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Professor A G Constantinides 1 Z - transform Defined as power series Examples:

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Professor A G Constantinides 2 Z - transform And since We get

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Professor A G Constantinides 3 Z - transform Define +ve and = 1 +ve and > 1 +ve and < 1

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Professor A G Constantinides 4 Z - transform We have ie Note that has a pole at on the z-plane.

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Professor A G Constantinides 5 Z - transform Note: (i) If magnitude of pole is > 1 then increases without bound (ii) If magnitude of pole is < 1 then has a bounded variation i.e. the contour on the z-plane is of crucial significance. It is called the Unit circle

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Professor A G Constantinides 6 The unit circle 1 1

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Professor A G Constantinides 7 Z – transform properties (i) Linearity The z-transform operation is linear Z Where Z, i = 1, 2 (ii) Shift Theorem Z

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Professor A G Constantinides 8 Z - transform Let Z But for negative i.....

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Professor A G Constantinides 9 Z - transform Examples: (i) Consider generation of new discrete time signal from via Recall linearity and shift (ii) Z write

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Professor A G Constantinides 10 Z - transform From With from earlier result We obtain Z Z

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Professor A G Constantinides 11 Inverse Z - transform Given F(z) to determine. Basic relationship is may be obtained by power series expansion. It suffers from cumulative errors

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Professor A G Constantinides 12 Inverse Z - transform Alternatively Use for m = -1 otherwise where closed contour encloses origin

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Professor A G Constantinides 13 Inverse Z - transform Integrate to yield Examples (i) write

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Professor A G Constantinides 14 Inverse Z - transform And hence Pole at of Residue (ii)Let where and To determine

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Professor A G Constantinides 15 Inverse Z - transform From inversion formula But

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Professor A G Constantinides 16 Inverse Z - transform Hence Thus

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Professor A G Constantinides 17 Inverse Z - transform Note: (i) For causal signals for negative i. Thus upper convolution summation limit is in this case equal to k. (ii) Frequency representation of a discrete- time signal is obtained from its z-transform by replacing where T is the sampling period of interest. (Justification will be given later.)

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1 7.1 Discrete Fourier Transform (DFT) 7.2 DFT Properties 7.3 Cyclic Convolution 7.4 Linear Convolution via DFT Chapter 7 Discrete Fourier Transform Section.

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