| Preface |
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xv | |
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1 | (36) |
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Digital signal processing and its benefits |
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1 | (2) |
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3 | (2) |
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5 | (8) |
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5 | (2) |
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7 | (2) |
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9 | (2) |
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11 | (1) |
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11 | (2) |
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Digital signal processors |
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13 | (1) |
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Overview of real-world applications of DSP |
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13 | (2) |
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Audio applications of DSP |
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15 | (8) |
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15 | (1) |
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Speech synthesis and recognition |
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16 | (3) |
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The compact disc digital audio system |
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19 | (4) |
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Telecommunication applications of DSP |
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23 | (6) |
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Digital cellular mobile telephony |
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23 | (4) |
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Set-top box for digital television reception |
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27 | (1) |
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Adaptive telephone echo cancellation |
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28 | (1) |
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Biomedical applications of DSP |
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29 | (6) |
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30 | (3) |
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DSP-based closed loop controlled anaesthesia |
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33 | (2) |
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35 | (2) |
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35 | (1) |
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35 | (1) |
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36 | (1) |
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Analog I/O interface for real-time DSP systems |
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37 | (67) |
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Typical real-time DSP systems |
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38 | (1) |
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Analog-to-digital conversion process |
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39 | (1) |
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Sampling - lowpass and bandpass signals |
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40 | (25) |
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40 | (16) |
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Sampling bandpass signals |
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56 | (9) |
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Uniform and non-uniform quantization and encoding |
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65 | (6) |
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Uniform quantization and encoding (linear pulse code modulation (PCM)) |
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66 | (2) |
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Non-uniform quantization and encoding (nonlinear PCM) |
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68 | (3) |
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Oversampling in A/D conversion |
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71 | (13) |
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71 | (1) |
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Oversampling and anti-aliasing filtering |
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71 | (3) |
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Oversampling and ADC resolution |
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74 | (4) |
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An application of oversampling - single-bit (oversampling) ADC |
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78 | (6) |
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Digital-to-analog conversion process: signal recovery |
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84 | (1) |
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84 | (2) |
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86 | (1) |
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Oversampling in D/A conversion |
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86 | (4) |
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Oversampling D/A conversion in the CD player |
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87 | (3) |
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Constraints of real-time signal processing with analog input/output signals |
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90 | (1) |
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91 | (1) |
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92 | (12) |
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92 | (10) |
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102 | (1) |
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102 | (2) |
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104 | (68) |
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104 | (7) |
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106 | (3) |
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109 | (2) |
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111 | (7) |
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118 | (2) |
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Computational complexity of the DFT |
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120 | (1) |
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The decimation-in-time fast Fourier transform algorithm |
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121 | (11) |
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127 | (1) |
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128 | (4) |
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Computational advantages of the FFT |
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132 | (1) |
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Inverse fast Fourier transform |
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132 | (1) |
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Implementation of the FFT |
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133 | (2) |
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The decimation-in-frequency FFT |
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134 | (1) |
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Comparison of DIT and DIF algorithms |
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134 | (1) |
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Modifications for increased speed |
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134 | (1) |
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Other discrete transforms |
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135 | (16) |
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Discrete cosine transform |
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135 | (1) |
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136 | (3) |
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139 | (2) |
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141 | (3) |
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Multiresolution analysis by the wavelet method |
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144 | (3) |
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Signal representation by singularities: the wavelet transform method |
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147 | (4) |
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An application of the DCT: image compression |
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151 | (3) |
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The Discrete Cosine transform |
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152 | (1) |
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2D DCT coefficient quantization |
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153 | (1) |
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153 | (1) |
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154 | (18) |
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158 | (2) |
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160 | (1) |
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161 | (1) |
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C language program for direct DFT computation |
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161 | (6) |
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C program for radix-2 decimation-in-time FFT |
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167 | (3) |
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170 | (1) |
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References for Appendices |
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171 | (1) |
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The z-transform and its applications in signal processing |
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172 | (70) |
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Discrete-time signals and systems |
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173 | (1) |
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174 | (5) |
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179 | (15) |
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179 | (3) |
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Partial fraction expansion method |
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182 | (6) |
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188 | (6) |
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Comparison of the inverse z-transform methods |
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194 | (1) |
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Properties of the z-transform |
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194 | (3) |
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Some applications of the z-transform in signal processing |
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197 | (21) |
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Pole-zero description of discrete-time systems |
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197 | (3) |
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Frequency response estimation |
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200 | (1) |
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Geometric evaluation of frequency response |
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201 | (3) |
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Direct computer evaluation of frequency response |
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204 | (1) |
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Frequency response estimation via FFT |
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205 | (1) |
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Frequency units used in discrete-time systems |
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205 | (3) |
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208 | (1) |
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209 | (2) |
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Impulse response estimation |
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211 | (2) |
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Applications in digital filter design |
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213 | (1) |
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Realization structures for digital filters |
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213 | (5) |
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218 | (24) |
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218 | (5) |
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223 | (1) |
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223 | (1) |
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223 | (1) |
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Recursive algorithm for the inverse z-transform |
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223 | (2) |
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C program for evaluating the inverse z-transform and for cascade-to-parallel structure conversion |
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225 | (6) |
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C program for estimating frequency response |
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231 | (2) |
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z-transform operations with MATLAB |
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233 | (8) |
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References for Appendices |
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241 | (1) |
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Correlation and convolution |
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242 | (75) |
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242 | (1) |
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243 | (30) |
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Cross - and autocorrelation |
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249 | (8) |
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Applications of correlation |
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257 | (10) |
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267 | (6) |
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273 | (28) |
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Properties of convolution |
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282 | (1) |
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283 | (1) |
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283 | (2) |
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285 | (1) |
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286 | (2) |
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288 | (1) |
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Computational advantages of fast linear convolution |
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289 | (1) |
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Convolution and correlation by sectioning |
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290 | (2) |
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292 | (5) |
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297 | (3) |
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Computational advantages of fast convolution by sectioning |
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300 | (1) |
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The relationship between convolution and correlation |
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301 | (1) |
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Implementation of correlation and convolution |
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301 | (1) |
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302 | (8) |
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302 | (5) |
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307 | (3) |
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310 | (7) |
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311 | (4) |
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315 | (1) |
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316 | (1) |
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C language program for computing cross- and autocorrelation |
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316 | (1) |
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A framework for digital filter design |
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317 | (25) |
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Introduction to digital filters |
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318 | (1) |
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Types of digital filters: FIR and IIR filters |
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319 | (2) |
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Choosing between FIR and IIR filters |
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321 | (3) |
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324 | (10) |
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Specification of the filter requirements |
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324 | (3) |
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327 | (1) |
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Representation of a filter by a suitable structure (realization) |
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328 | (4) |
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Analysis of finite wordlength effects |
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332 | (1) |
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Implementation of a filter |
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333 | (1) |
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334 | (5) |
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339 | (3) |
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339 | (2) |
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341 | (1) |
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341 | (1) |
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Finite impulse response (FIR) filter design |
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342 | (112) |
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343 | (6) |
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Summary of key characteristic features of FIR filters |
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343 | (1) |
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Linear phase response and its implications |
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344 | (3) |
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Types of linear phase FIR filters |
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347 | (2) |
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349 | (1) |
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FIR filter specifications |
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350 | (1) |
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FIR coefficient calculation methods |
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351 | (1) |
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352 | (15) |
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Some common window functions |
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354 | (4) |
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Summary of the window method of calculating FIR filter coefficients |
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358 | (8) |
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Advantages and disadvantages of the window method |
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366 | (1) |
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367 | (13) |
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367 | (3) |
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Parameters required to use the optimal program |
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370 | (1) |
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Relationships for estimating filter length, N |
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371 | (1) |
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Summary of procedure for calculating filter coefficients by the optimal method |
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372 | (1) |
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373 | (7) |
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Frequency sampling method |
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380 | (18) |
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Nonrecursive frequency sampling filters |
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380 | (9) |
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Recursive frequency sampling filters |
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389 | (1) |
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Frequency sampling filters with simple coefficients |
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390 | (8) |
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Summary of the frequency sampling method |
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398 | (1) |
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Comparison of the window, optimum and frequency sampling methods |
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398 | (4) |
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Special FIR filter design topics |
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402 | (5) |
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402 | (2) |
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404 | (2) |
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Computationally efficient FIR filters |
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406 | (1) |
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Realization structures for FIR filters |
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407 | (4) |
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407 | (1) |
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408 | (2) |
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410 | (1) |
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Choosing between structures |
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410 | (1) |
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Finite wordlength effects in FIR digital filters |
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411 | (9) |
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Coefficient quantization errors |
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412 | (7) |
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419 | (1) |
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419 | (1) |
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FIR implementation techniques |
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420 | (2) |
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422 | (3) |
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425 | (1) |
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Application examples of FIR filters |
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425 | (29) |
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426 | (9) |
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435 | (1) |
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436 | (1) |
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437 | (1) |
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C programs for FIR filter design |
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437 | (3) |
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FIR filter design with MATLAB |
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440 | (14) |
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Design of infinite impulse response (IIR) digital filters |
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454 | (125) |
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Introduction: summary of the basic features of IIR filters |
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455 | (1) |
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Design stages for digital IIR filters |
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456 | (1) |
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Performance specification |
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457 | (2) |
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Coefficient calculation methods for IIR filters |
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459 | (1) |
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Pole-zero placement method of coefficient calculation |
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459 | (4) |
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Basic concepts and illustrative design examples |
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459 | (4) |
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Impulse invariant method of coefficient calculation |
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463 | (5) |
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Basic concepts and illustrative design examples |
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463 | (3) |
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Summary of the impulse invariant method |
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466 | (1) |
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Remarks on the impulse invariant method |
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466 | (2) |
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Matched z-transform (MZT) method of coefficient calculation |
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468 | (3) |
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Basic concepts and illustrative design examples |
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468 | (2) |
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Summary of the matched z-transform method |
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470 | (1) |
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Remarks on the matched z-transform method |
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471 | (1) |
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Bilinear z-transform (BZT) method of coefficient calculation |
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471 | (11) |
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Basic concepts and illustrative design examples |
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471 | (2) |
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Summary of the BZT method of coefficient calculation |
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473 | (5) |
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Comments on the bilinear transformation method |
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478 | (4) |
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Use of BZT and classical analog filters to design IIR filters |
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482 | (18) |
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Characteristic features of classical analog filters |
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483 | (2) |
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The BZT methodology using classical analog filters |
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485 | (6) |
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Illustrative design examples (lowpass, highpass, bandpass and bandstop filters) |
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491 | (9) |
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Calculating IIR filter coefficients by mapping s-plane poles and zeros |
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500 | (8) |
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500 | (5) |
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505 | (3) |
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Using IIR filter design programs |
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508 | (1) |
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Choice of coefficient calculation methods for IIR filters |
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509 | (8) |
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510 | (7) |
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Realization structures for IIR digital filters |
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517 | (7) |
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Practical building blocks for IIR filters |
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518 | (2) |
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Cascade and parallel realization structures for higher-order IIR filters |
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520 | (4) |
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Finite wordlength effects in IIR filters |
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524 | (5) |
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Coefficient quantization errors |
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526 | (3) |
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Implementation of IIR filters |
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529 | (1) |
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A detailed design example of an IIR digital filter |
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530 | (5) |
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535 | (1) |
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Application examples in digital audio and instrumentation |
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536 | (2) |
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536 | (1) |
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536 | (1) |
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Digital frequency oscillators |
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536 | (2) |
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Application examples in telecommunication |
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538 | (41) |
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Touch-tone generation and reception for digital telephones |
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538 | (2) |
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Digital telephony: dual tone multifrequency (DTMF) detection using the Goertzel algorithm |
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540 | (6) |
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Clock recovery for data communication |
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546 | (3) |
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549 | (5) |
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554 | (1) |
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555 | (2) |
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557 | (1) |
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C programs for IIR digital filter design |
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557 | (5) |
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IIR filter design with MATLAB |
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562 | (15) |
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Evaluation of complex square roots using real arithmetic |
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577 | (2) |
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Multirate digital signal processing |
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579 | (66) |
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579 | (2) |
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Some current uses of multirate processing in industry |
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580 | (1) |
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Concepts of multirate signal processing |
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581 | (9) |
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Sampling rate reduction: decimation by integer factors |
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582 | (2) |
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Sampling rate increase: interpolation by integer factors |
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584 | (2) |
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Sampling rate conversion by non-integer factors |
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586 | (3) |
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Multistage approach to sampling rate conversion |
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589 | (1) |
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Design of practical sampling rate converters |
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590 | (11) |
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590 | (1) |
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Filter requirements for individual stages |
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591 | (1) |
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Determining the number of stages and decimation factors |
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592 | (2) |
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Illustrative design examples |
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594 | (7) |
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Software implementation of sampling rate converters-decimators |
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601 | (5) |
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Program for multistage decimation |
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602 | (2) |
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Test example for the decimation program |
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604 | (2) |
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Software implementation of interpolators |
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606 | (6) |
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Program for multistage interpolation |
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610 | (1) |
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610 | (2) |
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Sample rate conversion using polyphase filter structure |
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612 | (5) |
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Polyphase implementation of interpolators |
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612 | (5) |
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617 | (15) |
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High quality analog-to-digital conversion for digital audio |
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618 | (1) |
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Efficient digital-to-analog conversion in compact hi-fi systems |
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618 | (2) |
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Application in the acquisition of high quality data |
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620 | (6) |
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Multirate narrowband digital filtering |
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626 | (5) |
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High resolution narrowband spectral analysis |
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631 | (1) |
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632 | (13) |
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633 | (4) |
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637 | (1) |
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638 | (1) |
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639 | (1) |
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C programs for multirate processing and systems design |
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639 | (1) |
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Multirate digital signal processing with MATLAB |
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640 | (5) |
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645 | (36) |
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When to use adaptive filters and where they have been used |
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646 | (1) |
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Concepts of adaptive filtering |
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647 | (4) |
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Adaptive filters as a noise canceller |
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647 | (1) |
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Other configurations of the adaptive filter |
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648 | (1) |
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Main components of the adaptive filter |
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648 | (1) |
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648 | (3) |
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Basic Wiener filter theory |
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651 | (3) |
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The basic LMS adaptive algorithm |
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654 | (8) |
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Implementation of the basic LMS algorithm |
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655 | (3) |
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Practical limitations of the basic LMS algorithm |
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658 | (3) |
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Other LMS-based algorithms |
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661 | (1) |
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Recursive least squares algorithm |
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662 | (4) |
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Recursive least squares algorithm |
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663 | (1) |
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Limitations of the recursive least squares algorithm |
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664 | (1) |
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665 | (1) |
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Application example 1 - adaptive filtering of ocular artefacts from the human EEG |
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666 | (2) |
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The physiological problem |
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666 | (1) |
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Artefact processing algorithm |
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667 | (1) |
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668 | (1) |
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Application example 2 - adaptive telephone echo cancellation |
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668 | (2) |
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670 | (11) |
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670 | (1) |
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670 | (1) |
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Adaptive jammer suppression |
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671 | (1) |
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672 | (1) |
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Separation of speech signals from background noise |
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672 | (1) |
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Fetal monitoring - cancelling of maternal ECG during labour |
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673 | (1) |
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674 | (1) |
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674 | (1) |
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675 | (1) |
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676 | (1) |
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C language programs for adaptive filtering |
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676 | (4) |
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MATLAB programs for adaptive filtering |
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680 | (1) |
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Spectrum estimation and analysis |
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681 | (46) |
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682 | (2) |
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Principles of spectrum estimation |
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684 | (3) |
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687 | (20) |
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687 | (3) |
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690 | (13) |
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The periodogram method and periodogram properties |
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703 | (1) |
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Modified periodogram methods |
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704 | (1) |
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The Blackman-Tukey method |
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705 | (1) |
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The fast correlation method |
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706 | (1) |
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Comparison of the power spectral density estimation methods |
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706 | (1) |
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Modern parametric estimation methods |
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707 | (1) |
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Autoregressive spectrum estimation |
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708 | (7) |
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Autoregressive model and filter |
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708 | (1) |
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Power spectrum density of AR series |
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709 | (1) |
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Computation of model parameters - Yule-Walker equations |
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710 | (3) |
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Solution of the Yule-Walker equations |
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713 | (1) |
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714 | (1) |
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Comparison of estimation methods |
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715 | (1) |
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|
|
715 | (6) |
|
Use of spectral analysis by a DFT for differentiating between brain diseases |
|
|
715 | (4) |
|
Spectral analysis of EEGs using autoregressive modelling |
|
|
719 | (2) |
|
|
|
721 | (1) |
|
|
|
721 | (6) |
|
|
|
722 | (2) |
|
|
|
724 | (1) |
|
|
|
725 | (1) |
|
MATLAB programs for spectrum estimation and analysis |
|
|
725 | (2) |
|
General- and special-purpose digital signal processors |
|
|
727 | (78) |
|
|
|
728 | (1) |
|
Computer architectures for signal processing |
|
|
728 | (18) |
|
|
|
730 | (2) |
|
|
|
732 | (5) |
|
Hardware multiplier-accumulator |
|
|
737 | (1) |
|
|
|
738 | (3) |
|
|
|
741 | (1) |
|
|
|
742 | (1) |
|
Extended parallelism - SIMD, VLIW and static superscalar processing |
|
|
742 | (4) |
|
General-purpose digital signal processors |
|
|
746 | (13) |
|
Fixed-point digital signal processors |
|
|
747 | (9) |
|
Floating-point digital signal processors |
|
|
756 | (3) |
|
Selecting digital signal processors |
|
|
759 | (2) |
|
Implementation of DSP algorithms on general-purpose digital signal processors |
|
|
761 | (26) |
|
|
|
761 | (9) |
|
|
|
770 | (7) |
|
|
|
777 | (5) |
|
|
|
782 | (4) |
|
|
|
786 | (1) |
|
Special-purpose DSP hardware |
|
|
787 | (5) |
|
|
|
789 | (1) |
|
|
|
790 | (2) |
|
|
|
792 | (13) |
|
|
|
793 | (3) |
|
|
|
796 | (1) |
|
|
|
797 | (1) |
|
|
|
798 | (1) |
|
TMS320 assembly language programs for real-time signal processing and a C language program for constant geometry radix-2 FFT |
|
|
798 | (7) |
|
Analysis of finite wordlength effects in fixed-point DSP systems |
|
|
805 | (68) |
|
|
|
805 | (1) |
|
|
|
806 | (9) |
|
|
|
808 | (4) |
|
Floating-point arithmetic |
|
|
812 | (3) |
|
ADC quantization noise and signal quality |
|
|
815 | (2) |
|
Finite wordlength effects in IIR digital filters |
|
|
817 | (43) |
|
Influence of filter structure on finite wordlength effects |
|
|
818 | (4) |
|
Coefficient quantization errors in IIR digital filters |
|
|
822 | (1) |
|
Coefficient wordlength requirements for stability and desired frequency response |
|
|
823 | (5) |
|
Addition overflow errors and their effects |
|
|
828 | (1) |
|
|
|
829 | (3) |
|
Scaling in cascade realization |
|
|
832 | (2) |
|
Scaling in parallel realization |
|
|
834 | (1) |
|
Output overflow detection and prevention |
|
|
835 | (1) |
|
Product roundoff errors in IIR digital filters |
|
|
836 | (1) |
|
Effects of roundoff errors on filter performance |
|
|
837 | (4) |
|
Roundoff noise in cascade and parallel realizations |
|
|
841 | (4) |
|
Effects of product roundoff noise in modern DSP systems |
|
|
845 | (1) |
|
Roundoff noise reduction schemes |
|
|
846 | (7) |
|
Determining practical values for error feedback coefficients |
|
|
853 | (4) |
|
Limit cycles due to product roundoff errors |
|
|
857 | (2) |
|
Other nonlinear phenomena |
|
|
859 | (1) |
|
Finite wordlength effects in FFT algorithms |
|
|
860 | (4) |
|
|
|
860 | (2) |
|
Overflow errors and scaling in FFT |
|
|
862 | (2) |
|
Coefficient quantization in FFT |
|
|
864 | (1) |
|
|
|
864 | (9) |
|
|
|
865 | (3) |
|
|
|
868 | (1) |
|
|
|
868 | (2) |
|
|
|
870 | (1) |
|
Finite wordlength analysis program for IIR filters |
|
|
870 | (1) |
|
L2 scaling factor equations |
|
|
870 | (3) |
|
Applications and design studies |
|
|
873 | (52) |
|
Evaluation boards for real-time signal processing |
|
|
874 | (3) |
|
|
|
874 | (1) |
|
|
|
874 | (2) |
|
DSP56002 evaluation module for real-time DSP |
|
|
876 | (1) |
|
TMS320C54 and D5P56300 evaluation boards |
|
|
876 | (1) |
|
|
|
877 | (27) |
|
Detection of fetal heartbeats during labour |
|
|
877 | (8) |
|
Adaptive removal of ocular artefacts from human EEGs |
|
|
885 | (16) |
|
Equalization of digital audio signals |
|
|
901 | (3) |
|
|
|
904 | (7) |
|
Computer-based multiple choice DSP questions |
|
|
911 | (9) |
|
|
|
920 | (5) |
|
|
|
921 | (1) |
|
|
|
921 | (2) |
|
|
|
923 | (1) |
|
|
|
923 | (1) |
|
The modified UD factorization algorithm |
|
|
923 | (2) |
| Index |
|
925 | |
