1
Sampling Theory & Data Acquisition
© 2017 School of Information Technology and Electrical Engineering at The University of Queensland
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π-day (
http://elec3004.com
Lecture Schedule: Week Date Lecture Title
1 28-Feb Introduction
2-Mar Systems Overview
2 7-Mar Systems as Maps & Signals as Vectors
9-Mar Systems: Linear Differential Systems
3 14-Mar Sampling Theory & Data Acquisition 16-Mar Antialiasing Filters
4 21-Mar Discrete System Analysis
23-Mar Convolution Review
5 28-Mar Frequency Response
30-Mar Filter Analysis
5 4-Apr Digital Filters (IIR)
6-Apr Digital Windows
6 11-Apr Digital Filter (FIR)
13-Apr FFT
18-Apr
Holiday 20-Apr
25-Apr
7 27-Apr Active Filters & Estimation
8 2-May Introduction to Feedback Control
4-May Servoregulation/PID
10 9-May Introduction to (Digital) Control
11-May Digitial Control
11 16-May Digital Control Design
18-May Stability
12 23-May Digital Control Systems: Shaping the Dynamic Response
25-May Applications in Industry
13 30-May System Identification & Information Theory
1-Jun Summary and Course Review
14 March 2017 - ELEC 3004: Systems 2
2
Follow Along Reading:
B. P. Lathi
Signal processing
and linear systems
1998
TK5102.9.L38 1998
• Chapter 5:
Sampling
– § 5.1 The Sampling Theorem
– § 5.2 Numerical Computation of
Fourier Transform: The Discrete
Fourier Transform (DFT)
Also:
– § 4.6 Signal Energy
14 March 2017 - ELEC 3004: Systems 3
Linear Differential Systems (Recap)
14 March 2017 - ELEC 3004: Systems 4
3
Equivalence Across Domains
Source: Dorf & Bishop, Modern Control Systems, 12th Ed., p. 73
14 March 2017 - ELEC 3004: Systems 5
Source: Dorf & Bishop, Modern Control Systems, 12th Ed., p. 74
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4
Example: Quarter-Car Model
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Example: Quarter-Car Model (2)
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5
Materials, parts, labour, etc. (inputs) are combined to make a
number of products (outputs):
• 𝑥𝑗: price per unit of production input 𝑗
• 𝑎𝑖𝑗: input 𝑗 required to manufacture one unit of product 𝑖
• 𝑦𝑖: production cost per unit of product 𝑖
• For 𝑦 = 𝐴𝑥:
o 𝑖𝑡ℎ row of 𝐴 is bill of materials for unit of product 𝑖
• Production inputs needed: – 𝑞𝑖 is quantity of product 𝑖 to be produced
– 𝑟𝑗 is total quantity of production input 𝑗 needed
∴ 𝑟 = 𝐴𝑇𝑞
& Total production cost is:
𝑟𝑇𝑥 = 𝐴𝑇𝑞 𝑇𝑥 = 𝑞𝑇𝐴𝑥
Economics: Cost of Production
Source: Boyd, EE263, Slide 2-18
14 March 2017 - ELEC 3004: Systems 9
𝑦 = 𝐴𝑥
• 𝑦𝑖 is 𝑖𝑡ℎ measurement or sensor reading (which we have)
• 𝑥𝑗 is 𝑗𝑡ℎ parameter to be estimated or determined
• 𝑎𝑖𝑗 is sensitivity of 𝑖𝑡ℎ sensor to 𝑗𝑡ℎ parameter
• sample problems: o find 𝑥, given 𝑦
o find all x’s that result in y (i.e., all x’s consistent with measurements)
o if there is no x such that 𝑦 = 𝐴𝑥, find 𝑥 s.t. 𝑦 ≈ 𝐴𝑥 (i.e., if the sensor readings are inconsistent, find x which is almost consistent)
Estimation (or inversion)
Source: Boyd, EE263, Slide 2-26
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6
Signals & Systems
14 March 2017 - ELEC 3004: Systems 11
Signal: A carrier of (desired) information [1]
• Need NOT be electrical: • Thermometer
• Clock hands
• Automobile speedometer
• Need NOT always being given
– “Abnormal” sounds/operations
– Ex: “pitch” or “engine hum” during machining as an
indicator for feeds and speeds
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7
Signal: A carrier of (desired) information [2]
• Electrical signals
– Voltage
– Current
• Digital signals
– Convert analog electrical signals to an appropriate
digital electrical message
– Processing by a microcontroller or microprocessor
14 March 2017 - ELEC 3004: Systems 13
Transduction (sensor to an electrical signal) • Sensor reacts to environment (physics)
• Turn this into an electrical signal: – V: voltage source
– I: current source
• Measure this signal
– Resistance
– Capacitance
– Inductance
Happy π-day - ELEC 3004: Systems 14
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• Representation of a signal against a discrete set
• The set is fixed in by computing hardware
• Can be scaled or normalized … but is limited
• Time is also discretized
Digital Signal
14 March 2017 - ELEC 3004: Systems 15
Analog vs Digital
• Analog Signal: An analog or analogue signal is any
variable signal continuous in both time and
amplitude
• Digital Signal: A digital signal is a signal that is both
discrete and quantized
E.g. Music stored in a
CD: 44,100 Samples
per second and 16 bits
to represent amplitude
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9
• Continuous:
:
Digital Systems
14 March 2017 - ELEC 3004: Systems 17
Digital Systems ∵ Better SNR
• We trade-off “certainty in time” for “signal noise/uncertainty”
• Analog: ∞ time resolution
– Digital has fixed time steps
• This avoids the noise and uncertainty in component values that affect analogue signal processing.
Better Processing
• Digital microprocessors are in a range of objects, from obvious (e.g. phone) to disposable (e.g. Go cards). (what doesn’t have one?)
Compared to antilog computing (op-amp):
• Accuracy: digital signals are usually represented using 12 bits or more.
• Reliability: The ALU is stable over time.
• Flexibility: limited only programming ability!
• Cost: advances in technology make microcontrollers economical even for small, low cost applications. (Raspberry Pi 3: US$35)
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10
Signals & Systems
WHY?
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11
BUT there is Noise …
Note: this picture illustrates the concepts but it is not quantitatively precise
Source: Prof. M. Siegel, CMU 14 March 2017 - ELEC 3004: Systems 21
• Cross-coupled measurements
• Cross-talk (at a restaurant or even a lecture)
• A bright sunny day obstructing picture subject
• Strong radio station near weak one
• observation-to-observation variation – Measurement fluctuates (ex: student)
– Instrument fluctuates (ex: quiz !)
• Unanticipated effects / variation (Temperature)
• One man’s noise might be another man’s signal
Noise: “Unwanted” Signals Carrying Errant Information
14 March 2017 - ELEC 3004: Systems 23
12
Noise: Fundamental Natural Sources • Voltage (EMF) – Capacitive & Inductive Pickup
• Johnson Noise – thermal / Brownian
• 1/f (Hooge Noise)
• Shot noise (interval-to-interval statistical count)
14 March 2017 - ELEC 3004: Systems 24
SNR : Signal to Noise Ratio
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13
BREAK
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Not this type of sampling …
14 March 2017 - ELEC 3004: Systems 28
This type of sampling…
Source: Wikipedia: http://en.wikipedia.org/wiki/File:Signal_Sampling.png
14 March 2017 - ELEC 3004: Systems 29
15
• The Nyquist criterion states:
To prevent aliasing, a bandlimited signal of bandwidth wB
rad/s must be sampled at a rate greater than 2wB rad/s
ws > 2wB
Sampling Theorem
Note: this is a > sign not a
Also note: Most real world signals require band-limiting
with a lowpass (anti-aliasing) filter
14 March 2017 - ELEC 3004: Systems 30
Mathematics of Sampling and Reconstruction
DSP Ideal
LPF
x(t) xc(t) y(t)
Impulse train
T(t)= (t - nt)
… … t
Sampling frequency fs = 1/t
Gain
fc Freq
1
0
Cut-off frequency = fc
reconstruction sampling
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16
• x(t) multiplied by impulse train T(t)
Mathematical Model of Sampling
• xc(t) is a train of impulses of height x(t)|t=nt
n
Tc
tnttnx
ttttttx
ttxtx
)()(
)2()()()(
)()()(
14 March 2017 - ELEC 3004: Systems 32
-10 -8 -6 -4 -2 0 2 4 6 8 10 -2
-1
0
1
2
t
x(t
)
Continuous-time
-10 -8 -6 -4 -2 0 2 4 6 8 10 -2
-1
0
1
2
t
xc(t
)
Discrete-time
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17
• Image a signal…
Discrete Time Signal
-8 -6 -4 -2 0 2 4 6 8-1
-0.5
0
0.5
1
time (s)
Am
plit
ud
e
Signal
Digitized Signal
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• Digitization helps beat the Noise!
Discrete Time Signals
-8 -6 -4 -2 0 2 4 6 8-1.5
-1
-0.5
0
0.5
1
1.5
time (s)
Am
plit
ud
e
Signal + 5% Gausian Noise
Digitized Noisy Signal
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• But only so much…
Discrete Time Signals
-8 -6 -4 -2 0 2 4 6 8-1.5
-1
-0.5
0
0.5
1
1.5
time (s)
Am
plit
ud
e
Signal + 20% Gausian Noise
Digitized Noisy Signal
14 March 2017 - ELEC 3004: Systems 36
• Shifting
• Reversal
• Time Scaling (Down Sampling)
(Up Sampling)
Signal Manipulations
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• Can make control tricky!
Discrete Time Signals
14 March 2017 - ELEC 3004: Systems 38
• Can make control tricky!
Discrete Time Signals
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20
•
Nyquist Sampling Theorem and Aliasing
14 March 2017 - ELEC 3004: Systems 40
• The Nyquist criterion states:
To prevent aliasing, a bandlimited signal of bandwidth wB
rad/s must be sampled at a rate greater than 2wB rad/s
–ws > 2wB
Sampling Theorem
Note: this is a > sign not a
Also note: Most real world signals require band-limiting
with a lowpass (anti-aliasing) filter
14 March 2017 - ELEC 3004: Systems 41
21
• Frequency domain analysis of sampling is very useful to understand – sampling (X(w)* (w - 2n/t) )
– reconstruction (lowpass filter removes replicas)
– aliasing (if ws 2wB)
• Time domain analysis can also illustrate the concepts – sampling a sinewave of increasing frequency
– sampling images of a rotating wheel
Time Domain Analysis of Sampling
14 March 2017 - ELEC 3004: Systems 42
Original signal
Discrete-time samples
Reconstructed signal
A signal of the original frequency is reconstructed 14 March 2017 - ELEC 3004: Systems 43
22
Original signal
Discrete-time samples
Reconstructed signal
A signal with a reduced frequency is recovered, i.e., the signal is
aliased to a lower frequency (we recover a replica) 14 March 2017 - ELEC 3004: Systems 44
Sampling < Nyquist Aliasing
0 5 10 15-1.5
-1
-0.5
0
0.5
1
1.5
time
sig
na
l
True signal
Aliased (under sampled) signal
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23
Nyquist is not enough …
0 1 2 3 4 5 6 7-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
11Hz Sin Wave: Sin(2t) 2 Hz Sampling
Time(s)
Norm
aliz
ed
mag
nitu
de
14 March 2017 - ELEC 3004: Systems 46
A little more than Nyquist is not enough …
0 1 2 3 4 5 6 7-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
11Hz Sin Wave: Sin(2t) 4 Hz Sampling
Time(s)
Norm
aliz
ed
mag
nitu
de
14 March 2017 - ELEC 3004: Systems 47
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• Consider the case where the DSP performs no filtering
operations – i.e., only passes xc(t) to the reconstruction filter
• To understand we need to look at the frequency domain
• Sampling: we know – multiplication in time convolution in frequency
– F{x(t)} = X(w)
– F{T(t)} = (w - 2n/t),
– i.e., an impulse train in the frequency domain
Frequency Domain Analysis of Sampling
14 March 2017 - ELEC 3004: Systems 48
• In the frequency domain we have
Frequency Domain Analysis of Sampling
n
n
c
t
nwX
t
t
nw
twXwX
21
22*)(
2
1)(
Let’s look at an example where X(w) is triangular function
with maximum frequency wm rad/s
being sampled by an impulse train, of frequency ws rad/s
Remember
convolution with
an impulse?
Same idea for an
impulse train
14 March 2017 - ELEC 3004: Systems 49
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• In this example it was possible to recover the original signal
from the discrete-time samples
• But is this always the case?
• Consider an example where the sampling frequency ws is
reduced – i.e., t is increased
Sampling Frequency
14 March 2017 - ELEC 3004: Systems 50
Fourier transform of original signal X(ω) (signal spectrum)
w
… …
Fourier transform of impulse train T(/2) (sampling signal)
0 ws = 2/t 4/t
Original spectrum
convolved with
spectrum of
impulse train …
Fourier transform of sampled signal
w
…
Original Replica 1 Replica 2
1/t
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26
Spectrum of sampled signal
Spectrum of reconstructed signal
w -wm wm
Reconstruction filter
removes the replica
spectrums & leaves
only the original
Reconstruction filter (ideal lowpass filter)
w -wc wc = wm
t
…
w
…
Original Replica 1 Replica 2
1/t
14 March 2017 - ELEC 3004: Systems 52
Sampled Spectrum ws > 2wm
w -wm wm ws
orignal replica 1 …
…
LPF
original freq recovered
Sampled Spectrum ws < 2wm
w -wm wmws
…
orignal …
replica 1
LPF
Original and replica spectrums overlap
Lower frequency
recovered (ws – wm)
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14 March 2017 - ELEC 3004: Systems 54
Reconstruction
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• Signal is bandlimited to bandwidth WB – Problem: real signals are not bandlimited
• Therefore, require (non-ideal) anti-aliasing filter
• Signal multiplied by ideal impulse train – problems: sample pulses have finite width
– and not in practice, but sample & hold circuit
• Samples discrete-time, continuous valued – Problem: require discrete values for DSP
• Therefore, require A/D converter (quantisation)
• Ideal lowpass reconstruction (‘sinc’ interpolation) – problems: ideal lowpass filter not available
• Therefore, use D/A converter and practical lowpass filter
Sampling and Reconstruction Theory and Practice
14 March 2017 - ELEC 3004: Systems 56
• Frequency domain: multiply by ideal LPF – ideal LPF: ‘rect’ function (gain t, cut off wc) – removes replica spectrums, leaves original
• Time domain: this is equivalent to – convolution with ‘sinc’ function – as F
-1{t rect(w/wc)} = t wc sinc(wct/)
– i.e., weighted sinc on every sample
• Normally, wc = ws/2
Time Domain Analysis of Reconstruction
n
ccr
tntwtwtnxtx
)(sinc)()(
14 March 2017 - ELEC 3004: Systems 57
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• Whittaker–Shannon interpolation formula
Reconstruction
14 March 2017 - ELEC 3004: Systems 58
Zero Order Hold (ZOH)
ZOH impulse response
ZOH amplitude response
ZOH phase response
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• Zero-Order Hold [ZOH]
Reconstruction
14 March 2017 - ELEC 3004: Systems 60
• Whittaker–Shannon interpolation formula
Reconstruction
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Ideal 𝑠𝑖𝑛𝑐 Interpolation of sample values [0 0 0.75 1 0.5 0 0]
-4 -3 -2 -1 0 1 2 3 4
-0.2
0
0.2
0.4
0.6
0.8
1
Sample
Valu
e
reconstructed signal xr(t)
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‘staircase’ output from D/A converter (ZOH)
0 1 2 3 4 5 6 7 8 9 10 0
2
4
6
8
10
12
14
16
Time (sec)
Am
plit
ud
e (
V)
output samples
D/A output
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32
Smooth output from reconstruction filter
0 2 4 6 8 10 12 2
4
6
8
10
12
14
16
Time (sec)
Am
plit
ud
e (
V)
D/A output
Reconstruction filter output
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Example: error due to signal quantisation
0 1 2 3 4 5 6 7 8 9 10 0
2
4
6
8
10
12
14
16
Sample number
Am
plit
ud
e (
V)
original signal x(t) quantised samples xq(t)
14 March 2017 - ELEC 3004: Systems 65
33
• Impulse train sampling not realisable – sample pulses have finite width (say nanosecs)
• This produces two effects,
• Impulse train has sinc envelope in frequency domain – impulse train is square wave with small duty cycle
– Reduces amplitude of replica spectrums • smaller replicas to remove with reconstruction filter
• Averaging of signal during sample time – effective low pass filter of original signal
• can reduce aliasing, but can reduce fidelity
• negligible with most S/H
Finite Width Sampling
14 March 2017 - ELEC 3004: Systems 67
• Sample and Hold (S/H) 1. takes a sample every t seconds 2. holds that value constant until next sample
• Produces ‘staircase’ waveform, x(nt)
Practical Sampling
t
x(t)
hold for t
sample instant
x(nt)
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Two stage process:
1. Digital to analogue converter (D/A) – zero order hold filter – produces ‘staircase’ analogue output
2. Reconstruction filter – non-ideal filter: wc = ws /2 – further reduces replica spectrums – usually 4th – 6th order e.g., Butterworth
• for acceptable phase response
Practical Reconstruction
14 March 2017 - ELEC 3004: Systems 69
• Analogue output y(t) is – convolution of output samples y(nt) with hZOH(t)
D/A Converter
2/
)2/sin(
2exp)(
otherwise,0
0,1)(
)()()(
tw
twtjwtwH
ttth
tnthtnyty
ZOH
ZOH
ZOH
n
D/A is lowpass filter with sinc type frequency response
It does not completely remove the replica spectrums
Therefore, additional reconstruction filter required
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Data Acquisition (A/D Conversion)
14 March 2017 - ELEC 3004: Systems 71
Representation of Signal
• Time Discretization • Digitization
0 5 10 150
100
200
300
400
500
600
time (s)
Ex
pec
ted
sig
nal (m
V)
Coarse time discretization
True signal
Discrete time sampled points
0 5 10 150
100
200
300
400
500
600
time (s)
Ex
pec
ted
sig
nal
(mV
)
Coarse signal digitization
True signal
Digitization
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36
• Analogue to digital converter (A/D) – Calculates nearest binary number to x(nt)
• xq[n] = q(x(nt)), where q() is non-linear rounding fctn
– output modeled as xq[n] = x(nt) + e[n]
• Approximation process – therefore, loss of information (unrecoverable) – known as ‘quantisation noise’ (e[n]) – error reduced as number of bits in A/D increased
• i.e., x, quantisation step size reduces
Quantisation
2][
xne
14 March 2017 - ELEC 3004: Systems 73
Input-output for 4-bit quantiser (two’s compliment)
Analogue
Digital
7 0111
6 0110
5 0101
4 0100
3 0011
2 0010
1 0001
0 0000
-1 1111
-2 1110
-3 1101
-4 1100
-5 1011
-6 1010
-7 1000
x
quantisation
step size
12
2
m
Ax
where A = max amplitude
m = no. quantisation bits
14 March 2017 - ELEC 3004: Systems 74
37
• To estimate SQNR we assume – e[n] is uncorrelated to signal and is a
– uniform random process
• assumptions not always correct! – not the only assumptions we could make…
• Also known a ‘Dynamic range’ (RD) – expressed in decibels (dB)
– ratio of power of largest signal to smallest (noise)
Signal to Quantisation Noise
noise
signal
DP
PR 10log10
14 March 2017 - ELEC 3004: Systems 75
Need to estimate:
1. Noise power – uniform random process: Pnoise = x2/12
2. Signal power – (at least) two possible assumptions 1. sinusoidal: Psignal = A2/2 2. zero mean Gaussian process: Psignal = 2
• Note: as A/3: Psignal A2/9
• where 2 = variance, A = signal amplitude
Dynamic Range
Regardless of assumptions: RD increases by 6dB
for every bit that is added to the quantiser
1 extra bit halves x
i.e., 20log10(1/2) = 6dB
14 March 2017 - ELEC 3004: Systems 76
38
-6 -4 -2 0 2 4 6
-1
-0.5
0
0.5
1
t
sin(10 t) + 0.1 sin(100 t)
-6 -4 -2 0 2 4 6
-1
-0.5
0
0.5
1
t
sin(10 t) + 0.1 sin(100 t)
Derivatives magnify noise!
•
•
•
•
-6 -4 -2 0 2 4 6
-20
-15
-10
-5
0
5
10
15
20
t
10 cos(10 t) + 10 cos(100 t)-6 -4 -2 0 2 4 6
-10
-5
0
5
10
t
10 cos(10 t)
14 March 2017 - ELEC 3004: Systems 77
• Theoretical model of Sampling – bandlimited signal (wB)
– multiplication by ideal impulse train (ws > 2wB) • convolution of frequency spectrums (creates replicas)
– Ideal lowpass filter to remove replica spectrums • wc = ws /2
• Sinc interpolation
• Practical systems – Anti-aliasing filter (wc < ws /2)
– A/D (S/H and quantisation)
– D/A (ZOH)
– Reconstruction filter (wc = ws /2)
Summary
Don’t confuse
theory and
practice!
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• Aliasing and Anti-Aliasing
• Review: – Chapter 5 of Lathi
• A signal has many signals
[Unless it’s bandlimited]
Next Time…
14 March 2017 - ELEC 3004: Systems 79
• Aliasing - through sampling, two entirely different analog
sinusoids take on the same “discrete time” identity
For f[k]=cosΩk, Ω=ωT:
The period has to be less than Fh (highest frequency):
Thus:
ωf: aliased frequency:
Alliasing
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Aliasing: Another view of this
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• Non-ideal filter – wc = ws /2
• Filter usually 4th – 6th order (e.g., Butterworth) – so frequencies > wc may still be present
– not higher order as phase response gets worse
• Luckily, most real signals – are lowpass in nature
• signal power reduces with increasing frequency
– e.g., speech naturally bandlimited (say < 8KHz)
– Natural signals have a (approx) 1/f spectrum
– so, in practice aliasing is not (usually) a problem
Practical Anti-aliasing Filter
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Amplitude spectrum of original signal
w -wm wm
…
Fourier transform of sampled signal
w
…
Original Replica 1 Replica 2
1/t
w
… …
Fourier transform of sampling signal (pulses have finite width)
0 ws = 2/t 4/t
sinc envelope
Zero at harmonic
1/duty cycle
14 March 2017 - ELEC 3004: Systems 83
Fourier transform of original signal X(ω) (signal spectrum) [AGAIN!]
w
… …
Fourier transform of impulse train T(/2) (sampling signal)
0 ws = 2/t 4/t
Original spectrum
convolved with
spectrum of
impulse train …
Fourier transform of sampled signal
w
…
Original Replica 1 Replica 2
1/t
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Original Spectrum
w -wm wm
Replica spectrums
overlap with original
(and each other)
This is Aliasing
w
… …
Fourier transform of impulse train (sampling signal)
0 2/t 4/t 6/t
…
Amplitude spectrum of sampled signal
w
…
Original Replica 1 Replica 2 … 14 March 2017 - ELEC 3004: Systems 85
Amplitude spectrum of sampled signal
Due to overlapping
replicas (aliasing)
the reconstruction
filter cannot recover
the original spectrum
Reconstruction filter (ideal lowpass filter)
w -wc wc = wm
Spectrum of reconstructed signal
w -wm wm
…
w
…
Original Replica 1 Replica 2 …
sampled signal
spectrum
The effect of aliasing is
that higher frequencies
of “alias to” (appear as)
lower frequencies 14 March 2017 - ELEC 3004: Systems 86
43
Rotating wheel and peg
Top
View
Front
View
Need both top and front
view to determine rotation
Another way to see Aliasing Too!
14 March 2017 - ELEC 3004: Systems 87
Temporal Aliasing
90o clockwise rotation/frame
clockwise rotation perceived
270o clockwise rotation/frame
(90o) anticlockwise rotation
perceived i.e., aliasing
Require LPF to ‘blur’ motion
14 March 2017 - ELEC 3004: Systems 88