Search NASA⌕ Search

SEARCH · Search NASA

Results for “ICA”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Coping with Trial-to-Trial Variability of Event Related Signals: A Bayesian Inference Approach

In electro-neurophysiology, single-trial brain responses to a sensory stimulus or a motor act are commonly assumed to result from the linear superposition of a stereotypic event-related signal (e.g. the event-related potential or ERP) that is invariant across trials and some ongoing brain activity often referred to as noise. To extract the signal, one performs an ensemble average of the brain responses over many identical trials to attenuate the noise. To date, h s simple signal-plus-noise (SPN) model has been the dominant approach in cognitive neuroscience. Mounting empirical evidence has shown that the assumptions underlying this model may be overly simplistic. More realistic models have been proposed that account for the trial-to-trial variability of the event-related signal as well as the possibility of multiple differentially varying components within a given ERP waveform. The variable-signal-plus-noise (VSPN) model, which has been demonstrated to provide the foundation for separation and characterization of multiple differentially varying components, has the potential to provide a rich source of information for questions related to neural functions that complement the SPN model. Thus, being able to estimate the amplitude and latency of each ERP component on a trial-by-trial basis provides a critical link between the perceived benefits of the VSPN model and its many concrete applications. In this paper we describe a Bayesian approach to deal with this issue and the resulting strategy is referred to as the differentially Variable Component Analysis (dVCA). We compare the performance of dVCA on simulated data with Independent Component Analysis (ICA) and analyze neurobiological recordings from monkeys performing cognitive tasks.

Ding, Mingzhou↗
NASA NTRS1999

3-D VLSI Architecture Implementation for Data Fusion Problems

This paper gives an overview of hardware implementation techniques employed in solving real-time classification problems using Neural Network, Principle Component Analysis (PCA), and Independent Component Analysis (ICA) techniques.

3-D VLSI architecture data fusion neural networks↗
NASA NTRS2006

Point-to-Point! Validation of the Small Aircraft Transportation System Higher Volume Operations Concept

Described is the research process that NASA researchers used to validate the Small Aircraft Transportation System (SATS) Higher Volume Operations (HVO) concept. The four phase building-block validation and verification process included multiple elements ranging from formal analysis of HVO procedures to flight test, to full-system architecture prototype that was successfully shown to the public at the June 2005 SATS Technical Demonstration in Danville, VA. Presented are significant results of each of the four research phases that extend early results presented at ICAS 2004. HVO study results have been incorporated into the development of the Next Generation Air Transportation System (NGATS) vision and offer a validated concept to provide a significant portion of the 3X capacity improvement sought after in the United States National Airspace System (NAS).

Williams, Daniel M.↗
NASA NTRS2007

Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment

In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.

ENose↗
NASA NTRS2008

Sensing a Changing Chemical Mixture Using an Electronic Nose

A method of using an electronic nose to detect an airborne mixture of known chemical compounds and measure the temporally varying concentrations of the individual compounds is undergoing development. In a typical intended application, the method would be used to monitor the air in an inhabited space (e.g., the interior of a building) for the release of solvents, toxic fumes, and other compounds that are regarded as contaminants. At the present state of development, the method affords a capability for identifying and quantitating one or two compounds that are members of a set of some number (typically of the order of a dozen) known compounds. In principle, the method could be extended to enable monitoring of more than two compounds. An electronic nose consists of an array of sensors, typically made from polymer carbon composites, the electrical resistances of which change upon exposure to a variety of chemicals. By design, each sensor is unique in its responses to these chemicals: some or all of the sensitivities of a given sensor to the various vapors differ from the corresponding sensitivities of other sensors. In general, the responses of the sensors are nonlinear functions of the concentrations of the chemicals. Hence, mathematically, the monitoring problem is to solve the set of time-dependent nonlinear equations for the sensor responses to obtain the time dependent concentrations of individual compounds. In the present developmental method, successive approximations of the solution are generated by a learning algorithm based on independent-component analysis (ICA) an established information theoretic approach for transforming a vector of observed interdependent signals into a set of signals that are as nearly statistically independent as possible.

Duong, Tuan↗
NASA NTRS2009

NASA Tech Briefs, July 2009

Topics covered include: Dual Cryogenic Capacitive Density Sensor; Hail Monitor Sensor; Miniature Six-Axis Load Sensor for Robotic Fingertip; Improved Blackbody Temperature Sensors for a Vacuum Furnace; Wrap-Around Out-the-Window Sensor Fusion System; Wide-Range Temperature Sensors with High-Level Pulse Train Output; Terminal Descent Sensor Simulation; A Robust Mechanical Sensing System for Unmanned Sea Surface Vehicles; Additive for Low-Temperature Operation of Li-(CF)n Cells; Li/CFx Cells Optimized for Low-Temperature Operation; Number Codes Readable by Magnetic-Field-Response Recorders; Determining Locations by Use of Networks of Passive Beacons; Superconducting Hot-Electron Submillimeter-Wave Detector; Large-Aperture Membrane Active Phased-Array Antennas; Optical Injection Locking of a VCSEL in an OEO; Measuring Multiple Resistances Using Single-Point Excitation; Improved-Bandwidth Transimpedance Amplifier; Inter-Symbol Guard Time for Synchronizing Optical PPM; Novel Materials Containing Single-Wall Carbon Nanotubes Wrapped in Polymer Molecules; Light-Curing Adhesive Repair Tapes; Thin-Film Solid Oxide Fuel Cells; Zinc Alloys for the Fabrication of Semiconductor Devices; Small, Lightweight, Collapsible Glove Box; Radial Halbach Magnetic Bearings; Aerial Deployment and Inflation System for Mars Helium Balloons; Steel Primer Chamber Assemblies for Dual Initiated Pyrovalves; Voice Coil Percussive Mechanism Concept for Hammer Drill; Inherently Ducted Propfans and Bi-Props; Silicon Nanowire Growth at Chosen Positions and Orientations; Detecting Airborne Mercury by Use of Gold Nanowires; Detecting Airborne Mercury by Use of Palladium Chloride; Micro Electron MicroProbe and Sample Analyzer; Nanowire Electron Scattering Spectroscopy; Electron-Spin Filters Would Offer Spin Polarization Greater than 1; Subcritical-Water Extraction of Organics from Solid Matrices; A Model for Predicting Thermoelectric Properties of Bi2Te3; Integrated Miniature Arrays of Optical Biomolecule Detectors; A Software Rejuvenation Framework for Distributed Computing; Kurtosis Approach to Solution of a Nonlinear ICA Problem; Robust Software Architecture for Robots; R4SA for Controlling Robots; Bio-Inspired Neural Model for Learning Dynamic Models; Evolutionary Computing Methods for Spectral Retrieval; Monitoring Disasters by Use of Instrumented Robotic Aircraft; Complexity for Survival of Living Systems; Using Drained Spacecraft Propellant Tanks for Habitation; Connecting Node; and Electrolytes for Low-Temperature Operation of Li-CFx Cells.

Source record↗
NASA NTRS2007

All-Particle Spectrum Measured by the ATIC Experiment

The Advanced Thin Ionization Calorimeter (ATIC), a balloon-borne experiment, is designed to investigate the composition and energy spectra of cosmic rays of charge Z = 1 to 26 over the energy range - 10(exp 11) - 10(exp 14) ev. The instrument consists of a silicon matrix charge detector, plastic-scintillator strip hodoscopes interleaved with graphite interaction targets, and an 18 radiation length deep, fully active bismuth germanate (BGO) calorimeter. ATIC has had two successful long duration balloon (LDB) flights launched from McMurdo Station, Antarc't'ica in 2000 and 2002. In this paper, we present the all-particle spectrum extracted from data collected during the ATIC flights, and compare it with results from other experiments at both lower and higher energies.

Ahn, H. S.↗
NASA NTRS2008

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Srivastava, Askok N.↗
NASA NTRS2000

Mir Contamination Observations and Implications to the International Space Station

A series of external contamination measurements were made on the Russian Mir Space Station. The Mir external contamination observations summarized in this paper were essential in assessing the system level impact of Russian Segment induced contamination on the International Space Station (ISS). Mir contamination observations include results from a series of flight experiments: CNES Comes-Aragatz, retrieved NASA camera bracket, Euro-Mir '95 ICA, retrieved NASA Trek blanket, Russian Astra-II, Mir Solar Array Return Experiment (SARE), etc. Results from these experiments were studied in detail to characterize Mir induced contamination. In conjunction with Mir contamination observations, Russian materials samples were tested for condensable outgassing rates in the U.S. These test results were essential in the characterization of Mir contamination sources. Once Mir contamination sources were identified and characterized, activities to assess the implications to ISS were implemented. As a result, modifications in Russian materials selection and/or usage were implemented to control contamination and mitigate risk to ISS.

Soares, Carlos↗
NASA NTRS2011

EEG Artifact Removal Using a Wavelet Neural Network

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Nguyen, Hoang-Anh T.↗
NASA NTRS2011

Allowing for Horizontally Heterogeneous Clouds and Generalized Overlap in an Atmospheric GCM

While fully accounting for 3D effects in Global Climate Models (GCMs) appears not realistic at the present time for a variety of reasons such as computational cost and unavailability of 3D cloud structure in the models, incorporation in radiation schemes of subgrid cloud variability described by one-point statistics is now considered feasible and is being actively pursued. This development has gained momentum once it was demonstrated that CPU-intensive spectrally explicit Independent Column Approximation (lCA) can be substituted by stochastic Monte Carlo ICA (McICA) calculations where spectral integration is accomplished in a manner that produces relatively benign random noise. The McICA approach has been implemented in Goddard's GEOS-5 atmospheric GCM as part of the implementation of the RRTMG radiation package. GEOS-5 with McICA and RRTMG can handle horizontally variable clouds which can be set via a cloud generator to arbitrarily overlap within the full spectrum of maximum and random both in terms of cloud fraction and layer condensate distributions. In our presentation we will show radiative and other impacts of the combined horizontal and vertical cloud variability on multi-year simulations of an otherwise untuned GEOS-5 with fixed SSTs. Introducing cloud horizontal heterogeneity without changing the mean amounts of condensate reduces reflected solar and increases thermal radiation to space, but disproportionate changes may increase the radiative imbalance at TOA. The net radiation at TOA can be modulated by allowing the parameters of the generalized overlap and heterogeneity scheme to vary, a dependence whose behavior we will discuss. The sensitivity of the cloud radiative forcing to the parameters of cloud horizontal heterogeneity and comparisons of CERES-derived forcing will be shown.

Lee, D.↗
NASA NTRS2013

Interface Anywhere: Development of a Voice and Gesture System for Spaceflight Operations

The Interface Anywhere Project was funded through Innovation Charge Account (ICA) at NASA JSC in the Fall of 2012. The project was collaboration between human factors and engineering to explore the possibility of designing an interface to control basic habitat operations through gesture and voice control; (a) Current interfaces require the users to be physically near an input device in order to interact with the system; and (b) By using voice and gesture commands, the user is able to interact with the system anywhere they want within the work environment.

Thompson, Shelby↗
NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗
NASA NTRS2016

Influence of Exercise Modality on Cerebral-Ocular Hemodynamics and Pressures

Background: Moderate and high intensity aerobic or resistance exercise has clearly identified benefits for cardiac, muscle, and bone health. However, the impact of such exercise - either as a mitigating or an exacerbating factor - on the development of the visual impairment and intracranial pressure syndrome (VIIP) is unknown. Accordingly, our aim was to characterize the effect of an acute bout of resistance (RE), moderate-intensity continuous (CE), and high-intensity interval exercise (IE) during a cephalad fluid shift on cerebral-ocular hemodynamics and pressures. Methods: 10 male subjects (36 plus or minus 9 years) completed 4 testing days in a 15 degree head-down tilt (HDT): (1) assessment of maximum volume of O (sub 2), (2) RE session (4 sets of 12 repetition maximum leg press exercise), (3) CE session (30 minutes of cycling at 60 percent maximum volume of O (sub 2)), and (4) IE session (4 by 4-minute intervals of exercise at 85 percent maximum volume of O (sub 2) with 3-minute active rest periods). During each session, blood flow (Vivid-e, GE Healthcare) in extracranial arteries (common carotid artery, CCA; internal carotid artery, ICA; external carotid artery, ECA and vertebral artery, VA), and mean blood flow velocity in middle cerebral artery (MCA), internal jugular pressure (IJP; VeinPress), and intraocular pressure (IOP; Icare PRO) were measured at rest, at the end of each resistance or interval set, and every 5 minutes during continuous exercise. Translaminar pressure gradient (TLPG) was estimated by subtracting IJP from IOP. Results: There were no differences across days in pre-exercise resting blood flows or pressures. IOP decreased slightly from HDT rest (20.2 plus or minus 2.3 millimeters of mercury) to exercise (RE: 19.2 plus or minus 2.8 millimeters of mercury; CE: 18.9 plus or minus 3.2 millimeters of mercury; IE: 20.1 plus or minus 2.8 millimeters of mercury), while IJP decreased during CE (31.6 plus or minus 9.5 millimeters of mercury) and RE (32.0 plus or minus 8.1 millimeters of mercury), and increased during IE (35.1 plus or minus 9.5 millimeters of mercury) from HDT rest (33.3 plus or minus 6.5 millimeters of mercury). Estimated TLPG was increased during IE only. Compared to RE and CE, IE resulted in the greatest increase in MCA blood flow velocity and extracranial artery blood flow. Conclusions: These preliminary results suggest that high-intensity IE acutely increases cerebral blood flow, IJP, and TLPG. Alterations in TLPG is one mechanism that may contribute to optic nerve sheath edema in astronauts. Accordingly, acutely raising IOP and/or orbital pressure during exercise could optimize cerebral-ocular pressures during spaceflight.

Scott, J.↗
NASA NTRS2016

Biodigester Feasibility and Design for Space & Earth

Anaerobic digestion converts organic waste into methane gas and fertilizer effluent. The ICA-developed prototype system is designed for planetary surface operation. It uses passive hydrostatic control for reliability, and is modular and redundant. The serpentine configuration accommodates tight geometric constraints similar to the ISS ECLSS rack architectures. Its shallow, low-tilt design enables (variable) lower-g convection than standard Earth (1 g) digesters. This technology will reuse and recycle materials including human waste, excess food, as well as packaging (if biodegradable bags are used).

Shutts, Stacy↗
NASA NTRS2016

RFI Detection and Mitigation using Independent Component Analysis as a Pre-Processor

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

independent component analysis↗
NASA NTRS2016

Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters : Aerosols - Part 2

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

aerosol retrieval↗