Active Learning in an Information-rich Age Early 20 th century, how do you study physics? Early 21 st century, how do you study wavelets?

Slides:



Advertisements
Similar presentations
Chapter 11 Signal Processing with Wavelets. Objectives Define and illustrate the difference between a stationary and non-stationary signal. Describe the.
Advertisements

Study of Change Blindness EEG Synchronization using Wavelet Coherence Analysis Professor: Liu Student: Ruby.
1 / 13 Fourier, bandwidth, filter. 2 / 13 The important roles of Fourier series and Fourier transforms: –To analysis and synthesis signals in frequency.
Signal Denoising with Wavelets. Wavelet Threholding Assume an additive model for a noisy signal, y=f+n K is the covariance of the noise Different options.
Lecture 7 Linear time invariant systems
Statistical properties of Random time series (“noise”)
Extensions of wavelets
EE591U Wavelets and Filter Banks Copyright Xin Li EE591U: Wavelets and Filter Banks Xin Li LDCSEE, Fall 2008.
0 - 1 © 2007 Texas Instruments Inc, Content developed in partnership with Tel-Aviv University From MATLAB ® and Simulink ® to Real Time with TI DSPs Wavelet.
An Introduction to S-Transform for Time-Frequency Analysis S.K. Steve Chang SKC-2009.
Spectral analysis Kenneth D. Harris 18/2/15. Continuous processes A continuous process defines a probability distribution over the space of possible signals.
EE322 Digital Communications
Sep 22, 2005CS477: Analog and Digital Communications1 Random Processes and PSD Analog and Digital Communications Autumn
CS CS 175 – Week 4 Triangle Mesh Smoothing Signal Processing, Diffusion, Curvature Flow.
Nov 01, 2005CS477: Analog and Digital Communications1 Bandpass Noise Analog and Digital Communications Autumn
Undecimated wavelet transform (Stationary Wavelet Transform)
7th IEEE Technical Exchange Meeting 2000 Hybrid Wavelet-SVD based Filtering of Noise in Harmonics By Prof. Maamar Bettayeb and Syed Faisal Ali Shah King.
Paul Heckbert Computer Science Department Carnegie Mellon University
Subband-based Independent Component Analysis Y. Qi, P.S. Krishnaprasad, and S.A. Shamma ECE Department University of Maryland, College Park.
Chapter 7. Random Process – Spectral Characteristics
Lecture 16 Random Signals and Noise (III) Fall 2008 NCTU EE Tzu-Hsien Sang.
Signal and System I Continuous-time filters described by differential equations + Recall in Ch. 2 Continuous time Fourier transform. LTI system.
(1) A probability model respecting those covariance observations: Gaussian Maximum entropy probability distribution for a given covariance observation.
ELEC 303 – Random Signals Lecture 21 – Random processes
Image Denoising using Wavelet Thresholding Techniques Submitted by Yang
Probability Theory and Random Processes
WEIGHTED OVERCOMPLETE DENOISING Onur G. Guleryuz Epson Palo Alto Laboratory Palo Alto, CA (Please view in full screen mode to see.
Wavelets: theory and applications
Random Processes ECE460 Spring, Power Spectral Density Generalities : Example: 2.
1 Part 5 Response of Linear Systems 6.Linear Filtering of a Random Signals 7.Power Spectrum Analysis 8.Linear Estimation and Prediction Filters 9.Mean-Square.
Rajeev Aggarwal, Jai Karan Singh, Vijay Kumar Gupta, Sanjay Rathore, Mukesh Tiwari, Dr.Anubhuti Khare International Journal of Computer Applications (0975.
May 20-22, 2010, Brasov, Romania 12th International Conference on Optimization of Electrical and Electronic Equipment OPTIM 2010 Electrocardiogram Baseline.
Digtial Image Processing, Spring ECES 682 Digital Image Processing Week 5 Oleh Tretiak ECE Department Drexel University.
EE565 Advanced Image Processing Copyright Xin Li Image Denoising Theory of linear estimation Spatial domain denoising techniques Conventional Wiener.
CCN COMPLEX COMPUTING NETWORKS1 This research has been supported in part by European Commission FP6 IYTE-Wireless Project (Contract No: )
ارتباطات داده (883-40) فرآیندهای تصادفی نیمسال دوّم افشین همّت یار دانشکده مهندسی کامپیوتر 1.
EE565 Advanced Image Processing Copyright Xin Li Image Denoising: a Statistical Approach Linear estimation theory summary Spatial domain denoising.
3.3.1 Synchronized averaging
1 Wavelet Transform. 2 Definition of The Continuous Wavelet Transform CWT The continuous-time wavelet transform (CWT) of f(x) with respect to a wavelet.
Lecture#10 Spectrum Estimation
I3/CA Europlanet - EC Contract Space Research Institute Austrian Academy of Sciences Non-thermal radio emission fundamentals:
Optimal Adaptive Wavelet Bases
Discrete-time Random Signals
APPLICATION OF A WAVELET-BASED RECEIVER FOR THE COHERENT DETECTION OF FSK SIGNALS Dr. Robert Barsanti, Charles Lehman SSST March 2008, University of New.
Geology 6600/7600 Signal Analysis 09 Oct 2015 © A.R. Lowry 2015 Last time: A Periodogram is the squared modulus of the signal FFT! Blackman-Tukey estimates.
傅思維. How to implement? 2 g[n]: low pass filter h[n]: high pass filter :down sampling.
1 Review of Probability and Random Processes. 2 Importance of Random Processes Random variables and processes talk about quantities and signals which.
Wavelet Thresholding for Multiple Noisy Image Copies S. Grace Chang, Bin Yu, and Martin Vetterli IEEE TRANSACTIONS
Jun Li 1, Zhongdong Yang 1, W. Paul Menzel 2, and H.-L. Huang 1 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison 2 NOAA/NESDIS/ORA.
EE565 Advanced Image Processing Copyright Xin Li Application of Wavelets (I): Denoising Problem formulation Frequency-domain solution: linear Wiener.
1.1 What is Noise? any ‘unwanted” part of the analytical signal always some noise in a signal 1.2 Signal-to-noise ratio (S/N) for a set of data (replicate.
WAVELET NOISE REMOVAL FROM BASEBAND DIGITAL SIGNALS IN BANDLIMITED CHANNELS Dr. Robert Barsanti SSST March 2010, University of Texas At Tyler.
CARMA Models for Stochastic Variability (or how to read a PSD) Jim Barrett University of Birmingham.
Feature Matching and Signal Recognition using Wavelet Analysis Dr. Robert Barsanti, Edwin Spencer, James Cares, Lucas Parobek.
Complex demodulation Windowed Fourier Transform burst data Appropriate for burst data continuous data Questionable for continuous data (may miss important.
PERFORMANCE OF A WAVELET-BASED RECEIVER FOR BPSK AND QPSK SIGNALS IN ADDITIVE WHITE GAUSSIAN NOISE CHANNELS Dr. Robert Barsanti, Timothy Smith, Robert.
Properties of the power spectral density (1/4)
Speech Enhancement Summer 2009
CARMA Models for Stochastic Variability (or how to read a PSD)
3.4.3 Notch and comb filters To remove periodic artifacts
DCT – Wavelet – Filter Bank
Image Denoising in the Wavelet Domain Using Wiener Filtering
The Use of Wavelet Filters to De-noise µPET Data
Outline Linear Shift-invariant system Linear filters
Wavelet-Based Denoising Using Hidden Markov Models
Windowed Fourier Transform
Comments on Rebecca Willett’s paper
copyright Robert J. Marks II
Chapter 15: Wavelets (i) Fourier spectrum provides all the frequencies
Additive Manufacturing: Denoising and Particle Tracking
Presentation transcript:

Active Learning in an Information-rich Age Early 20 th century, how do you study physics? Early 21 st century, how do you study wavelets?

Use Tools Like Caveman Did Publish or Perish (a good tool for scientific research)

Small-World Phenomenon Donoho Johnstone Coifman Vertteli DeVore Daubechies Sweldens Mallat Mayer Morlet, Grossmann Vaidyanathan Schröder

Signal Denoising Study by Toy Examples taken from Donoho’s paper “Translation-invariant Denoising”

Four Classes of Signals

Noisy Observations Y=X+W W~N(0,1)

An Easy Attack Use a Low-Pass filter such as moving averaging Why low-pass? What if noise is periodic? How far are we from the optimal performance?

A Different Class of Signal

A Different Class of Noise ECG signal

Signal in Nature Self-similar property (Global) –1/f noise is a signal or process with a frequency spectrum such that the power spectral density is proportional to the reciprocal of the frequency Transient/nonstationary property (Local) –Changes in space and time –Motivate the study of wavelet transforms

Noise in Nature Always Gaussian? Stationary or transient? Random or structural (perceptually meaningful)?