Lecture 3, Signals and Systems: Part II | MIT RES.6.007 Signals and Systems, Spring 2011

Lecture 3, Signals and Systems: Part II
Instructor: Alan V. Oppenheim
View the complete course: ocw.mit.edu/RES...
This video covers the unit step and impulse signals. System properties are discussed, including memory, invertibility, causality, stability, and time invariance.
ocw.mit.edu; Creative Commons Attribution-NonCommercial-ShareAlike 3.0; ocw.mit.edu/Ocw... Album photo courtesy of Professor Alan Oppenheim

[@lucys4226]

What he accomplished in the first 11 minutes, my professor would spend 2 50minute lectures on. THANK YOU!

[@fazlerabbi2666]

wow i wish i had teachers like this in my university!

[@christoskettenis880]

Me also!

[@BlaziNomad]

I wish I watched these videos earlier. Not the day before my midterm lol.

[@mrnarason]

Wish I found this and watched it and took notes before I stated the semester

[@ew98]

I have 9 days left for my midterm. Wondering if the videos will do the trick. Wish me luck

[@ew98]

@@mehdi4031 1.25 is the fastest I can manage, you are a beast haha btw you should check out the assignment section too (ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011/assignments/). There are some great problems with explained solutions. Anyways, good luck man. Hope you'll do great!

[@shivanshanand8818]

feels bro

[@scoobs6389]

I have my mid in 15 minutes ....... Well I'm trying 😭😭😭

[@alirezaghodsipoor2239]

Damn good! Especially the stick horse. Especially ballancing it on his hand! And explaining feedback!

[@ChatagaiDuru]

Watching these amazing lectures is really helpful Thanks Mr Oppenheim and MIT!

[@caseyli5580]

@MIT OCW: please consider renaming this video to reflect the content taught in it, as this is the best explanation I have come across so far on KZbin for the unit impulse signal, and you'd have no way of knowing from the title or watching the first few minutes. I didn't even realize there was a video description. Thanks for posting these fantastic lectures!

[@mitocw]

You should check out the Readings section of the course on MIT OpenCourseWare: ocw.mit.edu/RES-6.007S11. We think it would give you a better insight into the contents of each lecture at a glance. Best wishes on your studies!

[@nikos4677]

​@@mitocwIt helps the algorithm recommend the video on people who need it.

[@arv_sajeev]

Thank you internet, MIT, Alan ❤️

[@李愚-f7j]

so clear !!! less calculation, more insight, I wished I have watched this earlier thank you

[@carlesgelada2043]

at 32:40 he talks about an integrator. but the way i understand it, the function it is not an integrator but a "calculator of the area from infinite to t", a very easy example: if we take x(t) to be 1 (more simple is impossible), y(t) = x + infinite + C An integrator should output: y(t) = x + C

[@carlesgelada2043]

i just realized that for LTI systems it is the same. because h of t = 0 for t < 0. So the example above is not a LTI system :)

[@CT7ALW]

Well, then you integrate using the "previous area" method your C becomes y(-inf). It's valid for non-LTI systems.

[@iwonakozlowska6134]

Thank you , especially for "feedback" demonstration !

[@AhmedKMoustafa2]

0:54 skip and start if you want

[@melihtopal5328]

Shame on you ! what a disrespectful behavior against Oppenheim.

[@AhmedKMoustafa2]

@@melihtopal5328 😂😂😂😂😂😂

[@waero9923]

amazingggggggg no words to explain how amazing this explanation is

[@abhiram721]

"borrowed from my son with some reluctance on his part". im glad he borrowed it hahaha

[@MrImmi34]

Playing these amazing lectures at twice the speed really saves time.

[@burningsilicon149]

Note that u_delta(t) still has discontinuity its just in the derivative of the function instead of the value of the function.

[@quimicomusi]

this is so amazing! thanks MIT!...from Portugal ;)

[@sudarshanh.v993]

If someone feels that his speaking speed is slow, play the video at 1.25x speed. It becomes perfect.

[@harish786786]

This the man of concept solving....

[@akshaydurgapal4874]

he is the bible of signal .. u r great opphenemim

[@vivekrai1974]

12:09 How is derivative a rectangle? Shouldn't it be equal to the slope of the line at delta t?

[@aanchalg20]

check out MIT OCW webpage for 6-007-signals-and-systems-spring-2011/lecture-notes/

[@vivekrai1974]

6:37 If we add impulse signals as shown here, won't the addition lead to a step signal which is an monotonically increasing arithmetic progression?

[@vishnureddy7557]

I want to know where I can get video manual for practiceing problems.Thanks in advance And its very interesting to learn by this video course

[@ravikumarmeena01]

really great work you people are doing , hats off

[@thespectre2012]

Thank you Alan.

[@AjinkyaBobadepictpune]

great explanation of feedback

[@tusharikas5447]

crisp like his textbook!

[@bjornarya]

5:12 can someone please explain this equation to me? it doesn't seem right to me. please help.

[@xKINGkhanSRKx]

you are summing the values of the unit impulse function, starting from -infinity to n, where n is less than 0. In the unit impulse function there exists only 1 value, which is at n = 0. Thus, when you take the sum of all values from -infinity to n (where n is less than 0), you are essentially adding "0 + 0 + 0.... + 0" but no value other than 0 exists within this range.

[@munikrishnatelagam6934]

for y(n)= cos x(n) ..... is this stable or not????

[@maike2332]

yes

[@AbhishekKumar-xf9td]

munikrishna telagam ..i think it is stable

[@samyamshrestha]

Thank You very much for this lecture. 😍

[@ovichitayat5796]

Is 'Inverted pendulum' really unstable? at some point the movement will stop, hitting the ground or swinging from the anchor point...I agree that the initial motion is a much-amplified response to the trigger but BIBO is not about gain/amplification, it's about the final state as a response to the initial impulse...or maybe I don't really know the detailed definition of the 'Inverted Pendulum' system.

[@kanishhh]

If y(n)=x(-n) and time invariant property is checked then in case of input delay x(n-k) output is y(n,k)= x(-n-k) i.e system only applies folding on (n), why it doesn't does the same thing over delay (k)?

[@treya111]

Anyone might explaining 9:06? 😿

[@AncientAthlete77]

LEGEND !! Respect !!

[@shady86570]

Please, is there any lectures that undertake the illustration of Modern digital and analog communication systems by Lathi?

[@stevenan93]

can someone explain the modulator not being time invariant? I can't intuitively understand that part.

[@Antz7Freak]

+stevenan93 I suggest that you look at what modulation means first.

[@DF-ss5ep]

I don't think the equations are written in the most clear way. I think a good intuitive description is that there is a modulator device that's doing it's thing independently of the input signal. The output signal in modulated form depends on the pair of initial states of the input and modulator. So shifting the input without shifting the modulator gives a different result, they go out of sync. I guess to make a time invariant modulator, you would need a modulator function that doesn't depend on time, like a multiplication, or taking the logarithm of the input.

[@ahmedesmail6684]

Excuse me, is y[n] = nx[n] a time-invariant system ?? Thanks,

[@arjunmuraleedharan2210]

No I guess.

[@abinashpuhan8512]

What about a running integrator will it be time invariant too??

[@osamaelzubair1203]

Where can someone access the video manual ?

[@mitocw]

The book is Oppenheim, Alan V., and A. S. Willsky. Signals and Systems. Prentice Hall, 1982. ISBN: 9780138097318. Which is available at some libraries or can be bought. We are not sure if the book is available in an online format.

[@ashutoshgupta1668]

can someone tell where can I get the text manual, he is talking about in the video? is it same as his text book on singnal and systems?

[@mitocw]

+ashutosh gupta The videotaped lectures are designed to be closely integrated with the text: Oppenheim, Alan V., and A. S. Willsky. Signals and Systems. Prentice Hall, 1982. ISBN: 9780138097318. (www.amazon.com/exec/obidos/ASIN/0138097313/ref=nosim/mitopencourse-20)

[@MexterO123]

For that signal y[n] = x[n-1] is this considered linear? How would that be proven?

[@tonystark7224]

ax[n-1] = ay[n] -> linear

[@privateprivate6459]

@@tonystark7224 linearity needs to obey homogeneity and additivity. You need to include additivity as well.

[@senanurkok3075]

Barış Hoca bizi çok üzüyorsun

[@FahimKhan-vd8yp]

where can I find the "video manual" the kind instructor is referring to?

[@mitocw]

Under the Lecture Notes section of the resource on MIT OpenCourseWare at: ocw.mit.edu/RES-6.007S11. Best wishes on your studies!

[@mohamedessam1397]

As Always ,The Best.

[@parshudgv]

If y[n]=x[5n] its non Causal system then what is system integration from -infinity to t x[5n]..?plz any one tell me.

[@abhisangeetha]

i think your signal is a discrete one and how can we integrate from - infty to t

[@maresfillies6041]

What a gangster. XD

[@bryansbooks]

I see you all over lol. Like, about 6 years ago you were facebook friends with two of my friends and now I just see you all the time in comments sections and forums and stuff.

[@bryansbooks]

Probably because EE

[@thivuxhale]

I don't quite get the stick horse example. Can someone explain?

[@privateprivate6459]

he's demonstrating feedback. Regular (noninverting pendullum) = stable, bounded I/P = bounded O/P. Inverted pendulum is not stable b/c the smallest(bounded) I/P will created unbounded O/P. He then goes to demonstrate that with feedback, it is possible to turn an unstable system(inverted pend) to a stable system.

[@asemabdou1126]

he is incredibly

[@steveocho]

Was this filmed 30 years ago?

[@albertoochoa7657]

+steveocho yes

[@orlandosoplon77]

40+ years ago, actually (1975).

[@aamirbangash1578]

you're a legend

[@nagabellyshravan3708]

nice understandable teaching, but please make literal work also, sir.

[@stevenan93]

+moopannar anandan lol

[@damny0utoobe]

and he has a text. search the web

[@nagabellyshravan3708]

thank you..for your suggestion

[@nagabellyshravan3708]

damny0utoobe thank you for your suggestion

[@dabulls1g]

this lectures are from 3-4 decades ago, this guy is beyond retired. they were reformatted and uploaded on 2011.

[@sadiyamahmood0263]

Humans are an example of NON-CAUSAL systems. It can be understood with an example. when we are moving on a road and if we see sign of danger ahead, we immediately change our route..This OUTPUT of changing route upon knowledge of dangerous FUTURE INPUTS clarifies that we humans can behave as non-causal system...This means that all of our ACTIONS(OUTPUTS) in this world are related to the world of FUTURE INPUTS (Heaven and Hell are the world of future inputs as per religious books)... The outputs and inputs are related through TRANSFER FUNCTION of non-causal systems.. This leads to the conclusion that there exists the Creater of non-causal systems and only He has the power to subject us to FUTURE INPUTS based on our actions(outputs) in this world...With the help of knowledge of Transfer Function Humans and the Fourier/laplace transform of our actions( ouput), we can KNOW what will be the FUTURE INPUTS i.e what heaven and hell truly is!

[@RyaliVenkataSatyaSaiSriRamaVar]

excuse me but can you see future

[@sadiyamahmood0263]

​@@RyaliVenkataSatyaSaiSriRamaVar If the world to which the FUTURE belongs is a CERTAIN world( the world which we will FACE for SURE), then, KNOWLEDGE of that future world is sufficient...SEEING the future world is not necessary...but if you really want to decode what truly does this CERTAIN WORLD HAVE which we will face after the Day of Judgement then do just one experiment....Find out the TRANSFER FUNCTION OF HUMANS( We can treat humans as a SYSTEM)..you can do it in this manner...when you see a sign board on a road telling you that there is danger AHEAD, then you get to KNOW FUTURE INPUTS you will be facing if you do not change your route!! This knowledge creates an electrical signal in human brain..write expression of this signal... the signal you got is the future input...and when you DECIDE to CHANGE the route then this decision( your OUTPUT) generates another electrical signal in the brain..this is your OUTPUT Signal... Find the ratio of FOURIER/LAPLACE TRANSFORM of the OUTPUT SIGNAL and the FUTURE INPUT SIGNAL...this is how we calculate TRANSFER FUNCTION of any SYSTEM.... Once you have this transfer function then it is very easy to KNOW future inputs( belonging to the certain world i.e Heaven and Hell) for every output signals we generate( you can call it your actions) in this uncertain world....

[@alirezaghodsipoor2239]

So good mathematically. Brilliant. But it's too abstract. If the professor had made examples of what is unit impulse/step in nature or where it has risen from, it would have been perfect.

[@MohamedElsheikh22]

شكرررررا

[@SuvitJoshia]

11:56 /

[@RahulKumar-pz4wv]

great

[@MexterO123]

haz tres you're*

[@fahadimran6961]

My teacher doesn't explain well! She's just way too worried about covering the course.

[@karimkhan1312]

i wish u were my teacher

[@sevdaimany6710]

bro are u alive?

[@Anveshana837]

He looks like the science professor from stranger things.

[@user_528dhf]

wow....

[@yasinozatl2363]

why the fuck 480p! It is MIT!

[@jihoonbang3830]

You can say in that way if you paid at least once for MIT OCW.

[@coolbeans486]

did u guys get good grades by watching this course??

[@byanghor]

Classic

[@DirtyToenailZ]

true hipster

[@sevdaimany6710]

im from future

[@brianko4311]

gang gang

[@mnesvat]

funny horse :D