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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

A Low-Power High-Speed Smart Sensor Design for Space Exploration Missions

A low-power high-speed smart sensor system based on a large format active pixel sensor (APS) integrated with a programmable neural processor for space exploration missions is presented. The concept of building an advanced smart sensing system is demonstrated by a system-level microchip design that is composed with an APS sensor, a programmable neural processor, and an embedded microprocessor in a SOI CMOS technology. This ultra-fast smart sensor system-on-a-chip design mimics what is inherent in biological vision systems. Moreover, it is programmable and capable of performing ultra-fast machine vision processing in all levels such as image acquisition, image fusion, image analysis, scene interpretation, and control functions. The system provides about one tera-operation-per-second computing power which is a two order-of-magnitude increase over that of state-of-the-art microcomputers. Its high performance is due to massively parallel computing structures, high data throughput rates, fast learning capabilities, and advanced VLSI system-on-a-chip implementation.

Fang, Wai-Chi↗
NASA NTRS1988

Introduction to a system for implementing neural net connections on SIMD architectures

Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized communication. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.

Tomboulian, Sherryl↗
NASA NTRS1988

Introduction to a system for implementing neural net connections on SIMD architectures

Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized elements. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network.

Tomboulian, Sherryl↗
NASA NTRS1991

Parallel algorithms for boundary value problems

A general approach to solve boundary value problems numerically in a parallel environment is discussed. The basic algorithm consists of two steps: the local step where all the P available processors work in parallel, and the global step where one processor solves a tridiagonal linear system of the order P. The main advantages of this approach are twofold. First, this suggested approach is very flexible, especially in the local step and thus the algorithm can be used with any number of processors and with any of the SIMD or MIMD machines. Secondly, the communication complexity is very small and thus can be used as easily with shared memory machines. Several examples for using this strategy are discussed.

Lin, Avi↗
NASA NTRS1992

Parallelization of irregularly coupled regular meshes

Regular meshes are frequently used for modeling physical phenomena on both serial and parallel computers. One advantage of regular meshes is that efficient discretization schemes can be implemented in a straight forward manner. However, geometrically-complex objects, such as aircraft, cannot be easily described using a single regular mesh. Multiple interacting regular meshes are frequently used to describe complex geometries. Each mesh models a subregion of the physical domain. The meshes, or subdomains, can be processed in parallel, with periodic updates carried out to move information between the coupled meshes. In many cases, there are a relatively small number (one to a few dozen) subdomains, so that each subdomain may also be partitioned among several processors. We outline a composite run-time/compile-time approach for supporting these problems efficiently on distributed-memory machines. These methods are described in the context of a multiblock fluid dynamics problem developed at LaRC.

Chase, Craig↗
NASA NTRS1990

Parallel algorithms for boundary value problems

A general approach to solve boundary value problems numerically in a parallel environment is discussed. The basic algorithm consists of two steps: the local step where all the P available processors work in parallel, and the global step where one processor solves a tridiagonal linear system of the order P. The main advantages of this approach are two fold. First, this suggested approach is very flexible, especially in the local step and thus the algorithm can be used with any number of processors and with any of the SIMD or MIMD machines. Secondly, the communication complexity is very small and thus can be used as easily with shared memory machines. Several examples for using this strategy are discussed.

Lin, Avi↗
NASA NTRS1994

Some Problems and Solutions in Transferring Ecosystem Simulation Codes to Supercomputers

Many computer codes for the simulation of ecological systems have been developed in the last twenty-five years. This development took place initially on main-frame computers, then mini-computers, and more recently, on micro-computers and workstations. Recent recognition of ecosystem science as a High Performance Computing and Communications Program Grand Challenge area emphasizes supercomputers (both parallel and distributed systems) as the next set of tools for ecological simulation. Transferring ecosystem simulation codes to such systems is not a matter of simply compiling and executing existing code on the supercomputer since there are significant differences in the system architectures of sequential, scalar computers and parallel and/or vector supercomputers. To more appropriately match the application to the architecture (necessary to achieve reasonable performance), the parallelism (if it exists) of the original application must be exploited. We discuss our work in transferring a general grassland simulation model (developed on a VAX in the FORTRAN computer programming language) to a Cray Y-MP. We show the Cray shared-memory vector-architecture, and discuss our rationale for selecting the Cray. We describe porting the model to the Cray and executing and verifying a baseline version, and we discuss the changes we made to exploit the parallelism in the application and to improve code execution. As a result, the Cray executed the model 30 times faster than the VAX 11/785 and 10 times faster than a Sun 4 workstation. We achieved an additional speed-up of approximately 30 percent over the original Cray run by using the compiler's vectorizing capabilities and the machine's ability to put subroutines and functions "in-line" in the code. With the modifications, the code still runs at only about 5% of the Cray's peak speed because it makes ineffective use of the vector processing capabilities of the Cray. We conclude with a discussion and future plans.

Skiles, J. W.↗
NASA NTRS1992

From pilot's associate to satellite controller's associate

Associate technology is an emerging engineering discipline wherein intelligent automation can significantly augment the performance of man-machine systems. An associate system is one that monitors operator activity and adapts its operational behavior accordingly. Associate technology is most effectively applied when mapped into management of the human-machine interface and display-control loop in typical manned systems. This paper addresses the potential for application of associate technology into the arena of intelligent command and control of satellite systems, from diagnosis of onboard and onground of satellite systems fault conditions, to execution of nominal satellite control functions. Rather than specifying a specific solution, this paper draws parallels between the Pilot's Associate concept and the domain of satellite control.

Neyland, David L.↗
NASA NTRS1988

Eigenvalue computation of large symmetric tridiagonal matrices on concurrent processors

Symmetric tridiagonal eigenvalue problems may arise indirectly in structural dynamic analysis. An algorithm for eigenvalue computation of large symmetric tridiagonal matrices on concurrent processors to meet the challenge of the new emerging computer hardware technology is presented. A standard bisection method in conjunction with Sylvester's Theorem is chosen to be converted into a parallel N-section algorithm. This parallel algorithm takes advantage of the multi-processor environment by carrying out N (number of processors) triangular factorizations of chosen shifted matrices in all processors concurrently and by minimizing communication between processors. The algorithm is designed for local-memory concurrent processors, i.e. message passing type processors. The efficiency and speed-up are given in terms of problem and machine parameters. The algorithm is very efficient when both the number of processors and the number of eigenvalues to be extracted are much smaller than the order of the tridiagonal matrix.

Chang, H. Y.↗
NASA NTRS2001

Multimodal Neuroelectric Interface Development

This project aims to improve performance of NASA missions by developing multimodal neuroelectric technologies for augmented human-system interaction. Neuroelectric technologies will add completely new modes of interaction that operate in parallel with keyboards, speech, or other manual controls, thereby increasing the bandwidth of human-system interaction. We recently demonstrated the feasibility of real-time electromyographic (EMG) pattern recognition for a direct neuroelectric human-computer interface. We recorded EMG signals from an elastic sleeve with dry electrodes, while a human subject performed a range of discrete gestures. A machine-teaming algorithm was trained to recognize the EMG patterns associated with the gestures and map them to control signals. Successful applications now include piloting two Class 4 aircraft simulations (F-15 and 757) and entering data with a "virtual" numeric keyboard. Current research focuses on on-line adaptation of EMG sensing and processing and recognition of continuous gestures. We are also extending this on-line pattern recognition methodology to electroencephalographic (EEG) signals. This will allow us to bypass muscle activity and draw control signals directly from the human brain. Our system can reliably detect P-rhythm (a periodic EEG signal from motor cortex in the 10 Hz range) with a lightweight headset containing saline-soaked sponge electrodes. The data show that EEG p-rhythm can be modulated by real and imaginary motions. Current research focuses on using biofeedback to train of human subjects to modulate EEG rhythms on demand, and to examine interactions of EEG-based control with EMG-based and manual control. Viewgraphs on these neuroelectric technologies are also included.

Trejo, Leonard J.↗
NASA NTRS1977

Combustion of hydrogen injected into a supersonic airstream (the SHIP computer program)

The mathematical and physical basis of the SHIP computer program which embodies a finite-difference, implicit numerical procedure for the computation of hydrogen injected into a supersonic airstream at an angle ranging from normal to parallel to the airstream main flow direction is described. The physical hypotheses built into the program include: a two-equation turbulence model, and a chemical equilibrium model for the hydrogen-oxygen reaction. Typical results for equilibrium combustion are presented and exhibit qualitatively plausible behavior. The computer time required for a given case is approximately 1 minute on a CDC 7600 machine. A discussion of the assumption of parabolic flow in the injection region is given which suggests that improvement in calculation in this region could be obtained by use of the partially parabolic procedure of Pratap and Spalding. It is concluded that the technique described herein provides the basis for an efficient and reliable means for predicting the effects of hydrogen injection into supersonic airstreams and of its subsequent combustion.

Markatos, N. C.↗
NASA NTRS1995

Nonlinear structural analysis on distributed-memory computers

A computational strategy is presented for the nonlinear static and postbuckling analyses of large complex structures on massively parallel computers. The strategy is designed for distributed-memory, message-passing parallel computer systems. The key elements of the proposed strategy are: (1) a multiple-parameter reduced basis technique; (2) a nested dissection (or multilevel substructuring) ordering scheme; (3) parallel assembly of global matrices; and (4) a parallel sparse equation solver. The effectiveness of the strategy is assessed by applying it to thermo-mechanical postbuckling analyses of stiffened composite panels with cutouts, and nonlinear large-deflection analyses of HSCT models on Intel Paragon XP/S computers. The numerical studies presented demonstrate the advantages of nested dissection-based solvers over traditional skyline-based solvers on distributed memory machines.

Watson, Brian C.↗
NASA NTRS1995

Postbuckling and large-deflection nonlinear analyses on distributed-memory computers

A computational strategy is presented for postbuckling and nonlinear static analyses of large complex structures on distributed-memory parallel computers. The strategy is designed for message-passing parallel computer systems. The key elements of the proposed strategy are: (1) a multiple-parameter reduced basis technique; (2) a nested dissection (or multilevel substructuring) ordering scheme; (3) parallel assembly of global matrices; and (4) a parallel sparse equation solver. The effectiveness of the strategy is assessed by performing thermomechanical postbuckling analyses of stiffened composite panels with cutouts, and nonlinear large-deflection analyses of High Speed Civil Transport models on three distributed-memory computers. The numerical studies presented demonstrate the advantages of nested dissection-based solvers over traditional skyline-based solvers on distributed-memory machines.

Watson, Brian C.↗
NASA NTRSDate not supplied

Bingo: A Customizable Framework for Symbolic Regression with Genetic Programming

In this paper, we introduce Bingo, a flexible and customizable yet performant Python framework for symbolic regression with genetic programming. Bingo maintains a modular code structure for simple abstraction and easily swappable components. Fitness functions, selection methods, and constant optimization methods allow for easy problem-specific customization. Bingo also maintains several features for increased efficiency such as parallelism, equation simplification, and a C++ backend. We compare Bingo’s performance to other genetic programming for symbolic regression (GPSR) methods to show that it is both competitive and flexible.

machine learning↗
NASA NTRS1994

Missileborne Artificial Vision System (MAVIS)

Several years ago when INTEL and China Lake designed the ETANN chip, analog VLSI appeared to be the only way to do high density neural computing. In the last five years, however, digital parallel processing chips capable of performing neural computation functions have evolved to the point of rough equality with analog chips in system level computational density. The Naval Air Warfare Center, China Lake, has developed a real time, hardware and software system designed to implement and evaluate biologically inspired retinal and cortical models. The hardware is based on the Adaptive Solutions Inc. massively parallel CNAPS system COHO boards. Each COHO board is a standard size 6U VME card featuring 256 fixed point, RISC processors running at 20 MHz in a SIMD configuration. Each COHO board has a companion board built to support a real time VSB interface to an imaging seeker, a NTSC camera, and to other COHO boards. The system is designed to have multiple SIMD machines each performing different corticomorphic functions. The system level software has been developed which allows a high level description of corticomorphic structures to be translated into the native microcode of the CNAPS chips. Corticomorphic structures are those neural structures with a form similar to that of the retina, the lateral geniculate nucleus, or the visual cortex. This real time hardware system is designed to be shrunk into a volume compatible with air launched tactical missiles. Initial versions of the software and hardware have been completed and are in the early stages of integration with a missile seeker.

Andes, David K.↗
NASA NTRS2007

Distributed Antenna-Coupled TES for FIR Detectors Arrays

We describe a new architecture for a superconducting detector for the submillimeter and far-infrared. This detector uses a distributed hot-electron transition edge sensor (TES) to collect the power from a focal-plane-filling slot antenna array. The sensors lay directly across the slots of the antenna and match the antenna impedance of about 30 ohms. Each pixel contains many sensors that are wired in parallel as a single distributed TES, which results in a low impedance that readily matches to a multiplexed SQUID readout These detectors are inherently polarization sensitive, with very low cross-polarization response, but can also be configured to sum both polarizations. The dual-polarization design can have a bandwidth of 50The use of electron-phonon decoupling eliminates the need for micro-machining, making the focal plane much easier to fabricate than with absorber-coupled, mechanically isolated pixels. We discuss applications of these detectors and a hybridization scheme compatible with arrays of tens of thousands of pixels.

antenna↗
NASA NTRS1931

Strength of Rectangular Flat Plates Under Edge Compression

Flat rectangular plates of duralumin, stainless iron, monel metal, and nickel were tested under loads applied at two opposite edges and acting in the plane of the plate. The edges parallel to the direction of loading were supported in V grooves. The plates were all 24 inches long and varied in width from 4 to 24 inches by steps of 4 inches, and in thickness from 0.015 to 0.095 inch by steps of approximately 0.015 inch. There were also a few 1, 2, 3, and 6 inch wide specimens. The loads were applied in the testing machine at the center of a bar which rested along the top of the plate. Load was applied until the plate failed to take any more load. The tests show that the loads carried by the plates generally reached a maximum for the 8 or 12 inch width and that there was relatively small drop in load for the greater widths. Deflection and set measurement perpendicular to the plane of the plate were taken and the form of the buckle determined. The number of buckles were found to correspond in general to that predicted by the theory of buckling of a plate uniformly loaded at two opposite edges and simply supported at the edges.

Schuman, Louis↗
NASA NTRS1972

A robot conditioned reflex system modeled after the cerebellum.

Reduction of a theory of cerebellar function to computer software for the control of a mechanical manipulator. This reduction is achieved by considering the cerebellum, along with the higher-level brain centers which control it, as a type of finite-state machine with input entering the cerebellum via mossy fibers from the periphery and output from the cerebellum occurring via Purkinje cells. It is hypothesized that the cerebellum learns by an error-correction system similar to Perceptron training algorithms. An electromechanical model of the cerebellum is then developed for the control of a mechanical arm. The problem of modeling the granular layer which selects the set of parallel fibers which are active at any instant of time is considered, and a relevance matrix is constructed to model the relative degree of influence which mossy fibers from the various joints have on the sets of granule cells unique to each joint.

Albus, J. S.↗