Chebyshev-derived spindown parameters for gravitational wave signals from pulsars

2008 ◽  
Vol 86 (4) ◽  
pp. 597-600 ◽  
Author(s):  
S R Valluri ◽  
M D Fried
Keyword(s):  
Fourier Transform ◽  
Gravitational Wave ◽  
Master Equation ◽  
Chebyshev Polynomial ◽  
Polynomial Method ◽  
Frequency Pattern ◽  
Time Frequency ◽  
The Fourier Transform

The master equation described by Badri Krishnan et al. (Phys Rev. D, 70, 082001 (2004)) for the time-frequency pattern using the F-statistic is studied in the context of Chebyshev-polynomial modified spindown parameters for the case of gravitational wave pulsar signals. The Chebyshev-polynomial method enables an analytic and numeric evaluation of the Fourier transform (FT) for both the non-demodulated and F-statistic demodulated FT.PACS Nos.: 04.30.Tv, 95.85.sz, 02.30.Gp, 02.40.Re

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

An automatic means to discriminate between earthquakes and quarry blasts

1990 ◽  
Vol 80 (6B) ◽  
pp. 2143-2160
Author(s):  
Michael A. H. Hedlin ◽  
J. Bernard Minster ◽  
John A. Orcutt
Keyword(s):  
Fourier Transform ◽  
Spectral Features ◽  
Single Event ◽  
Vertical Array ◽  
Signal To Noise ◽  
Spectral Character ◽  
Frequency Pattern ◽  
Time Frequency ◽  
Cepstral Analysis ◽  
Double Fourier Transform

Abstract In this article we discuss our efforts to use the NORESS array to discriminate between regional earthquakes and ripple-fired quarry blasts (events that involve a number of subexplosions closely grouped in space and time). The method we describe is an extension of the time versus frequency “pattern-based” discriminant proposed by Hedlin et al. (1989b). At the heart of the discriminant is the observation that ripple-fired events tend to give rise to coda dominated by prominent spectral features that are independent of time and periodic in frequency. This spectral character is generally absent from the coda produced by earthquakes and “single-event” explosions. The discriminant originally proposed by Hedlin et al. (1989b) used data collected at 250 sec−1 by single sensors in the 1987 NRDC network in Kazakhstan, U.S.S.R. We have found that despite the relatively low digitization rate provide by the NORESS array (40 sec−1) we have had good success in our efforts to discriminate between earthquakes and quarry blasts by stacking all vertical array channels to improve signal-to-noise ratios. We describe our efforts to automate the method, so that visual pattern recognition is not required, and to make it less susceptible to spurious time-independent spectral features not originating at the source. In essence, we compute a Fourier transform of the time-frequency matrix and examine the power levels representing energy that is periodic in frequency and independent of time. Since a double Fourier transform is involved, our method can be considered as an extension of “cepstral” analysis (Tribolet, 1979). We have found, however, that our approach is superior since it is cognizant of the time independence of the spectral features of interest. We use earthquakes to define what cepstral power is to be expected in the absence of ripple firing and search for events that violate this limit. The assessment of the likelihood that ripple firing occurred at the source is made automatically by the computer and is based on the extent to which the limit is violated.

scholarly journals Design and construction of a prototype for the analysis of vibrations in an induction motor for the detection of faults

2020 ◽  
pp. 27-33
Author(s):  
Javier Garrido ◽  
Beatris Escobedo-Trujillo ◽  
Guillermo Miguel Martínez-Rodríguez ◽  
Oscar Fernando Silva-Aguilar
Keyword(s):  
Fourier Transform ◽  
Induction Motor ◽  
Wavelet Transforms ◽  
Data Acquisition System ◽  
Time Frequency ◽  
Different Types ◽  
Control Devices ◽  
The Fourier Transform ◽  
And Control ◽  
Types Of Faults

The contribution of this work is to present the design of a prototype integrated by an induction motor, a data acquisition system, accelerometers and control devices for stop and start, to generate and identify different types of faults by means of vibration analysis. in the domain: time, frequency or frequency-time, through the use of the Fourier Transform, Fast Fourier Transform or Wavelet Transforms (wavelet transform). In this prototype, failures can be generated in the induction motor such as: unbalance, different types of misalignment, mechanical looseness, and electrical failures such as broken bars or short-circuited rings, an example of a misalignment failure is presented to show the process of analysis and detection.

scholarly journals Time-frequency tools of signal processing for EISCAT data analysis

1996 ◽  
Vol 14 (12) ◽  
pp. 1513-1525
Author(s):  
J. Lilensten ◽  
P. O. Amblard
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Data Analysis ◽  
Gravity Waves ◽  
Velocity Measurements ◽  
Time Frequency ◽  
Synthetic Signal ◽  
Stationary Analysis ◽  
The Fourier Transform ◽  
Short Time

Abstract. We demonstrate the usefulness of some signal-processing tools for the EISCAT data analysis. These tools are somewhat less classical than the familiar periodogram, squared modulus of the Fourier transform, and therefore not as commonly used in our community. The first is a stationary analysis, "Thomson's estimate'' of the power spectrum. The other two belong to time-frequency analysis: the short-time Fourier transform with the spectrogram, and the wavelet analysis via the scalogram. Because of the highly non-stationary character of our geophysical signals, the latter two tools are better suited for this analysis. Their results are compared with both a synthetic signal and EISCAT ion-velocity measurements. We show that they help to discriminate patterns such as gravity waves from noise.

A SIGNAL-TUNED GABOR TRANSFORM WITH APPLICATION TO EEG ANALYSIS

2013 ◽  
Vol 24 (04) ◽  
pp. 1350017 ◽  
Author(s):  
JOSÉ R. A. TORREÃO ◽  
SILVIA M. C. VICTER ◽  
JOÃO L. FERNANDES
Keyword(s):  
Experimental Study ◽  
Fourier Transform ◽  
Input Signal ◽  
Fourier Component ◽  
Gabor Transform ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
The Fourier Transform

We introduce a time-frequency transform based on Gabor functions whose parameters are given by the Fourier transform of the analyzed signal. At any given frequency, the width and the phase of the Gabor function are obtained, respectively, from the magnitude and the phase of the signal's corresponding Fourier component, yielding an analyzing kernel which is a representation of the signal's content at that particular frequency. The resulting Gabor transform tunes itself to the input signal, allowing the accurate detection of time and frequency events, even in situations where the traditional Gabor and S-transform approaches tend to fail. This is the case, for instance, when considering the time-frequency representation of electroencephalogram traces (EEG) of epileptic subjects, as illustrated by the experimental study presented here.

scholarly journals Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 597 ◽  
Author(s):  
Miguel Iglesias-Martínez ◽  
Jose Antonino-Daviu ◽  
Pedro Fernández de Córdoba ◽  
J. Conejero
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Induction Motor ◽  
Mean Value ◽  
High Order ◽  
One Dimensional ◽  
Time Frequency ◽  
Temporal Domain ◽  
The Fourier Transform ◽  
The One

The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor in an induction motor from the processing of the stray flux signals. For this, the calculation of two indicators is proposed: the first is based on the frequency domain and it relies on the calculation of the sum of the mean value of the bispectrum of the flux signal. The use of high order spectral analysis is justified in that with the one-dimensional analysis resulting from the Fourier Transform, there may not always be solid differences at the spectral level that enable us to distinguish between healthy and faulty conditions. Also, based on the high-order spectral analysis, differences may arise that, with the classical analysis with the Fourier Transform, are not evident, since the high order spectra from the Bispectrum are immune to Gaussian noise, but not the results that can be obtained using the one-dimensional Fourier transform. On the other hand, a second indicator based on the temporal domain that is based on the calculation of the square value of the median of the autocovariance function of the signal is evaluated. The obtained results are satisfactory and let us conclude the affirmative hypothesis of using flux signals for determining the condition of the rotor of an induction motor.

scholarly journals A study of the gravitational wave pulsar signal with orbital and spindown effects

2006 ◽  
Vol 84 (6-7) ◽  
pp. 607-613
Author(s):  
S R Valluri ◽  
K M Rao ◽  
P Wiegert ◽  
F A Chishtie
Keyword(s):  
Fourier Transform ◽  
Gravitational Wave ◽  
Orbital Motion ◽  
Special Functions ◽  
The Moon ◽  
The Earth ◽  
Source Signal ◽  
Phase Corrections ◽  
The Fourier Transform ◽  
Monochromatic Source

In this work, we present an analytic and a preliminary numerical analysis of the gravitational wave signal from a pulsar that includes simple spindown effects. We estimate the phase corrections to a monochromatic source signal due to rotational and elliptical orbital motion of the Earth, and perturbations due to Jupiter and the Moon. We briefly discuss the Fourier transform of such a signal, expressed in terms of well-known special functions, and its applications. PACKS Nos.: 04.30.-w

SOME MATHEMATICAL TOOLS FOR THE ANALYSIS OF BIOLOGICAL SIGNALS

2007 ◽  
Vol 07 (02) ◽  
pp. 215-227 ◽  
Author(s):  
MICHELE NICHELATTI ◽  
PAOLO PETTAZZONI ◽  
GIOVANNI PALLOTTI
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Special Focus ◽  
Transient Phenomena ◽  
Time Frequency ◽  
Window Functions ◽  
Biological Signals ◽  
Recent Developments ◽  
The Fourier Transform

The paper presents an informal review of some techniques available for signal analysis. In the interpretation of biomedical signals, the individuation of hidden transient phenomena in the spectrum can have a crucial role for diagnostic purposes. Since most biological signals are nonstationary, the Fourier transform is not sufficient to detect possible transient phenomena in the spectrum; therefore, some improvements in the Fourier transform technique have been carried out by means of window functions in the transformation kernel. Some of the most important features of recent developments in signal analysis are discussed here, with special focus on the uncertainty principle governing any time–frequency analysis.

scholarly journals A simple Spectral Observer

2018 ◽  
Author(s):  
Lizeth Torres ◽  
Javier Jiménez-Cabas ◽  
José Francisco Gómez-Aguilar ◽  
Pablo Pérez-Alcazar
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Online Algorithm ◽  
Duffing Oscillator ◽  
Design Procedure ◽  
Recursive Identification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
The Fourier Transform ◽  
Short Time

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

scholarly journals Time Frequency Analysis of Wavelet and Fourier Transform

2020 ◽  
Author(s):  
Karlton Wirsing
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Efficient Access ◽  
The Fourier Transform

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.

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