2:49 what is band-limited signal? 4:05 what is omega m? (in fourier transform of message siganal m(t)) 5:14 c(t) is periodic impulse train 8:27 sample signal s(t) is m(t) x c(t) 15:53 condition for no overlapping 17:15 what condition the overlapping will occur
You covered almost 2 weeks of portion my faculty taught. Idk why they make it so complicated and actual concept never goes in our minds. Thank you Neso Academy
Bro you are terrific, we need professors like you in colleges, infact in IITs also, I am saying it because I am an IIT student( i am not trying to show off here, it's true). Your concepts are strong, and you know how to make students understand the things easily. Plus point is, your English fluency is so damn good.
I know, bra. I wish i had these opportunity to see such channels earlier. Well, never to late. I am 51, machine-leaning with python. Q=I am between contracts, gathering skills.
Best explanation sir. Thank u so much. Mujhe sir book se samajh me nahi aa Raha tha. Thank you so much sir samjha ne k liye. Sampling theorem ka is se best explanation aur koi nahi ho Sakta. Thank you sir.
Thank you so much! Finally there is someone who can explain the sampling theorem clearly to me. I sincerely wish we have professors like you in universities.
@@phoenix-dt2uj It is from M(W-Ws), shifting the original spectrum by Ws, so the "-Wm" from the original spectrum shifted to "-Wm+Ws"="Ws-Wm". And the "+Wm" from the original spectrum shifted to "+Wm+Ws"="Ws+Wm"
🎯 Key points for quick navigation: 00:00 *🎙️ Introduction to Sampling* - Explanation of the need for converting continuous signals to discrete signals, - Importance of sampling due to the prevalence of digital technologies. 01:17 *📉 Defining Sampling and Band-limited Signals* - Detailed definition of sampling and its reversible conversion process, - Introduction to the concept of band-limited signals and their importance, - Description of the signal spectrum being nonzero within a finite frequency range. 03:29 *⚙️ Conversion Process and Signal Spectrum* - Explanation of the sampler as a device multiplying signals, - Description of sampling frequency and its relation to signal frequency, - Presentation of the signal amplitude in terms of instantaneous values. 08:36 *🔄 Fourier Transform and Convolution* - Derivation of the spectrum of the sampled signal using convolution, - Use of Fourier transform properties for simplifying signal representation. 13:18 *🎯 Spectrum Analysis and Overlapping Conditions* - Expansion of the Fourier transform expression and plotting of the spectrum, - Analysis of conditions for spectrum overlap and the implication for signal recovery, - Introduction of the "guard band" concept between non-overlapping spectra. 16:42 *📏 Sampling Theorem Explanation* - Formal definition of the sampling theorem and its conditions, - Importance of the condition Omega s ≥ 2*Omega M for successful signal recovery. 19:16 *🔍 Practical Implications and Example* - Discussion of why band-limited signals are essential to avoid overlapping, - Presentation of an example illustrating the consequences of non-band-limited signals. Made with HARPA AI
If I ever get to pursue my masters and PhD in EEE which I will for sure, all my innermost feelings and profound love will reach out to neso Academy no matter how long it takes
It would be beneficial to illustrate a band-pass filter before the sampler. This would highlight how a signal is frequency band-limited prior to sampling, so that the Nyquest sampling theorem is valid for anti-aliasing.
At last signal system is back,a ray of hope and superb explaination,thank you sir..But one thing ,in competitive exam some questions are coming from bandpass signal,how to tackle that type of problem?
100% valuable information is all that You present! Strong impact to ours knowledge! Illustrated The Sampling Theorem for the essence to the 19min when you input Fourier transform and all clearly present with a diagram is for better researching. I would thank you how You guid us step by step and define signal by equations as well as the Fourier transform but also indirectly and highest frequency - folding frequency also regarded to the Nyquist frequency! Thank you and good luck in Yours mindfull Channel!
sir .. which signal should be band limited because if we take m(t) as band limited then fourier transform will be unbounded. eg. rect.(t)----ft----- sinc(f)
Can you please upload the explanation of the Fourier transform applications which related to the signals and systems course before our final exam that will be held on 17/5/2018? You would help us so much , thank you.
at 16:52 you said that for both cases reconstruction is possible but it need not be true. for the nyquist sampling rate it may be wrong or maybe true. Ex.A signal x(t) = sin(2π500t ) is sampled at fs = 1000 samples/second rate. Can we recover this signal back ? We can't
I am a machine-learning programmer. Did my first contract but now I am off to gather skills in image recognition/computer vision programming. For that reason I am learning these topics. I found your channel just now. I hope I will get a job soon and start donating. I will also have some questions I can ask other your viewers and yourself on the subject, but I would like to ask you and them clearly. In order to do that, can you tell me the tools you use for your videos so my questions can come out very clearly?
@@dawn-of-newday Most people don't really understand it anyway. I got diverted from that, looking at the job market. Good skill to have, but I followed the market, and now things are turning better. Nine interview, three job offers, just last month and this month and now I've been working after I accepted one. I think if I keep on, I will get comfortable.
Hi, thanks for your wonderful video. I'm really confused about priodization and sampling. Can you please clarify it. If sampling is the reduction of a continuous-time signal to a discrete-time signal, does that mean it makes an analog continuous-time signal periodic in discrete time?
You need to multiply it with a carrier signal c(t) so that you can get the samples of the message signal m(t) and then finally use it for digital communications.
Once I get employed , I will donate for sure
Yes, we must.
Azazel hjk
Same thoughts. He has my life!!!
Same ...
Job mili
2:49 what is band-limited signal?
4:05 what is omega m? (in fourier transform of message siganal m(t))
5:14 c(t) is periodic impulse train
8:27 sample signal s(t) is m(t) x c(t)
15:53 condition for no overlapping
17:15 what condition the overlapping will occur
You covered almost 2 weeks of portion my faculty taught. Idk why they make it so complicated and actual concept never goes in our minds. Thank you Neso Academy
Bro you are terrific, we need professors like you in colleges, infact in IITs also, I am saying it because I am an IIT student( i am not trying to show off here, it's true). Your concepts are strong, and you know how to make students understand the things easily.
Plus point is, your English fluency is so damn good.
I know, bra. I wish i had these opportunity to see such channels earlier. Well, never to late. I am 51, machine-leaning with python. Q=I am between contracts, gathering skills.
yeah Most of Indians can not talk like this thanks for lecture.
True
++
i am also IIT studnet
It is great that how in such a simple and efficient manner you clear all our concepts. Keep it up and yes....thanks a lot!!
Thank you so much sir, ur effort helps many students lyk me in understanding concepts. Pls keep going
Best explanation sir. Thank u so much. Mujhe sir book se samajh me nahi aa Raha tha. Thank you so much sir samjha ne k liye.
Sampling theorem ka is se best explanation aur koi nahi ho Sakta.
Thank you sir.
Thanks!
Thank you so much! Finally there is someone who can explain the sampling theorem clearly to me. I sincerely wish we have professors like you in universities.
15:05 can u tell how it's Ws-Wm,and ws+wm , plz
@@phoenix-dt2uj It is from M(W-Ws), shifting the original spectrum by Ws, so
the "-Wm" from the original spectrum shifted to "-Wm+Ws"="Ws-Wm".
And the "+Wm" from the original spectrum shifted to "+Wm+Ws"="Ws+Wm"
Happy Teachers Day to the sir. Probably the most selfless and one of the best teachers on UA-cam!
15:05 can u tell how it's Ws-Wm,
@@phoenix-dt2ujHe defined it in the first graph os M(w) = w where he draws a triangle bounded by [-wm, wm]
🎯 Key points for quick navigation:
00:00 *🎙️ Introduction to Sampling*
- Explanation of the need for converting continuous signals to discrete signals,
- Importance of sampling due to the prevalence of digital technologies.
01:17 *📉 Defining Sampling and Band-limited Signals*
- Detailed definition of sampling and its reversible conversion process,
- Introduction to the concept of band-limited signals and their importance,
- Description of the signal spectrum being nonzero within a finite frequency range.
03:29 *⚙️ Conversion Process and Signal Spectrum*
- Explanation of the sampler as a device multiplying signals,
- Description of sampling frequency and its relation to signal frequency,
- Presentation of the signal amplitude in terms of instantaneous values.
08:36 *🔄 Fourier Transform and Convolution*
- Derivation of the spectrum of the sampled signal using convolution,
- Use of Fourier transform properties for simplifying signal representation.
13:18 *🎯 Spectrum Analysis and Overlapping Conditions*
- Expansion of the Fourier transform expression and plotting of the spectrum,
- Analysis of conditions for spectrum overlap and the implication for signal recovery,
- Introduction of the "guard band" concept between non-overlapping spectra.
16:42 *📏 Sampling Theorem Explanation*
- Formal definition of the sampling theorem and its conditions,
- Importance of the condition Omega s ≥ 2*Omega M for successful signal recovery.
19:16 *🔍 Practical Implications and Example*
- Discussion of why band-limited signals are essential to avoid overlapping,
- Presentation of an example illustrating the consequences of non-band-limited signals.
Made with HARPA AI
If I ever get to pursue my masters and PhD in EEE which I will for sure, all my innermost feelings and profound love will reach out to neso Academy no matter how long it takes
Exam was easy because of you. Huge thanks sir. The best sir out there.
This is an excellent quality video.
Why I am going college if such videos are available on youtube.
Thanks for this lesson.
U r my college
You are fantastic. This is the strongest Signal and Systems course available on the internet. Thank you from the bottom of my ❤
Very clear! I couldn't understand this topic until I saw it.
Bro I like the way of teaching 👌
Now I can happily go to exam because of you we need a sir like u to teach us thanks for the vedio 🥰
Great, very clear! If I had money, I would surely contribute. Surely in the future.
It would be beneficial to illustrate a band-pass filter before the sampler. This would highlight how a signal is frequency band-limited prior to sampling, so that the Nyquest sampling theorem is valid for anti-aliasing.
Thanks always helps before exams
This is what I've been looking for the whole time, Thanks :D
Your explaining ability is fabulous.
Maan gye boss!!!
I am from Bangladesh. I follow this channel. This is very nice. Thanks to you all.
Really better than our teacher! Thanks...
the best lecture i found
At last signal system is back,a ray of hope and superb explaination,thank you sir..But one thing ,in competitive exam some questions are coming from bandpass signal,how to tackle that type of problem?
These types of videos clear the concept and help out in the exams the neso academy
Great video!
100% valuable information is all that You present! Strong impact to ours knowledge! Illustrated The Sampling Theorem for the essence to the 19min when you input Fourier transform and all clearly present with a diagram is for better researching. I would thank you how You guid us step by step and define signal by equations as well as the Fourier transform but also indirectly and highest frequency - folding frequency also regarded to the Nyquist frequency!
Thank you and good luck in Yours mindfull Channel!
Thank you sir..please keep helping us like this😇
Helped me alot 1 day before my exam 🤩🤩. Thanks a lot finally had a question in my exam for 10 marks. Thank you sooo much
when there's no one to help you
you come to NESO academy
This is amazing. Currently reviewing Signals and Systems, and this was an unbelievable recap.
best explanation on the internet
That is exactly what I just needed the night before the exam :) some coffee, make speed x3 and chill
Thanq sir. The best explanation. Keep it up.. Keep making videos to help us
you are the best professor
great content
Crystal clear explanation
Thank you🙏
15:05 can u tell how it's Ws-Wm,
Tnxs neso academy ur video are very to understand and it saves my time
Thank you so much sir for clearing all my doubts...specialy why we take band limited signal... keep it up
Awesome Explanation, thanks sir.
I want to gain knowledge what he has.🤔✌. Can't describe your knwoledge and the explanation in words.
Even in our NIT they didn't taught this much.👌
You are a hero... ❤️
sir .. which signal should be band limited because if we take m(t) as band limited then fourier transform will be unbounded.
eg. rect.(t)----ft----- sinc(f)
Bro Fourier Transform of signal should be band limited, not the signal itself.
Thank u neso
Thank you very much. You are a genius. 👍👍👌👌🙏🙏🔝🔝
I love your videos so much❤️❤️
Love from Japan
after my startup settled , i will donate sure...
Can you please upload the explanation of the Fourier transform applications which related to the signals and systems course before our final exam that will be held on 17/5/2018?
You would help us so much , thank you.
amazing way to explanation keep going
Thanks for such an easy explanation!!
900k soon sir kepp it up
at 16:52 you said that for both cases reconstruction is possible but it need not be true.
for the nyquist sampling rate it may be wrong or maybe true.
Ex.A signal x(t) = sin(2π500t ) is sampled at fs = 1000 samples/second rate. Can
we recover this signal back ? We can't
Simply incredible.
Very helpful, as always.
Sampling frequency should preferably be greater than 2*Wm, not equal to 2*Wm, due to non-idealities of practical lowpass filters.
My absolute goat.
This was a really good explanation
My two weeks class is fully completed in 20 minutes video 🔥🔥
Amazing! Thanks so much!!!!
Awesome explanation sir
Hey this is very helpful for me thank you so much neso academy... And please can you upload the video of explanation of types of sampling methods? 😊
Wow u r the Best of Best lecturer
We are 'Indebted to you' tq
sir pls upload videos as early as possible of signals and system
I have hope because Neso is here to save me on SAS
I believe that If I don't understand any topic here, I can't understand anywhere.
I love this channel❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤
Good explanation...!! Thanks a lot sir..!!!
Thank you sir. very helpful
Neatly explained. Very nice!
excellent job
I am a machine-learning programmer. Did my first contract but now I am off to gather skills in image recognition/computer vision programming. For that reason I am learning these topics. I found your channel just now. I hope I will get a job soon and start donating. I will also have some questions I can ask other your viewers and yourself on the subject, but I would like to ask you and them clearly. In order to do that, can you tell me the tools you use for your videos so my questions can come out very clearly?
Bro, I have a subject next semester on Imagr processing. I don't understand not a thing about dsp.
@@dawn-of-newday Most people don't really understand it anyway. I got diverted from that, looking at the job market. Good skill to have, but I followed the market, and now things are turning better. Nine interview, three job offers, just last month and this month and now I've been working after I accepted one. I think if I keep on, I will get comfortable.
Hi, thanks for your wonderful video. I'm really confused about priodization and sampling. Can you please clarify it. If sampling is the reduction of a continuous-time signal to a discrete-time signal, does that mean it makes an analog continuous-time signal periodic in discrete time?
explained perfectly
Sir please make a video on quantization process in signal and system.
Ultimate
Tqsm
Crystal Clear ✴️
Oh my God! Thank you!
you must include also complex signals, where nyquist theorem is a fallacy
Many thanks 😊
Thank you for this video.
Thanks man
It really helped😊😊
At 5:12 what is the need for multiplication of m(t) with another signal c(t) ?
to get samples bro
You need to multiply it with a carrier signal c(t) so that you can get the samples of the message signal m(t) and then finally use it for digital communications.
Thanks a lot!!! I have one question: how can we get band-limited signal?
well explained sir love you
sir, please do make concepts of electrical engineering also...
Yes sir plzzz
Thank u sir👍
super explanation
we need z transform these are very useful bro please uplod fast
You are awesome! Thank you!
Sir @ 12:04 you took w(s) outside the bracket.Why M(w) was not divided by w(s)? Is it coz convolution ...M(w) was not divided by w(s)?
Very good explanation😎😎
Pls make videos on ⚡ engineering
sir please do upload alias and sampling techniques too
When will u start z transform
So laplace is completed fully by previous lecture??????
Best explanation....
Your lessons are much helpful
thanks (;