Electronic Circuit Design
(EE-313)
DE-42 Mechatronics
Syndicate – A
Title:
Complex Engineering Problem
Names of Group Members as per CMS:
1. NC Aliza Naeem
2. NC Maryam Awan
Reg # 337922
Reg # 347962
Submitted to: Assistant Professor Kanwal Naveed
Problem Statement:
A stereo noise creates a huge disturbance, especially when you are listening
to weak radio stations. Peaks of unwanted background noise take over the broadcast signal, making
it unpleasant resulting in a low Signal to Noise Ratio (SNR). This attenuation might cause the
signal to be inaudible most of the time. Therefore, the application of an appropriate amplifier is
essential. We have to make a filter to remove noise and have to use an amplifier to amplify the
noise free signal.
Solution:
Designing of filter:
We have used a gaussian filter in MATLAB to remove the noise from our
input signal. A Gaussian filter is a filter whose impulse response is a Gaussian function (or an
approximation of it since the true Gaussian response has an infinite impulse response). Gaussian
filters have the property of having no ringing on the step function input while minimizing rise and
fall times. This behavior is closely related to the fact that Gaussian filters have the lowest possible
group delay. Gaussian filters have the best combination of high frequency rejection while
minimizing spatial dispersion. This is the key point of the uncertainty principle.
INPUT and OUTPUTS:
Figure 1 Original Signal
Figure 3 Gaussian low pass filter
Figure 2 Noisy Signal
Figure 4 Signal after noise removal
Design of Butterworth 3rd Order Filter:
A Butterworth filter is a type of signal processing filter
designed to have a passband response that is as flat as possible. Butterworth had a reputation for
solving "impossible" math problems. At the time, filter design required a significant amount of
designer experience due to the limitations of the theory in use at the time. This filter was out of
common use for over 30 years after its introduction. Butterworth explained: “An ideal electrical
filter should not only completely eliminate unwanted frequencies, but should also have uniform
sensitivity to desired frequencies.” Although such an ideal filter cannot be achieved, Butterworth
showed that as the number of filter elements with correct values increases, a closer
approximation is obtained. At the time, filters produced large passband ripples and component
value selection was highly interactive. Butterworth showed that it is possible to design a lowpass
filter whose cutoff frequency is normalized to 1 radian per second and whose frequency response
(gain) is
Where ɷ is the angular frequency in radians per second and is the number of poles in the filter—
equal to the number of reactive elements in a passive filter. If ɷ = 1, the amplitude response of this
type of filter in the passband is 1/√2 ≈ 0.7071, which is half power or −3 dB.
This was our attempt to design a filter. Implementing the electronics part.
Proteus Simulations:
Figure 5 Proteus Implementation
Figure 6 Output Audio
Figure 7 Input Audio
Amplifier Design:
Figure 8 Proteus Implementation
Figure 9 Input Output
ANALYSIS:
We choose a two stage BJT amplifier in common emitter configuration. The audio
file we chose was “forest-3-26722”. The input signal was very low in magnitude. The filter we
designed was a third order butter-worth filter.
Understanding SNR:
The SNR understanding was done in MATLAB. Where we generated our own noise signal with
a particular SNR. We simulated a sine wave and noise signal. The filter designed in MATLAB
was 3rd order butterworth Filter to understand the SNR and noise better.
Code:
clc;
close all;
alphap=4;
alphas=30;
fp=1200;
fs=1000;
f=8000;
ohmp=(2*pi*fp)/f;
ohms=(2*pi*fs)/f;
[N,wn]=buttord(ohmp,ohms,alphap,alphas);
[B,A]=butter(N,wn,"low");
t=0:0.01:pi;
[h,ohm]=freqz(B,A,t);
subplot(3,1,1)
plot(ohm/pi,abs(h))
grid on;
xlabel('normalized frequency')
ylabel('gain')
title('frequency response')
subplot(3,1,2)
plot(ohm/pi,20*log(abs(h)))
grid on;
xlabel('normalized frequency')
ylabel('gain in db')
title('frequency response in db')
subplot(3,1,3)
plot(ohm/pi,angle(h))
grid on;
xlabel('normalized frequency')
ylabel('gain in db')
title('frequency response in db')
Amplifier
Cascaded Two Stage BJT Amplifier:
The calculations of Amplifier were done with following assumptions:
Vcc = 12V, VE = 10% of VCC. 2NPN BJTs were considered for design. BC547 and 2N3904.
Cut-off Frequency = 1kHz.
Input Signal => Vp-p = 200mV
Av for first stage is a little less than second stage.
We acquired our Quiescent point by midpoint biasing.
Dc gain: β = 100.
Using Equations of Common emitter, we calculated the values of our resistors and capacitances
with cut off frequency. Higher cut-off frequency 44.68 M Hz was calculated with miller effects.