- Видео 149
- Просмотров 143 938
Ankit Bhurane
Добавлен 28 сен 2015
Lecture 18 Module 1 Inverse Laplace Transform: Distinct Poles
Lecture 18 Module 1 Inverse Laplace Transform Distinct Poles
Просмотров: 741
Видео
Lecture 17 Module 2 Analyzing Systems with Laplace Transform: Causal and Stable Systems
Просмотров 4526 лет назад
Lecture 17 Module 2 Analyzing Systems with Laplace Transform
Lecture 17 Module 1 Properties of Laplace Transform
Просмотров 3026 лет назад
Lecture 17 Module 1 Properties of Laplace Transform
Lecture 16 Module 5 Properties of Region of Convergence
Просмотров 3136 лет назад
Lecture 16 Module 5 Properties of Region of Convergence
Lecture 16 Module 4 Concept of Poles and Zeros
Просмотров 3046 лет назад
Lecture 16 Module 4 Concept of Poles and Zeros
Lecture 16 Module 3 Laplace Transform Examples Two Sided Signal
Просмотров 1,5 тыс.6 лет назад
Lecture 16 Module 3 Laplace Transform Examples Two Sided Signal
Lecture 16 Module 2 Laplace Transform Numerical. Can two Signals have the same Laplace Transform?
Просмотров 3816 лет назад
Lecture 16 Module 2 Laplace Transform: Numerical Can two different signals have the same Laplace transform?
Lecture 16 Module 1 Introducing Laplace Transform
Просмотров 4316 лет назад
Introducing Laplace Transform
Lecture 15 Module 1 Sampling of Discrete-Time Signals
Просмотров 1,4 тыс.6 лет назад
Lecture 15 Module 1 Sampling of Discrete-Time Signals
Lecture 14 Module 5 Discrete-Time Processing of Continuous-Time Signals
Просмотров 2,3 тыс.6 лет назад
Lecture 14 Module 5 Discrete-Time Processing of Continuous-Time Signals
Lecture 14 Module 4 Recovering Continuous-Time Signal from its Samples Time Domain Perspective
Просмотров 4616 лет назад
Lecture 14 Module 4 Recovering Continuous-Time Signal from its Samples Time Domain Perspective
Lecture 14 Module 3 Recovering Continuous Time Signal from its Samples: Sampling Theorem
Просмотров 6776 лет назад
Lecture 14 Module 3 Recovering Continuous-Time Signal from its Samples: Sampling Theorem
Lecture 14 Module 2 Sampling of Continuous-Time Signals: Frequency Domain Perspective
Просмотров 6626 лет назад
Lecture 14 Module 2 Sampling of Continuous-Time Signals: Frequency Domain Perspective
Lecture 14 Module 1 Introducing Sampling of Continuous-Time Signals
Просмотров 8356 лет назад
Lecture 14 Module 1 Introducing Sampling of Continuous-Time Signals
Lecture 13 Module 4 Properties of Discrete -Time Fourier Transform
Просмотров 3656 лет назад
Lecture 13 Module 4 Properties of Discrete-Time Fourier Transform
Lecture 13 Module 3 Discrete-Time Fourier Transform: Examples of Aperiodic and Periodic Signals
Просмотров 5926 лет назад
Lecture 13 Module 3 Discrete-Time Fourier Transform: Examples of Aperiodic and Periodic Signals
Lecture 13 Module 2 Discrete Time Fourier Transform Examples
Просмотров 3426 лет назад
Lecture 13 Module 2 Discrete Time Fourier Transform Examples
Lecture 13 Module 1 Discrete-Time Fourier Transform
Просмотров 3566 лет назад
Lecture 13 Module 1 Discrete-Time Fourier Transform
Lecture 12 Module 5 Fourier Transform of Periodic Signals
Просмотров 5296 лет назад
Lecture 12 Module 5 Fourier Transform of Periodic Signals
Lecture 12 Module 4 Fourier Transform of Unit Step and Signum Function
Просмотров 1,8 тыс.6 лет назад
Lecture 12 Module 4 Fourier Transform of Unit Step and Signum Function
Lecture 12 Module 3 Fourier Transform of Unit Impulse Dirac Delta and Constant Signal
Просмотров 1,1 тыс.6 лет назад
Lecture 12 Module 3 Fourier Transform of Unit Impulse Dirac Delta and Constant Signal
Lecture 12 Module 2 Properties of Continuous Time Fourier Transform
Просмотров 4316 лет назад
Lecture 12 Module 2 Properties of Continuous Time Fourier Transform
Lecture 11 Module 3 Discrete Time Fourier Series Properties
Просмотров 5226 лет назад
Lecture 11 Module 3 Discrete Time Fourier Series Properties
Lecture 12 Module 1 Introducing Fourier Transform
Просмотров 5556 лет назад
Lecture 12 Module 1 Introducing Fourier Transform
Lecture 11 Module 2 Discrete Time Fourier Series Example Square Wave
Просмотров 4,5 тыс.6 лет назад
Lecture 11 Module 2 Discrete Time Fourier Series Example Square Wave
Lecture 11 Module 1 Fourier Series Comparison of Continuous and Discrete Time Periodic Signals
Просмотров 6026 лет назад
Lecture 11 Module 1 Fourier Series Comparison of Continuous and Discrete Time Periodic Signals
Lecture 10 Module 2 Properties of Fourier Series Parsevals Theorem
Просмотров 4116 лет назад
Lecture 10 Module 2 Properties of Fourier Series Parsevals Theorem
Lecture 10 Module 3 Dirichlet Conditions
Просмотров 5896 лет назад
Lecture 10 Module 3 Dirichlet Conditions
Lecture 10 Module 1 Properties of Fourier Series
Просмотров 5056 лет назад
Lecture 10 Module 1 Properties of Fourier Series
Lecture 9 Module 4 Fourier Series of Full Wave Rectified Wave
Просмотров 6 тыс.6 лет назад
Lecture 9 Module 4 Fourier Series of Full Wave Rectified Wave
the great video i had a lot of confusions but finished in not more than 30 mins you're life saver
how we got the integration at 18:08 please and thank you
Solve it step by step: The intermediate expression before the limit substitution is: = (t•Tau) - (Tau^2)/2 Then after substitution of limits you get the shown expression.
Isidro Burgs
in case 2 how did u get the t^2 ? it should have been taken outside the integral sign because you are integrating with respect to tau
At 20:20, your integration is incorrect. I did it many times and got the same answer, but different than yours. I also checked it with online calculators.
I'm sorry to say there are a lot of mistakes in the explanations and even in the equations you gave at the end the function y(t) exists only between 0 and 1.
When you reach the time period of {2 ≤ t ≤3} wouldn't the function in your integral be just 1? Since the area of convergence/overlapping occuring is just up to the amplitude (or just a square decreasing) of x(t) = 1?
Could one exolpalin to me why he didn't put 2 in equation of line is 2-t why in his integrations didn't put 2 ??
nice explanation
Bro I can actually understand you and hear you very clearly. Legend
Thanks a lot
Hey! Through a projection, you were able to demonstrate that Cos and Sin are two different components of eiθ. But Mathematically, we show that eiθ = cos θ + isinθ through Taylor's theorem, on the argument that both eiθ and cos θ + isin θ can be expressed into the same exponential. Since a direct visual demonstration of the identity can be established, isn't it possible to go directly from eiθ to cos θ + isin θ without showing that both can be converted to some same other thing?
Hello Oosman, You are correct. Of course we use the Eulers identity to represent e^j● = cos(●)+jsin(●) This is just to visualize and get a feel of how a complex function can be visualized which is otherwise not that intuitive! This also demonstrates that a complex function in fact has two real functions combined.
so for homogeneity(scaling) we use the power of x and y?
Neso waale bhaiya ho kya aap
I think WRONG SOLVING AT 18:00
your voice is similar to neso academy signals and systems teacher
😍
Спасибоо❤😊
thank you.
Example 4 is function
Yes! Thanks for noting and correcting the typo. 🙏
Disappointing, too much playing about with the computer graphics.
U Neso Academy Guy??
No dear. My name is Ankit A. Bhurane
The blue hatching is often wrong. It should run all the way up to the red response function h, wherever x is 1.
I have considered blue hatching only for the overlap between the two signals as for non-overlapping regions, it's zero.
what is slope?
Here the function is h(t)=t which means slope is 45⁰ so tan(45)=1 so slope is 1
@@noone7692 no it's not like that, after I learned from one of my friend
o nooo, how you find t- tawo
very nice explanation
amplitude of impulse is infinity , right ? how this converter works ?
Yes, but area of impulse function is unity (1).
@@ankitbhurane9762 yes but how this converter operator takes each value as samples and turns it to a sequence? By a kind of integration or something? I know unit impulse function has scalable area and infinity amplitude. But I don't understand how the transforms " x(nT). impulse( t - nT) >> x[n]" or just " impulse (t) >> impulse [n] as amplitude of 1" are performed.
According to the first condition if we suppose a function f=1/√x in for [0,2] with time period 2 will it have Fourier transform bcoz it is absolutely integrable but has infinite value at 0??
I want to ask, what software do you use in drawing this lecture?
Hello Juan, I use OpenBoard software. Link is here: openboard.ch/download.en.html
if in the convolution I assume t=0, then how will you solve the integration?
For t=0, both first and second sub equations are valid. i.e. 0 and t^2/2
thank you , it's so practical
really helpfull. lot of love
Please someone clearly tell me how we are getting (t-2)on the left hand side after time reversal, I can not get that. In my sense it would be (t+2) but. Please clearly tell someone shifting process after time reversal.
Dear Jamila, After flipping, as the current axis is t (the point on right) so anything to the left of it would be t minus something. As the width of the signal is 2, the point on left will be t-2. Consider t as some value say 5 so t-2 will be less than that, say 3 and so on.
thank u!
Sir, what is difference between Verilog and VHDL? Which is better?
thankyou very much !!!!!
Y(t)=0 for t>3....I think
Yes, you are correct! That's a typo. Please consider.🙏
U explain in the best way i have read
Thank you Jaitavya.
Thank you for the explanation, good sir
ur tone of voice makes me want to break my headphones
Could you please give your constructive inputs? I am using professional Blue Snowball microphone 🎤 with filter.
your useless comment wants me to kick your ass
Why are you g*y ?
Why are you g @y ??
Stop being a gey then
Nice one sir🙏
Thank you for a very nice explanation. This is by far the best signal processing series I have seen on youtube! More people should watch this :)
Thank you ! very well explained
Brilliant explaination , sir
Thanks Tejas!
This is the example of dirichilet condition ?
Yes they are!
32:00
Sorry but the concept of recovering a signal from its samples is an easy concept it doesn't need 18min it's too long you should instead make the computations using the fourier transform to explain how mathematically you can recover it ! Thanks for your effort anyway !!
Hello Khadidja Bennour, Thanks for your inputs. The mathematical recovery is already explained in the next module o this lecture i.e. Lecture 14 Module 4.
Please note that the vertical axis in the last diagram x(-t) is at -1.
I have seen various videos in order to understand the topic .....got that into my mind after seeing the elaborate explanation in video......thankyou sir
Your handwriting and drawings , symbols such as Ω and x , 𝜏 are very attentive, speech is very clear. Thank you so much for your effort 👌🏻🙏🏻
Thank you, Kadir!