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Presents

Presents. The Story of Wavelets. Robi Polikar Dept. of Electrical & Computer Engineering Rowan University. The Story of Wavelets. Technical Overview But…We cannot do that with Fourier Transform…. Time - frequency representation and the STFT Continuous wavelet transform

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Presents

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  1. Presents

  2. The Story of Wavelets Robi PolikarDept. of Electrical & Computer EngineeringRowan University

  3. The Story of Wavelets • Technical Overview • But…We cannot do that with Fourier Transform…. • Time - frequency representation and the STFT • Continuous wavelet transform • Multiresolution analysis and discrete wavelet transform (DWT) • Application Overview • Conventional Applications: Data compression, denoising, solution of PDEs, biomedical signal analysis. • Unconventional applications • Yes…We can do that with wavelets too… • Historical Overview • 1807 ~ 1940s: The reign of the Fourier Transform • 1940s ~ 1970s: STFT and Subband Coding • 1980s & 1990s: The Wavelet Transform and MRA

  4. What is a Transformand Why Do we Need One ? • Transform: A mathematical operation that takes a function or sequence and maps it into another one • Transforms are good things because… • The transform of a function may give additional /hidden information about the original function, which may not be available /obvious otherwise • The transform of an equation may be easier to solve than the original equation (recall your fond memories of Laplace transforms in DFQs) • The transform of a function/sequence may require less storage, hence provide data compression / reduction • An operation may be easier to apply on the transformed function, rather than the original function (recall other fond memories on convolution).

  5. Jean B. Joseph Fourier (1768-1830) December, 21, 1807 “An arbitrary function, continuous or with discontinuities, defined in a finite interval by an arbitrarily capricious graph can always be expressed as a sum of sinusoids” J.B.J. Fourier

  6. Complex function representation through simple building blocks • Basis functions • Using only a few blocks  Compressed representation • Using sinusoids as building blocks  Fourier transform • Frequency domain representation of the function

  7. How Does FT Work Anyway? • Recall that FT uses complex exponentials (sinusoids) as building blocks. • For each frequency of complex exponential, the sinusoid at that frequency is compared to the signal. • If the signal consists of that frequency, the correlation is high  large FT coefficients. • If the signal does not have any spectral component at a frequency, the correlation at that frequency is low / zero,  small / zero FT coefficient.

  8. FT At Work

  9. FT At Work F F F

  10. FT At Work F

  11. FT At Work Complex exponentials (sinusoids) as basis functions: F An ultrasonic A-scan using 1.5 MHz transducer, sampled at 10 MHz

  12. Stationary and Non-stationary Signals • FT identifies all spectral components present in the signal, however it does not provide any information regarding the temporal (time) localization of these components. Why? • Stationary signals consist of spectral components that do not change in time • all spectral components exist at all times • no need to know any time information • FT works well for stationary signals • However, non-stationary signals consists of time varying spectral components • How do we find out which spectral component appears when? • FT only provides what spectral components exist , not where in time they are located. • Need some other ways to determine time localization of spectral components

  13. Stationary and Non-stationary Signals • Stationary signals’ spectral characteristics do not change with time • Non-stationary signals have time varying spectra Concatenation

  14. Stationary vs. Non-Stationary X4(ω) Perfect knowledge of what frequencies exist, but no information about where these frequencies are located in time X5(ω)

  15. Shortcomings of the FT • Sinusoids and exponentials • Stretch into infinity in time, no time localization • Instantaneous in frequency, perfect spectral localization • Global analysis does not allow analysis of non-stationary signals • Need a local analysis scheme for a time-frequency representation (TFR) of nonstationary signals • Windowed F.T. or Short Time F.T. (STFT) : Segmenting the signal into narrow time intervals, narrow enough to be considered stationary, and then take the Fourier transform of each segment, Gabor 1946. • Followed by other TFRs, which differed from each other by the selection of the windowing function

  16. Short Time Fourier Transform(STFT) • Choose a window function of finite length • Place the window on top of the signal at t=0 • Truncate the signal using this window • Compute the FT of the truncated signal, save. • Incrementally slide the window to the right • Go to step 3, until window reaches the end of the signal • For each time location where the window is centered, we obtain a different FT • Hence, each FT provides the spectral information of a separate time-slice of the signal, providing simultaneous time and frequency information

  17. STFT Frequency parameter Time parameter Signal to be analyzed FT Kernel (basis function) STFT of signal x(t): Computed for each window centered at t=t’ Windowing function Windowing function centered at t=t’

  18. STFT t’=-8 t’=-2 t’=4 t’=8

  19. STFT at Work

  20. STFT At Work

  21. STFT At Work

  22. STFT • STFT provides the time information by computing a different FTs for consecutive time intervals, and then putting them together • Time-Frequency Representation (TFR) • Maps 1-D time domain signals to 2-D time-frequency signals • Consecutive time intervals of the signal are obtained by truncating the signal using a sliding windowing function • How to choose the windowing function? • What shape? Rectangular, Gaussian, Elliptic…? • How wide? • Wider window require less time steps  low time resolution • Also, window should be narrow enough to make sure that the portion of the signal falling within the window is stationary • Can we choose an arbitrarily narrow window…?

  23. Selection of STFT Window Two extreme cases: • W(t) infinitely long:  STFT turns into FT, providing excellent frequency information (good frequency resolution), but no time information • W(t) infinitely short:  STFT then gives the time signal back, with a phase factor. Excellent time information (good time resolution), but no frequency information Wide analysis window poor time resolution, good frequency resolution Narrow analysis windowgood time resolution, poor frequency resolution Once the window is chosen, the resolution is set for both time and frequency.

  24. Heisenberg Principle Frequency resolution: How well two spectral components can be separated from each other in the transform domain Time resolution: How well two spikes in time can be separated from each other in the transform domain Both time and frequency resolutions cannot be arbitrarily high!!! We cannot precisely know at what time instance a frequency component is located. We can only know what interval of frequencies are present in which time intervals

  25. The Wavelet Transform • Overcomes the preset resolution problem of the STFT by using a variable length window • Analysis windows of different lengths are used for different frequencies: • Analysis of high frequencies Use narrower windows for better time resolution • Analysis of low frequencies  Use wider windows for better frequency resolution • This works well, if the signal to be analyzed mainly consists of slowly varying characteristics with occasional short high frequency bursts. • Heisenberg principle still holds!!! • The function used to window the signal is called the wavelet

  26. The Wavelet Transform A normalization constant Translation parameter, measure of time Scale parameter, measure of frequency Signal to be analyzed Continuous wavelet transform of the signal x(t) using the analysis wavelet (.) The mother wavelet. All kernels are obtained by translating (shifting) and/or scaling the mother wavelet Scale = 1/frequency

  27. High frequency (small scale) Low frequency (large scale) WT at Work

  28. WT at Work

  29. WT at Work

  30. WT at Work

  31. Matlab Demos on CWT

  32. Discrete Wavelet Transform • CWT computed by computers is really not CWT, it is a discretized version of the CWT. • The resolution of the time-frequency grid can be controlled (within Heisenberg’s inequality), can be controlled by time and scale step sizes. • Often this results in a very redundant representation • How to discretize the continuous time-frequency plane, so that the representation is non-redundant? • Sample the time-frequency plane on a dyadic (octave) grid

  33. Discrete Wavelet Transform • Dyadic sampling of the time –frequency plane results in a very efficient algorithm for computing DWT: • Subband coding using multiresolution analysis • Dyadic sampling and multiresolution is achieved through a series of filtering and up/down sampling operations H x[n] y[n]

  34. x[n] x[n] ~ G 2 2 2 2 2 2 2 2 2 2 ~ ~ G H G G + + ~ H H H Decomposition Reconstruction Discrete Wavelet TransformImplementation Down-sampling Up-sampling Half band high pass filter Half band low pass filter G H 2-level DWT decomposition. The decomposition can be continues as long as there are enough samples for down-sampling.

  35. |H(jw)| w /2 -/2 2 2 2 2 2 DWT - Demystified Length: 512 B: 0 ~  g[n] h[n] Length: 256 B: 0 ~ /2 Hz Length: 256 B: /2 ~  Hz a1 |G(jw)| d1: Level 1 DWT Coeff. g[n] h[n] Length: 128 B: 0 ~  /4 Hz w Length: 128 B: /4 ~ /2 Hz -/2 /2 -  a2 d2: Level 2 DWT Coeff. g[n] h[n] 2 Length: 64 B: 0 ~ /8 Hz Length: 64 B: /8 ~ /4 Hz …a3…. d3: Level 3 DWT Coeff. Level 3 approximation Coefficients

  36. Implementation of DWT on MATLAB Choose wavelet and number of levels Load signal Hit Analyze button s=a5+d5+…+d1 Approx. coef. at level 5 Level 1 coeff. Highest freq. (Wavedemo_signal1)

  37. Applications of Wavelets

  38. Applications of Wavelets • Compression • De-noising • Feature Extraction • Discontinuity Detection • Distribution Estimation • Data analysis • Biological data • NDE data • Financial data

  39. Compression • DWT is commonly used for compression, since most DWT are very small, can be zeroed-out!

  40. Compression

  41. Compression

  42. ECG- Compression

  43. Denoising Implementation in Matlab First, analyze the signal with appropriate wavelets Hit Denoise (Noisy Doppler)

  44. Denoising Using Matlab Choose thresholding method Choose noise type Choose thrsholds Hit Denoise

  45. Denosing Using Matlab

  46. Discontinuity Detection (microdisc.mat)

  47. Discontinuity Detectionwith CWT (microdisc.mat)

  48. Application Overview • Data Compression • Wavelet Shrinkage Denoising • Source and Channel Coding • Biomedical Engineering • EEG, ECG, EMG, etc analysis • MRI • Nondestructive Evaluation • Ultrasonic data analysis for nuclear power plant pipe inspections • Eddy current analysis for gas pipeline inspections • Numerical Solution of PDEs • Study of Distant Universes • Galaxies form hierarchical structures at different scales

  49. Application Overview • Wavelet Networks • Real time learning of unknown functions • Learning from sparse data • Turbulence Analysis • Analysis of turbulent flow of low viscosity fluids flowing at high speeds • Topographic Data Analysis • Analysis of geo-topographic data for reconnaissance / object identification • Fractals • Daubechies wavelets: Perfect fit for analyzing fractals • Financial Analysis • Time series analysis for stock market predictions

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