Signal analyzer i - a signal analysis system for manual control studies.
Signal Analyzer I as signal analysis system for manual control studies, using digital computer
DIGITAL COMPUTER↗
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Electrocardiogram signal analyzer
Algorithm based on Taylor series expansion of Fourier transform has been developed and used for detection of cardiac arrhythmias in real-time electrocardiogram signal.
Portnoy, W. M.↗
Analog systems for analyzing infrasonic signals monitored in field experimentation.
Analog systems for analyzing infrasonic signals monitored in field experimentation with F-1 engine static firing
Fehr, U.↗
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.↗
Signal Processing and Calibration of Continuous-Wave Focused CO2 Doppler Lidars for Atmospheric Backscatter Measurement
Two continuous-wave(CW)focused C02 Doppler lidars (9.1 and 10.6 micrometers) were developed for airborne in situ aerosol backscatter measurements. The complex path of reliably calibrating these systems, with different signal processors, for accurate derivation of atmospheric backscatter coefficients is documented. Lidar calibration for absolute backscatter measurement for both lidars is based on range response over the lidar sample volume, not solely at focus. Both lidars were calibrated with a new technique using well-characterized aerosols as radiometric standard targets and related to conventional hard-target calibration. A digital signal processor (DSP), a surface acoustic and spectrum analyzer and manually tuned spectrum analyzer signal analyzers were used. The DSP signals were analyzed with an innovative method of correcting for systematic noise fluctuation; the noise statistics exhibit the chi-square distribution predicted by theory. System parametric studies and detailed calibration improved the accuracy of conversion from the measured signal-to-noise ratio to absolute backscatter. The minimum backscatter sensitivity is approximately 3 x 10(exp -12)/m/sr at 9.1 micrometers and approximately 9 x 10(exp -12)/m/sr at 10.6 micrometers. Sample measurements are shown for a flight over the remote Pacific Ocean in 1990 as part of the NASA Global Backscatter Experiment (GLOBE) survey missions, the first time to our knowledge that 9.1-10.6 micrometer lidar intercomparisons were made. Measurements at 9.1 micrometers, a potential wavelength for space-based lidar remote-sensing applications, are to our knowledge the first based on the rare isotope C-12 O(2)-18 gas.
Rothermel, Jeffry↗
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.↗
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.↗
Magnitude and sign correlations in heartbeat fluctuations
We propose an approach for analyzing signals with long-range correlations by decomposing the signal increment series into magnitude and sign series and analyzing their scaling properties. We show that signals with identical long-range correlations can exhibit different time organization for the magnitude and sign. We find that the magnitude series relates to the nonlinear properties of the original time series, while the sign series relates to the linear properties. We apply our approach to the heartbeat interval series and find that the magnitude series is long-range correlated, while the sign series is anticorrelated and that both magnitude and sign series may have clinical applications.
NASA Discipline Cardiopulmonary↗
Developing Signal-Pattern-Recognition Programs
Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.
Shelton, Robert O.↗
Noise-Contamination Detector
Deviations in periods of output from tracking filter measured. Signal-analyzing system measures degree to which sinusoid contaminated by noise. Noise-contamination detector indirectly measures noise in noisy sinusoidal signal by measuring average value of fractional absolute dither in period of signal. Developed to measure noise in outputs of vibration sensors used to test bearings and other components of rotating machinery.
Randall, Richard L.↗
Speech analyzer
A speech signal is analyzed by applying the signal to formant filters which derive first, second and third signals respectively representing the frequency of the speech waveform in the first, second and third formants. A first pulse train having approximately a pulse rate representing the average frequency of the first formant is derived; second and third pulse trains having pulse rates respectively representing zero crossings of the second and third formants are derived. The first formant pulse train is derived by establishing N signal level bands, where N is an integer at least equal to two. Adjacent ones of the signal bands have common boundaries, each of which is a predetermined percentage of the peak level of a complete cycle of the speech waveform.
Lokerson, D. C.↗
Assessing blood brain barrier dynamics or identifying or measuring selected substances, including ethanol or toxins, in a subject by analyzing Raman spectrum signals
A non-invasive method for analyzing the blood-brain barrier includes obtaining a Raman spectrum of a selected portion of the eye and monitoring the Raman spectrum to ascertain a change to the dynamics of the blood brain barrier.Also, non-invasive methods for determining the brain or blood level of an analyte of interest, such as glucose, drugs, alcohol, poisons, and the like, comprises: generating an excitation laser beam at a selected wavelength (e.g., at a wavelength of about 400 to 900 nanometers); focusing the excitation laser beam into the anterior chamber of an eye of the subject so that aqueous humor, vitreous humor, or one or more conjunctiva vessels in the eye is illuminated; detecting (preferably confocally detecting) a Raman spectrum from the illuminated portion of the eye; and then determining the blood level or brain level (intracranial or cerebral spinal fluid level) of an analyte of interest for the subject from the Raman spectrum. In certain embodiments, the detecting step may be followed by the step of subtracting a confounding fluorescence spectrum from the Raman spectrum to produce a difference spectrum; and determining the blood level and/or brain level of the analyte of interest for the subject from that difference spectrum, preferably using linear or nonlinear multivariate analysis such as partial least squares analysis. Apparatus for carrying out the foregoing methods are also disclosed.
Lambert, James L.↗
Automated eddy current analysis of materials
This research effort focused on the use of eddy current techniques for characterizing flaws in graphite-based filament-wound cylindrical structures. A major emphasis was on incorporating artificial intelligence techniques into the signal analysis portion of the inspection process. Developing an eddy current scanning system using a commercial robot for inspecting graphite structures (and others) has been a goal in the overall concept and is essential for the final implementation for expert system interpretation. Manual scans, as performed in the preliminary work here, do not provide sufficiently reproducible eddy current signatures to be easily built into a real time expert system. The expert systems approach to eddy current signal analysis requires that a suitable knowledge base exist in which correct decisions as to the nature of the flaw can be performed. In eddy current or any other expert systems used to analyze signals in real time in a production environment, it is important to simplify computational procedures as much as possible. For that reason, we have chosen to use the measured resistance and reactance values for the preliminary aspects of this work. A simple computation, such as phase angle of the signal, is certainly within the real time processing capability of the computer system. In the work described here, there is a balance between physical measurements and finite element calculations of those measurements. The goal is to evolve into the most cost effective procedures for maintaining the correctness of the knowledge base.
Workman, Gary L.↗
Diagnostic Analyzer for Gearboxes (DAG): User's Guide
This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software.
Jammu, Vinay B.↗
Processing of Signals from Fiber Bragg Gratings Using Unbalanced Interferometers
Fiber Bragg gratings (FBG) have become preferred sensory structures in fiber optic sensing system. High sensitivity, embedability, and multiplexing capabilities make FBGs superior to other sensor configurations. The main feature of FBGs is that they respond in the wavelength domain with the wavelength of the returned signal as the indicator of the measured parameter. The wavelength is then converted to optical intensity by a photodetector to detect corresponding changes in intensity. This wavelength-to-intensity conversion is a crucial part in any FBG-based sensing system. Among the various types of wavelength-to-intensity converters, unbalanced interferometers are especially attractive because of their small weight and volume, lack of moving parts, easy integration, and good stability. In this paper we investigate the applicability of unbalanced interferometers to analyze signals reflected from Bragg gratings. Analytical and experimental data are presented.
Adamovsky, Grigory↗
Real Time Phase Noise Meter Based on a Digital Signal Processor
A digital signal-processing meter for phase noise measurement on sinusoidal signals is dealt with. It enlists a special hardware architecture, made up of a core digital signal processor connected to a data acquisition board, and takes advantage of a quadrature demodulation-based measurement scheme, already proposed by the authors. Thanks to an efficient measurement process and an optimized implementation of its fundamental stages, the proposed meter succeeds in exploiting all hardware resources in such an effective way as to gain high performance and real-time operation. For input frequencies up to some hundreds of kilohertz, the meter is capable both of updating phase noise power spectrum while seamlessly capturing the analyzed signal into its memory, and granting as good frequency resolution as few units of hertz.
phase noise↗
Characteristics of ocean-reflected short radar pulses with application to altimetry and surface roughness determination
Current work related to geodetic altimetry is summarized. Special emphasis is placed on the effects of pulse length on both altimetry and sea-state estimation. Some discussion is also given of system tradeoff parameters and sea truth requirements to support scattering studies. The problem of analyzing signal characteristics and altimeter waveforms arising from rough surface backscattering is also considered.
Miller, L. S.↗
Flight monitor for jet engine disk cracks and the use of critical length criterion of fracture mechanics
A disk crack detector is discussed which is intended to operate under flight conditions. It monitors the disk rim for surface cracks emanating from the blade root interface. An eddy current type sensor, with a remotely located capacitance/conductance bridge and signal analyzer, can reliably detect a simulated crack 3 mm long. The sensor was tested on a spinning turbine disk at 540 C. Tests indicate that the system is useful at disk rim velocities to 460 m/sec. By using fracture mechanics, it is shown for Inconel 718 th at a crack operating under a rim stress of 34 x ten to the 7th power N/sqm has a critical length of 18 mm.
Barranger, J. P.↗