Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “blind source separation”

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.

Kurtosis Approach Nonlinear Blind Source Separation

In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation Keywords: Independent Component Analysis, Kurtosis, Higher order statistics.

Duong, Vu A.↗

Kurtosis Approach for Nonlinear Blind Source Separation

In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation.

kurtosis↗

Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. 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 [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

microwave↗

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↗

An RFI Detection Algorithm for Microwave Radiometers Using Sparse Component Analysis

Radio Frequency Interference (RFI) is a threat to passive microwave measurements and if undetected, can corrupt science retrievals. The sparse component analysis (SCA) for blind source separation has been investigated to detect RFI in microwave radiometer data. Various techniques using SCA have been simulated to determine detection performance with continuous wave (CW) RFI.

radiometers↗

An RFI Detection Algorithm for Microwave Radiometers Using Sparse Component Analysis

Radio Frequency Interference (RFI) is a threat to passive microwave measurements and if undetected, can corrupt science retrievals. The sparse component analysis (SCA) for blind source separation has been investigated to detect RFI in microwave radiometer data. Various techniques using SCA have been simulated to determine detection performance with continuous wave (CW) RFI.

radiometers↗

Independent Component Analysis for Preprocessing Optical Signals in Support of Multi-User Communication

In an effort to construct an optical transceiver for supporting multi-point communication, researchers developed large field-of-view FSO transceivers with wide apertures for facilitating rapid acquisition and optical signal tracking. The design is constructed from fiber-bundle(s) intelligently arranged to provide multiple optical pathways within a single transceiver, with each pathway limited to a particular optical directionality. Because designs with wide apertures are susceptible to receiving several optical signals simultaneously from multiple transmitters, accurately decoding individual signals and achieving true multi-user communication can be difficult. The work detailed in this paper investigated the use of blind source separation techniques, mainly independent component analysis (ICA), to estimate transmitted signals from their observed, combined mixtures sans information. Effects of signal power, format, data rate, wavelength, and turbulence severity on signal separation, as well as signal demodulation accuracy was also analyzed using experimental optical signals and corresponding mixtures. Signal generators, laser sources, turbulence emulator, beam profiler, photodiodes, and oscilloscopes comprised the experiment setup. Optical signals with varying power, format, and wavelength were generated and propagated through turbulence with varying degree severity, and finally received by the fiber-bundle-based optical transceiver. Results demonstrate that an ICA technique can accurately process the combined signals into corresponding individual components. The investigation further identified signal characteristics (e.g., power levels, format, data rate) and turbulence severity under which ICA is unable to accurately perform signal separations.

Optics↗

Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Non-NASA Center↗

Comparative Soot Diagnostics Experiment Looks at the Smoky World of Microgravity Combustion

From an economic standpoint, soot is one of the most important combustion intermediates and products. It is a major industrial product and is the dominant medium for radiant heat transport in most flames used to generate heat and power. The nonbuoyant structure of most flames of practical interest (turbulent flames) makes the understanding of soot processes in microgravity flames important to our ability to predict fire behavior on Earth. In addition, fires in spacecraft are considered a credible possibility. To respond to this risk, NASA has flown fire (or smoke) detectors on Skylab and the space shuttles and included them in the International Space Station design. The design of these detectors, however, was based entirely on normal gravity (1g) data. The detector used in the shuttle fleet is an ionization detector, whereas the system planned for the space station uses forward scattering of near-infrared light. The ionization detector, which is similar to smoke detectors used in homes, has a comparative advantage for submicron particulates. In fact, the space shuttle model uses a separation system that makes it blind to particles larger than a micron (believed to be dust). In the larger size range, the lightscattering detector is most sensitive. Without microgravity smoke data, the difference in the particle size sensitivities of the two detectors cannot be evaluated. As part of the Comparative Soot Diagnostics (CSD) experiment, these systems were tested to determine their response to particulates generated during long periods of low gravity. This experiment provided the first such measurements toward understanding soot processes on Earth and for designing and implementing improved spacecraft smoke detection systems. The objectives of CSD were to examine how particulates form from a variety of sources and to quantify the performance of several diagnostic techniques. The sources tested included four overheated materials (paper, silicone rubber, Teflon-coated (DuPont) wire, and Kapton-coated (DuPont) wires), each tested at three heating rates, and a candle tested at three air velocities. Paper, silicone rubber, and wire insulation, materials found in spacecraft crew cabins, were selected because of their different smoke properties. The candle yielded hydrocarbon soot typical of many 1g flames. Four diagnostic techniques were employed: thermophoretic sampling collected particulates for size analysis; laser light extinction measurements near the source tallied total particulate production; and laser light scattering and ionization detector measurements far from the particulate source provided data for evaluating the performance of smoke detection systems for these particulate sources.

Urban, David L.↗

A Fermi Gamma-Ray Burst Monitor Search for Electromagnetic Signals Coincident with Gravitational-Wave Candidates in Advanced LIGOs First Observing Run

We present a search for prompt gamma-ray counterparts to compact binary coalescence gravitational wave (GW)candidates from Advanced LIGO's first observing run (O1). As demonstrated by the multimessenger observations ofGW170817/GRB 170817A, electromagnetic and GW observations provide complementary information about theastrophysical source, and in the case of weaker candidates, may strengthen the case for an astrophysical origin. Here weinvestigate low-significance GWcandidates from the O1 compact binary coalescence searches using the Fermi Gamma-Ray Burst Monitor (GBM), leveraging its all sky and broad energy coverage. Candidates are ranked and compared tobackground to measure the significance. Those with false alarm rates (FARs) of less than 105 Hz (about one per day,yielding a total of 81 candidates) are used as the search sample for gamma-ray follow-up. No GW candidates werefound to be coincident with gamma-ray transients independently identified by blind searches of the GBM data. Inaddition, GW candidate event times were followed up by a separate targeted search of GBM data. Among the resultingGBM events, the two with the lowest FARs were the gamma-ray transient GW150914-GBM presented in Connaughtonet al. and a solar flare in chance coincidence with a GW candidate.

Burns, E.↗

Galileo Probe Measurements of Thermal and Solar Radiation Fluxes in the Jovian Atmosphere

The Galileo probe net flux radiometer (NFR) measured radiation fluxes in Jupiter's atmosphere from about 0.44 to 14 bars, using five spectral channels to separate solar and thermal components. Onboard calibration results confirm that the NFR responded to radiation approximately as expected. NFR channels also responded to a superimposed thermal perturbation, which can be approximately removed using blind channel measurements and physical constraints. Evidence for the expected NH3 cloud was seen in the spectral character of spin-induced modulations of the direct solar beam signals. These results are consistent with an overlying cloud of small NH3 ice particles (0.5-0.75 microns in radius) of optical depth 1.5-2 at 0.5 microns. Such a cloud would have so little effect on thermal fluxes that NFR thermal channels provide no additional constraints on its properties. However, evidence for heating near 0.45 bar in the NFR thermal channels would seem to require either an additional opacity source beyond this small-particle cloud, implying a heterogeneous cloud structure to avoid conflicts with solar modulation results, or a change in temperature lapse rate just above the probe measurements. The large thermal flux levels imply water vapor mixing ratios that are only 6% of solar at 10 bars, but possibly increasing with depth, and significantly subsaturated ammonia at pressures less than 3 bars. If deep NH3 mixing ratios at the probe entry site are 3-4 times ground-based inferences, as suggested by probe radio signal attenuation, then only half as much water is needed to match NFR observations. No evidence of a water cloud was seen near the 5-bar level. The 5-microns thermal channel detected the presumed NH4SH cloud base near 1.35 bars. Effects of this cloud were also seen in the solar channel upflux measurements but not in the solar net fluxes, implying that the cloud is a conservative scatterer of sunlight. The minor thermal signature of this cloud is compatible with particle radii near 3 gm, but it cannot rule out smaller particles. Deeper than about 3 bars, solar channels indicate unexpectedly large absorption of sunlight at wavelengths longer than 0.6 microns, which might be due to unaccounted-for absorption by NH3 between 0.65 and 1.5 microns.

Sromovsky, L. A.↗

Galileo Probe Measurements of Thermal and Solar Radiation Fluxes in the Jovian Atmosphere

The Galileo probe net flux radiometer (NFR) measured radiation fluxes in Jupiter's atmosphere from about 0.44 to 14 bars, using five spectral channels to separate solar and thermal components. Onboard calibration results confirm that the NFR responded to radiation approximately as expected. NFR channels also responded to a superimposed thermal perturbation, which can be approximately removed using blind channel measurements and physical constraints. Evidence for the expected NH3 cloud was seen in the spectral character of spin-induced modulations of the direct solar beam signals. These results are consistent with an overlying cloud of small NH3 ice particles (0.5-0.75 microns in radius) of optical depth 1.5-2 at 0.5 microns. Such a cloud would have so little effect on thermal fluxes that NFR thermal channels provide no additional constraints on its properties. However, evidence for heating near 0.45 bar in the NFR thermal channels would seem to require either an additional opacity source beyond this small-particle cloud, implying a heterogeneous-cloud structure to avoid conflicts with solar modulation results, or a change in temperature lapse rate just above the probe measurements. The large thermal flux levels imply water vapor mixing ratios that are only 6% of solar at 10 bars, but possibly increasing with depth, and significantly subsaturated ammonia at pressures less than 3 bars. If deep NH3 mixing ratios at the probe entry site are 3-4 times ground-based inferences, as suggested by probe radio signal attenuation, then only half as much water is needed to match NFR observations. No evidence of a water cloud was seen near the 5-bar level. The 5 microns thermal channel detected the presumed NH4SH cloud base near 1.35 bars. Effects of this cloud were also seen in the solar channel upflux measurements but not in the solar net fluxes, implying that the cloud is a conservative scatterer of sunlight. The minor thermal signature of this cloud is compatible with particle radii near 3 microns, but it cannot rule out smaller particles. Deeper than about 3 bars, solar channels indicate unexpectedly large absorption of sunlight at wavelengths longer than 0.6 microns, which might be due to unaccounted-for absorption by NH3 between 0.65 and 1.5 microns.

Sromovsky, L. A.↗