The Story of Wavelets Theory and Engineering Applications

Slides:



Advertisements
Similar presentations
Islamic university of Gaza Faculty of engineering Electrical engineering dept. Submitted to: Dr.Hatem Alaidy Submitted by: Ola Hajjaj Tahleel.
Advertisements

Ch 3 Analysis and Transmission of Signals
Transform Techniques Mark Stamp Transform Techniques.
Applications in Signal and Image Processing
1 Chapter 16 Fourier Analysis with MATLAB Fourier analysis is the process of representing a function in terms of sinusoidal components. It is widely employed.
Time-Frequency Analysis of Non-stationary Phenomena in Electrical Engineering Antonio Bracale, Guido Carpinelli Universita degli Studi di Napoli “Federico.
An Introduction to S-Transform for Time-Frequency Analysis S.K. Steve Chang SKC-2009.
Computer Graphics Recitation 6. 2 Motivation – Image compression What linear combination of 8x8 basis signals produces an 8x8 block in the image?
Time and Frequency Representations Accompanying presentation Kenan Gençol presented in the course Signal Transformations instructed by Prof.Dr. Ömer Nezih.
FFT-based filtering and the Short-Time Fourier Transform (STFT) R.C. Maher ECEN4002/5002 DSP Laboratory Spring 2003.
Time-Frequency and Time-Scale Analysis of Doppler Ultrasound Signals
Wavelet Transform 國立交通大學電子工程學系 陳奕安 Outline Comparison of Transformations Multiresolution Analysis Discrete Wavelet Transform Fast Wavelet Transform.
Short Time Fourier Transform (STFT)
Lecture 19 The Wavelet Transform. Some signals obviously have spectral characteristics that vary with time Motivation.
Presents.
Total Variation Imaging followed by spectral decomposition using continuous wavelet transform Partha Routh 1 and Satish Sinha 2, 1 Boise State University,
Image Fourier Transform Faisal Farooq Q: How many signal processing engineers does it take to change a light bulb? A: Three. One to Fourier transform the.
Wavelet Transform. What Are Wavelets? In general, a family of representations using: hierarchical (nested) basis functions finite (“compact”) support.
Basic Concepts and Definitions Vector and Function Space. A finite or an infinite dimensional linear vector/function space described with set of non-unique.
Introduction to Wavelets
Wavelet-based Coding And its application in JPEG2000 Monia Ghobadi CSC561 project
Transforms: Basis to Basis Normal Basis Hadamard Basis Basis functions Method to find coefficients (“Transform”) Inverse Transform.
Spectral Analysis Spectral analysis is concerned with the determination of the energy or power spectrum of a continuous-time signal It is assumed that.
CELLULAR COMMUNICATIONS DSP Intro. Signals: quantization and sampling.
Goals For This Class Quickly review of the main results from last class Convolution and Cross-correlation Discrete Fourier Analysis: Important Considerations.
Wavelets: theory and applications
Details, details… Intro to Discrete Wavelet Transform The Story of Wavelets Theory and Engineering Applications.
WAVELET TUTORIALS.
CSE &CSE Multimedia Processing Lecture 8. Wavelet Transform Spring 2009.
The Story of Wavelets.
The Wavelet Tutorial Dr. Charturong Tantibundhit.
Lecture 13 Wavelet transformation II. Fourier Transform (FT) Forward FT: Inverse FT: Examples: Slide from Alexander Kolesnikov ’s lecture notes.
Sep.2008DISP Time-Frequency Analysis 時頻分析  Speaker: Wen-Fu Wang 王文阜  Advisor: Jian-Jiun Ding 丁建均 教授   Graduate.
README Lecture notes will be animated by clicks. Each click will indicate pause for audience to observe slide. On further click, the lecturer will explain.
Wavelet transform Wavelet transform is a relatively new concept (about 10 more years old) First of all, why do we need a transform, or what is a transform.
EE104: Lecture 5 Outline Review of Last Lecture Introduction to Fourier Transforms Fourier Transform from Fourier Series Fourier Transform Pair and Signal.
Systems (filters) Non-periodic signal has continuous spectrum Sampling in one domain implies periodicity in another domain time frequency Periodic sampled.
Basics Course Outline, Discussion about the course material, reference books, papers, assignments, course projects, software packages, etc.
Digital Image Processing CSC331 Image Enhancement 1.
ECE472/572 - Lecture 13 Wavelets and Multiresolution Processing 11/15/11 Reference: Wavelet Tutorial
Chapter 6 Spectrum Estimation § 6.1 Time and Frequency Domain Analysis § 6.2 Fourier Transform in Discrete Form § 6.3 Spectrum Estimator § 6.4 Practical.
1 Using Wavelets for Recognition of Cognitive Pattern Primitives Dasu Aravind Feature Group PRISM/ASU 3DK – 3DK – September 21, 2000.
“Digital stand for training undergraduate and graduate students for processing of statistical time-series, based on fractal analysis and wavelet analysis.
Pre-Class Music Paul Lansky Six Fantasies on a Poem by Thomas Campion.
Time Frequency Analysis
Ch4 Short-time Fourier Analysis of Speech Signal z Fourier analysis is the spectrum analysis. It is an important method to analyze the speech signal. Short-time.
2D Sampling Goal: Represent a 2D function by a finite set of points.
The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit.
Fourier Transform.
Frequency Domain Coding of Speech 主講人:虞台文. Content Introduction The Short-Time Fourier Transform The Short-Time Discrete Fourier Transform Wide-Band Analysis/Synthesis.
May 2 nd 2012 Advisor: John P. Castagna.  Background---STFT, CWT and MPD  Fractional Matching Pursuit Decomposition  Computational Simulation  Results:
Analysis of Traction System Time-Varying Signals using ESPRIT Subspace Spectrum Estimation Method Z. Leonowicz, T. Lobos
By Dr. Rajeev Srivastava CSE, IIT(BHU)
Time Compression/Expansion Independent of Pitch. Listening Dies Irae from Requiem, by Michel Chion (1973)
Eeng Chapter4 Bandpass Signalling  Bandpass Filtering and Linear Distortion  Bandpass Sampling Theorem  Bandpass Dimensionality Theorem  Amplifiers.
The Frequency Domain Digital Image Processing – Chapter 8.
Short Time Fourier Transform (STFT) CS474/674 – Prof. Bebis.
Wavelet Transform Advanced Digital Signal Processing Lecture 12
… Sampling … … Filtering … … Reconstruction …
Spectral Analysis Spectral analysis is concerned with the determination of the energy or power spectrum of a continuous-time signal It is assumed that.
FFT-based filtering and the
Homework 1 (Due: 11th Oct.) (1) Which of the following applications are the proper applications of the short -time Fourier transform? Also illustrate.
Outline Linear Shift-invariant system Linear filters
Gary Margrave and Michael Lamoureux
Wavelet transform Wavelet transform is a relatively new concept (about 10 more years old) First of all, why do we need a transform, or what is a transform.
Lecture 18 DFS: Discrete Fourier Series, and Windowing
6. Time and Frequency Characterization of Signals and Systems
INTRODUCTION TO THE SHORT-TIME FOURIER TRANSFORM (STFT)
Chapter 7 Finite Impulse Response(FIR) Filter Design
Lec.6:Discrete Fourier Transform and Signal Spectrum
Presentation transcript:

The Story of Wavelets Theory and Engineering Applications Time frequency representation Instantaneous frequency and group delay Short time Fourier transform –Analysis Short time Fourier transform – Synthesis Discrete time STFT

Time – Frequency Representation Why do we need it? Time info difficult to interpret in frequency domain Frequency info difficult to interpret in time domain Perfect time info in time domain , perfect freq. info in freq. domain …Why? How to handle non-stationary signals Instantaneous frequency Group Delay

Instantaneous Frequency & Group Delay Instantaneous frequency: defined as the rate of change in phase A dual quantity group delay defined as the rate of change in phase spectrum Frequency as a function of time Time as a function of frequency What is wrong with these quantities???

Time Frequency Representation in Two-dimensional Space TFR Linear STFT, WT, etc. Non-Linear Quadratic Spectrogram, WD

STFT Amplitude ….. ….. time t0 t1 tk tk+1 tn ….. ….. Frequency

The Short Time Fourier Transform Take FT of segmented consecutive pieces of a signal. Each FT then provides the spectral content of that time segment only Spectral content for different time intervals Time-frequency representation Time parameter Signal to be analyzed FT Kernel (basis function) Frequency parameter STFT of signal x(t): Computed for each window centered at t= (localized spectrum) Windowing function (Analysis window) Windowing function centered at t=

Properties of STFT Linear Complex valued Time invariant Time shift Frequency shift Many other properties of the FT also apply.

Alternate Representation of STFT STFT : The inverse FT of the windowed spectrum, with a phase factor

Filter Interpretation of STFT X(t) is passed through a bandpass filter with a center frequency of Note that (f) itself is a lowpass filter.

Filter Interpretation of STFT x(t) X X x(t)

Resolution Issues All signal attributes located within the local window interval around “t” will appear at “t” in the STFT Amplitude time k n Frequency

Time-Frequency Resolution Closely related to the choice of analysis window Narrow window  good time resolution Wide window (narrow band)  good frequency resolution Two extreme cases: (T)=(t) excellent time resolution, no frequency resolution (T)=1 excellent freq. resolution (FT), no time info!!! How to choose the window length? Window length defines the time and frequency resolutions Heisenberg’s inequality Cannot have arbitrarily good time and frequency resolutions. One must trade one for the other. Their product is bounded from below.

Time-Frequency Resolution

Time Frequency Signal Expansion and STFT Synthesis Basis functions Coefficients (weights) Synthesis window Synthesized signal Each (2D) point on the STFT plane shows how strongly a time frequency point (t,f) contributes to the signal. Typically, analysis and synthesis windows are chosen to be identical.

STFT Example 300 Hz 200 Hz 100Hz 50Hz

STFT Example

STFT Example a=0.01

STFT Example a=0.001

STFT Example a=0.0001

STFT Example a=0.00001

Discrete Time Stft