Thanks for your video. If I have a desired vector uo=[1 2 3 4] and I add awgn noise to it i.e. u=awgn(uo,35); Now if I take 100 such vectors that are not exactly equal but are nearly the same as uo, i.e. [1.001 1.998 2.989 4.001] [1.021 2.008 3.011 4.021] [0.989 1.978 2.979 3.8901] . . . . [0.899 2.040 3.110 3.998] Then how can I use these 100 vectors to remove the noise and get the desired vector uo using your program? Regards
@Knowledge Amplifier. Thanks alot for the video, it has gave me idea how to program it. Can you please help me how to program a code for the below question as i am not getting the output as required. I will be very very thankful to you. Write a program that will smooth an array of noisy data using a two-point average. Assume the array of data is a row vector and that a backwards average will be used for the smoothing. For a backwards two-point average, Xavg,𝑘 =(X𝑘+X𝑘−1)/2, except for 𝑘 = 1 where Xavg,1 = X1. Use loops and conditional statements rather than array operations. Use the following statement (for MATLAB) to create a row vector of noisy data from a normal distribution with a mean of 10.0 and a standard deviation of 2.0 to test your program: • noisydata = 10.0 + 2.0 ∗ 𝑟andn(1,100);
The moving average filter has all coefficients equal ... For example, a 5 point filter has the filter kernel: ˛ 0, 0, 1/5, 1/5, 1/5, 1/5, 1/5, 0, 0 ˛ . That is, the moving average filter is a convolution of the input signal with a rectangular pulse having an area of one. Hope you got my point. Happy Learning :-)
Very useful … too good! Thanks a lot ! Plz make more such MATLAB videos …
Thank you, I will @Tom-sp3gy. Happy Learning !
Great lesson
Glad to hear this Shimaal Carrim! Happy Learning
Sir, very nice, please keep on producing such helpful material for dsp
Thank You Abubakar Safi for this inspiring comment! Happy Learning :-)
How do we know a and b (denominator and numenator) in a real data?
Hi bro can you please explain how to calculate numarator and denominator values please
I appreciate your help. Please uploade a video with taka real temperature data and how to filter it? I will expect you
Very nice man , very nice , you helped me a lot
Thank You athlene x :-)
@@KnowledgeAmplifier1 I want to implement this but without using builtin filter function. Can you help me in this regard
It would have been much bettter presentation if you had made a PowerPoint slide instead of digital pen
how can we filter 3 number at the same time?
Great content
Thank You Pablo marchesi selma! Happy Learning & have a great new year ahead :-)
What does window size means
Very clear explanation
Thank You Abhijeet Mishra. Happy Coding :-)
Thanks for your video.
If I have a desired vector uo=[1 2 3 4] and I add awgn noise to it i.e. u=awgn(uo,35); Now if I take 100 such vectors that are not exactly equal but are nearly the same as uo, i.e.
[1.001 1.998 2.989 4.001]
[1.021 2.008 3.011 4.021]
[0.989 1.978 2.979 3.8901]
.
.
.
.
[0.899 2.040 3.110 3.998]
Then how can I use these 100 vectors to remove the noise and get the desired vector uo using your program?
Regards
Thank you man! Really helpful!
Glad it helped ViralTube! Happy Learning :-)
thank you for the video, you help me a lot...
Welcome Daniel Alejandro Dominguez
. Happy Learning :-)
@Knowledge Amplifier. Thanks alot for the video, it has gave me idea how to program it.
Can you please help me how to program a code for the below question as i am not getting the output as required. I will be very very thankful to you.
Write a program that will smooth an array of noisy data using a two-point average. Assume the array of data is a row vector
and that a backwards average will be used for the smoothing. For a backwards two-point
average, Xavg,𝑘 =(X𝑘+X𝑘−1)/2, except for 𝑘 = 1 where Xavg,1 = X1. Use loops and
conditional statements rather than array operations. Use the following statement (for
MATLAB) to create a row vector of noisy data from a normal distribution with a mean
of 10.0 and a standard deviation of 2.0 to test your program:
• noisydata = 10.0 + 2.0 ∗ 𝑟andn(1,100);
gj
moving averrage. hhahah
😅
It is ridiculous that you chose those coefficients equal. this makes your video useless
The moving average filter has all coefficients equal ...
For example, a 5 point filter has the filter kernel:
˛ 0, 0, 1/5, 1/5, 1/5, 1/5, 1/5, 0, 0 ˛ . That is, the moving average filter is a
convolution of the input signal with a rectangular pulse having an area of one.
Hope you got my point.
Happy Learning :-)