Figure 6.2 Example of a block diagram representation of a difference equation.

Copyright ©2010, ©1999, ©1989 by Pearson Education, Inc.All rights reserved.

Discrete-Time Signal Processing, Third EditionAlan V. Oppenheim • Ronald W. Schafer

Figure 6.1 Block diagram symbols. (a) Addition of two sequences. (b) Multiplication of a sequence by a constant. (c) Unit delay.



Figure 6.3 Block diagram representation for a general Nth-order difference equation.



Figure 6.4 Rearrangement of block diagram of Figure 6.3. We assume for convenience that N = M. If N ≠ M, some of the coefficients will be zero.



Figure 6.5 Combination of delays in Figure 6.4.



Figure 6.6 Direct form I implementation of Eq. (6.16).



Figure 6.7 Direct form II implementation of Eq. (6.16).



Figure 6.8 Example of nodes and branches in a signal flow graph.



Figure 6.9 Example of a signal flow graph showing source and sink nodes.



Figure 6.10 (a) Block diagram representation of a 1st-order digital filter. (b) Structure of the signal flow graph corresponding to the block diagram in (a).



Figure 6.11 Signal flow graph of Figure 6.10(b) with the delay branch indicated by z−1.



Figure 6.12 Flow graph not in standard direct form.



Figure 6.13 Direct form I equivalent of Figure 6.12.



Figure 6.14 Signal flow graph of direct form I structure for an Nth-order system.



Figure 6.15 Signal flow graph of direct form II structure for an Nth-order system.



Figure 6.16 Direct form I structure for Example 6.4.



Figure 6.17 Direct form II structure for Example 6.4.



Figure 6.18 Cascade structure for a 6th-order system with a direct form II realization of each 2nd-order subsystem.



Figure 6.19 Cascade structures for Example 6.5. (a) Direct form I subsections. (b) Direct form II subsections.



Figure 6.20 Parallel form structure for 6th-order system (M = N = 6) with the real and complex poles grouped in pairs.



Figure 6.21 Parallel form structure for Example 6.6 using a 2nd-order system.



Figure 6.22 Parallel form structure for Example 6.6 using 1st-order systems.



Figure 6.23 (a) System with feedback loop. (b) FIR system with feedback loop. (c) Noncomputable system.



Figure 6.24 (a) Flow graph of simple 1st-order system. (b) Transposed form of (a). (c) Structure of (b) redrawn with input on left.



Figure 6.25 Direct form II structure for Example 6.8.



Figure 6.26 Transposed direct form II structure for Example 6.8.



Figure 6.27 General flow graph resulting from applying the transposition theorem to the direct form I structure of Figure 6.14.



Figure 6.28 General flow graph resulting from applying the transposition theorem to the direct form II structure of Figure 6.15.



Figure 6.29 Direct form realization of an FIR system.



Figure 6.30 Transposition of the network of Figure 6.29.



Figure 6.31 Cascade form realization of an FIR system.



Figure 6.32 Direct form structure for an FIR linear-phase system when M is an even integer.



Figure 6.33 Direct form structure for an FIR linear-phase system when M is an odd integer.



Figure 6.34 Symmetry of zeros for a linear-phase FIR filter.



Figure 6.35 One section of the lattice structure for FIR lattice filters. (a) Block diagram representation of a two-port building block (b) Equivalent flow graph.



Figure 6.36 Cascade connection of M basic building block sections.



Figure 6.37 Lattice flow graph for an FIR system based on a cascade of M two-port building blocks of Figure 6.35(b).



Figure 6.38 Algorithm for converting from k-parameters to FIR filter coefficients.



Figure 6.39 Algorithm for converting from FIR filter coefficients to k-parameters.



Figure 6.40 Flow graphs for example. (a) Direct form. (b) Lattice form (coefficients rounded).



Figure 6.41 One stage of computation for an all-pole lattice system.



Figure 6.42 All-pole lattice system.



Figure 6.43 Signal flow graph of IIR filter; (a) direct form, (b) lattice form.



Figure 6.44 Nonlinear relationships representing two’s-complement (a) rounding and (b) truncation for B = 2.



Figure 6.45 Two’s-complement rounding. (a) Natural overflow. (b) Saturation.



Figure 6.46 Implementation of discrete-time filtering of an analog signal. (a) Ideal system. (b) Nonlinear model. (c) Linearized model.



Table 6.1 UNQUANTIZED DIRECT-FORM COEFFICIENTS FOR A 12TH-ORDER ELLIPTIC FILTER



Figure 6.47 IIR coefficient quantization example. (a) Log magnitude for unquantized elliptic bandpass filter. (b) Magnitude in passband for unquantized (solid line) and 16-bit quantized cascade form (dashed line).



Table 6.2 ZEROS AND POLES OF UNQUANTIZED 12TH-ORDER ELLIPTIC FILTER.



Figure 6.48 IIR coefficient quantization example. (a) Poles and zeros of H(z) for unquantized coefficients. (b) Poles and zeros for 16-bit quantization of the direct form coefficients.



Table 6.3 UNQUANTIZED CASCADE-FORM COEFFICIENTS FOR A 12TH-ORDER ELLIPTIC FILTER



Table 6.4 SIXTEEN-BIT QUANTIZED CASCADE-FORM COEFFICIENTS FOR A 12TH-ORDER ELLIPTIC FILTER



Figure 6.49 Direct form implementation of a complex-conjugate pole pair.



Figure 6.50 Pole-locations for the 2nd-order IIR direct form system of Figure 6.49. (a) Four-bit quantization of coefficients. (b) Seven-bit quantization.



Figure 6.51 Coupled form implementation of a complex-conjugate pole pair.



Figure 6.52 Pole locations for coupled form 2nd-order IIR system of Figure 6.51. (a) Four-bit quantization of coefficients. (b) Seven-bit quantization.



Figure 6.53 Representation of coefficient quantization in FIR systems.



Table 6.5 UNQUANTIZED AND QUANTIZED COEFFICIENTS FOR AN OPTIMUM FIR LOWPASS FILTER (M = 27)



Figure 6.54 FIR quantization example. (a) Log magnitude for unquantized case. (b) Approximation error for unquantized case. (Error not defined in transition band.) (c) Approximation error for 16-bit quantization.



Figure 6.54 (continued) (d) Approximation error for 14-bit quantization. (e) Approximation error for 13-bit quantization. (f) Approximation error for 8-bit quantization.



Figure 6.55 Effect of impulse response quantization on zeros of H(z). (a) Unquantized. (b) 16-bit quantization. (c) 13-bit quantization. (d) 8-bit quantization.



Figure 6.56 Subsystem to implement 4th-order factors in a linear-phase FIR system such that linearity of the phase is maintained independently of parameter quantization.



Figure 6.57 Models for direct form I system. (a) Infinite-precision model. (b) Nonlinear quantized model. (c) Linear-noise model.



Figure 6.58 Probability density function for quantization errors. (a) Rounding. (b) Truncation.



Figure 6.59 Linear-noise model for direct form I with noise sources combined.



Figure 6.60 1st-order linear noise model.



Figure 6.61 Linear-noise models for direct form II. (a) Showing quantization of individual products. (b) With noise sources combined.



Figure 6.62 Scaling of direct form systems. (a) Direct form I. (b) Direct form II.



Figure 6.63 Scaled 1st-order system.



Table 6.6 COEFFICIENTS FOR ELLIPTIC LOWPASS FILTER IN CASCADE FORM



Figure 6.64 Models for 6th-order cascade system with transposed direct form II subsystems. (a) Infinite-precision model. (b) Linear-noise model for scaled system, showing quantization of individual multiplications. (c) Linear-noise model with noise sources combined.



Figure 6.65 Pole–zero plot for 6th-order system of Figure 6.64, showing pairing of poles and zeros.



Figure 6.67 Output noise power spectrum for 123 ordering (solid line) and 321 ordering (dashed line) of 2 nd-order sections.



Figure 6.68 Direct form realization of an FIR system. (a) Infinite-precision model. (b) Linear-noise model.



Figure 6.69 1st-order IIR system. (a) Infinite-precision linear system. (b) Nonlinear system due to quantization.



Figure 6.70 Response of the 1st-order system of Figure 6.69 to an impulse. (a) a = ½. (b) a = −½.



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Figure P6.35-2 Polyphase structure of the system.



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Figure 6.2 Example of a block diagram representation of a difference equation.

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Transcript of Figure 6.2 Example of a block diagram representation of a difference equation.