Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Computational network”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Telemedicine Program

Since the 1970s, NASA has been involved in the research and demonstration of telemedicine for its potential in the care of astronauts in flight and Earth-bound applications. A combination of NASA funding, expertise and off-the-shelf computer and networking systems made telemedicine possible for a medically underserved hospital in Texas. Through two-way audio/video relay, the program links pediatric oncology specialists at the University of Texas Health Science Center in San Antonio to South Texas Hospital in Harlingen, providing easier access and better care to children with cancer. Additionally, the hospital is receiving teleclinics on pediatric oncology nursing, family counseling and tuberculosis treatment. VTEL Corporation, Sprint, and the Healthcare Open Systems and Trials Consortium also contributed staff and hardware.

Source record↗
NASA NTRS2002

Adaptive Resource Management Technology for Satellite Constellations

This manuscript describes the Sensor Web Adaptive Resource Manager (SWARM) project. The primary focus of the project is on the design and prototyping of middleware for managing computing and network resources in a way that enables the information systems of satellite constellations to provide realtime performance within dynamic environments. The middleware has been prototyped, and it has been evaluated by employing it to manage a pool of distributed resources for the ITOS (Integrated Test and Operations System) satellite command and control software system. The design of the middleware is discussed and a summary of the evaluation effort is provided.

Welch, Lonnie↗
NASA NTRS2002

RIACS FY2002 Annual Report

The Research Institute for Advanced Computer Science (RIACS) carries out basic research and technology development in computer science, in support of the National Aeronautics and Space Administration's missions. Operated by the Universities Space Research Association (a non-profit university consortium), RIACS is located at the NASA Ames Research Center, Moffett Field, California. It currently operates under a multiple year grant/cooperative agreement that began on October 1, 1997 and is up for renewal in September 2003. Ames has been designated NASA's Center of Excellence in Information Technology. In this capacity, Ames is charged with the responsibility to build an Information Technology (IT) Research Program that is preeminent within NASA. RIACS serves as a bridge between NASA Ames and the academic community, and RIACS scientists and visitors work in close collaboration with NASA scientists. RIACS has the additional goal of broadening the base of researchers in these areas of importance to the nation's space and aeronautics enterprises. RIACS research focuses on the three cornerstones of IT research necessary to meet the future challenges of NASA missions: 1) Automated Reasoning for Autonomous Systems; 2) Human-Centered Computing; and 3) High Performance Computing and Networking. In addition, RIACS collaborates with NASA scientists to apply IT research to a variety of NASA application domains including aerospace technology, earth science, life sciences, and astrobiology. RIACS also engages in other activities, such as workshops, seminars, visiting scientist programs and student summer programs, designed to encourage and facilitate collaboration between the university and NASA IT research communities.

Leiner, Barry M.↗
NASA NTRS2001

External Data and Attribute Hyperlink Programs for Promis*e(Registered Trademark)

External Data and Attribute Hyperlink are computer programs that can be added to Promis*e(trademark) which is a commercial software system that automates routine tasks in the design (including drawing schematic diagrams) of electrical control systems. The programs were developed under the Stennis Space Center's (SSC) Dual Use Technology Development Program to provide capabilities for SSC's BMCS configuration management system which uses Promis*e(trademark). The External Data program enables the storage and management of information in an external database linked to a drawing. Changes can be made either in the database or on the drawing. Information that originates outside Promis*e(trademark) can be stored in custom fields that can be added to the database. Although this information is not available in Promis*e(trademark) printed drawings, it can be associated with symbols in the drawings, and can be retrieved through the drawings when the software is running. The Attribute Hyperlink program enables the addition of hyperlink information as attributes of symbols. This program enables the formation of a direct hyperlink between a schematic diagram and an Internet site or a file on a compact disk, on the user's hard drive, or on another computer on a network to which the user's computer is connected. The user can then obtain information directly related to the part (e.g., maintenance, or troubleshooting information) associated with the hyperlink.

Derengowski, Rich↗
NASA NTRS1998

Research and Technology 1997

This report highlights the challenging work accomplished during fiscal year 1997 by Ames research scientists and engineers. The work is divided into accomplishments that support the goals of NASA s four Strategic Enterprises: Aeronautics and Space Transportation Technology, Space Science, Human Exploration and Development of Space (HEDS), and Earth Science. NASA Ames Research Center s research effort in the Space, Earth, and HEDS Enterprises is focused i n large part to support Ames lead role for Astrobiology, which broadly defined is the scientific study of the origin, distribution, and future of life in the universe. This NASA initiative in Astrobiology is a broad science effort embracing basic research, technology development, and flight missions. Ames contributions to the Space Science Enterprise are focused in the areas of exobiology, planetary systems, astrophysics, and space technology. Ames supports the Earth Science Enterprise by conducting research and by developing technology with the objective of expanding our knowledge of the Earth s atmosphere and ecosystems. Finallv, Ames supports the HEDS Enterprise by conducting research, managing spaceflight projects, and developing technologies. A key objective is to understand the phenomena surrounding the effects of gravity on living things. Ames has also heen designated the Agency s Center of Evcellence for Information Technnlogv. The three cornerstones of Information Technology research at Ames are automated reasoning, human-centered computing, and high performance computing and networking.

Source record↗
NASA NTRS1999

Microphone Array Phased Processing System (MAPPS): Version 4.0 Manual

A processing system has been developed to meet increasing demands for detailed noise measurement of individual model components. The Microphone Array Phased Processing System (MAPPS) uses graphical user interfaces to control all aspects of data processing and visualization. The system uses networked parallel computers to provide noise maps at selected frequencies in a near real-time testing environment. The system has been successfully used in the NASA Ames 7- by 10-Foot Wind Tunnel.

Watts, Michael E.↗
NASA NTRS2003

The NASA Smart Probe Project for real-time multiple microsensor tissue recognition

BACKGROUND: Remote surgery requires automated sensors, effectors and sensor-effector communication. The NASA Smart Probe Project has focused on the sensor aspect. METHODS: The NASA Smart Probe uses neural networks and data from multiple microsensors for a unique tissue signature in real time. Animal and human trials use several probe configurations: (1) 8-microsensor probe (2.5 mm in diameter) for rodent studies (normal and subcutaneous mammary tumor tissues), and (2) 21-gauge needle probe with 3 spectroscopic fibers and an impedance microelectrode for breast cancer diagnosis in humans. Multisensor data are collected in real time (update 100 times/s) using PCs. RESULTS: Human data (collected by NASA licensee BioLuminate) from 15 women undergoing breast biopsy distinguished normal tissue from both benign tumors and breast carcinoma. Tumor margins and necrosis are rapidly detected. CONCLUSION: Real-time tissue identification is achievable. Potential applications, including probes incorporating nanoelectrode arrays, are presented. Copyright 2003 S. Karger AG, Basel.

Biopsy/methods↗
NASA NTRS2001

Do nonlinearities play a significant role in short term, beat-to-beat variability?

Numerous studies of short-term beat-to-beat variability in cardiovascular signals have not resolved the debate about the completeness of linear analysis techniques. This aim of this paper is to evaluate further the role of nonlinearities in short-term, beat-to-beat variability. We compared linear autoregressive moving average (ARMA) and nonlinear neural network (NN) models for predicting instantaneous heart rate (HR) and mean arterial blood pressure (BP) from past HR and BP. To evaluate these models, we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that ARMA linear analysis techniques provide adequate characterization of the system dynamics responsible for generating short-term, beat-to-beat variability.

NASA Discipline Cardiopulmonary↗
NASA NTRS2003

Predicting protein functions from redundancies in large-scale protein interaction networks

Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

Proteins/chemistry/metabolism↗
NASA NTRS2002

Single-base-pair discrimination of terminal mismatches by using oligonucleotide microarrays and neural network analyses

The effects of single-base-pair near-terminal and terminal mismatches on the dissociation temperature (T(d)) and signal intensity of short DNA duplexes were determined by using oligonucleotide microarrays and neural network (NN) analyses. Two perfect-match probes and 29 probes having a single-base-pair mismatch at positions 1 to 5 from the 5' terminus of the probe were designed to target one of two short sequences representing 16S rRNA. Nonequilibrium dissociation rates (i.e., melting profiles) of all probe-target duplexes were determined simultaneously. Analysis of variance revealed that position of the mismatch, type of mismatch, and formamide concentration significantly affected the T(d) and signal intensity. Increasing the concentration of formamide in the washing buffer decreased the T(d) and signal intensity, and it decreased the variability of the signal. Although T(d)s of probe-target duplexes with mismatches in the first or second position were not significantly different from one another, duplexes with mismatches in the third to fifth positions had significantly lower T(d)s than those with mismatches in the first or second position. The trained NNs predicted the T(d) with high accuracies (R(2) = 0.93). However, the NNs predicted the signal intensity only moderately accurately (R(2) = 0.67), presumably due to increased noise in the signal intensity at low formamide concentrations. Sensitivity analysis revealed that the concentration of formamide explained most (75%) of the variability in T(d)s, followed by position of the mismatch (19%) and type of mismatch (6%). The results suggest that position of the mismatch at or near the 5' terminus plays a greater role in determining the T(d) and signal intensity of duplexes than the type of mismatch.

Non-NASA Center↗
NASA NTRS1998

Predicting lettuce canopy photosynthesis with statistical and neural network models

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Non-NASA Center↗
NASA NTRS1999

Classification of ion mobility spectra by functional groups using neural networks

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Non-NASA Center↗
NASA NTRS1999

Unsupervised classification of Space Acceleration Measurement System (SAMS) data using ART2-A

The Space Acceleration Measurement System (SAMS) has been developed by NASA to monitor the microgravity acceleration environment aboard the space shuttle. The amount of data collected by a SAMS unit during a shuttle mission is in the several gigabytes range. Adaptive Resonance Theory 2-A (ART2-A), an unsupervised neural network, has been used to cluster these data and to develop cause and effect relationships among disturbances and the acceleration environment. Using input patterns formed on the basis of power spectral densities (psd), data collected from two missions, STS-050 and STS-057, have been clustered.

STS-57 Shuttle Project↗
NASA NTRS1999

Self-organizing map (SOM) of space acceleration measurement system (SAMS) data

In this paper, space acceleration measurement system (SAMS) data have been classified using self-organizing map (SOM) networks without any supervision; i.e., no a priori knowledge is assumed regarding input patterns belonging to a certain class. Input patterns are created on the basis of power spectral densities of SAMS data. Results for SAMS data from STS-50 and STS-57 missions are presented. Following issues are discussed in details: impact of number of neurons, global ordering of SOM weight vectors, effectiveness of a SOM in data classification, and effects of shifting time windows in the generation of input patterns. The concept of 'cascade of SOM networks' is also developed and tested. It has been found that a SOM network can successfully classify SAMS data obtained during STS-50 and STS-57 missions.

STS-57 Shuttle Project↗
NASA NTRS2000

Neural network recognition of chemical class information in mobility spectra obtained at high temperatures

A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

NASA Discipline Environmental Health↗
NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗
NASA NTRS1998

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Cervix Neoplasms/pathology↗