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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Huang, Norden E.↗
NASA NTRS2010

Parallel Processing of Broad-Band PPM Signals

A parallel-processing algorithm and a hardware architecture to implement the algorithm have been devised for timeslot synchronization in the reception of pulse-position-modulated (PPM) optical or radio signals. As in the cases of some prior algorithms and architectures for parallel, discrete-time, digital processing of signals other than PPM, an incoming broadband signal is divided into multiple parallel narrower-band signals by means of sub-sampling and filtering. The number of parallel streams is chosen so that the frequency content of the narrower-band signals is low enough to enable processing by relatively-low speed complementary metal oxide semiconductor (CMOS) electronic circuitry. The algorithm and architecture are intended to satisfy requirements for time-varying time-slot synchronization and post-detection filtering, with correction of timing errors independent of estimation of timing errors. They are also intended to afford flexibility for dynamic reconfiguration and upgrading. The architecture is implemented in a reconfigurable CMOS processor in the form of a field-programmable gate array. The algorithm and its hardware implementation incorporate three separate time-varying filter banks for three distinct functions: correction of sub-sample timing errors, post-detection filtering, and post-detection estimation of timing errors. The design of the filter bank for correction of timing errors, the method of estimating timing errors, and the design of a feedback-loop filter are governed by a host of parameters, the most critical one, with regard to processing very broadband signals with CMOS hardware, being the number of parallel streams (equivalently, the rate-reduction parameter).

Gray, Andrew↗
NASA NTRS2006

Medical Signal-Conditioning and Data-Interface System

A general-purpose portable, wearable electronic signal-conditioning and data-interface system is being developed for medical applications. The system can acquire multiple physiological signals (e.g., electrocardiographic, electroencephalographic, and electromyographic signals) from sensors on the wearer s body, digitize those signals that are received in analog form, preprocess the resulting data, and transmit the data to one or more remote location(s) via a radiocommunication link and/or the Internet. The system includes a computer running data-object-oriented software that can be programmed to configure the system to accept almost any analog or digital input signals from medical devices. The computing hardware and software implement a general-purpose data-routing-and-encapsulation architecture that supports tagging of input data and routing the data in a standardized way through the Internet and other modern packet-switching networks to one or more computer(s) for review by physicians. The architecture supports multiple-site buffering of data for redundancy and reliability, and supports both real-time and slower-than-real-time collection, routing, and viewing of signal data. Routing and viewing stations support insertion of automated analysis routines to aid in encoding, analysis, viewing, and diagnosis.

Braun, Jeffrey↗
NASA NTRS2005

Estimating Effects of Multipath Propagation on GPS Signals

Multipath Simulator Taking into Account Reflection and Diffraction (MUSTARD) is a computer program that simulates effects of multipath propagation on received Global Positioning System (GPS) signals. MUSTARD is a very efficient means of estimating multipath-induced position and phase errors as functions of time, given the positions and orientations of GPS satellites, the GPS receiver, and any structures near the receiver as functions of time. MUSTARD traces each signal from a GPS satellite to the receiver, accounting for all possible paths the signal can take, including all paths that include reflection and/or diffraction from surfaces of structures near the receiver and on the satellite. Reflection and diffraction are modeled by use of the geometrical theory of diffraction. The multipath signals are added to the direct signal after accounting for the gain of the receiving antenna. Then, in a simulation of a delay-lock tracking loop in the receiver, the multipath-induced range and phase errors as measured by the receiver are estimated. All of these computations are performed for both right circular polarization and left circular polarization of both the L1 (1.57542-GHz) and L2 (1.2276-GHz) GPS signals.

Byun, Sung↗
NASA NTRS2004

Signal Design for Improved Ranging Among Multiple Transceivers

"Ultra-BOC" (where "BOC" signifies "binary offset carrier") is the name of an improved generic design of microwave signals to be used by a group of spacecraft flying in formation to measure ranges and bearings among themselves and to exchange telemetry needed for these measurements. Ultra-BOC could also be applied on Earth for diverse purposes -- for example, measuring relative positions of vehicles on highways for traffic-control purposes and determining the relative alignments of machines operating in mines and of construction machines and structures at construction sites. Ultra-BOC provides for rapid and robust acquisition of signals, even when signal-to-noise ratios are low. The design further provides that each spacecraft or other platform constantly strives to acquire and track the signals from the other platforms while simultaneously transmitting signals that provide full range, bearing, and telemetry service to the other platforms. In Ultra-BOC, unlike in other signal designs that have been considered for the same purposes, it is not necessary to maneuver the spacecraft or other platforms to obtain the data needed for resolving integer-carrier-cycle phase ambiguities.

Young, Lawrence↗
NASA NTRS2004

Low-Cutoff, High-Pass Digital Filtering of Neural Signals

The figure depicts the major functional blocks of a system, now undergoing development, for conditioning neural signals acquired by electrodes implanted in a brain. The overall functions to be performed by this system can be summarized as preamplification, multiplexing, digitization, and high-pass filtering. Other systems under development for recording neural signals typically contain resistor-capacitor analog low-pass filters characterized by cutoff frequencies in the vicinity of 100 Hz. In the application for which this system is being developed, there is a requirement for a cutoff frequency of 5 Hz. Because the resistors needed to obtain such a low cutoff frequency would be impractically large, it was decided to perform low-pass filtering by use of digital rather than analog circuitry. In addition, it was decided to timemultiplex the digitized signals from the multiple input channels into a single stream of data in a single output channel. The signal in each input channel is first processed by a preamplifier having a voltage gain of approximately 50. Embedded in each preamplifier is a low-pass anti-aliasing filter having a cutoff frequency of approximately 10 kHz. The anti-aliasing filters make it possible to couple the outputs of the preamplifiers to the input ports of a multiplexer. The output of the multiplexer is a single stream of time-multiplexed samples of analog signals. This stream is processed by a main differential amplifier, the output of which is sent to an analog-to-digital converter (ADC). The output of the ADC is sent to a digital signal processor (DSP).

Mojarradi,Mohammad↗
NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗
NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗
NASA NTRS2013

Stacked Transformer for Driver Gain and Receive Signal Splitting

In a high-speed signal transmission system that uses transformer coupling, there is a need to provide increased transmitted signal strength without adding active components. This invention uses additional transformers to achieve the needed gain. The prior art uses stronger drivers (which require an IC redesign and a higher power supply voltage), or the addition of another active component (which can decrease reliability, increase power consumption, reduce the beneficial effect of serializer/deserializer preemphasis or deemphasis, and/or interfere with fault containment mechanisms), or uses a different transformer winding ratio (which requires redesign of the transformer and may not be feasible with high-speed signals that require a 1:1 winding ratio). This invention achieves the required gain by connecting the secondaries of multiple transformers in series. The primaries of these transformers are currently either connected in parallel or are connected to multiple drivers. There is also a need to split a receive signal to multiple destinations with minimal signal loss. Additional transformers can achieve the split. The prior art uses impedance-matching series resistors that cause a loss of signal. Instead of causing a loss, most instantiations of this invention would actually provide gain. Multiple transformers are used instead of multiple windings on a single transformer because multiple windings on the same transformer would require a redesign of the transformer, and may not be feasible with high-speed transformers that usually require a bifilar winding with a 1:1 ratio. This invention creates the split by connecting the primaries of multiple transformers in series. The secondary of each transformer is connected to one of the intended destinations without the use of impedance-matching series resistors.

Driscoll, Kevin R.↗
NASA NTRS2017

System and Method for Multi-Wavelength Optical Signal Detection

The system and method for multi-wavelength optical signal detection enables the detection of optical signal levels significantly below those processed at the discrete circuit level by the use of mixed-signal processing methods implemented with integrated circuit technologies. The present invention is configured to detect and process small signals, which enables the reduction of the optical power required to stimulate detection networks, and lowers the required laser power to make specific measurements. The present invention provides an adaptation of active pixel networks combined with mixed-signal processing methods to provide an integer representation of the received signal as an output. The present invention also provides multi-wavelength laser detection circuits for use in various systems, such as a differential absorption light detection and ranging system.

McGlone, Thomas D.↗
NASA NTRS2017

HydroCube Mission Concept: P-Band Signals of Opportunity for Remote Sensing of Snow and Root Zone Soil Moisture

We have developed the HydroCube mission concept with a constellation of small satellites to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The HydroCube satellites would operate at sun-synchronous 3-day repeat polar orbits with a spatial resolution of about 1-3 Km. The mission goals would be to improve the estimation of terrestrial water storage and weather forecasts. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The HydroCube Signals of Opportunity (SoOp) concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals from geostationary Mobile Use Objective System (MUOS) communication satellites. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE. To address the ionospheric delay at P-band frequencies, the signals from both MUOS bands (360-380 MHz and 250-270 MHz) would be used. We have conducted an analysis to trade off the spatial resolution for a space-based sensor and measurement accuracy. Through modeling analysis, we find that the dual-band MUOS signals would allow estimation of soil moisture and surface roughness together. From the two MUOS frequencies at 260 MHz and 370 MHz, we can retrieve the soil moisture from the reflectivity ratio scaled by wavenumbers using the two P-band frequencies for MUOS. A modeling analysis using layered stratified model has been completed to determine the sensitivity requirements of HydroCube measurements. For mission concept demonstration, a field campaign has been conducted at the Fraser Experimental Forest in Colorado since February 2016. The data acquired has provided support to the HydroCube concept.

Derksen, Chris↗
NASA NTRS1982

Propagation of wide bandwidth signals through strongly turbulent ionized media

Analytic and numerical techniques are presented which directly address the problem of propagation of wide bandwidth signals through random ionized media. This work is applicable to the problems of satellite communication and space based radar observation through a disturbed ionospheric propagation channel that would result from a high altitude chemical release or nuclear detonation. An analytic solution is presented for the two-position, two-frequency mutual coherence function for spherical wave propagation in the strong scattering limit. This solution is used to derive simple expressions for the power impulse response function and to determine its relationship to the important parameters of decorrelation distance and coherence bandwidth which describe the disturbed propagation channel. Results for mean time delay and time delay jitter are presented and compared to direct simulation results and to other theoretical calculations. A numerical/analytical solution to the parabolic wave equation is presented in the form of a multiple phase-screen (MPS) propagation simulation. In this very general solution technique, the random medium is divided into a finite number of layers. The field fluctuation through each layer is obtained by replacing the layer by a centrally located thin phase-screen, whose statistical properties are determined from the statistics of the electron-density irregularities. The waveform then propagates from phase-screen to phase-screen via an exact solution to the Fresnel-Kirchnoff equation. For wide bandwidth waveforms, numerical solutions are obtained at a number of discrete frequencies centered about the carrier and then time-domain solutions are obtained by Fourier transform techniques. Detailed results are presented for a number of interesting cases including propagation of a 20 MHz bandwidth signal through a finite barium cloud at a carrier frequency of 100 MHz. One of the major uses of the MPS propagation simulation has been to provide realizations of the received signal after propagation through a disturbed channel. The MPS simulation obtains a general solution of the parabolic wave equation under both weak and strong scattering conditions. A second technique to directly obtain realizations of wide bandwidth waveforms is presented here. This technique is limited to the case of strong scattering but requires only a fraction of the computer resources needed for MPS signal generation. Detailed comparisons of the two signal generation techniques are presented.

Random Media↗
NASA NTRS2001

Signal transduction mechanisms in plants: an overview

This article provides an overview on recent advances in some of the basic signalling mechanisms that participate in a wide variety of stimulus-response pathways. The mechanisms include calcium-based signalling, G-protein-mediated-signalling and signalling involving inositol phospholipids, with discussion on the role of protein kinases and phosphatases interspersed. As a further defining feature, the article highlights recent exciting findings on three extracellular components that have not been given coverage in previous reviews of signal transduction in plants, extracellular calmodulin, extracellular ATP, and integrin-like receptors, all of which affect plant growth and development.

Non-NASA Center↗
NASA NTRS1997

Signal transduction in T lymphocytes in microgravity

More than 120 experiments conducted in space in the last 15 years have shown that dramatic changes are occurring in several types of single cells during their exposure to microgravity. One focus of today's research on cells in space is on signal transduction, especially those steps involving the cytoskeleton and cell-cell interactions. Signal transduction is often altered in microgravity as well as in hypergravity. This leads to changes in cell proliferation, genetic expression and differentiation. Interesting examples are leukocytes, HeLa cells, epidermoid cells and osteoblastic cells. Signalling pathways were studied in T lymphocytes in microgravity by several investigators after the discovery that mitogenic activation in vitro is virtually nil at 0g. T cells are a good model to study signal transduction because three extracellular signals (mitogen, IL-1 and IL-2) are required for full activation, and two classical pathways (via proteins G and PKC) are activated within the cell. In addition, low molecular weight GTP-binding proteins (Ras and Rap) are interacting with the cytoskeleton. The data at 0g support the notion that the expression of IL-2 receptor is inhibited at 0g, while mitogen binding and the transmission of IL-1 by accessory cells occur normally. In addition, alterations of the cytoskeleton suggest that the interaction with Rap proteins is disturbed. Data obtained with phorbol esters indicate that the function of PKC is changed in microgravity. Similar conclusions are drawn from the results with epidermoid cells A431.

short duration↗
NASA NTRS2001

Mechanisms of cell signaling by nitric oxide and peroxynitrite: from mitochondria to MAP kinases

Many of the biological and pathological effects of nitric oxide (NO) are mediated through cell signaling pathways that are initiated by NO reacting with metalloproteins. More recently, it has been recognized that the reaction of NO with free radicals such as superoxide and the lipid peroxyl radical also has the potential to modulate redox signaling. Although it is clear that NO can exert both cytotoxic and cytoprotective actions, the focus of this overview are those reactions that could lead to protection of the cell against oxidative stress in the vasculature. This will include the induction of antioxidant defenses such as glutathione, activation of mitogen-activated protein kinases in response to blood flow, and modulation of mitochondrial function and its impact on apoptosis. Models are presented that show the increased synthesis of glutathione in response to shear stress and inhibition of cytochrome c release from mitochondria. It appears that in the vasculature NO-dependent signaling pathways are of three types: (i) those involving NO itself, leading to modulation of mitochondrial respiration and soluble guanylate cyclase; (ii) those that involve S-nitrosation, including inhibition of caspases; and (iii) autocrine signaling that involves the intracellular formation of peroxynitrite and the activation of the mitogen-activated protein kinases. Taken together, NO plays a major role in the modulation of redox cell signaling through a number of distinct pathways in a cellular setting.

Non-NASA Center↗
NASA NTRS2016

Real-time Identification and Control of Satellite Signal Impairments Solution and Application of the Stratonovich Equation Part 1. Theoretical Development

As satellite communications systems become both more complex and reliant with respect to their operating environment, it has become imperative to be able to identify, during real-time operation, the onset of one or more impairments to the quality of overall communications system integrity. One of the most important aspects to monitor of a satellite link operating within the Earth's atmosphere is the signal fading due to the occurrence of rain and/or phase scintillations. This, of course, must be done in the presence of the associated measurement uncertainty or potentially faulty measurement equipment such as in the Advanced Communication Technology Satellite (ACTS) experiment. In the present work, an approach originally suggested in 1991, and apparently still considered iconoclastic, will be significantly developed and applied to the satellite communications link on which the deleterious composite signal fade is the result of one or many component fade mechanisms. Through the measurement (with the attendant uncertainty or 'error' in the measurement) of such a composite fading satellite signal, it is desired to extract the level of each of the individual fading mechanisms so they can be appropriately mitigated before they impact the overall performance of the communications network. Rather than employing simple-minded deterministic filtering to the real-time fading, the present approach is built around all the models and/or descriptions used to describe the individual fade components, including their dynamic evolution. The latter is usually given by a first-order Langevin equation. This circumstance allows the description of the associated temporal transition probability densities of each of the component processes. By using this description, along with the real-time measurements of the composite fade (along with the measurement errors), one can obtain statistical estimates of the levels of each of the component fading mechanisms as well as their predicted values into the future. This is all accomplished by the use of the well-known Stratonovich integro-differential equation that results from the model of the measured signal fade that is also tailored to adaptively adjust the values of the parameters used in the statistical models of the individual fade mechanisms. Three examples of increasing complexity are addressed and solved for the iterative determination of fade component levels from the measured composite signal fade in the presence of measurement error and, in the last case, with uncertainty in the model parameters.

Countermeasure↗
NASA NTRS2017

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Signal Recognition↗
NASA NTRS2017

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Signal Classification↗