This is an excellent video. I'm starting a summer cell counting project that uses a lock in amplifier. I don't have specific experience with the circuit yet, so this has been a valuable resource. Thanks.
It is overall a very intuitive video! Thanks! Somehow I feel it is a bit scattered. For instance, the transition from time domain to frequency domain would have gotten me really confused if I didn't know lock in detection before. The "speed up" hint is really thoughtful. But it is not actually due to the length of this video. Instead, I really slowed down when you went through the figures. That would mean some of the contents can really be reduced. Rather, spending some time on the figures would really help the audience to get your logic behind it. Again, thanks for this nice video.
Really good video, thanks! I have a question at 28:26 : Why does the square wave have so many harmonics and the sine wave has only the fundamental frequency in frequency domain?
Good question. In the frequency domain, we are showing how much of different sine waves we have to add together to get the wave. A sine wave is made up of one sine wave - it only takes one sine wave to write a sine wave. But a square wave is not a sine wave, so we have to add a bunch of sine waves to get a square wave.
I didn't really understand where the DC offset A comes from? And isn't R time-dependent, so that we need to time average several times? Is time-averaging the same as low-pass filtering? Sorry if my questions are a bit dumb....
The offset A was added because for the example given (detection of light) there is no such thing as a negative signal (you can't have a negative light power). And, yes, time-averaging is essentially the same as low-pass filtering. And, again, yes, R is time dependent. So you want to average over a time long enough to average away noise, but not so long that you average away the time dependent features of R that you want to measure.
Very small signal amid high noise identified by knowing the shape of the signal - is this what LIGO does to detect a black hole merger? Or at least the principle behind it?
This is a tremendously insightful video. A must, in truly understanding lock-in amplification.
This is an excellent video. I'm starting a summer cell counting project that uses a lock in amplifier. I don't have specific experience with the circuit yet, so this has been a valuable resource. Thanks.
Beautiful ... CAT example is so appropriate. I enjoyed the entire video. It's very good.
This is a phenomenal explanation. Thank you.
Great video!
Thanks!
Good cat analogy!
Very good video on the principles of lock-in detection.
Great explanation！
this is an amazing explanation !
It is overall a very intuitive video! Thanks! Somehow I feel it is a bit scattered. For instance, the transition from time domain to frequency domain would have gotten me really confused if I didn't know lock in detection before.
The "speed up" hint is really thoughtful. But it is not actually due to the length of this video. Instead, I really slowed down when you went through the figures. That would mean some of the contents can really be reduced. Rather, spending some time on the figures would really help the audience to get your logic behind it.
Again, thanks for this nice video.
LockIn Amplifier is a kind of modulation and demodulation for signal detection?
Is a kind of Communication theory for signal processing ?
Thanks.
THANKS for your concise explanation
You are welcome!
Dear sir, Does a Pir sensor, use the locking amplifier concept when working?
Really good video, thanks! I have a question at 28:26 : Why does the square wave have so many harmonics and the sine wave has only the fundamental frequency in frequency domain?
Good question. In the frequency domain, we are showing how much of different sine waves we have to add together to get the wave. A sine wave is made up of one sine wave - it only takes one sine wave to write a sine wave. But a square wave is not a sine wave, so we have to add a bunch of sine waves to get a square wave.
@@dallinschannel Aaaah, nice, thanks, that was even understandable for me! :)
I didn't really understand where the DC offset A comes from? And isn't R time-dependent, so that we need to time average several times? Is time-averaging the same as low-pass filtering? Sorry if my questions are a bit dumb....
The offset A was added because for the example given (detection of light) there is no such thing as a negative signal (you can't have a negative light power). And, yes, time-averaging is essentially the same as low-pass filtering. And, again, yes, R is time dependent. So you want to average over a time long enough to average away noise, but not so long that you average away the time dependent features of R that you want to measure.
Thank you a lot!!
Very small signal amid high noise identified by knowing the shape of the signal - is this what LIGO does to detect a black hole merger? Or at least the principle behind it?
I don't know. LIGO uses a bunch of amazing "tricks" to get those signals, but I don't know if there is something akin to lock-in detection going on.
Probably they use it for interferometer stabilization, I used it for that works amazing
34:37
Hello
confusing. too much content