Advance in Digital Signal Processing EE6802 °Name:Dr. Peter Tsang Room: G6505 ·Ext:7763 Email: eewmtsan@cityu. edu. hk
• Name: Dr. Peter Tsang • Room: G6505 • Ext: 7763 • Email: eewmtsan@cityu.edu.hk
Advance in Signal Processing EE6802 Introduction to Digital Signal Processing Digital Filter Design Multi-rate Signal Processing Wavelet Applications http://www.ee.cityu.edu.hk/-csl/adsp/adsp.html
• Introduction to Digital Signal Processing • Digital Filter Design • Multi-rate Signal Processing • Wavelet • Applications http://www.ee.cityu.edu.hk/~csl/adsp/adsp.html
Advance in Signal Processing EE6802 A.N. Akansu et. al., "Multiresolution Signal Decomposition", Academic Press. P.P. Vaidyanathan, "Multirate Systems and Filter Banks", Prentice Hall. Students are strongly recommended to look for reference books in the library
• A.N. Akansu et. al., “Multiresolution Signal Decomposition”, Academic Press. • P.P. Vaidyanathan, “Multirate Systems and Filter Banks”, Prentice Hall. Students are strongly recommended to look for reference books in the library
Advance in Signal Processing EE6802 Special Arrangement Class cancellation: Week 5(6th Oct 2007 Course Assessment: 100%o Laboratory Sessions 1 st weeks 8 2nd weeks 10 to 13 Subject to change if necessary
Course Assessment: 100% Laboratory Sessions : 1 st weeks 8 2 nd weeks 10 to 13 Subject to change if necessary Special Arrangement Class cancellation: Week 5 (6th Oct 2007)
Advance in Signal Processing EE6802 Expected outcome from students Familiarize with FIR and IIR Digital Filter Design 2. Establish the concept of Multi-resolution Signal Decomposition 3. Understanding the basic mathematical framework of Wavelet Decomposition 4. Capable of designing and building Digital Filters, Multi-resolution and Wavelet filter banks Applying Wavelet Decomposition in image processing
Expected outcome from students: 1. Familiarize with FIR and IIR Digital Filter Design. 2. Establish the concept of Multi-resolution Signal Decomposition. 3. Understanding the basic mathematical framework of Wavclet Decomposition. 4. Capable of designing and building Digital Filters, Multi-resolution and Wavelet filter banks. 5. Applying Wavelet Decomposition in image processing
Signal Processing y(t) System G{x(} Input Output signal signal Transfer function ( filter) Allow certain frequency band to pass, and reject others Figure 1
x(t) System y(t) = G{x(t)} Input signal Transfer function (filter) Allow certain frequency band to pass, and reject others Output signal Figure 1 Signal Processing
Signal Processing y(t) hr(0) G{x(} Input Output signal signal Non-recursive system hr(0) Feed forward response Figure 2
x(t) Input signal Non-recursive system Output signal hFF(t) hFF(t) Feed forward response y(t) = G{x(t)} Figure 2 Signal Processing
Signal Processing y() x() hr(0) G{() Input Output signal signal Non-recursive system y()=x(0)hF(0 (1) Igure 3
x(t) Input signal Non-recursive system Output signal hFF(t) y(t) = x(t) * hFF(t) (1) y(t) = G{x(t)} Figure 3 Signal Processing
Convolution=x(0)*h(=y(0=]moxt-rdr h(t) x(t) 2-10123456 -2-10123456 y(O)=AREAh(r)x(T) →r 5-4-3-2-10123456 y(1)=AREAh(t)(x(1-t 5-4-3-2-10123456
Convolution y(t) = x(t) * h (t) -2 -1 0 1 2 3 4 5 6 h( ) -2 -1 0 1 2 3 4 5 6 x( ) -2 -1 0 1 2 3 4 5 6 y(0)= AREA[h( ) x(- )] -5 -4 -3 = ( ) ( − ) − y(t) h x t d -2 -1 0 1 2 3 4 5 6 y(1)= AREA[h( ) x(1- )] -5 -4 -3
Convolution=x(0)*h(=y(0=]moxt-rdr h(t) x(t) 2-10123456 2-10123456 (2)=AREA()∩x2) →r -5-4-3-2-10123456 y(3)=AREAh()x(3-T) 5-4-3-2-10123456
Convolution y(t) = x(t) * h (t) -2 -1 0 1 2 3 4 5 6 h( ) -2 -1 0 1 2 3 4 5 6 x( ) -2 -1 0 1 2 3 4 5 6 y(2)= AREA[h( ) x(2- )] -5 -4 -3 = ( ) ( − ) − y(t) h x t d -2 -1 0 1 2 3 4 5 6 y(3)= AREA[h( ) x(3- )] -5 -4 -3
