Results for “Nonstationary signals” — Page 2

NASA NTRS1998

Decadal Air-Sea Interaction in the North Atlantic Based on Observations and Modeling Results

The decadal, 12-14 year, cycle observed in the North Atlantic SST and tide gauge data was examined using the NCEP/NCAR reanalyses, COADS data and an ocean model simulation. Besides this decadal mode, a shorter, subdecadal period of about 8 years exists in tide gauge data north of 40N, in the subpolar SST and in the winter North Atlantic Oscillation (NAO) index and in subpolar winter heat flux values. The decadal cycle is a well separated mode in a singular spectrum analysis (SSA) for a time series of SST EOF mode 1 with a center over the Gulf Stream extension. Tide gauge and SST data are consistent in that both show a significant subdecadal periodicity exclusively in the subpolar gyre, but in subtropics the 12-14 year period is the prominent, but nonstationary, decadal signal. The main finding of this study is that this 12-14 year cycle can be constructed based on the leading mode of the surface heat flux. This connection to the surface heat flux implicates the participation of the thermohaline circulation in the decadal cycle. During the cycle starting from the positive index phase of NAO, SST and oceanic heat content anomalies are created in subtropics due to local heat flux and intensification of the thermohaline circulation. The anomalies advect to the subpolar gyre where they are amplified by local heat flux and are part of the negative feedback of thermohaline circulation on itself. Consequently the oceanic thermohaline circulation slows down and the opposite cycle starts. The oscillatory nature would not be possible without the active atmospheric participation in the cycle, because it provides the unstable interaction through heat flux, without it, the oceanic mode would be damped. This analysis suggests that the two principal modes of heat flux variability, corresponding to patterns similar to North Atlantic Oscillation (NAO) and Western Atlantic (WA), are part of the same decadal cycle and an indirect measure of the north-south movement of the storm tracks.

Hakkinen, Sirpa↗

NASA NTRS1975

Bioelectric signal analysis and measurement

Nonstationary time series techniques are used to analyze EEG signals for the estimation of alertness. A time varying order is extracted in sequential time series measurement of these data and strategies are devised for obtaining optimal representation of the EEG signal.

Lai, D. C.↗

NASA NTRS1993

Effects of signal analysis parameters and noise removal on measured aircraft spectra

Special techniques must be applied when analyzing acoustic noise data from nonstationary sources such as aircraft flyover measurements. Since the Fourier transform is time dependent, the noise signal is divided into short time segments by introducing a window function so that the spectral characteristics remain reasonably stationary. Reducing the window width reduces the frequency resolution while increasing the window duration can lead to spectral smearing. A trade-off must be made between time resolution and frequency resolution. The effects of varying the window duration on narrow-band acoustic spectra and thus the frequency bin width are addressed in this study. The influence of window functions (rectangular, Hamming, etc.) are also investigated. Both a tonal noise source, XV-15 aircraft in the airplane mode, and a broadband noise source, a F-18 aircraft, are considered. When dealing with flight test data, not only is the signal nonstationary, often it is contaminated by both ambient background noise and internal noise generated by the data acquisition system and power generators. Since generator noise is highly tonal, this can be particularly troublesome when computing tone-corrected perceived noise level (PNLT). A scheme is presented in this paper to eliminate unwanted background and internal noise.

Kelly, Jeffrey J.↗

NASA NTRS2014

Transient Convection Due to Imposed Heat Flux: Application to Liquid-Acquisition Devices

A model problem is considered that addresses the effect of heat load from an ambient laboratory environment on the temperature rise of liquid nitrogen inside an enclosure. This model has applications to liquid acquisition devices inside the cryogenic storage tanks used to transport vapor-free propellant to the main engine. We show that heat loads from Q = 0.001 to 10 W, with corresponding Rayleigh numbers from Ra = 109 to 1013, yield a range of unsteady convective states and temperature rise in the liquid. The results show that Q = 1 to 10 W (Ra = 1012 to 1013) yield temperature distributions along the enclosure height that are similar in trend to experimental measurements. Unsteady convection, which shows selfsimilarity in its planforms, is predicted for the range of heat-load conditions. The onset of convection occurs from a free-convection-dominated base flow that becomes unstable against convective instability generated at the bottom of the enclosure while the top of the enclosure is convectively stable. A number of modes are generated with small-scale thermals at the bottom of the enclosure in which the flow selforganizes into two symmetric modes prior to the onset of the propagation of the instability. These symmetric vertical modes transition to asymmetric modes that propagate as a traveling-wave-type motion of convective modes and are representative of the asymptotic convective state of the flow field. Intense vorticity production is created in the core of the flow field due to the fact that there is shear instability between the vertical and horizontal modes. For the higher Rayleigh numbers, 1012 to 1013, there is a transition from a stationary to a nonstationary response time signal of the flow and temperature fields with a mean value that increases with time over various time bands and regions of the enclosure.

Duval, Walter M. B.↗

NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: An Application to XCO2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

Calle, Leonardo↗

NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: an Application to Xco2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

segmentation algorithm↗

NASA NTRS1974

Processing electrophysiological signals for the monitoring of alertness

Mathematical techniques are described for processing EEG signals associated with varying states of alertness. Fast algorithms for implementing real-time computations of alertness estimates were developed. A realization of the phase-distortionless digital filter is presented which approaches real-time filtering and a transform for EEG signals. This transform provides information for the alertness estimates and can be performed in real time. A statistical test for stationarity in EEG signals is being developed that will provide a method for determining the duration of the EEG signals necessary for estimating the short-time power or energy spectra for nonstationary analysis of EEG signals.

Lai, D. C.↗

NASA NTRS2003

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Huang, Norden E.↗

NASA NTRS1973

Multipath modeling for aeronautical communications.

One of the fundamental technical problems in aeronautical digital communications is that of multipath propagation between aircraft and ground terminal. This paper examines in detail a model of the received multipath signal that is useful for application of modern detection and estimation theories. The model treats arbitrary modulation and covers the selective and nonselective cases. The necessarily nonstationary statistics of the received signal are determined from the link geometry and the surface roughness parameters via a Kirchhoff solution.

Painter, J. H.↗

NASA NTRS1994

Develop advanced nonlinear signal analysis topographical mapping system

The Space Shuttle Main Engine (SSME) has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature, pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system; (2) develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amount of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. High compression ratio can be achieved to allow minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities; and (3) integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.

Source record↗

NASA NTRS1993

Develop advanced nonlinear signal analysis topographical mapping system

The SSME has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) Develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system. (2) Develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amounts of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. A high compression ratio can be achieved to allow the minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities. (3) Integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for a quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.

Jong, Jen-Yi↗

NASA NTRS1993

An introduction to wavelet analysis in oceanography and meteorology - With application to the dispersion of Yanai waves

Wavelet analysis is a relatively new technique that is an important addition to standard signal analysis methods. Unlike Fourier analysis that yields an average amplitude and phase for each harmonic in a dataset, the wavelet transform produces an instantaneous estimate or local value for the amplitude and phase of each harmonic. This allows detailed study of nonstationary spatial or time-dependent signal characteristics. The wavelet transform is discussed, examples are given, and some methods for preprocessing data for wavelet analysis are compared. By studying the dispersion of Yanai waves in a reduced gravity equatorial model, the usefulness of the transform is demonstrated. The group velocity is measured directly over a finite range of wavenumbers by examining the time evolution of the transform. The results agree well with linear theory at higher wavenumber but the measured group velocity is reduced at lower wavenumbers, possibly due to interaction with the basin boundaries.

Meyers, Steven D.↗

NASA NTRS2008

Precision Measurement of Black Hole Binary Dynamics: Analyzing the LISA Data Stream

One of the richest potential sources of insight into fundamental physics that LISA will be capable of observing is the inspiral of supermassive black hole binaries (BHBs). However, the data analysis challenge presented by the LISA data stream is quite unlike the situation for present day gravitational wave detectors. In order to make the precision measurements necessary to achieve LISA's science goals, the BHB signal must be distinguished from a data stream that not only contains instrumental noise, but potentially thousands of other signals as well, so that the "background" we wish to separate out to focus on the BHB signal is likely to be highly nonstationary and nongaussian, as well as being of scientific interest in its own right. In addition, whereas the theoretical templates that we calculate in order to ultimately estimate the parameters can afford to be somewhat inaccurate and still be effective for present day and near future detectors, this is not the case for LISA, and extremely high fidelity of the theoretical templates for high signal-to-noise signals will be required to prevent theoretical errors from dominating the parameter estimates. NVe, will describe efforts in the community of LISA data analysts to address the challenges regarding the specific issue of BHB signals. These efforts include using a Markov Chain Monte Carlo approach with the freedom to model the BHB and the other signals present in the data stream simultaneously, rather than trying to remove other signals and risk biasing the remaining data. The Mock LISA Data Challenge is a community of LISA scientists who generate rounds of simulated LISA noise with increasingly difficult signal content, and invite the LISA data analysis community to exercise their methods, or develop new methods, in an attempt to extract the parameters for the signals embedded in the mock data. In addition to practical approaches such ,is this to assess the level of parameter accuracy, one can apply the Fisher matrix formalism to assess both the statistical errors from noise and the theoretical errors

McWilliams, Sean T.↗

NASA NTRS2005

Empirical mode decomposition for analyzing acoustical signals

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

Huang, Norden E.↗

NASA NTRS1974

An implicit sampling theorem for bounded bandlimited functions

A rigorous proof of the 'strong bias tone' scheme is embodied in the implicit sampling theorem. The representation of signals that are sample functions of possible nonstationary random processes being of principal interest, the proof could not directly invoke results from classical analysis, which depend on the existence of the Fourier transform of the function under consideration; rather, it is based on Zakai's (1965) theorem on the series expansion of functions, band-limited under a suitably extended definition. A practical circuit that restores an approximate version of the signal from its sine-wave-crossings is presented and possible improvements to it are discussed.

Bar-David, I.↗

NASA NTRS1968

A recursive approach to signal detection.

Recursive solution to problem of signal detection in presence of additive Gaussian nonstationary nonwhite noise

Boorstyn, R. R.↗

NASA NTRS1973

Note on the error signal of block quantizers.

Demonstration that the error signal incurred by block quantizing stationary data is nonstationary under some conditions. Examples are presented which indicate that the mean square error is largest at the block edges. When block quantizers are used to encode pictures at low bit rates, this effect tends to make the block edges visible in the reconstructed picture.

Tasto, M.↗

NASA NTRS1989

Spectral and correlation analysis with applications to middle-atmosphere radars

The correlation and spectral analysis methods for uniformly sampled stationary random signals, estimation of their spectral moments, and problems arising due to nonstationary are reviewed. Some of these methods are already in routine use in atmospheric radar experiments. Other methods based on the maximum entropy principle and time series models have been used in analyzing data, but are just beginning to receive attention in the analysis of radar signals. These methods are also briefly discussed.

Rastogi, Prabhat K.↗