# Basic terminology by decree

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```									                                   DSP Basics

1.0    DSP SYSTEMS                               2

Example 1     Delay                             3

Example 2     Moving average filter             4

Example 3     Differentiator                    5

Example 4     Integrator / Accumulator          6

Example 5     A 3 term Controller               6

Example 6     Audio Tone Control                7

Example 7     Sine wave Generator               8

Example 8     AC measurement                    9

2.0 TRANSFER FUNCTIONS                          10

3.0    SAMPLING                                 11

3.1    Aliasing                                12

3.2    Reconstruction (Spectral Images)        13

3.3    Reconstruction filtering                14

3.4    A 2 times Oversampling DSP System       16

3.5    DSP interface hardware                  17

4.0    DIGITAL SIGNALS                          18

4.1    The impulse (n)                        18

4.2    The unit step u(n)                      18

4.3    The ramp r(n) or n.u(n)                 18

4.4    Digital sine wave                       19

5.0 CORRELATION                                 20

5.1    Cross correlation                       20

5.2    Auto Correlation                        21

6.0    CONVOLUTION                              22

1
1.0            DSP systems

X(t)

X(n)

0     T     2T            3T                       nT

Anti alias             Sampling             Processing               Reconstruction

X(t)               X(n)                      Y(n)              Y(t)
Filter                 ADC                   DSP                DAC               Filter

X(t)       The analog (continuous) input signal
X(n)       The sampled (discrete) input signal
Y(n)       The sampled (discrete) output signal
Y(t)       The analog (continuous) output signal

Sampling theorem
An analog signal may be completely represented by sampling
(and completely recovered ) provided

Sampling frequency (Fs) = 2. max. input frequency

(b)        Digital input / output

X(n)                  Y(n)
Sequence of Numbers                    DSP                      Sequence of Numbers

2
Example 1 Delay
(a) One Sample Delay                                                                                 Simulation Diagram
Difference Equation                           Y(n) = X(n-1)
X(n)
Notation
X(n)              The present input sample                                                                D
X(n-1)            The previous input sample
X(n+1)            The next input sample                                                                               Y(n)
Y(n)              The calculated output sample

n             =              0,          1,          2,       3,     4, ……
Input             =             X(n)          =              5,          3,          7,       9,     3, ……
Output            =             Y(n)          =              0,          5,          3,       7,     9, ……

(a) Two Sample Delay

Difference Equation                           Y(n) = X(n-2)

Input =           X(n)          =             5,             3,          7,          9,       3, ……
Output =          Y(n)          =             0,             0,          5,          3,       7,   9, ……

2 sample delay at frequency 250Hz (Fs = 16 KHz)                                                          Simulation Diagram

X(n)
1.50

1.00                                                                                                           D
0.50
Input
0.00                                                                                                           D
output
13

17

21

25

29

33

37

41

45

49
1

5

9

-0.50                                                                                                                       Y(n)
-1.00

-1.50

2 sample delay at frequency 1000Hz (Fs = 16 KHz)

1.50

1.00

0.50
Input
0.00
output
1
5
9

13
17
21
25
29

33
37
41
45

49

-0.50

-1.00

-1.50

3
Example 2 Moving average filter
Gets the average of a number of input values
A two term moving average filter gets the average of the present value and the
previous value of the input. (A 5 term averager will calculate the average of 5
samples)

Difference equation: Y(n) = 0.5X(n) + 0.5X(n-1)

Operates as a smoothing or low pass filter

Simulation Diagram                                           Frequency response

0.5
X(n)                                +                     Y(n)
1
D
0.5
       

Apply this filter to records of daily temperature data given below
Day: n          1       2       3       4      5       6      7                     8      9
Temp: X         15      14      16      13     12      15     14                    16     15

At day 4 i.e. n = 4
Y(4) = 0.5 X(4) + 0.5X(3)
= 0.5 (13) + 0.5 (16) = 14.5               i.e. the output for day 4 is 14.5

Note the first day calculation has incomplete data. Standard practice is to use zero for
samples not available. If using Exel input data needs to be filled with leading zeros.

Result of a 7 point moving average on Wind speed data

25
20
15                                                                                     input data
10                                                                                     Filtered data
5
0
1    3   5   7     9   11 13 15 17 19 21 23 25 27 29

4
Example 3               Differentiator                                                                                   x
Δx/ Δt The change in x divided by the time difference
Δx
Y(n) = [X(n) - X(n-1)]/T
A differentiator is a high pass filter                                                                          Δt
i.e. d/dt (sin 2ft) = 2f Cos 2ft
Gain is proportional to frequency.
T            t

The 1/T multiplier can be removed
resulting in a simpler equation which still acts as a differentiator but with less Gain

Difference Equation Y(n) = X(n) - X(n-1)

Simulation Diagram                                                        Frequency response
1                                                                                          
X(n)                                 +                            Y(n)
2
D
-1
           

Apply this filter to records of daily temperature data given below
Day: n          1       2       3       4      5       6      7                                   8      9
Temp: X         15      14      16      13     12      15     14                                  16     15

At day 4 i.e. n = 4
Y(4) = X(4) - X(3)         = 13 - 16 = 3
o
The output for day 4 is 3 . i.e. the change in temp between day 3 and day 4.

The Differentiator detects sudden changes in data trends as shown below

2.50
2.00
1.50
1.00
0.50
Input
0.00
output
1
4
7

10
13
16

19
22
25

28
31
34

37
40
43

46
49

-0.50
-1.00
-1.50
-2.00
-2.50

5
Example 4                 Integrator / Accumulator

Piecewise Integration is calculated by adding the next rectangle to the previously
accumulated result i.e. new area = old area + new rectangle

x
y(n) = Tx(n) + y(n-1) where T is the sampling period

Tz
Transfer Function
z 1
T

T
Simulation Diagram                   X(n)                  +                                                  Y(n)

D
1

This process is also used as an accumulator without the T i.e. y(n) = x(n) + y(n-1)

Not usually used as a standalone process as the output is unbounded for constant
input. Cannot be used if the average input is not zero i.e. no DC, marginally stable.
Can be used as part of a feedback system      e.g. AGC or 2 or 3 term controllers see
below

Example 5      A 3 term Controller
PID controller
Sampling time is 0.01 secs

5.56

Proportional

Speed output       0.1
Vin
In1 Out1              15                                                    Tacho         Speed
Integ Gain              DAC             DC motor
Required
speed           Integerator

In1 Out1              1

Diff Gain
Differentiator

Integrator reduces the error between required speed and actual speed.
Differentiator increases system reaction time i.e. motor speeds up quicker.
The Motor and tacho are the only analogue blocks everything else is digital.
There should be an ADC after the tacho.

6
Example 6                                    Audio Tone Control

Shelving Filter
The amount of boost/cut is controlled by the coefficients. (0.5 in the example below)
Positive values for bass boost and negative for treble boost.
The middle frequency is unchanged.
The effect is increased as the coefficient approaches 1 and decreased as the coefficient
approaches 0.

Example Bass boost
Difference Equation:                                       y(n) = x(n) + 0.5x(n-1)+ 0.5y(n-1)

z  0. 5
Transfer Function:
z  0. 5

Simulation Diagram
1
X(n)                              +                 +                            Y(n)

D                                                  D
0.5
0.5

Frequency Response
3

2.5

2

1.5

1

0.5

0
0   1000      2000       3000   4000   5000   6000    7000   8000

7
Example 7                   Sine wave Generator

If 2 values of a sine wave are known the next one can be calculated.
The first 2 values are initialised then all values can be calculated using the preceding 2
values.
i.e. start with 2 values then calculate 3rd value, wait one sample then calculate 4th
value using 2nd and 3rd values, discard 1st value.
5th value is calculated using 3rd and 4th values. Etc.

The coefficient in the Difference equation determines the frequency.
The initial values determine the voltage.

Difference Equation for a 500Hz sine wave if Fs is 16KHz.

Y(n) = 1.96 Y(n-1) – Y(n-2)

First value = 0                                               1*sin(0)
Second value= 0.195 ( for a peak of 1)                        1*sin(pi/16)

Third value = 1.96*0.195 – 0                                  =         0.382
Forth value = 1.96*0.382 - 0.195                              =         0.554

output

1.50
1.00
0.50
0.00
1

4
7
10

13
16

19

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25

28
31

34
37
40

43
46

49

-0.50
-1.00
-1.50

The above equation uses only one multiply and a subtraction per sample.

Other options:
High level function: y = sin(n*angle)               Disadvantage: slow
Lookup table: Preload a full cycle into memory, frequency can be changed by
skipping some values.
Disadvantage: Only a fixed no of different frequencies may be produced

Theory
A Filter is created with a pole on the unit circle at an angle corresponding to the
frequency of oscillation. See poles and zeros later.

8
Example 8             AC measurement

AC                            Rectifier                     Smoothing       Scale   Read
signal                                                      Filter                  Value

Before an AC signal can me measured it must be converted to a steady value.
Shown above is one method of AC measurement.
 The signal is first rectified (lose the sign)
 Converted to an average value using a digital smoothing or low pass filter
(same idea as in a circuit for a power supply),
 The value is scaled to give RMS or peak value, this is a simple multiply.

The rectifier is needed in the above system since the average value of an AC signal is
0.

The effectiveness of the smoothing filter is crucial to the overall accuracy of the
measurement.
Considerations:
 Will there be a ripple after smoothing?
 For what range of frequencies will the system work.
 Will there be a significant delay in the filter before we can read the value?
The example below shows a slight measurement error of 0.006V and a delay of about
200 samples, which is 0.0125 Seconds at 16KHz sampling.

input
1

0.5

0

-0.5

-1
0   50   100   150    200    250     300   350   400   450   500

output
1

0.8

0.6

0.4

0.2

0
0   50   100   150    200    250     300   350   400   450   500

9
2.0 Transfer functions
Definition TF = Output / Input
Output is calculated by simple multiplication of the transfer function by the input.
Systems can be created easily by combining Blocks.

TF                  Output = TF. Input

TF1            TF2              TF = TF1. TF2

TF1

+            TF = TF1 + TF2

TF2

(a)     Convert a difference equation to a Transfer Function
Note : Multiply by z is a time shift of one sample

Difference equation  y(n) = x(n) + 0.5y(n-1)
Convert to z         Y(z) = X(z) + 0.5 z-1 Y(z)
Collect y’s to left  Y(z) - 0.5 z-1 Y(z) = X(z)
Y(z)(1 - 0.5 z-1 ) = X(z)
Y            1              z
Transfer Function =                  1

X       1  0.5 z        z  0.5

(b)     Convert a transfer function to a difference equation
Y ( z)          z  0.5
Shelving filter H ( z )                 
X ( z)          z  0.5

Multiply out any factors in the transfer function and cross multiply

Y(z) [z – 0.5] = X(z) [z+0.5]
z Y(z) – 0.5 Y(z) = z X(z) + 0.5 X(z)

Divide by highest power of z to ensure all powers of z are negative
Y(z) –0.5 z-1 Y(z) = X(z) + 0.5 z-1X(z)

Y(z) = X(z) + 0.5 z-1X(z) +0.5 z-1 Y(z)

Convert to difference equation using time shift theorem
Y(n) = X(n) + 0.5X(n-1) +0.5Y(n-1)

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3.0   Sampling

11
3.1   Aliasing

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3.2     Reconstruction                    (Spectral Images)
When a 20 KHz sine wave is sampled at 40 KHz the Reconstructed signal will be a Square wave
(without any reconstruction filtering)

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3.3   Reconstruction filtering

14
Calculating the filter order
Example CD player reconstruction filter. Sampling frequency = 44.1 Khz
Music bandwidth B = 20 Khz.
Attenuation required at first image = 60 dBs

(a) no oversampling first image = start of stop band = 44K - 20K = 24 KHz.
60 = 20 n log(24/20) => n = 37

(b) 2X oversampling first image = 88K - 20K = 68 KHz.
60 = 20 n log(68/20) => n = 5.6

(c) 8X oversampling first image = 352K - 20K = 332 KHz.
60 = 20 n log(332/20) => n = 2.5

note: A digital interpolation filter is required in all oversampling cases to remove
spectral images caused by the system sampling frequency of 44 Khz.
Digital filter order is 37 as calculated in (a) for this example.

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3.4   A 2 times Oversampling DSP System

16
3.5   DSP interface hardware

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4.0               Digital signals

4.1      The impulse (n)
1
(n) = 1 at n = 0
(n) = 0 at n  0                              -2    -1       0       1   2               n

What are the following signals             (n-1),                (n+3),             (2),       (0).

4.2      The unit step u(n)
1
u (n) = 1 at n > 0
u (n) = 0 at n < 0
-2    -1       0       1   2   3   4           n

4.3      The ramp r(n) or n.u(n)

r(n) = 0, 1, 2, 3, 4, 5, .... etc. .
-2    -1       0       1   2   3   4           n

Exercise 5.0      find expressions for the signals below (see example 1.1 p 15 Lynn)

1                            1

0 1 2 3 4 5 6 7 8 9              n                  -4 -3 -2 -1 0 1 2 3 4 5 6                     n

4

-4 -3 -2 -1 0 1 2 3 4 5 6        n                  -6 -5 -4 -3 -2 -1 0 1 2 3 4                   n
-2

18
4.4            Digital sine wave
Methods of specifying frequency in DSP
Examples are for a 2KHz sine wave if Fs is 16KHz.
(a) Actual Frequency Sin(2 f nT) f is between 0 and 8000
Example: f = 2000           Sin(2 2000 n 0.0000625)

(b) Normalised Frequency         Sin(2 f n ) f is between 0 and 0.5
Example: f = 0.125               Sin(2 0.125 n )

(c) Angle    Sin(n )             is between 0 and 
Example:  = /4                 Sin(0.25 n)

x = Sin n/4 = 0, 0.7, 1, 0.7, 0, -0.7, -1, -0.7, 0, etc.

1

0.5

0
0     1     2     3     4         5    6    7       8
-0.5

-1

n

The above signal has period = 8 samples = 8T => frequency 1/8T = Fs/8

Relationship between frequency and 

f                                              
  2                            and             f         Fs
Fs                                              2

 = 2 corresponds to a frequency of Fs or 16KHz for our example.
 =  corresponds to a frequency of Fs/2 or 8KHz etc.

2            Fs
Number of samples /period                or
             f

Periodic signals               See p18 Lynn
A digital signal will be periodic if it repeats after an integer number of samples.
e.g. if Fs = 16 KHz : 2 KHz ( = /4) will repeat after 8 samples (one period)
3 KHz ( = 0.375 or 3/8 ) will repeat after 16 samples (3 periods)

A digital signal is periodic if the no of samples in a period is a rational number.
                                   m           f    m
is a rational number i. e.             or     
2                                   N          Fs N 
where m is any integer (no of periods) and N is the repetition time(no of samples)
i.e.  = 0.125 (500Hz)          /2 = 0.0625 = 1/16 => repeats after 16 samples
but  = 0.5 (636.619Hz) /2 = 0.0795774                        does not repeat
19
5.0 Correlation

5.1     Cross correlation
The correlation of two signals provides a statistical comparison between the two
signals and is calculated as the average cross product of the signals as a function of the
time shift between them. The output will reflect any components held in common.

1 N 1
 x ( n) y ( n  k )
rxy ( k ) 
N n0
output is a function of k not n.
where N is the length of the signal.
if the signal is periodic we only need to average over a period, ( i.e. N = the period )
note x(n)y(n-k) is often used as it contains the same information i.e. rxy(-k) or ryx(k)

Example 5.1 Calculate the crosscorrelation of the two signals
x(n) = 0.5,1,1,1 and y(n) = 1,0.5,0.2,0.1
x(n)                                                                                  y(n)
1                                                       1
.5                                                                                           .5         .2   .1
0          1         2         3                   n                                  0         1          2     3    n
n
X(n)
1
.5

0      1            2          3
1                                           n
at k = 3 we get        Y(n+3)                                            .5                                         shift
.2    .1

Crosscorrelation = .05, 0.2, 0.55, 1.3, 1.7, 1.5, 1                                                     as k goes from +3 to -3

Example 5.2
A noisy FSK signal (top) is first correlated with 1650Hz representing logic 1(middle)
and then correlated against 1850 Hz representing logic 0 (bottem)
noisy fsk signal
2

1

0

-1

-2
0        20        40        60        80             100             120   140    160     180       200
correlation with 1850Hz
100

50

0

-50

-100
-100        -80        -60       -40   -20                 0              20    40     60      80        100
correlation with 1650Hz
100

50

0

-50

-100
-100        -80        -60       -40   -20                 0              20    40     60      80        100

20
5.2        Auto Correlation
The average product of a signal with a time shifted version of itself
1 N 1
rxx ( k )       x ( n) x ( n  k )
N n0
Used to identify repetitive signals in noise
A random signal will produce a output only at time shift = 0 while a repetitive signal
will come through the correlation
The shift (k) is often kept to about 15 -20% of the signal length since the number of
products decreases as the shift increases and the results are less reliable.
i.e. if the sample size is 100 use k = -15 to +15 for analysis.

Examples
Original Signal                       Autocorrelation

2                                     200

0                                       0

-2                                   -200
0   50     100    150    200       -100    -50     0        50   100
2                                     200

0                                       0

-2                                   -200
0   50     100    150    200       -100    -50     0        50   100
2                                     200

0                                       0

-2                                   -200
0   50     100    150    200       -100    -50     0        50   100
2                                     200

0                                       0

-2                                   -200
0   50     100    150    200       -100    -50     0        50   100

Plot 1: Sine Wave. Repetitive patterns come through the correlation unchanged
Plot 2: DC. Same for constant levels.
Plot 3: Random noise is only correlated at zero shift so produces a single spike at 0.
Plot 4: Noisy signal. The repetitive pattern is more obvious after correlation since
random noise is correlated into a single spike.

21
6.0       Convolution


x ( n ) * y ( n)       x ( k ) y (n  k )
k  

One signal is reversed and shifted through the other with the sum of the cross products
calculated at each shift. Note Convolution output is a function of n (time).
Either signal may be shifted since X * Y = Y * X
Calculation method: set a value for n then calculate the sum for all the k values, this
gives the result for the n value chosen, repeat for all other values of n.
We will see later how convolution is used to implement FIR filters by getting the
convolution of the input signal with the filters impulse response.

Example 6.1 Calculate the convolution of the two signals
x(n) = 1,0.5,0.2,0.1 and y(n) = 0.5,1,1,1
X(n)                                                                                            Y(n)

1                                                                                                                     1

.5                                                                                    .5
.2           .1

0     1         2             3                 n                                         0             1       2   3         n

Y(n) is reversed and shifted through X(n)

1
X(k)
.5
.2             .1

0        1        2              3                      k
n=0                        1

.5                                              Output(0) = 0.5
Y(-k)
only k= 0 produces an
output
-3               -2       -1       0             k

1
n=1
.5
Y(1-k)
Output(1) = 1+0.25=1.25
only k=0 and k=1
-2           -1       0        1                 k
produce outputs

1.758
1.6
1.25
0.8
Output(n)                         .5
0.3
0.1

0            1        2        3       4         5             6          n

22

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