PPT-Time-Frequency Analysis and Wavelet

Transform Oral Presentation Advisor 丁建均 and All Class Members Student 李境嚴 ID D00945001 Whats Today 3 1 Finding Instantaneous Frequency 2 Signal Decomposition. It cascades wavelet trans form convolutions with nonlinear modulus and averaging op erators The 64257rst network layer outputs SIFTtype descriptors whereas t he next layers provide complementary invariant information which improv es classi64257ca ti Student: . r03521101 Chun-Hsiang . Wang. Lecturer: . Jian-Jiun. . Ding. Date: 2014/11/27. 1. O. utline. Introduction. Wavelet Transformation. . Wavelet Zoom. Wavelet Transform Modulus Maxima. Application-Stratigraphic profiling. Signal Analysis. 09 . Oct 2015. © A.R. Lowry . 2015. Last time. :. • . A . Periodogram. . is the squared modulus of the signal FFT. !. • . Blackman-. Tukey. estimates autocorrelation from signal, then. S. S. A. 1. D. 1. A. 2. D. 2. A. 3. D. 3. Bhushan D Patil. PhD Research Scholar . Department of Electrical Engineering. Indian Institute of Technology, Bombay. Powai, Mumbai. 400076. Outline of Talk. By. Dr. Rajeev . Srivastava. CSE, IIT(BHU). Dr.. Rajeev . Srivastava. 1. Its Understanding. Dr. Rajeev Srivastava. 2. 3. Wavelet Analysis and Synthesis . Dr. Rajeev Srivastava. Dr. Rajeev Srivastava. SRS Synthesis. 1. . Wavelets. 2. Damped Sinusoids. Wavelet Synthesis. Goal: . Synthesis acceleration time history that can be used for a shaker test or for a numerical simulation. 3. Shaker Shock. (Section 13.10.6-13.10.8). Michael Phipps. Vallary. S. . Bhopatkar. The most useful thing about wavelet transform is that it can turned into sparse expansion i.e. it can be truncated. Truncated Wavelet Approximation. Federica Caselli. . Department of Civil Engineering University . of Rome Tor . Vergata. Corso. . di. . Modellazione. e . Simulazione. . di. . Sistemi. . Fisiologici. Medical Imaging. X-Ray. CT. S. S. A. 1. D. 1. A. 2. D. 2. A. 3. D. 3. Bhushan D Patil. PhD Research Scholar . Department of Electrical Engineering. Indian Institute of Technology, Bombay. Powai, Mumbai. 400076. Outline of Talk. Showcasing work by N. Morizet, N.Godin, J.Tang, E.Maillet, M.Fregonese, and B.Normand on "Classification of Acoustic Emission Signals Using Wavelets. and Random Forests: Application to localized corrosion".. Lecture . 5. DCT & Wavelets. Tammy . Riklin. Raviv. Electrical and Computer Engineering. Ben-Gurion University of the Negev. Spatial Frequency Analysis. images of naturally occurring scenes or objects (trees, rocks, . ECE, UA. 12 Lead ECG Interpretation. Anatomy Revisited. RCA. right ventricle. inferior wall of LV. posterior wall of LV (75%). SA Node (60%). AV Node (>80%). LCA. septal wall of LV. anterior wall of LV. - . An Image Coding Algorithm. Shufang Wu . http://www.sfu.ca/~vswu. vswu@cs.sfu.ca. Friday, June 14, 2002. Agenda. Overview. Discrete Wavelet Transform. Zerotree Coding of Wavelet Coefficients. Successive-Approximation Quantization (SAQ). Presenter : Ke-Jie Liao. NTU,GICE,DISP Lab,MD531. An Introduction to Discrete Wavelet Transforms. 1. Introduction. Continuous Wavelet Transforms. Multiresolution Analysis Backgrounds. Image Pyramids.