Ankit Bhurane
Ankit Bhurane
  • Видео 149
  • Просмотров 143 938

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

Комментарии

  • @mugusengajeannepo6746
    @mugusengajeannepo6746 2 месяца назад

    the great video i had a lot of confusions but finished in not more than 30 mins you're life saver

  • @omerahmet-r1u
    @omerahmet-r1u 3 месяца назад

    how we got the integration at 18:08 please and thank you

    • @ankitbhurane9762
      @ankitbhurane9762 3 месяца назад

      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.

  • @MosesFrances-c4r
    @MosesFrances-c4r 4 месяца назад

    Isidro Burgs

  • @lisaprince1313
    @lisaprince1313 4 месяца назад

    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

  • @bendustin7609
    @bendustin7609 9 месяцев назад

    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.

  • @tshibengabukalonji8067
    @tshibengabukalonji8067 Год назад

    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.

  • @jaimeayala6969
    @jaimeayala6969 Год назад

    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?

  • @papammeheia68
    @papammeheia68 Год назад

    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 ??

  • @vaibhavkapadia1
    @vaibhavkapadia1 Год назад

    nice explanation

  • @irving9509
    @irving9509 Год назад

    Bro I can actually understand you and hear you very clearly. Legend

  • @mohammedmorad8013
    @mohammedmorad8013 Год назад

    Thanks a lot

  • @oosmanbeekawoo
    @oosmanbeekawoo Год назад

    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?

    • @ankitbhurane9762
      @ankitbhurane9762 Год назад

      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.

  • @ifeanyinneji7704
    @ifeanyinneji7704 2 года назад

    so for homogeneity(scaling) we use the power of x and y?

  • @Hashira_slayer
    @Hashira_slayer 2 года назад

    Neso waale bhaiya ho kya aap

  • @yelinyigitoglu3375
    @yelinyigitoglu3375 2 года назад

    I think WRONG SOLVING AT 18:00

  • @premat.j.2333
    @premat.j.2333 2 года назад

    your voice is similar to neso academy signals and systems teacher

  • @alibhai1877
    @alibhai1877 2 года назад

    😍

  • @Kazakhwoman
    @Kazakhwoman 2 года назад

    Спасибоо❤😊

  • @jason-92
    @jason-92 2 года назад

    thank you.

  • @mohamedfardinkhan
    @mohamedfardinkhan 2 года назад

    Example 4 is function

    • @ankitbhurane9762
      @ankitbhurane9762 2 года назад

      Yes! Thanks for noting and correcting the typo. 🙏

  • @philcooper279
    @philcooper279 2 года назад

    Disappointing, too much playing about with the computer graphics.

  • @GeraltOfRivia69
    @GeraltOfRivia69 2 года назад

    U Neso Academy Guy??

  • @benheideveld4617
    @benheideveld4617 2 года назад

    The blue hatching is often wrong. It should run all the way up to the red response function h, wherever x is 1.

    • @ankitbhurane9762
      @ankitbhurane9762 2 года назад

      I have considered blue hatching only for the overlap between the two signals as for non-overlapping regions, it's zero.

  • @safeegull22
    @safeegull22 2 года назад

    what is slope?

    • @noone7692
      @noone7692 2 года назад

      Here the function is h(t)=t which means slope is 45⁰ so tan(45)=1 so slope is 1

    • @safeegull22
      @safeegull22 2 года назад

      @@noone7692 no it's not like that, after I learned from one of my friend

  • @safeegull22
    @safeegull22 2 года назад

    o nooo, how you find t- tawo

  • @Vanz-ff5lg
    @Vanz-ff5lg 2 года назад

    very nice explanation

  • @tıbhendese
    @tıbhendese 2 года назад

    amplitude of impulse is infinity , right ? how this converter works ?

    • @ankitbhurane9762
      @ankitbhurane9762 2 года назад

      Yes, but area of impulse function is unity (1).

    • @tıbhendese
      @tıbhendese 2 года назад

      @@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.

  • @kamalsandhu650
    @kamalsandhu650 3 года назад

    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??

  • @Juan-iq8br
    @Juan-iq8br 3 года назад

    I want to ask, what software do you use in drawing this lecture?

    • @ankitbhurane9762
      @ankitbhurane9762 3 года назад

      Hello Juan, I use OpenBoard software. Link is here: openboard.ch/download.en.html

  • @utkarshkathuria2931
    @utkarshkathuria2931 3 года назад

    if in the convolution I assume t=0, then how will you solve the integration?

    • @ankitbhurane9762
      @ankitbhurane9762 3 года назад

      For t=0, both first and second sub equations are valid. i.e. 0 and t^2/2

  • @mjavadrajabi7401
    @mjavadrajabi7401 3 года назад

    thank you , it's so practical

  • @responsiblestudent7938
    @responsiblestudent7938 3 года назад

    really helpfull. lot of love

  • @jamilajamila3658
    @jamilajamila3658 3 года назад

    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.

    • @ankitbhurane9762
      @ankitbhurane9762 3 года назад

      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.

  • @pietronotarimazzocchi6316
    @pietronotarimazzocchi6316 3 года назад

    thank u!

  • @kaustubhkalkonde9298
    @kaustubhkalkonde9298 3 года назад

    Sir, what is difference between Verilog and VHDL? Which is better?

  • @shubhamkashyap6733
    @shubhamkashyap6733 3 года назад

    thankyou very much !!!!!

  • @surendrabachina9681
    @surendrabachina9681 3 года назад

    Y(t)=0 for t>3....I think

    • @ankitbhurane5130
      @ankitbhurane5130 3 года назад

      Yes, you are correct! That's a typo. Please consider.🙏

  • @jaitavyamishra8138
    @jaitavyamishra8138 3 года назад

    U explain in the best way i have read

  • @NatPatrick-l3w
    @NatPatrick-l3w 3 года назад

    Thank you for the explanation, good sir

  • @jacklaughter1219
    @jacklaughter1219 4 года назад

    ur tone of voice makes me want to break my headphones

  • @anchalsatapathy4175
    @anchalsatapathy4175 4 года назад

    Nice one sir🙏

  • @ninjanothing8343
    @ninjanothing8343 4 года назад

    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 :)

  • @serignesalioumbackesourang3835
    @serignesalioumbackesourang3835 4 года назад

    Thank you ! very well explained

  • @tejasdeshpande485
    @tejasdeshpande485 4 года назад

    Brilliant explaination , sir

  • @لُبنى7
    @لُبنى7 4 года назад

    This is the example of dirichilet condition ?

  • @anushkachaturvedy3624
    @anushkachaturvedy3624 4 года назад

    32:00

  • @khaben6986
    @khaben6986 5 лет назад

    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 !!

    • @ankitbhurane5130
      @ankitbhurane5130 4 года назад

      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.

  • @ankitbhurane2
    @ankitbhurane2 5 лет назад

    Please note that the vertical axis in the last diagram x(-t) is at -1.

  • @sahanaravinder7058
    @sahanaravinder7058 5 лет назад

    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

  • @ssahinnkadir
    @ssahinnkadir 5 лет назад

    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 👌🏻🙏🏻