wow this course is soo well made. Splendid. Thank you very much Sir!

Speaking of filters, what about filtering out that low frequency from your audio?

Thank you for the whole library of Radio design !

thank you

thank you sir

Exactly that I was want to see! Thanks from Kazakhstan! I was wondering about sine wave modulated but at least square is good too

is there any books i should read in parallel to this course?

For 3rd order: I think 1dB compression point resides approximately 10dB below the 3rd order intercept point.

very good

Thank you 🙏

First time I had a professor tell me to go and watch a Bond movie.

Great explanation. Need it for my bachelor thesis and understand now the basics

You really mess up the whole concept of dB and dBm; you have made this look more complicated. Where is the dBc concept? If you do not know something do not try to make things up worst.

Quick comment David, on slide 12, you're asking for the maximum bitrate while defining the minimum SNR. Since the former is monotonically increasing with the latter, the maximum bitrate should be infinity (assuming an SNR being only lower bounded). What am I missing?

This is a good explanation in short term, of what is main bij Fourier who how investigate de fourier transform..

At 4:56 there is a bit of simplification -> if x and y are in dB, then: log(x+y)=log(10*log(x)+10*log(y))=log(10*log(x*y)) which doesn't mean it is usefull in practice

Very useful!

I wish I could watch this & all the other lectures in this series, but I'm very sensitive to audio & the mix here is intolerably wrong for my ears 😖

such amazing video!!

I don't understand the maximum bit rate C to be in kHz?

Some of the videos have been cut short towards the end , why is that ? I cannot find conclusions to the videos

Man had a factory reset when he tried doing the example

at around minute 2 this f(t) = del_t (phi(t)); this f should be an omega

finally understand what is pulse shaping, my professor only mentioned ISI, sinc and raised cosine, which made me very confused with why do we need these things.

2:37 Hi professor, does very low intermediate frequency in heterodyne detection mean it is basically homodyne detection?

Transcript Search in video 0:05 so the time domain is what we think 0:07 about and we perceive in our everyday 0:10 life so in our world time marches 0:13 forward and we think about things 0:14 happening over time and so the time 0:18 domain is very comfortable and familiar 0:21 to us and typically is represented by a 0:24 plot versus time not much to say in here 0:29 but if we think about the other domains 0:32 we work with namely the frequency domain 0:34 this is a little bit different the 0:36 frequency domain is an abstract concepts 0:38 that we've generated that really helps 0:40 us deal with signals in a more robust 0:43 way and these are representing the 0:45 signals by the frequency components that 0:47 make them up if you remember from the 0:50 Fourier series that you could take any 0:53 time waveform and break it up into a 0:56 series of cosines and sines and the 0:58 frequency domain just simply plots the 1:01 magnitude and phase of those cosines and 1:05 sines that when you add up create the 1:07 time domain signal that you are 1:09 interested in and we generally represent 1:11 this in F or we can also talk about 1:15 Omega typically as engineers we talk 1:18 about F because all of our 1:19 instrumentation is in frequency directly 1:23 Omega is used quite often because 1:26 oftentimes analytical calculations end 1:28 up in radians and a make is more useful 1:30 so we can go back and forth between the 1:32 two almost interchangeably the only 1:35 thing you need to make sure of is that 1:37 if you're doing a calculation it may 1:39 likely be in terms of Omega and if you 1:42 want to convert to F you need to use the 1:44 formula right here 1:47 all right so the question comes in if we 1:50 have time domain is what we see in real 1:52 life if you will in frequency domain is 1:54 this concept how do we measure these two 1:56 and so you should know that there's two 2:00 primary instruments that we're going to 2:01 use to measure time and frequency domain 2:04 so let's start with the time domain 2:09 and this is a picture of an old 2:12 oscilloscope but you probably already 2:14 know that we use the escola scope here 2:16 to represent the time domain and so the 2:20 time axis is actually right right here 2:23 and then of course if we want to do the 2:25 frequency domain we use a spectrum 2:28 analyzer where this axis right here is 2:30 in terms of F and these are simply plots 2:33 of the magnitude of each frequency then 2:36 we're going to learn to use both of 2:37 these it's expected that you probably 2:39 already know how to use an oscilloscope 2:40 and we're going to go over some of the 2:42 basic functionality of a spectrum 2:45 analyzer and also go into some of its 2:47 advanced features because one of the 2:49 things about radio systems is that we 2:51 deal with signals of very low magnitude 2:53 and our very low power so microwatts 2:56 maybe even pico watts and it turns out 2:59 that we have to use some of these 3:00 advanced features in order to be able to 3:01 see these really small signals so we'll 3:03 talk about that more as time comes along 3:06 alright now we've been talking about the 3:09 time versus frequency domain and as you 3:11 know we can go between those two using 3:14 the Fourier transform and so the Fourier 3:16 transform will take us from a time 3:18 domain signal into a frequency domain 3:20 signal as you know and just as a 3:26 reminder if you change a signal in one 3:28 domain it gets changed in the other 3:30 domain however the change is not the 3:33 same if we change this signal here X of 3:37 Omega does change but the change happens 3:39 to the Fourier transform so except for 3:41 linear multiplications by scalars 3:44 changing X of T for instance shifting in 3:47 time does not just simply shift that on 3:50 the axis but this should be review for 3:52 you and so here is a couple examples of 3:57 Fourier transforms and one of the things 3:59 we want to show you here is sort of how 4:04 things look in the time domain and how 4:05 things look in the frequency domain so 4:08 if you remember the Fourier transform of 4:10 a square ends up with a sinc function 4:14 here and you're probably familiar with 4:14 that if we were though to take a 4:17 low-pass filter and I'll draw that in 4:20 the frequency domain so imagine I put in 4:21 a filter 4:22 that has a passband like this so it's 4:27 pretty easy and intuitive from the 4:29 frequency domain to realize we're just 4:31 gonna basically filter out all these 4:33 sides and this is the signal we get but 4:36 you need to remember that in the time 4:37 domain it has an equivalent one and it 4:40 smooths it out and we could have 4:42 actually thought about this directly in 4:44 that a low-pass filter is going to 4:45 remove all the sharp edges and smooth 4:48 things out so here's a great example of 4:50 how operating in the frequency domain 4:53 here is very easy and quick but there is 4:56 an equivalent time domain change that we 4:58 just need to keep in mind all right so 5:03 I've shown on the left a a time domain 5:06 waveform and on the right its Fourier 5:08 transform and just to be accurate here I 5:13 know that this right here is a cosine 5:15 because it has two positive Delta 5:18 functions so I would need to set my zero 5:21 for instance right here to make this 5:24 exactly accurate and so we often say 5:28 that a sinusoid is a single tone because 5:30 it contains a single Delta function in 5:33 the frequency domain and we also need to 5:36 remember that all real signals have a 5:38 negative frequency component as well are 5:40 sometimes called an image and so 5:43 whenever you look at the frequency 5:45 domain you should always see the image 5:47 if it's a real signal now I should just 5:50 be real careful by real here we mean we 5:53 can measure it 5:56 in the lab 6:01 okay i specify that because we're gonna 6:04 be talking a little bit later about real 6:06 and imaginary components in terms of 6:10 in-phase and quadrature and that just 6:12 has to do with the fact that we're using 6:14 imaginary to represent a 90 degree phase 6:16 shift but those are both signals we can 6:19 measure in the lab so real here is more 6:20 of just realistic or stuff we can 6:22 measure in the lab as opposed to real 6:24 and imaginary so I want to talk a little 6:28 bit more about this negative frequency 6:30 if you actually did the Fourier 6:32 transform of cosine you would see that 6:36 one of the coefficients comes out and it 6:38 has a minus J Omega T component and 6:41 that's what gives us this delta function 6:43 another way that I think helps me 6:46 remember two things one is what this is 6:49 and also that we need both is this 6:53 identity here you probably remember if 6:55 you think about these is plotting e to 6:59 the J Omega T and this is e to the minus 7:02 J Omega T right we know that e to the J 7:06 Omega T is cosine Omega T plus J sine 7:10 Omega T so if I just had one of these 7:12 this is what I would get I'd get a real 7:15 part and I'd get an imaginary part so I 7:18 wouldn't have a completely real signal 7:20 if I want a completely real signal what 7:22 I need to do is combine two of these 7:24 either for a cosine or a sine and what 7:27 that does is it creates either a cosine 7:32 or a sine and removes the imaginary 7:34 component so if you think about this 7:36 plot simply as a 7:42 e to the J Omega plot it helps me out a 7:45 little bit and remembering why I need 7:47 both it's because I need this to be real 7:49 and that we can think about a cosine 7:52 here simply as these two components 7:54 right there

Great explanation, thank you please keep up!

Thank you for this. I am uncertain of something though. In WiFi, if using BPSK for example, do you apply pulse shaping before or after modulating the carrier. Or does it matter? You video at time kzbin.info/www/bejne/q4WsYaSCgceGbZY implies that you apply shaping in the transmitter BEFORE modulation. Other sources I have read say that shaping is applied after modulation???

Honestly. didn't really understand the purpose of this video...

Thank you sir ❤

I have watched most of the series, it is excellent. I did enjoy it. I wish there was also a longer version of these lectures for more interested people. The focus is on the signal processing side rather than the RF and circuit design, which is great. Thank you

Hello professor. I have a question about minute 6. If you move to different frequency isn't it frequency modulation as well? thank you

PLEASE do a video on how to build that PLASMA TAURUS thing plzzzz

You are the man

i q 訊號是方波?? 不是弦波??

I like how I started to get motivated to go back to school. So I decided to look up where Dr. Ricketts lectures.....makes sense I'll stay home haha. Life has interesting ways of calling me dumb 🤣

Your going to need some very high order Image rejection filter

Your RF BPF is what order?

Thank you for this great series.

Thanks for all videos

thank you so much

Thank you so much 🎉 you saved my day with this video. Have a great day.

2023.07.14 감사합니다.

Great explanation ....simplified !! Many thanks David

Thinks sir , this is great

Great courses. Thank you Professor.

Really good stuff. Good explanation. thank you

Well done

hallo sir can we talk?

Slide 7: The capacity units should be in kbps not kHz. From the perspective of physics, it does not matter, but I believe it is important to distinguish between bit rate and signal rate.