1. 1 Prof. Nizamettin AYDIN naydin@yildiz.edu.tr http://www.yildiz.edu.tr/~naydin Digital Signal Processing

2. 2 FIR Filtering Digital Signal Processing

3. 3 LECTURE OBJECTIVES INTRODUCE FILTERING IDEA –Weighted Average –Running Average FINITE IMPULSE RESPONSE FILTERS –FIR –FIR Filters –Show how to compute the output y[n] from the input signal, x[n]

4. 4 DIGITAL FILTERING CONCENTRATE on the COMPUTER – PROCESSING ALGORITHMS –SOFTWARE (MATLAB) –HARDWARE: DSP chips, VLSI DSP: DIGITAL SIGNAL PROCESSING COMPUTER D-to-AA-to-D x(t)y(t)y[n]x[n]

5. 5 DISCRETE-TIME SYSTEM OPERATE on x[n] to get y[n] WANT a GENERAL CLASS of SYSTEMS –ANALYZE the SYSTEM TOOLS: TIME-DOMAIN & FREQUENCY-DOMAIN –SYNTHESIZE the SYSTEM COMPUTER y[n]x[n]

6. 6 D-T SYSTEM EXAMPLES EXAMPLES: –POINTWISE OPERATORS SQUARING: y[n] = (x[n]) 2 –RUNNING AVERAGE RULE: “the output at time n is the average of three consecutive input values” SYSTEM y[n]x[n]

7. 7 DISCRETE-TIME SIGNAL x[n] is a LIST of NUMBERS –INDEXED by “n” STEM PLOT

8. 8 3-PT AVERAGE SYSTEM ADD 3 CONSECUTIVE NUMBERS –Do this for each “n” Make a TABLE n=0 n=1

9. 9 INPUT SIGNAL OUTPUT SIGNAL

10. 10 PAST, PRESENT, FUTURE “n” is TIME

11. 11 ANOTHER 3-pt AVERAGER Uses “PAST” VALUES of x[n] –IMPORTANT IF “n” represents REAL TIME WHEN x[n] & y[n] ARE STREAMS

12. 12 GENERAL FIR FILTER FILTER COEFFICIENTS {b k } –DEFINE THE FILTER –For example,

13. 13 GENERAL FIR FILTER FILTER COEFFICIENTS {b k } FILTER ORDER is M FILTER LENGTH is L = M+1 –NUMBER of FILTER COEFFS is L

14. 14 GENERAL FIR FILTER SLIDE a WINDOW across x[n] x[n]x[n]x[n-M]

15. 15 FILTERED STOCK SIGNAL OUTPUT INPUT 50-pt Averager

16. 16 SPECIAL INPUT SIGNALS x[n] = SINUSOID x[n] has only one NON-ZERO VALUE 1 n UNIT-IMPULSE FREQUENCY RESPONSE (LATER)

17. 17 UNIT IMPULSE SIGNAL  [n]  [n] is NON-ZERO When its argument is equal to ZERO

18. 18 MATH FORMULA for x[n] Use SHIFTED IMPULSES to write x[n]

19. 19 SUM of SHIFTED IMPULSES This formula ALWAYS works

20. 20 4-pt AVERAGER CAUSAL SYSTEM: USE PAST VALUES INPUT = UNIT IMPULSE SIGNAL =  [n] OUTPUT is called “IMPULSE RESPONSE”

21. 21 4-pt Avg Impulse Response  [n] “READS OUT” the FILTER COEFFICIENTS n=0 “h” in h[n] denotes Impulse Response 1 n n=0 n=1 n=4 n=5 n=–1 NON-ZERO When window overlaps  [n]

22. 22 FIR IMPULSE RESPONSE Convolution = Filter Definition –Filter Coeffs = Impulse Response CONVOLUTION

23. 23 FILTERING EXAMPLE 7-point AVERAGER –Removes cosine By making its amplitude (A) smaller 3-point AVERAGER –Changes A slightly

24. 24 3-pt AVG EXAMPLE USE PAST VALUES

25. 25 7-pt FIR EXAMPLE (AVG) CAUSAL: Use Previous LONGER OUTPUT

26. 26

27. 27 Linearity & Time-Invariance Convolution Digital Signal Processing

28. 28 LECTURE OBJECTIVES –BLOCK DIAGRAM REPRESENTATION Components for Hardware Connect Simple Filters Together to Build More Complicated Systems LTI SYSTEMS GENERAL PROPERTIES of FILTERS –L –LINEARITY –TI –TIME-INVARIANCE CONVOLUTION –==> CONVOLUTION

29. 29 IMPULSE RESPONSE, –FIR case: same as CONVOLUTION –GENERAL: –GENERAL CLASS of SYSTEMS –LINEAR and TIME-INVARIANT ALL LTI systems have h[n] & use convolution OVERVIEW

30. 30 CONCENTRATE on the FILTER (DSP) DISCRETE-TIME SIGNALS –FUNCTIONS of n, the “time index” –INPUT x[n] –OUTPUT y[n] DIGITAL FILTERING FILTER D-to-AA-to-D x(t)y(t)y[n]x[n]

31. 31 BUILDING BLOCKS BUILD UP COMPLICATED FILTERS –FROM SIMPLE MODULES –Ex: FILTER MODULE MIGHT BE 3-pt FIR FILTER y[n]x[n] FILTER + + INPUT OUTPUT

32. 32 GENERAL FIR FILTER FILTER COEFFICIENTS {b k } –DEFINE THE FILTER –For example,

33. 33 MATLAB for FIR FILTER yy = conv(bb,xx) –VECTOR bb contains Filter Coefficients –DSP-First: yy = firfilt(bb,xx) FILTER COEFFICIENTS {b k } conv2() for images

34. 34 SPECIAL INPUT SIGNALS x[n] = SINUSOID x[n] has only one NON-ZERO VALUE 1 n UNIT-IMPULSE FREQUENCY RESPONSE

35. 35 FIR IMPULSE RESPONSE Convolution = Filter Definition –Filter Coeffs = Impulse Response

36. 36 MATH FORMULA for h[n] Use SHIFTED IMPULSES to write h[n] 1 n –1 2 0 4

37. 37 LTI: Convolution Sum Output = Convolution of x[n] & h[n]Output = Convolution of x[n] & h[n] –NOTATION: –Here is the FIR case: FINITE LIMITS Same as b k

38. 38 CONVOLUTION Example

39. 39 GENERAL FIR FILTER SLIDE a Length-L WINDOW over x[n] x[n]x[n]x[n-M]

40. 40 POP QUIZ FIR Filter is “FIRST DIFFERENCE” y[n] = x[n] - x[n-1] INPUT is “UNIT STEP” Find y[n]

41. 41 HARDWARE STRUCTURES INTERNAL STRUCTURE of “FILTER” –WHAT COMPONENTS ARE NEEDED? –HOW DO WE “HOOK” THEM TOGETHER? SIGNAL FLOW GRAPH NOTATION FILTER y[n]x[n]

42. 42 HARDWARE ATOMS Add, Multiply & Store

43. 43 FIR STRUCTURE Direct Form SIGNAL FLOW GRAPH

44. 44 SYSTEM PROPERTIES MATHEMATICAL DESCRIPTIONMATHEMATICAL DESCRIPTION TIME-INVARIANCETIME-INVARIANCE LINEARITYLINEARITY CAUSALITY –“No output prior to input” SYSTEM y[n]x[n]

45. 45 TIME-INVARIANCE IDEA: –“Time-Shifting the input will cause the same time- shift in the output” EQUIVALENTLY, –We can prove that The time origin (n=0) is picked arbitrary

46. 46 TESTING Time-Invariance

47. 47 LINEAR SYSTEM LINEARITY = Two Properties SCALING –“Doubling x[n] will double y[n]” SUPERPOSITION: –“Adding two inputs gives an output that is the sum of the individual outputs”

48. 48 TESTING LINEARITY

49. 49 LTI SYSTEMS LTI: L inear & T ime- I nvariant COMPLETELY CHARACTERIZED by: –IMPULSE RESPONSE h[n] –CONVOLUTION: y[n] = x[n]*h[n] The “rule”defining the system can ALWAYS be re- written as convolution FIR Example: h[n] is same as b k

50. 50 POP QUIZ FIR Filter is “FIRST DIFFERENCE” –y[n] = x[n] - x[n -1] Write output as a convolution –Need impulse response –Then, another way to compute the output:

51. 51 CASCADE SYSTEMS Does the order of S 1 & S 2 matter? –NO, LTI SYSTEMS can be rearranged !!! –WHAT ARE THE FILTER COEFFS? {b k } S1S1 S2S2

52. 52 CASCADE EQUIVALENT –Find “overall” h[n] for a cascade ? S1S1 S2S2 S1S1 S2S2

53. 53 CONVOLUTION Example

54. 54

55. 55 Frequency Response of FIR Filters Digital Signal Processing

56. 56 LECTURE OBJECTIVES SINUSOIDAL INPUT SIGNAL –DETERMINE the FIR FILTER OUTPUT FREQUENCY RESPONSE of FIR –PLOTTING vs. Frequency –MAGNITUDE vs. Freq –PHASE vs. Freq MAG PHASE

57. 57 DOMAINS: Time & Frequency Time-Domain: “n” = time –x[n] discrete-time signal –x(t) continuous-time signal Frequency Domain (sum of sinusoids) –Spectrum vs. f (Hz) –ANALOG vs. DIGITAL –Spectrum vs. omega-hat Move back and forth QUICKLY

58. 58 LTI SYSTEMS LTI: L inear & T ime- I nvariant COMPLETELY CHARACTERIZED by: –IMPULSE RESPONSE h[n] –CONVOLUTION: y[n] = x[n]*h[n] The “rule”defining the system can ALWAYS be re- written as convolution FIR Example: h[n] is same as b k

59. 59 DIGITAL “FILTERING” SPECTRUMCONCENTRATE on the SPECTRUM SINUSOIDAL INPUT –INPUT x[n] = SUM of SINUSOIDS –Then, OUTPUT y[n] = SUM of SINUSOIDS FILTERD-to-AA-to-D x(t)y(t)y[n]y[n]x[n]x[n]

60. 60 SINUSOIDAL RESPONSE INPUT: x[n] = SINUSOID OUTPUT: y[n] will also be a SINUSOID –Different Amplitude and Phase –SAME Frequency AMPLITUDE & PHASE CHANGE FREQUENCY RESPONSE –Called the FREQUENCY RESPONSE

61. 61 COMPLEX EXPONENTIAL FIR DIFFERENCE EQUATION x[n] is the input signal—a complex exponential

62. 62 COMPLEX EXP OUTPUT Use the FIR “Difference Equation”

63. 63 FREQUENCY RESPONSE Complex-valued formula –Has MAGNITUDE vs. frequency –And PHASE vs. frequency Notation: FREQUENCY RESPONSE At each frequency, we can DEFINE

64. 64 EXAMPLE 6.1 EXPLOIT SYMMETRY

65. 65 PLOT of FREQ RESPONSE RESPONSE at  /3

66. 66 EXAMPLE 6.2 y[n]y[n]x[n]x[n]

67. 67 EXAMPLE 6.2 (answer)

68. 68 EXAMPLE: COSINE INPUT y[n]x[n]

69. 69 EX: COSINE INPUT Use Linearity

70. 70 EX: COSINE INPUT (ans-2)

71. 71 MATLAB: FREQUENCY RESPONSE HH = freqz(bb,1,ww) –VECTOR bb contains Filter Coefficients –SP-First: HH = freekz(bb,1,ww) FILTER COEFFICIENTS {b k }

72. 72 LTI SYSTEMS LTI: Linear & Time-Invariant COMPLETELY CHARACTERIZED by: –FREQUENCY RESPONSE, or –IMPULSE RESPONSE h[n] Sinusoid IN -----> Sinusoid OUT –At the SAME Frequency

73. 73 Time & Frequency Relation Get Frequency Response from h[n] –Here is the FIR case: IMPULSE RESPONSE

74. 74 BLOCK DIAGRAMS Equivalent Representations y[n]y[n]x[n]x[n] y[n]y[n]x[n]x[n]

75. 75 UNIT-DELAY SYSTEM y[n]y[n]x[n]x[n] y[n]y[n]x[n]x[n]

76. 76 FIRST DIFFERENCE SYSTEM y[n]y[n] x[n]x[n] y[n]y[n]x[n]x[n]

77. 77 CASCADE SYSTEMS Does the order of S 1 & S 2 matter? –NO, LTI SYSTEMS can be rearranged !!! –WHAT ARE THE FILTER COEFFS? {b k } –WHAT is the overall FREQUENCY RESPONSE ? S1S1 S2S2

78. 78 CASCADE EQUIVALENT MULTIPLY the Frequency Responses y[n]y[n]x[n]x[n] x[n]x[n]y[n]y[n] EQUIVALENT SYSTEM

79. 79

80. 80 Z Transforms: Introduction Digital Signal Processing

81. 81 LECTURE OBJECTIVES INTRODUCE the Z-TRANSFORM –Give Mathematical Definition –Show how the H(z) POLYNOMIAL simplifies analysis CONVOLUTION is SIMPLIFIED ! Z-Transform can be applied to –FIR Filter: h[n] --> H(z) –Signals: x[n] --> X(z)

82. 82 THREE DOMAINS Z-TRANSFORM-DOMAIN POLYNOMIALS: H(z) FREQ-DOMAIN TIME-DOMAIN

83. 83 Three main reasons for Z-Transform Offers compact and convenient notation for describing digital signals and systems Widely used by DSP designers, and in the DSP literature Pole-zero description of a processor is a great help in visualizing its stability and frequency response characteristic

84. 84 TRANSFORM CONCEPT Move to a new domain where –OPERATIONS are EASIER & FAMILIAR –Use POLYNOMIALS TRANSFORM both ways –x[n] ---> X(z) (into the z domain) –X(z) ---> x[n] (back to the time domain)

85. 85 “TRANSFORM” EXAMPLE Equivalent Representations y[n]y[n]x[n]x[n] y[n]y[n]x[n]x[n]

86. 86 Z-TRANSFORM IDEA POLYNOMIAL REPRESENTATION y[n]y[n]x[n]x[n] y[n]y[n]x[n]x[n] 2.12.13

87. 87 Z-Transform DEFINITION POLYNOMIAL Representation of LTI SYSTEM: EXAMPLE: APPLIES to Any SIGNAL POLYNOMIAL in z -1

88. 88 Z-Transform EXAMPLE ANY SIGNAL has a z-Transform:

89. 89 EXPONENT GIVES TIME LOCATION

90. 90 Example Find the Z-Transform of the exponentially decaying signal shown in the following figure, expressing it as compact as possible. x[n] 1 0.8 0.64 0.512 … n

91. The geometric series formulageometric series 91

92. 92 The Z-Transform of the signal:

93. 93 Example Find and sketch, the signal corresponding to the Z-Transform:

94. 94 Recasting X(z) as a power series in z -1, we obtain: Succesive values of x[n], starting at n=0, are therefore: 0, 1, -1.2, 1.44, -1.728, ···

95. 95 x[n] is shown in the following figure: x[n]x[n] … n 1 1.44 -1.728 -1.2

96. 96 Z-Transform of FIR Filter SYSTEM FUNCTIONCALLED the SYSTEM FUNCTION h[n] is same as {b k } FIR DIFFERENCE EQUATION CONVOLUTION SYSTEM FUNCTION

97. 97 Z-Transform of FIR Filter Get H(z) DIRECTLY from the {b k } Example 7.3 in the book:

98. 98 Ex. DELAY SYSTEM UNIT DELAY: find h[n] and H(z) y[n]y[n]x[n]x[n] y[n] = x[n-1]x[n]x[n]

99. 99 DELAY EXAMPLE UNIT DELAY: find y[n] via polynomials –x[n] = {3,1,4,1,5,9,0,0,0,...}

100. 100 DELAY PROPERTY

101. 101 GENERAL I/O PROBLEM Input is x[n], find y[n] (for FIR, h[n]) How to combine X(z) and H(z) ?

102. 102 FIR Filter = CONVOLUTION CONVOLUTION

103. 103 CONVOLUTION PROPERTY PROOF: MULTIPLY Z-TRANSFORMS

104. 104 CONVOLUTION EXAMPLE MULTIPLY the z-TRANSFORMS: MULTIPLY H(z)X(z)

105. 105 CONVOLUTION EXAMPLE Finite-Length input x[n] FIR Filter (L=4) MULTIPLY Z-TRANSFORMS y[n] = ?

106. 106 CASCADE SYSTEMS Does the order of S 1 & S 2 matter? –NO, LTI SYSTEMS can be rearranged !!! –Remember: h 1 [n] * h 2 [n] – How to combine H 1 (z) and H 2 (z) ? S1S1 S2S2

107. 107 CASCADE EQUIVALENT Multiply the System Functions x[n]x[n]y[n]y[n] y[n]y[n]x[n]x[n] EQUIVALENT SYSTEM

108. 108 CASCADE EXAMPLE y[n]y[n]x[n]x[n] x[n]x[n]y[n]y[n]w[n]w[n]

109. 109

110. IIR Filters: Feedback and H(z) Digital Signal Processing 110 21Kasim2k14

111. LECTURE OBJECTIVES –Show how to compute the output y[n] FIRST-ORDER CASE (N=1) Z-transform: Impulse Response h[n]  H(z)  INFINITE IMPULSE RESPONSE FILTERS IIR  Define IIR DIGITAL Filters FEEDBACK  Have FEEDBACK: use PREVIOUS OUTPUTS 111

112. THREE DOMAINS Z-TRANSFORM-DOMAIN POLYNOMIALS : H(z) FREQ-DOMAIN TIME-DOMAIN 112

113. Quick Review: Delay by n d IMPULSE RESPONSE SYSTEM FUNCTION FREQUENCY RESPONSE 113

114. Quick Review: L-pt Averager IMPULSE RESPONSE SYSTEM FUNCTION 114

115. LOGICAL THREAD FIND the IMPULSE RESPONSE, h[n] –INFINITELY LONG –IIR –IIR Filters EXPLOIT THREE DOMAINS: –Show Relationship for IIR: 115

116. ONE FEEDBACK TERM CAUSALITY –NOT USING FUTURE OUTPUTS or INPUTS FIR PART of the FILTER FEED-FORWARD PREVIOUS FEEDBACK  ADD PREVIOUS OUTPUTS 116

117. FILTER COEFFICIENTS ADD PREVIOUS OUTPUTS MATLAB –yy = filter([3,-2],[1,-0.8],xx) SIGN CHANGE FEEDBACK COEFFICIENT 117

118. COMPUTE OUTPUT 118

119. COMPUTE y[n] FEEDBACK DIFFERENCE EQUATION:  NEED y[-1] to get started 119

120. AT REST CONDITION y[n] = 0, for n<0 BECAUSE x[n] = 0, for n<0 120

121. COMPUTE y[0] THIS STARTS THE RECURSION: SAME with MORE FEEDBACK TERMS 121

122. COMPUTE MORE y[n] CONTINUE THE RECURSION: 122

123. PLOT y[n] 123

124. IMPULSE RESPONSE 124

125. IMPULSE RESPONSE DIFFERENCE EQUATION: Find h[n] CONVOLUTION in TIME-DOMAIN IMPULSE RESPONSE LTI SYSTEM 125

126. PLOT IMPULSE RESPONSE 126

127. Infinite-Length Signal: h[n] POLYNOMIAL Representation SIMPLIFY the SUMMATION APPLIES to Any SIGNAL 127

128. Derivation of H(z) Recall Sum of Geometric Sequence: Yields a COMPACT FORM 128

129. H(z) = z-Transform{ h[n] } FIRST-ORDER IIR FILTER: 129

130. H(z) = z-Transform{ h[n] } ANOTHER FIRST-ORDER IIR FILTER: 130

131. CONVOLUTION PROPERTY MULTIPLICATION of z-TRANSFORMS CONVOLUTION in TIME-DOMAIN IMPULSE RESPONSE 131

132. STEP RESPONSE: x[n]=u[n] 132

133. DERIVE STEP RESPONSE 133

134. PLOT STEP RESPONSE 134

135. MATH FORMULA for h[n] Use SHIFTED IMPULSES to write h[n] 0.2 n 0 4 135

136. LTI: Convolution Output = Convolution of x[n] & h[n] –NOTATION: y[n] = x[n]*h[n] –Here is the FIR case: FINITE LIMITS Same as b k 136

137. L-pt RUNNING AVG H(z) ZEROS on UNIT CIRCLE (z-1) in denominator cancels k=0 term 137

138. FILTER DESIGN: CHANGE L Passband Narrower for L bigger 138

139. H(z) = z-Transform{ h[n] } FIRST-ORDER CASE: 139

140. POP QUIZ Given: Plot the Magnitude and Phase Find the output, y[n] –When 140

141. POP QUIZ: MAG & PHASE Given: Derive Magnitude and Phase 141

142. Ans: FREQ RESPONSE 142

143. POP QUIZ : Answer #2 Find y[n] when 143

144. 11-pt RUNNING SUM H(z) NO zero at z=1 144

145. 3 DOMAINS MOVIE: FIR ZEROS MISSING h[n] H(  ) H(z) 145

146. L-pt RUNNING AVG: Step Response STEP RESPONSE 146

147. 147

148. IIR Filters: H(z) and Frequency Response Digital Signal Processing 148

149. LECTURE OBJECTIVES SYSTEM FUNCTION: H(z) H(z) has POLES and ZEROS FREQUENCY RESPONSE of IIR –Get H(z) first THREE-DOMAIN APPROACH

150. THREE DOMAINS Z-TRANSFORM-DOMAIN POLYNOMIALS: H(z) FREQ-DOMAIN TIME-DOMAIN Use H(z) to get Freq. Response

151. H(z) = z-Transform{ h[n] } FIRST-ORDER IIR FILTER:

152. Typical IMPULSE Response

153. First-Order Transform Pair GEOMETRIC SEQUENCE:

154. DELAY PROPERTY of X(z) DELAY in TIME Multiply X(z) by z -1

155. Z-Transform of IIR Filter DERIVE the SYSTEM FUNCTION H(z) DELAY –Use DELAY PROPERTY EASIER with DELAY PROPERTY

156. SYSTEM FUNCTION of IIR NOTE the FILTER COEFFICIENTS

157. SYSTEM FUNCTION DIFFERENCE EQUATION: READREAD the FILTER COEFFS: H(z)

158. CONVOLUTION PROPERTY MULTIPLICATIONMULTIPLICATION of z-TRANSFORMS CONVOLUTIONCONVOLUTION in TIME-DOMAIN IMPULSE RESPONSE

159. POLES & ZEROS ROOTS of Numerator & Denominator POLE: H(z)  inf ZERO: H(z)=0

160. EXAMPLE: Poles & Zeros INFINITEVALUE of H(z) at POLES is INFINITE POLE at z=0.8 ZERO at z= -1

161. POLE-ZERO PLOT ZERO at z = -1 POLE at z = 0.8

162. FREQUENCY RESPONSE SYSTEM FUNCTION: H(z) H(z) has DENOMINATOR FREQUENCY RESPONSE of IIR –We have H(z) THREE-DOMAIN APPROACH

163. FREQUENCY RESPONSE EVALUATE on the UNIT CIRCLE

164. FREQ. RESPONSE FORMULA

165. Frequency Response Plot

166. UNIT CIRCLE MAPPING BETWEEN

167. SINUSOIDAL RESPONSE x[n] = SINUSOID => y[n] is SINUSOID Get MAGNITUDE & PHASE from H(z)

168. POP QUIZ Given: Find the Impulse Response, h[n] Find the output, y[n] –When

169. Evaluate FREQ. RESPONSE zero at 

170. POP QUIZ: Eval Freq. Resp. Given: Find output, y[n], when –Evaluate at

171. CASCADE EQUIVALENT Multiply the System Functions y[n]x[n] y[n] EQUIVALENT SYSTEM

172. SUPERPOSITION

173. FILTER BANK in LAB (BPFs) CONSTANT BANDWIDTH if L is same 1/L controls BW VARIABLE BANDWIDTH ? MP3 match hearing Change L

174. MP-3 AUDIO CODING

175. H(z) from Linearity FIRST-ORDER IIR with 2 FIR terms:

176. INTERPRET ROOTS INFINITEVALUE of H(z) at POLES is INFINITE POLE ZERO

177. FREQ. RESPONSE FORMULA

178. THREE DOMAINS Use H(z) to get Freq. Response

179. FREQ. RESPONSE FORMULA

180. FREQ. RESPONSE from H(z)

181. THREE DOMAINS TIME-DOMAIN FREQ-DOMAIN Z-TRANSFORM-DOMAIN POLYNOMIALS: H(z) Use H(z) to get Freq. Response

182. POP QUIZ: Invert Z Given: Find the Impulse Response, h[n] –Use the DELAY PROPERTY

183. 183

184. 3-Domains for IIR Digital Signal Processing 184

185. LECTURE OBJECTIVES SECOND-ORDER IIR FILTERS –TWO FEEDBACK TERMS H(z) can have COMPLEX POLES & ZEROS THREE-DOMAIN APPROACH –BPFs have POLES NEAR THE UNIT CIRCLE

186. THREE DOMAINS Z-TRANSFORM-DOMAIN: poles & zeros POLYNOMIALS: H(z) Use H(z) to get Freq. Response FREQ-DOMAIN TIME-DOMAIN

187. Z-TRANSFORM TABLES

188. SECOND-ORDER FILTERS Two FEEDBACK TERMS

189. MORE POLES Denominator is QUADRATIC –2 Poles: REAL –or COMPLEX CONJUGATES

190. TWO COMPLEX POLES Find Impulse Response ? –Can OSCILLATE vs. n –“RESONANCE” FREQUENCY RESPONSEFind FREQUENCY RESPONSE –Depends on Pole Location –Close to the Unit Circle? BANDPASS FILTERMake BANDPASS FILTER 30.04.13

191. 2nd ORDER EXAMPLE

192. h[n]: Decays & Oscillates “PERIOD”=6

193. 2nd ORDER Z-transform PAIR GENERAL ENTRY for z-Transform TABLE

194. 2nd ORDER EX: n-Domain aa = [ 1, -0.9, 0.81 ]; bb = [ 1, -0.45 ]; nn = -2:19; hh = filter( bb, aa, (nn==0) ); HH = freqz( bb, aa, [-pi,pi/100:pi] );

195. Complex POLE-ZERO PLOT

196. UNIT CIRCLE MAPPING BETWEEN

197. FREQUENCY RESPONSE from POLE-ZERO PLOT

198. h[n]: Decays & Oscillates “PERIOD”=6

199. Complex POLE-ZERO PLOT

200. h[n]: Decays & Oscillates “PERIOD”=12

201. Complex POLE-ZERO PLOT

202. THREE INPUTS Given: Find the output, y[n] –When

203. SINUSOID ANSWER Given: The input: Then y[n]

204. SINUSOID Starting at n=0 Given: The input: Then y[n]

205. SINUSOID Starting at n=0

206. TransientSteady-State

207. Step Response Partial Fraction Expansion

208. Step Response

209. Stability Nec. & suff. condition: Pole must be Inside unit circle

210. SINUSOID starting at n=0 We’ll look at an example in MATLAB –cos(0.2  n) –Pole at –0.8, so a n is (–0.8) n There are two components: –TRANSIENT Start-up region just after n=0; (–0.8) n –STEADY-STATE Eventually, y[n] looks sinusoidal. Magnitude & Phase from Frequency Response

211. Cosine input

212. STABILITY When Does the TRANSIENT DIE OUT ?

213. STABILITY CONDITION ALL POLES INSIDE the UNIT CIRCLE UNSTABLE EXAMPLE: POLE @ z=1.1

214. BONUS QUESTION Given: The input is Then find y[n]

215. CALCULATE the RESPONSE Use the Z-Transform Method And PARTIAL FRACTIONS

216. GENERAL INVERSE Z (pole) n

217. SPLIT Y(z) to INVERT Need SUM of Terms:

218. INVERT Y(z) to y[n] Use the Z-Transform Table

219. TWO PARTS of y[n] TRANSIENTTRANSIENT –Acts Like (pole) n –Dies out ? IF |a 1 |<1 STEADY-STATESTEADY-STATE –Depends on the input –e.g., Sinusoidal

220. STEADY STATE HAPPENS When Transient dies out Limit as “n” approaches infinity Use Frequency Response to get Magnitude & Phase for sinusoid

221. NUMERICAL EXAMPLE

222. REALISTIC FIR BANDPASS FIR L = 24 M=23 23 zeros

223. FIR BPF: 23 ZEROS

224. Complex POLE-ZERO PLOT

225. POLES & ZEROS of IIR 3 POLES

226. IIR Elliptic LPF (N=3) 3 POLES