Lec1 : Introduction to filters Flashcards

1
Q

Powerline interference as an example of why filtering is required?

A
  • Powerline interference (or mains) is frustratingly common! In a perfect world we could remove it completely
  • Consider this example, a 500 Hz signal contaminated with 50 Hz interference
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

How can we define a filter system?

A

A filter system can be defined by a transfer function

H(ejω​)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

How is the transfer function used for the following analog circuit?

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

How do actual filters compare to ideal filters?

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Primary differences between actual and ideal filters?

Also seen as the compromises made when making real filters

A
  • Passband ripple
  • Stopband Ripple
  • Roll off(transition bandwidth)
  • phase response
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Equation for Gain(dB)?

A

Gain(dB) = 20log(Vout/Vin)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

How can the stopband, passband, bandwidth, be shown on a filter output graph?

(frequency response)

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

The graph showing the phase response of a filter?

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

The purpose of the phase reponce?

A
  • The phase response tells us how much each frequency component is shifted by in time.
  • We consider a filter to have a cut-off frequency fc at the point where the amplitude has reduced to -3 dB that of the passband.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Uses of the running average filter?

A

The is commonly used to “smooth” data such as stock market values, it is also the filter that excel uses to smooth plots

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

A non-casual filter?

A
  • If we have stored data then we can use past and future values – this is a non-causal filter
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

A casual filter?

A

A real time system can only use present and past values – this is a causal filter

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

How is filtering a continuous process?

A
  • It is important to realise that the filtering process is continuous, in the sense that the coefficients are static but as time passes by the values of x[n] will be constantly changing depending on the input waveform
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Convolution?

A

The concept of a set of static coefficients being applied to a sliding window is known as convolution, each input is multiplied by a coefficient and summed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

The convolution equation

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

The effect of a filter

A

Note that the output sequence is longer than the input (7 vs 5)

The output has been smoothed relative to input

17
Q

The difference equation for a three point running avergae filter?

A
18
Q

Filter coefficients overview?

A
19
Q

Low Pass Filter frequency response?

A
20
Q

High Pass filter frequency response?

A
21
Q

Band pass filter frequency repsonce?

A
22
Q

Band Stop filter frequency responce?

A
23
Q

Low band pass filter frequency responce?

A
24
Q

Band high pass filter response?

A
25
Q

Low band high pass filter response?

A
26
Q

Applications of LTI syestems

A

LTI systems are very useful as they are able to predict how a system will develop with time. This is very important in signal processing regarding the monitoring of signals per unit time