various new or proposed computer architectures for computational aerodynamics. The relationship, an efficiency measure that the single user of the computer system perceives, argues strongly in favor of separating scalar and vector processes, sometimes referred to as loosely coupled processes, to achieve optimum use of hardware.

Jones, W. P.↗

NASA NTRS1990

Optical computing and image processing using photorefractive gallium arsenide

Recent experimental results on matrix-vector multiplication and multiple four-wave mixing using GaAs are presented. Attention is given to a simple concept of using two overlapping holograms in GaAs to do two matrix-vector multiplication processes operating in parallel with a common input vector. This concept can be used to construct high-speed, high-capacity, reconfigurable interconnection and multiplexing modules, important for optical computing and neural-network applications.

Cheng, Li-Jen↗

NASA NTRS1966

Maximum A posteriori interval estimation

Maximum a posteriori estimate of random process state vector in interval

RANDOM PROCESS↗

NASA NTRS1993

Massively Parallel Neurocomputing for Aerospace Applications

An innovative hybrid, analog-digital charge-domain technology, for the massively parallel VLSI implementation of certain large scale matrix-vector operations, has recently been introduced. It employs arrays of Charge Coupled/Charge Injection Device cells holding an analog matrix of charge, which process digital vectors in parallel by means of binary, non-destructive charge transfer operations. The impact of this technology on massively parallel processing is discussed.

massively↗

NASA NTRS1987

Concurrent and vectorized mixed time, explicit nonlinear structural dynamics algorithms

A nonlinear structural dynamics program with an element library that exploits parallel processing is described. The aim is to exploit scheduling-allocation so that parallel processing and vectorization can effectively be treated in a general purpose program with explicit time integration and different time steps in different parts of the mesh. The program uses an element group scheme, which, as a by-product, also provides an automatic scheme for assigning different time steps to different parts of the mesh. The program has been tested on the Alliant FX/8; it shows a fivefold improvement in speed over compiler optimization.

Belytschko, Ted↗

NASA NTRS1972

Receiver processing for direct-detection optical communication systems.

A model that is sufficiently general to describe the predominant statistical characteristics of the output of many real optical detectors is formulated. This model is used to study the optimum receiver processing for direct-detection optical communication systems. In particular, the structures of detectors and estimators for filtered doubly stochastic Poisson processes (DSPP) observed in additive white Gaussian noise are considered. Representations for the posterior statistics of a vector Markov process that modulates the intensity of the DSPP are obtained.

Hoversten, E. V.↗

NASA NTRS1993

Massively parallel neurocomputing for aerospace applications

Fijany, Amir↗

NASA NTRS1963

Maximum likelihood estimation in finite stationary ergodic markaov chains

Maximum likelihood estimation of vector parameter in finite stationary ergodic markov chains

MARKOV CHAIN↗

NASA NTRS1990

Processing Laser-Velocimetric Data By Vector Scanning

Velocity plots obtained in minutes instead of hours. Velocity scanning technique for processing flow-field data from pulsed-laser-velocimetry images into two-dimensional velocity-vector maps. Requires no special equipment other than charge-coupled-device (CCD) camera, circuitry to digitize output of camera, and computer. Once image data digitized, all processing done in computer.

Wernet, Mark P.↗

NASA NTRS1972

Bayesian recursive image estimation.

The enhancement of images that are characterized only by statistical data where the picture contains additive noise is considered. The random process representing the output of the scanner is characterized by the output of a dynamic system with white noise input. The dynamic system describes a first-order vector-Markov process. The procedure of Kalman filtering is then utilized to recursively determine the minimum mean-square error estimate of the image. The result is then extended to obtain the smoothing of the data.

Nahi, N. E.↗

NASA NTRS1993

Planning collision free paths for two cooperating robots using a divide-and-conquer C-space traversal heuristic

A method was developed to plan feasible and obstacle-avoiding paths for two spatial robots working cooperatively in a known static environment. Cooperating spatial robots as referred to herein are robots which work in 6D task space while simultaneously grasping and manipulating a common, rigid payload. The approach is configuration space (c-space) based and performs selective rather than exhaustive c-space mapping. No expensive precomputations are required. A novel, divide-and-conquer type of heuristic is used to guide the selective mapping process. The heuristic does not involve any robot, environment, or task specific assumptions. A technique was also developed which enables solution of the cooperating redundant robot path planning problem without requiring the use of inverse kinematics for a redundant robot. The path planning strategy involves first attempting to traverse along the configuration space vector from the start point towards the goal point. If an unsafe region is encountered, an intermediate via point is identified by conducting a systematic search in the hyperplane orthogonal to and bisecting the unsafe region of the vector. This process is repeatedly applied until a solution to the global path planning problem is obtained. The basic concept behind this strategy is that better local decisions at the beginning of the trouble region may be made if a possible way around the 'center' of the trouble region is known. Thus, rather than attempting paths which look promising locally (at the beginning of a trouble region) but which may not yield overall results, the heuristic attempts local strategies that appear promising for circumventing the unsafe region.

Weaver, Johnathan M.↗

NASA NTRS1989

Selection of optimum median-filter-based ambiguity removal algorithm parameters for NSCAT

The NASA Scatterometer, NSCAT, is an active spaceborne radar designed to measure the normalized radar backscatter coefficient (sigma0) of the ocean surface. These measurements can, in turn, be used to infer the surface vector wind over the ocean using a geophysical model function. Several ambiguous wind vectors result because of the nature of the model function. A median-filter-based ambiguity removal algorithm will be used by the NSCAT ground data processor to select the best wind vector from the set of ambiguous wind vectors. This process is commonly known as dealiasing or ambiguity removal. The baseline NSCAT ambiguity removal algorithm and the method used to select the set of optimum parameter values are described. An extensive simulation of the NSCAT instrument and ground data processor provides a means of testing the resulting tuned algorithm. This simulation generates the ambiguous wind-field vectors expected from the instrument as it orbits over a set of realistic meoscale wind fields. The ambiguous wind field is then dealiased using the median-based ambiguity removal algorithm. Performance is measured by comparison of the unambiguous wind fields with the true wind fields. Results have shown that the median-filter-based ambiguity removal algorithm satisfies NSCAT mission requirements.

Shaffer, Scott↗

NASA NTRS1971

Bayesian recursive image estimation.

A procedure for recursively estimating images that are characterized statistically by the mean and correlation functions associated with the random process representing the brightness level is proposed for the case where the images are corrupted by additive noise. First, a dynamic model is developed with a response characteristic which matches that of the scanner output (the input of the estimator is the output of a horizontal line scanner) in a statistical sense. Such models have the form of an ordinary differential or difference equation with white noise input. An insignificant approximation is introduced by using a constant-coefficient model. The appropriate model is a vector valued difference equation with the solution representing a vector Markov process. The next step is to obtain the minimum mean square estimate of the image by using a Kalman filter. Since the image estimation is an interpolation problem, two successive runs over the observation are performed in opposite directions and the resultant estimates are averaged. Examples are included for illustration.

Nahi, N. E.↗