Analysis of discrete convolution in the matlab program

SCIENTIFIC PROGRESS
 
VOLUME 2 ǀ ISSUE 4 ǀ 2021 
ISSN: 2181-1601
Uzbekistan
 
www.scientificprogress.uz
  
Page 1025
Let’s say we turn on the voltage source for a while and then turn it off. In the time 
range, the voltage dependence of the source is rectangular. We also describe its impulse 
response, i.e., the reaction of the chain to the delta function. Recall the Delta function or 
Dirac function, this function is not zero and is only zero at the beginning of the 
coordinates or at time t, i.e. an infinitely short infinitely strong pulse. Thus the reaction 
of the system to such impulses is called the impulse reaction. With its help we can 
describe the dynamics of the system. 
DISCUSSION 
In our case, to calculate the output signal, we need to combine the voltage across 
the capacitor, the input signal of the circuit voltage, with the pulsed reaction of the 
electron. Convolution is used to calculate the output of a system with a known input 
signal and a known characteristic of the system. Now, let’s look at an example of using 
convolution to find signals. If the system has a pulsed response that is opposite to the 
value of the input sequence, then the result of the convolution operation will be 
maximal when the samples match. Based on this, it is possible to obtain the principle of 
compatible filtering. Appropriate filters are used when it is not important for us to 
maintain the received signal form, but when it is important to determine its presence on 
air and the time of arrival. 
Figure-5 
This is one of the usual radar functions, that is, if we know in advance the form of 
the signal we want to receive. It is not difficult to find the filter coefficients, i.e. the 
value of the second sequence for the convolution operation, otherwise they are equal to 
the signal report. In this case, the value at the filter output will be maximum when a 
noisy signal arrives at the input of the system. Similarly, signals can be detected in the 
background of noise. 
Let’s look at an example in MATLAB. In this example, we look for a random 
sequence of x using a filter that matches the pulse reaction of h. And we form the h 
impulse response by reflecting the sequence x. The fliplr function displays array 
elements.
RESULTS
We then add random elements before and after our sequence, as well as additional 
noise. We know where we put our sequence, so we write the last state of our sequence 
in the EXPECTED_NUM variable. We then calculate the conversion of our noisy signal 

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