John Larkin wrote:
On Wed, 4 Jan 2023 10:30:35 -0800, Joerg <news@analogconsultants.com
wrote:
On 1/2/23 2:34 PM, Joe Gwinn wrote:
On Mon, 2 Jan 2023 12:59:00 -0800, Joerg <news@analogconsultants.com
wrote:
On 1/2/23 12:20 PM, whit3rd wrote:
On Monday, January 2, 2023 at 10:25:28 AM UTC-8, John Larkin wrote:
\"QUESTION: which upper level math courses did you find most applicable
to your major or masters courses. Are there any other free/cheap
courses that can set me up for success in Power Electronics and/or
Embedded systems?\"
I don\'t think that higher-level math courses set people up for success
in any EE field except academics.
Sensing, measuring, and filtering get a lot of utility from Fourier transform
techniques; it\'s hard to imagine success in phase-shift measurement without
using a F-transform. Absence of high-level math courses sets people
up for failure, but they won\'t ever know that.
Actually during one of my consulting projects in the mid-90\'s I reversed
that trend at a client. They had a big DSP do lots of Fourier transforms
and the auto-calibration routine for that board took forever. Tens of
seconds. I reverted all that to time-domain and it was finished after a
few hundred msec, every single time.
In the 80\'s we often did it with zero-crossers. Less math but blazingly
fast.
What was this big DSP doing? This story rings a bell.
In radar, the initial calibration involves multiple alternating
conversions between time and frequency domains, because the desired
result is a clean pulse in the time domain, achieved by adjusting
phase and amplitude settings as a function of frequency.
I am not at liberty to go into great detail but in a nutshell the DSP
was there to calibrate a multi-channel RF system via FFT with respect to
amplitude and phase. High precision was required. Theoretically it
could, of course, be done with the FFT but it took way too long and it
didn\'t always converge to the precision they needed. The sofwtare also
was, let\'s say, a bit temperamental.
Once the correct settings have been found iteratively, subsequent
calibration is by adjusting the various settings back to those golden
numbers - the file containing those golden numbers is of course called
a golden database.
Antenna pattern is first calibrated by a like process.
My time-domain routine didn\'t need any golden numbers and converged
every single time within less than half a second. We let the uC handle
that because the computational load dropped to peanuts. The big DSP
became unemployed.
The project start was the usual, everyone saying that FFT was the name
of the game and there wasn\'t any other decent way. If it didn\'t work in
time domain I\'d have to buy everyone a beer at night. If it did,
everyone had to buy me a beer. I needed a designated driver that night ...
Given an actual waveform a(t) and a desired waveform d(t), we can fix
a to make d with an equalizer having impulse response e(t)
d(t) = a(t) ** e(t) ** is convolution
Finding e is the reverse convolution problem.
The classic way to find e(t) is to do complex FFTs on a and d and
complex divide to get the FFT of e, then reverse FFT. That usually
makes a bunch of divide-by-0 or divide-by-almost-0 points, which sort
of blows up.
\"Doctor, Doctor! It hurts when I go like this!\"
\"So don\'t go like that.\"
It\'s quite possible to do deconvolution badly as you say, but it\'s
generally not hard to do it well, and it\'s often very profitable.
Of course, how much that gets you depends on the situation. You just
have to keep an eye on the noise gain. In my long-ago thesis work, I
got a factor of two in resolution in an interferometric laser microscope
by deconvolving the analytically-known transfer function to something
more nearly rectangular.
The limitations had more to do with ringing than with noise gain or
singularities.
On the other hand, trying deconvolution to undo the effect of a Gaussian
lowpass is going to run out of gas very soon, because the noise gain
grows faster than exponentially with bandwidth.
Cheers
Phil Hobbs
John Larkin wrote:
On Wed, 4 Jan 2023 10:30:35 -0800, Joerg <news@analogconsultants.com
wrote:
On 1/2/23 2:34 PM, Joe Gwinn wrote:
On Mon, 2 Jan 2023 12:59:00 -0800, Joerg <news@analogconsultants.com
wrote:
On 1/2/23 12:20 PM, whit3rd wrote:
On Monday, January 2, 2023 at 10:25:28 AM UTC-8, John Larkin wrote:
\"QUESTION: which upper level math courses did you find most applicable
to your major or masters courses. Are there any other free/cheap
courses that can set me up for success in Power Electronics and/or
Embedded systems?\"
I don\'t think that higher-level math courses set people up for success
in any EE field except academics.
Sensing, measuring, and filtering get a lot of utility from Fourier transform
techniques; it\'s hard to imagine success in phase-shift measurement without
using a F-transform. Absence of high-level math courses sets people
up for failure, but they won\'t ever know that.
Actually during one of my consulting projects in the mid-90\'s I reversed
that trend at a client. They had a big DSP do lots of Fourier transforms
and the auto-calibration routine for that board took forever. Tens of
seconds. I reverted all that to time-domain and it was finished after a
few hundred msec, every single time.
In the 80\'s we often did it with zero-crossers. Less math but blazingly
fast.
What was this big DSP doing? This story rings a bell.
In radar, the initial calibration involves multiple alternating
conversions between time and frequency domains, because the desired
result is a clean pulse in the time domain, achieved by adjusting
phase and amplitude settings as a function of frequency.
I am not at liberty to go into great detail but in a nutshell the DSP
was there to calibrate a multi-channel RF system via FFT with respect to
amplitude and phase. High precision was required. Theoretically it
could, of course, be done with the FFT but it took way too long and it
didn\'t always converge to the precision they needed. The sofwtare also
was, let\'s say, a bit temperamental.
Once the correct settings have been found iteratively, subsequent
calibration is by adjusting the various settings back to those golden
numbers - the file containing those golden numbers is of course called
a golden database.
Antenna pattern is first calibrated by a like process.
My time-domain routine didn\'t need any golden numbers and converged
every single time within less than half a second. We let the uC handle
that because the computational load dropped to peanuts. The big DSP
became unemployed.
The project start was the usual, everyone saying that FFT was the name
of the game and there wasn\'t any other decent way. If it didn\'t work in
time domain I\'d have to buy everyone a beer at night. If it did,
everyone had to buy me a beer. I needed a designated driver that night ...
Given an actual waveform a(t) and a desired waveform d(t), we can fix
a to make d with an equalizer having impulse response e(t)
d(t) = a(t) ** e(t) ** is convolution
Finding e is the reverse convolution problem.
The classic way to find e(t) is to do complex FFTs on a and d and
complex divide to get the FFT of e, then reverse FFT. That usually
makes a bunch of divide-by-0 or divide-by-almost-0 points, which sort
of blows up.
\"Doctor, Doctor! It hurts when I go like this!\"
\"So don\'t go like that.\"
It\'s quite possible to do deconvolution badly as you say, but it\'s
generally not hard to do it well, and it\'s often very profitable.
Of course, how much that gets you depends on the situation. You just
have to keep an eye on the noise gain. In my long-ago thesis work, I
got a factor of two in resolution in an interferometric laser microscope
by deconvolving the analytically-known transfer function to something
more nearly rectangular.
The limitations had more to do with ringing than with noise gain or
singularities.
On the other hand, trying deconvolution to undo the effect of a Gaussian
lowpass is going to run out of gas very soon, because the noise gain
grows faster than exponentially with bandwidth.
Cheers
Phil Hobbs
John Larkin wrote:
On Wed, 4 Jan 2023 10:30:35 -0800, Joerg <news@analogconsultants.com
wrote:
On 1/2/23 2:34 PM, Joe Gwinn wrote:
On Mon, 2 Jan 2023 12:59:00 -0800, Joerg <news@analogconsultants.com
wrote:
On 1/2/23 12:20 PM, whit3rd wrote:
On Monday, January 2, 2023 at 10:25:28 AM UTC-8, John Larkin wrote:
\"QUESTION: which upper level math courses did you find most applicable
to your major or masters courses. Are there any other free/cheap
courses that can set me up for success in Power Electronics and/or
Embedded systems?\"
I don\'t think that higher-level math courses set people up for success
in any EE field except academics.
Sensing, measuring, and filtering get a lot of utility from Fourier transform
techniques; it\'s hard to imagine success in phase-shift measurement without
using a F-transform. Absence of high-level math courses sets people
up for failure, but they won\'t ever know that.
Actually during one of my consulting projects in the mid-90\'s I reversed
that trend at a client. They had a big DSP do lots of Fourier transforms
and the auto-calibration routine for that board took forever. Tens of
seconds. I reverted all that to time-domain and it was finished after a
few hundred msec, every single time.
In the 80\'s we often did it with zero-crossers. Less math but blazingly
fast.
What was this big DSP doing? This story rings a bell.
In radar, the initial calibration involves multiple alternating
conversions between time and frequency domains, because the desired
result is a clean pulse in the time domain, achieved by adjusting
phase and amplitude settings as a function of frequency.
I am not at liberty to go into great detail but in a nutshell the DSP
was there to calibrate a multi-channel RF system via FFT with respect to
amplitude and phase. High precision was required. Theoretically it
could, of course, be done with the FFT but it took way too long and it
didn\'t always converge to the precision they needed. The sofwtare also
was, let\'s say, a bit temperamental.
Once the correct settings have been found iteratively, subsequent
calibration is by adjusting the various settings back to those golden
numbers - the file containing those golden numbers is of course called
a golden database.
Antenna pattern is first calibrated by a like process.
My time-domain routine didn\'t need any golden numbers and converged
every single time within less than half a second. We let the uC handle
that because the computational load dropped to peanuts. The big DSP
became unemployed.
The project start was the usual, everyone saying that FFT was the name
of the game and there wasn\'t any other decent way. If it didn\'t work in
time domain I\'d have to buy everyone a beer at night. If it did,
everyone had to buy me a beer. I needed a designated driver that night ...
Given an actual waveform a(t) and a desired waveform d(t), we can fix
a to make d with an equalizer having impulse response e(t)
d(t) = a(t) ** e(t) ** is convolution
Finding e is the reverse convolution problem.
The classic way to find e(t) is to do complex FFTs on a and d and
complex divide to get the FFT of e, then reverse FFT. That usually
makes a bunch of divide-by-0 or divide-by-almost-0 points, which sort
of blows up.
\"Doctor, Doctor! It hurts when I go like this!\"
\"So don\'t go like that.\"
It\'s quite possible to do deconvolution badly as you say, but it\'s
generally not hard to do it well, and it\'s often very profitable.
Of course, how much that gets you depends on the situation. You just
have to keep an eye on the noise gain. In my long-ago thesis work, I
got a factor of two in resolution in an interferometric laser microscope
by deconvolving the analytically-known transfer function to something
more nearly rectangular.
The limitations had more to do with ringing than with noise gain or
singularities.
On the other hand, trying deconvolution to undo the effect of a Gaussian
lowpass is going to run out of gas very soon, because the noise gain
grows faster than exponentially with bandwidth.
Cheers
Phil Hobbs
On 1/4/2023 12:04 PM, Phil Hobbs wrote:
bitrex wrote:
On 1/4/2023 9:52 AM, bitrex wrote:
On 1/3/2023 7:30 PM, Phil Hobbs wrote:
RichD wrote:
On January 1, John Larkin wrote:
https://www.theregister.com/2022/07/18/electrical_engineers_extinction/?td=rt-9cp
I\'ve been thinking for some time now that EE schools don\'t turn out
people who like electricity, but maker culture might.
I advise younguns against an engineering degree, it\'s
over-specialized,
and obsolete in 5 years.
Only if you get sucked into spending all your time on the flavor of
the month. People who spend their time in school learning
fundamental things that are hard to master on your own (math,
mostly) and then pick up the other stuff as they go along don\'t get
obsolete. That\'s not difficult to do in your average EE program
even today, AFAICT. Signals and systems, electrodynamics, solid
state theory, and a bit of quantum are all good things to know.
Spending all your time in school programming in Javascript or VHDL
or memorizing compliance requirements is not a good career move for
an EE.
I tell them to get a physics education. Study hard. Then you
have the
tools to do anything you want.
Physicists turn up everywhere, it\'s true. Folks with bachelor\'s
degrees in physics can do most kinds of engineering, provided
they\'re willing to bone up on the specifics. Of course there are
some who assume they know everything and just bull ahead till they
fail, but, well, human beings are everyplace. Thing is, the
basic professional qualification for a physicist is a doctorate,
whereas in engineering it\'s a BSEE.
That is, first the academics, then the vocational training.
I agree that knowing the fundamentals cold is very important.
However, (a) physics isn\'t for everyone, by a long chalk; and (b)
there\'s a glorious intellectual heritage in engineering, so calling
it \'vocational training\' is pejorative.
Cheers
Phil \"Intermediate energy state\" Hobbs
Advanced engineering mathematics:
https://www.ebay.com/itm/194964206310
Which is pretty advanced, I don\'t know how many BS-type EEs know
about the orthogonality of Bessel functions, or regularly use
contour integration for anything.
But not as advanced as \"Advanced Mathematical Methods for Scientists
& Engineers\", which is largely about perturbation methods, boundary
layer theory, and WKB approximations. Sounds fun I guess, I just got
a used copy from Amazon for $8
I would expect stuff like the WKB approximation is regularly used
more in optics design than in circuit design, though.
WKB is common in approximate quantum theory, e.g. solid state.
Cheers
Phil Hobbs
I see, in a \"we can solve the hydrogen atom exactly & that\'s it\" sense.
On 1/4/2023 12:04 PM, Phil Hobbs wrote:
bitrex wrote:
On 1/4/2023 9:52 AM, bitrex wrote:
On 1/3/2023 7:30 PM, Phil Hobbs wrote:
RichD wrote:
On January 1, John Larkin wrote:
https://www.theregister.com/2022/07/18/electrical_engineers_extinction/?td=rt-9cp
I\'ve been thinking for some time now that EE schools don\'t turn out
people who like electricity, but maker culture might.
I advise younguns against an engineering degree, it\'s
over-specialized,
and obsolete in 5 years.
Only if you get sucked into spending all your time on the flavor of
the month. People who spend their time in school learning
fundamental things that are hard to master on your own (math,
mostly) and then pick up the other stuff as they go along don\'t get
obsolete. That\'s not difficult to do in your average EE program
even today, AFAICT. Signals and systems, electrodynamics, solid
state theory, and a bit of quantum are all good things to know.
Spending all your time in school programming in Javascript or VHDL
or memorizing compliance requirements is not a good career move for
an EE.
I tell them to get a physics education. Study hard. Then you
have the
tools to do anything you want.
Physicists turn up everywhere, it\'s true. Folks with bachelor\'s
degrees in physics can do most kinds of engineering, provided
they\'re willing to bone up on the specifics. Of course there are
some who assume they know everything and just bull ahead till they
fail, but, well, human beings are everyplace.
basic professional qualification for a physicist is a doctorate,
whereas in engineering it\'s a BSEE.
That is, first the academics, then the vocational training.
I agree that knowing the fundamentals cold is very important.
However, (a) physics isn\'t for everyone, by a long chalk; and (b)
there\'s a glorious intellectual heritage in engineering, so calling
it \'vocational training\' is pejorative.
Cheers
Phil \"Intermediate energy state\" Hobbs
Advanced engineering mathematics:
https://www.ebay.com/itm/194964206310
Which is pretty advanced, I don\'t know how many BS-type EEs know
about the orthogonality of Bessel functions, or regularly use
contour integration for anything.
But not as advanced as \"Advanced Mathematical Methods for Scientists
& Engineers\", which is largely about perturbation methods, boundary
layer theory, and WKB approximations. Sounds fun I guess, I just got
a used copy from Amazon for $8
I would expect stuff like the WKB approximation is regularly used
more in optics design than in circuit design, though.
WKB is common in approximate quantum theory, e.g. solid state.
Cheers
Phil Hobbs
I see, in a \"we can solve the hydrogen atom exactly & that\'s it\" sense.