Time/frequency analysis of some MOST data F. Baudin (IAS) & J. Matthews (UBC)

Slides:

Advertisements

Similar presentations

Noise in Radiographic Imaging

Advertisements

Time-Series Analysis of Astronomical Data

Biomedical Signal Processing

All slides © S. J. Luck, except as indicated in the notes sections of individual slides Slides may be used for nonprofit educational purposes if this copyright.

A Frequency Domain Technique for Correction of Audio Distortion Caused by Variable Recording Speed: Authors Robert Hembree Henry Skiba Alex Smith Advisor.

FilteringComputational Geophysics and Data Analysis 1 Filtering Geophysical Data: Be careful!  Filtering: basic concepts  Seismogram examples, high-low-bandpass.

Fourier Integrals For non-periodic applications (or a specialized Fourier series when the period of the function is infinite: L  ) L -L L  -L  - 

DFT/FFT and Wavelets ● Additive Synthesis demonstration (wave addition) ● Standard Definitions ● Computing the DFT and FFT ● Sine and cosine wave multiplication.

Harmonic Series and Spectrograms 220 Hz (A3) Why do they sound different? Instrument 1 Instrument 2Sine Wave.

Comparing different searches for gravitational-wave bursts on simulated LIGO and VIRGO data Michele Zanolin -MIT on behalf of the LIGO-VIRGO joint working.

Nonstationary Signal Processing Hilbert-Huang Transform Joseph DePasquale 22 Mar 07.

TVF: Theoretical Basis The Time Variable Filtering (TVF) is a filtering technique that does not give a dispersion curve but a smooth signal (a signal time-variable.

SIGNAL PROCESSING TECHNIQUES USED FOR THE ANALYSIS OF ACOUSTIC SIGNALS FROM HEART AND LUNGS TO DETECT PULMONARY EDEMA 1 Pratibha Sharma Electrical, Computer.

Exercise: ASK Consider the input signal with 3 levels shown above. Assume we want to use the following sine wave as the carrier. 10 sin(2p 2 t) And we.

An Introduction to S-Transform for Time-Frequency Analysis S.K. Steve Chang SKC-2009.

G O D D A R D S P A C E F L I G H T C E N T E R Upconversion Study with the Hilbert-Huang Transform Jordan Camp Kenji Numata John Cannizzo Robert Schofield.

Time-Frequency and Time-Scale Analysis of Doppler Ultrasound Signals

Intro to Fourier Analysis Definition Analysis of periodic waves Analysis of aperiodic waves Digitization Time-frequency uncertainty.

Statistical analysis and modeling of neural data Lecture 6 Bijan Pesaran 24 Sept, 2007.

Short Time Fourier Transform (STFT)

Variable Phenomena Nyquist Sampling Harry Nyquist (1889 – 1976)

Title of Your Investigation Group Members/Researchers.

Inputs to Signal Generation.vi: -Initial Distance (m) -Velocity (m/s) -Chirp Duration (s) -Sampling Info (Sampling Frequency, Window Size) -Original Signal.

UF S.Klimenko LIGO-G Z l Introduction l Goals of this analysis l Coherence of power monitors l Sign X-Correlation l H2-L1 x-correlation l Conclusion.

Harmonic Series and Spectrograms

The Story of Wavelets.

The Physical Layer Lowest layer in Network Hierarchy. Physical transmission of data. –Various flavors Copper wire, fiber optic, etc... –Physical limits.

Spectral analysis of multiple timeseries Kenneth D. Harris 18/2/15.

Question 1 Find the following using dB and dBm transformations: a) Power in dB for 22 W b) Power in dBm for 4.8 W c) Power in W for 25 dB.

A tool to simulate COROT light-curves R. Samadi 1 & F. Baudin 2 1 : LESIA, Observatory of Paris/Meudon 2 : IAS, Orsay.

Searching for Gravitational Waves with LIGO Andrés C. Rodríguez Louisiana State University on behalf of the LIGO Scientific Collaboration SACNAS

Digital modulation techniques. Modulations systems.

GLAST LAT Project Instrument Analysis Workshop June 8, 2004 W. Focke 1/12 Deadtime modeling and power density spectrum Warren Focke June 8, 2004.

Harmonic Series and Spectrograms BY JORDAN KEARNS (W&L ‘14) & JON ERICKSON (STILL HERE )

S.Klimenko, August 2003, Hannover LIGO-G Z How optimal are wavelet TF methods? S.Klimenko l Introduction l Time-Frequency analysis l Comparison.

CS434/534: Mobile Computing and Wireless Networks Y. Richard Yang 08/30/2012.

LIGO-G Z Center for Gravitational Wave Physics, Penn State 1 RayleighMonitor: A Time-Frequency Gaussianity Monitor for the DMT Lee Samuel Finn,

Fourier and Wavelet Transformations Michael J. Watts

Time-frequency analysis of thin bed using a modified matching pursuit algorithm Bo Zhang Graduated from AASP consortium of OU in 2014 currently with The.

Time/frequency analysis with COROT: what do we want to do? F. Baudin & E. Michel Time/frequency analysis : any method providing an information on the temporal.

Arrival time variations of pulses in shallow water and low frequency acoustical underwater positioning B.Katsnelson (Voronezh Uni, Russia) M.Badiey (Uni.

A Method to Correct Data Corrupted by Overflow Ben Christensen Jay Brady Dec. 5, 2013.

An Introduction to Time-Frequency Analysis Speaker: Po-Hong Wu Advisor: Jian-Jung Ding Digital Image and Signal Processing Lab GICE, National Taiwan University.

FINESSE FINESSE Frequency Domain Interferometer Simulation Andreas Freise European Gravitational Observatory 17. March 2004.

Outline Random variables –Histogram, Mean, Variances, Moments, Correlation, types, multiple random variables Random functions –Correlation, stationarity,

All slides © S. J. Luck, except as indicated in the notes sections of individual slides Slides may be used for nonprofit educational purposes if this copyright.

The Frequency Domain Digital Image Processing – Chapter 8.

CLASSIFICATION OF ECG SIGNAL USING WAVELET ANALYSIS

Searching the LIGO data for coincidences with Gamma Ray Bursts Alexander Dietz Louisiana State University for the LIGO Scientific Collaboration LIGO-G Z.

Short Time Fourier Transform (STFT) CS474/674 – Prof. Bebis.

II.1 Physical Reality II.1.1 (F Sept 08) Sound Anatomy.

Neural data-analysis Workshop

Filtering Geophysical Data: Be careful!

Fourier and Wavelet Transformations

Image Denoising in the Wavelet Domain Using Wiener Filtering

MA 527 Dr. Park.

POTSI Gaboring Advances

Lecture 35 Wave spectrum Fourier series Fourier analysis

Fourier Integrals For non-periodic applications (or a specialized Fourier series when the period of the function is infinite: L) -L L -L- L

FFTs, Windows, and Circularity

Gary Margrave and Michael Lamoureux

7.4 Fourier Transform Theorems, Part I

7.1 Introduction to Fourier Transforms

The M2 parameter in space ---beam quality factor

7.9 Comb Basis Set Transform comb-to-comb transform

Advance Warning of Supermassive Black Hole Mergers

Bandpass Modulation and Demodulation

bc POTSI Gaboring Advances

Discrete Fourier Transform

Noise and Requirement of BRT

Presentation transcript:

Time/frequency analysis of some MOST data F. Baudin (IAS) & J. Matthews (UBC)

Just few words about time/frequency analysis Classical Fourier transform: FT[S(t)](  )=  S(t) e i  t dt Windowed Fourier transform: WFT[S(t)]( ,t 0 ) =  S(t) W(t-t 0 ) e i  t dt If W(t) = gaussian => Gabor transform If W(t,  ) => wavelet transform

Just a drawing about time/frequency analysis

MOST data  Equ [roAp]  Oph [red giant]  Boo [Post MS] Procyon [MS]

 Equ : a simple case?

 Equ : a simple case of beating Confirmation with simulation: modulation due to beating

 Oph : a more interesting case

Signal + sine wave of constant amplitude => noise estimation

 Oph : a more interesting case Temporal modulation not due to noise: which origin?

[  Boo] Noise : not so interesting but…

Instrumental periodicities (CCD temperature?)

Procyon: variability of the signal?

Procyon: variability of the signal T < 10 days T > 10 days

Procyon: variability of the signal T < 10 days T > 10 days

Conclusion Time/Frequency analysis allows : variation with time of the (instrumental) noise [  Boo, Procyon] simple interpretation (beating) of amplitude modulation [  Equ] evidence of temporal variation of modes of unknown origin [  Oph]

[Procyon] Noise : not so interesting but…