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Biomedical Signal processing Chapter 2 Discrete-Time Signals and Systems

Biomedical Signal processing Chapter 2 Discrete-Time Signals and Systems. Zhongguo Liu Biomedical Engineering School of Control Science and Engineering, Shandong University. 2014/9/22. 1. Zhongguo Liu_Biomedical Engineering_Shandong Univ. Chapter 2 Discrete-Time Signals and Systems.

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Biomedical Signal processing Chapter 2 Discrete-Time Signals and Systems

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  1. Biomedical Signal processingChapter 2 Discrete-Time Signals and Systems Zhongguo Liu Biomedical Engineering School of Control Science and Engineering, Shandong University 2014/9/22 1 Zhongguo Liu_Biomedical Engineering_Shandong Univ.

  2. Chapter 2 Discrete-Time Signals and Systems 2.0 Introduction 2.1 Discrete-Time Signals: Sequences 2.2 Discrete-Time Systems 2.3 Linear Time-Invariant (LTI) Systems 2.4 Properties of LTI Systems 2.5 Linear Constant-Coefficient Difference Equations Zhongguo Liu_Biomedical Engineering_Shandong Univ. 2 9/22/2014

  3. Chapter 2 Discrete-Time Signals and Systems 2.6 Frequency-Domain Representation of Discrete-Time Signals and systems 2.7 Representation of Sequences by Fourier Transforms 2.8 Symmetry Properties of the Fourier Transform 2.9 Fourier Transform Theorems 2.10 Discrete-Time Random Signals 2.11 Summary Zhongguo Liu_Biomedical Engineering_Shandong Univ. 3 9/22/2014

  4. 2.0 Introduction Signal: something conveys information Signals are represented mathematically as functions of one or more independent variables. Continuous-time (analog) signals, discrete-time signals, digital signals Signal-processing systems are classified along the same lines as signals: Continuous-time (analog) systems, discrete-time systems, digital systems Discrete-time signal Sampling a continuous-time signal Generated directly by some discrete-time process Zhongguo Liu_Biomedical Engineering_Shandong Univ. 4 9/22/2014

  5. 2.1 Discrete-Time Signals: Sequences Discrete-Time signals are represented as In sampling, 1/T (reciprocal of T) : sampling frequency Cumbersome, so just use Zhongguo Liu_Biomedical Engineering_Shandong Univ. 5 9/22/2014

  6. Figure 2.1 Graphical representation of a discrete-time signal Abscissa: continuous line : is defined only at discrete instants Zhongguo Liu_Biomedical Engineering_Shandong Univ. 6 9/22/2014

  7. Sampling the analog waveform EXAMPLE Figure 2.2

  8. Sum of two sequences Product of two sequences Multiplication of a sequence by a numberα Delay (shift) of a sequence Basic Sequence Operations Zhongguo Liu_Biomedical Engineering_Shandong Univ. 8 9/22/2014

  9. Basic sequences Unit sample sequence (discrete-time impulse, impulse) Zhongguo Liu_Biomedical Engineering_Shandong Univ. 9 9/22/2014

  10. Basic sequences A sum of scaled, delayed impulses • arbitrary sequence Zhongguo Liu_Biomedical Engineering_Shandong Univ. 10 9/22/2014

  11. Basic sequences Unit step sequence First backward difference Zhongguo Liu_Biomedical Engineering_Shandong Univ. 11 9/22/2014

  12. Basic Sequences Exponential sequences • A and α are real: x[n] is real • A is positive and 0<α<1, x[n] is positive and decrease with increasing n • -1<α<0, x[n] alternate in sign, but decrease in magnitude with increasing n • :x[n] grows in magnitude as n increases Zhongguo Liu_Biomedical Engineering_Shandong Univ. 12 9/22/2014

  13. EX. 2.1 Combining Basic sequences Cumbersome simpler • If we want an exponential sequences that is zero for n <0, then Zhongguo Liu_Biomedical Engineering_Shandong Univ. 13 9/22/2014

  14. Basic sequences Sinusoidal sequence Zhongguo Liu_Biomedical Engineering_Shandong Univ. 14 9/22/2014

  15. ExponentialSequences Exponentially weighted sinusoids Exponentially growing envelope Exponentially decreasing envelope is refered to Complex Exponential Sequences Zhongguo Liu_Biomedical Engineering_Shandong Univ. 15 9/22/2014

  16. Frequency differencebetween continuous-time and discrete-time complex exponentials or sinusoids : frequency of the complex sinusoid or complex exponential : phase Zhongguo Liu_Biomedical Engineering_Shandong Univ. 16 9/22/2014

  17. Periodic Sequences A periodic sequence with integer period N Zhongguo Liu_Biomedical Engineering_Shandong Univ. 17 9/22/2014

  18. EX. 2.2Examples of Periodic Sequences Suppose it is periodic sequence with period N Zhongguo Liu_Biomedical Engineering_Shandong Univ. 18 9/22/2014

  19. Suppose it is periodic sequence with period N EX. 2.2Examples of Periodic Sequences Zhongguo Liu_Biomedical Engineering_Shandong Univ. 19 9/22/2014

  20. EX. 2.2Non-Periodic Sequences Suppose it is periodic sequence with period N Zhongguo Liu_Biomedical Engineering_Shandong Univ. 20 9/22/2014

  21. High and Low Frequencies in Discrete-time signal (a) w0 = 0 or 2 (b) w0 = /8 or 15/8 (c) w0 = /4 or 7/4 (d) w0 =  Zhongguo Liu_Biomedical Engineering_Shandong Univ. 21 9/22/2014

  22. 2.2 Discrete-Time System Discrete-Time Systemis a trasformation or operator that maps input sequence x[n] into a unique y[n] y[n]=T{x[n]}, x[n], y[n]: discrete-time signal x[n] y[n] T{‧} Discrete-Time System Zhongguo Liu_Biomedical Engineering_Shandong Univ. 22 9/22/2014

  23. EX. 2.3The Ideal Delay System • If is a positive integer: the delay of the system. Shift the input sequence to the right by samples to form the output . • If is a negative integer: the system will shift the input to the left by samples, corresponding to a time advance. Zhongguo Liu_Biomedical Engineering_Shandong Univ. 23 9/22/2014

  24. EX. 2.4 Moving Average dummy index m x[m] n-5 m n for n=7, M1=0, M2=5 Zhongguo Liu_Biomedical Engineering_Shandong Univ. 24 9/22/2014

  25. Properties of Discrete-time systems2.2.1 Memoryless (memory) system Memoryless systems: the output y[n] at every value of n depends only on the input x[n] at the same value of n Zhongguo Liu_Biomedical Engineering_Shandong Univ. 25 9/22/2014

  26. Properties of Discrete-time systems2.2.2 Linear Systems T{‧} T{‧} T{‧} T{‧} T{‧} • If • and only If: additivity property homogeneity or scaling 同(齐)次性 property • principle of superposition Zhongguo Liu_Biomedical Engineering_Shandong Univ. 26 9/22/2014

  27. Example of Linear System Ex. 2.6 Accumulator system for arbitrary when Zhongguo Liu_Biomedical Engineering_Shandong Univ. 27 9/22/2014

  28. Example 2.7Nonlinear Systems • For • counterexample • For • counterexample • Method: find one counterexample Zhongguo Liu_Biomedical Engineering_Shandong Univ. 28 9/22/2014

  29. Properties of Discrete-time systems2.2.3 Time-Invariant Systems Shift-Invariant Systems T{‧} T{‧} Zhongguo Liu_Biomedical Engineering_Shandong Univ. 29 9/22/2014

  30. Example of Time-Invariant System Ex. 2.8 Accumulator system Zhongguo Liu_Biomedical Engineering_Shandong Univ. 30 9/22/2014

  31. Example of Time-varying System Ex. 2.9 The compressor system T{‧} T{‧} T{‧} 0 0 0 0 Zhongguo Liu_Biomedical Engineering_Shandong Univ. 31 9/22/2014

  32. Properties of Discrete-time systems2.2.4 Causality A system is causal if, for every choice of , the output sequence value at the index depends only on the input sequence value for Zhongguo Liu_Biomedical Engineering_Shandong Univ. 32 9/22/2014

  33. Ex. 2.10 Example for Causal System Forward difference system is not Causal Backward difference system is Causal Zhongguo Liu_Biomedical Engineering_Shandong Univ. 33 9/22/2014

  34. Properties of Discrete-time systems2.2.5 Stability Bounded-Input Bounded-Output (BIBO) Stability: every bounded input sequence produces a bounded output sequence. if then Zhongguo Liu_Biomedical Engineering_Shandong Univ. 34 9/22/2014

  35. Ex. 2.11 Test for Stability or Instability if then is stable Zhongguo Liu_Biomedical Engineering_Shandong Univ. 35 9/22/2014

  36. Accumulator system Ex. 2.11 Test for Stability or Instability • Accumulator systemis not stable Zhongguo Liu_Biomedical Engineering_Shandong Univ. 36 9/22/2014

  37. 2.3 Linear Time-Invariant (LTI) Systems Impulse response T{‧} T{‧} Zhongguo Liu_Biomedical Engineering_Shandong Univ. 37 9/22/2014

  38. LTI Systems: Convolution • Representation of general sequence as a linear combination of delayed impulse • principle of superposition An Illustration Example（interpretation 1） Zhongguo Liu_Biomedical Engineering_Shandong Univ. 38 9/22/2014

  39. Zhongguo Liu_Biomedical Engineering_Shandong Univ. 39 9/22/2014

  40. Computation of the Convolution reflecting h[k] about the origion to obtain h[-k] Shifting the origin of the reflected sequence to k=n （interpretation 2） Zhongguo Liu_Biomedical Engineering_Shandong Univ. 40 9/22/2014

  41. Ex. 2.12 Zhongguo Liu_Biomedical Engineering_Shandong Univ. 41 9/22/2014

  42. Convolution can be realized by • Reflecting h[k] about the origin to obtain h[-k]. • Shifting the origin of the reflected sequences to k=n. • Computing the weighted moving average of x[k] by using the weights given by h[n-k].

  43. Ex. 2.13 Analytical Evaluation of the Convolution input Find the output at index n For system with impulse response h(k) 0 Zhongguo Liu_Biomedical Engineering_Shandong Univ. 43 9/22/2014

  44. h(-k) h(k) 0 0 h(n-k) x(k) 0 Zhongguo Liu_Biomedical Engineering_Shandong Univ. 44 9/22/2014

  45. h(-k) h(k) 0 0 Zhongguo Liu_Biomedical Engineering_Shandong Univ. 45 9/22/2014

  46. h(-k) h(k) 0 0 Zhongguo Liu_Biomedical Engineering_Shandong Univ. 46 9/22/2014

  47. Zhongguo Liu_Biomedical Engineering_Shandong Univ. 47 9/22/2014

  48. 2.4 Properties of LTI Systems Convolution is commutative(可交换的) x[n] y[n] h[n] y[n] h[n] x[n] • Convolution is distributed over addition Zhongguo Liu_Biomedical Engineering_Shandong Univ. 48 9/22/2014

  49. Cascade connection of systems h1[n] h2[n] h2[n] h1[n] y[n] y[n] x[n] x[n] h1[n] ]h2[n] y[n] x[n] Zhongguo Liu_Biomedical Engineering_Shandong Univ. 49 9/22/2014

  50. Parallel connection of systems Zhongguo Liu_Biomedical Engineering_Shandong Univ. 50 9/22/2014

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