[time-nuts] HP5065 - Some OP-Amp, FB and Teflon ...
If you are willing to take a China-risk here is a good price for lots of three of them. I've bought some stuff from this seller and it has all seemed legit and worked properly... Of courese, YMMV https://www.ebay.com/itm/3PCS-OP-AMP-IC-BURR-BROWN-BB-TI-TO-99-CAN-8-OPA111AM-100-Genuine-and-New/152770821747?hash=item2391d9be73:g:jKYAAOSwZlZZ-znn - > Any thaughts on a possible replacement for OPA111? ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
[time-nuts] HP5065 - Some OP-Amp, FB and Teflon ...
Corby, Thanks for the information. I was not aware of the relocated resistor. Yes, it is the DC feed-throughs. Any thaughts on a possible replacement for OPA111? I have also duplicated the revised A7 AC-Amplifier boardusing film capacitors for the notch filters and modernOP-Amps. Since I have one old A7 board as spare, I caneasily compare the performance. BR Ulf Kylenfall - SM6GXV ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Some more info on the 5065A optical unit
Hi The gotcha with coating (at least with RF coils is that the adsorption / desorption process gets really crazy. You may slow the “going in” part down to a week. The “getting out” part may turn into (many) months. That’s what makes the “stored on a shelf and then turned on” thing a mess. It takes a *long* time for things to stabilize. Again - this is all RF based. Some of what happens applies at DC. Some of what happens simply does not matter at very low frequencies. Bob > On Nov 27, 2017, at 6:41 PM, Bruce Griffiths > wrote: > > However the dimensions of the coil former vary with moisture content. > Impregnation with waxes and other organic materials merely serves to slow > down the rate of absorption and doesn't prevent it. > Bruce >> On 28 November 2017 at 12:36 Poul-Henning Kamp wrote: >> >> >> >> In message <2b66d682-15cf-465f-9a34-d7f7e7929...@n1k.org>, Bob kb8tq writes: >> >>> Straight cardboard *is* an issue on RF coils in humidity. >> >> The C-field coil is DC only. >> >> -- >> Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 >> p...@freebsd.org | TCP/IP since RFC 956 >> FreeBSD committer | BSD since 4.3-tahoe >> Never attribute to malice what can adequately be explained by incompetence. >> ___ >> time-nuts mailing list -- time-nuts@febo.com >> To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts >> and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
Hi Jim, On 11/28/2017 12:03 AM, jimlux wrote: On 11/27/17 2:45 PM, Magnus Danielson wrote: There is nothing wrong about attempting new approaches, or even just test and idea and see how it pans out. You should then compare it to a number of other approaches, and as you test things, you should analyze the same data with different methods. Prototyping that in Python is fine, but in order to analyze it, you need to be careful about the details. I would consider one just doing the measurements and then try different post-processings and see how those vary. Another paper then takes up on that and attempts analysis that matches the numbers from actual measurements. So, we might provide tough love, but there is a bit of experience behind it, so it should be listened to carefully. It is tough to come up with good artificial test data - the literature on generating "noise samples" is significantly thinner than the literature on measuring the noise. Agree completely. It's really the 1/f flicker noise which is hard. The white phase and frequency noise forms is trivial in comparison, but also needs its care to detail. Enough gaussian is sometimes harder than elsewhere. I always try to consider it a possible limitation. Enough random is another issue. What is the length of the noise source, what is the characteristics? When it comes to measuring actual signals with actual ADCs, there's also a number of traps - you can design a nice approach, using the SNR/ENOB data from the data sheet, and get seemingly good data. The challenge is really in coming up with good *tests* of your measurement technique that show that it really is giving you what you think it is. A trivial example is this (not a noise measuring problem, per se) - You need to measure the power of a received signal - if the signal is narrow band, and high SNR, then the bandwidth of the measuring system (be it a FFT or conventional spectrum analyzer) doesn't make a lot of difference - the precise filter shape is non-critical. The noise power that winds up in the measurement bandwidth is small, for instance. But now, let's say that the signal is a bit wider band or lower SNR or you're uncertain of its exact frequency, then the shape of the filter starts to make a big difference. Now, let’s look at a system where there’s some decimation involved - any decimation raises the prospect of “out of band signals” aliasing into the post decimation passband. Now, all of a sudden, the filtering before the decimator starts to become more important. And the number of bits you have to carry starts being more important. There is a risk of wasting bits too early when decimating. The trouble comes when the actual signal is way below the noise and you want to bring it out in post-processing, the limit of dynamic will haunt you. This have been shown many times before. Also, noise and quantization has an interesting interaction. It actually took a fair amount of work to *prove* that a system I was working on a) accurately measured the signal (in the presence of other large signals) b) that there weren’t numerical issues causing the strong signal to show up in the low level signal filter bins c) that the measured noise floor matched the expectation It's tricky business indeed. The cross-correlation technique could potentially measure below it's own noise-floor. Turns out it was very very VERY hard to do that safely. It remains a research topic. At best we just barely got to work around the issue. That is indeed a high dynamic setup. Cheers, Magnus ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Some more info on the 5065A optical unit
However the dimensions of the coil former vary with moisture content. Impregnation with waxes and other organic materials merely serves to slow down the rate of absorption and doesn't prevent it. Bruce > On 28 November 2017 at 12:36 Poul-Henning Kamp wrote: > > > > In message <2b66d682-15cf-465f-9a34-d7f7e7929...@n1k.org>, Bob kb8tq writes: > > >Straight cardboard *is* an issue on RF coils in humidity. > > The C-field coil is DC only. > > -- > Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 > p...@freebsd.org | TCP/IP since RFC 956 > FreeBSD committer | BSD since 4.3-tahoe > Never attribute to malice what can adequately be explained by incompetence. > ___ > time-nuts mailing list -- time-nuts@febo.com > To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts > and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Some more info on the 5065A optical unit
Hi Yup, I’ve never measured humidity on DC coils. I have done a lot of measurements on various combinations of cardboard and “stuff’ on RF coils. Bob > On Nov 27, 2017, at 6:36 PM, Poul-Henning Kamp wrote: > > > In message <2b66d682-15cf-465f-9a34-d7f7e7929...@n1k.org>, Bob kb8tq writes: > >> Straight cardboard *is* an issue on RF coils in humidity. > > The C-field coil is DC only. > > -- > Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 > p...@freebsd.org | TCP/IP since RFC 956 > FreeBSD committer | BSD since 4.3-tahoe > Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Some more info on the 5065A optical unit
In message <2b66d682-15cf-465f-9a34-d7f7e7929...@n1k.org>, Bob kb8tq writes: >Straight cardboard *is* an issue on RF coils in humidity. The C-field coil is DC only. -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
-Original Message- >From: Magnus Danielson >Sent: Nov 27, 2017 2:45 PM >To: time-nuts@febo.com >Cc: mag...@rubidium.se >Subject: Re: [time-nuts] Allan variance by sine-wave fitting > >Hi, > >There is nothing wrong about attempting new approaches, or even just >test and idea and see how it pans out. You should then compare it to a >number of other approaches, and as you test things, you should analyze >the same data with different methods. Prototyping that in Python is >fine, but in order to analyze it, you need to be careful about the details. > >I would consider one just doing the measurements and then try different >post-processings and see how those vary. >Another paper then takes up on that and attempts analysis that matches >the numbers from actual measurements. > >So, we might provide tough love, but there is a bit of experience behind >it, so it should be listened to carefully. > It is tough to come up with good artificial test data - the literature on generating "noise samples" is significantly thinner than the literature on measuring the noise. When it comes to measuring actual signals with actual ADCs, there's also a number of traps - you can design a nice approach, using the SNR/ENOB data from the data sheet, and get seemingly good data. The challenge is really in coming up with good *tests* of your measurement technique that show that it really is giving you what you think it is. A trivial example is this (not a noise measuring problem, per se) - You need to measure the power of a received signal - if the signal is narrow band, and high SNR, then the bandwidth of the measuring system (be it a FFT or conventional spectrum analyzer) doesn't make a lot of difference - the precise filter shape is non-critical. The noise power that winds up in the measurement bandwidth is small, for instance. But now, let's say that the signal is a bit wider band or lower SNR or you're uncertain of its exact frequency, then the shape of the filter starts to make a big difference. Now, let’s look at a system where there’s some decimation involved - any decimation raises the prospect of “out of band signals” (such as the noise) aliasing into the post decimation passband. Now, all of a sudden, the filtering before the decimator starts to become more important. And the number of bits you have to carry starts being more important. And some assumptions about noise being random and uncorrelated start to fall apart. It actually took a fair amount of work to *prove* that a recent system I was working on a) accurately measured the signal (in the presence of other large signals) b) that there weren’t numerical issues causing the strong signal to show up in the low level signal filter bins c) that the measured noise floor matched the expectation ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
On 11/27/17 2:45 PM, Magnus Danielson wrote: There is nothing wrong about attempting new approaches, or even just test and idea and see how it pans out. You should then compare it to a number of other approaches, and as you test things, you should analyze the same data with different methods. Prototyping that in Python is fine, but in order to analyze it, you need to be careful about the details. I would consider one just doing the measurements and then try different post-processings and see how those vary. Another paper then takes up on that and attempts analysis that matches the numbers from actual measurements. So, we might provide tough love, but there is a bit of experience behind it, so it should be listened to carefully. It is tough to come up with good artificial test data - the literature on generating "noise samples" is significantly thinner than the literature on measuring the noise. When it comes to measuring actual signals with actual ADCs, there's also a number of traps - you can design a nice approach, using the SNR/ENOB data from the data sheet, and get seemingly good data. The challenge is really in coming up with good *tests* of your measurement technique that show that it really is giving you what you think it is. A trivial example is this (not a noise measuring problem, per se) - You need to measure the power of a received signal - if the signal is narrow band, and high SNR, then the bandwidth of the measuring system (be it a FFT or conventional spectrum analyzer) doesn't make a lot of difference - the precise filter shape is non-critical. The noise power that winds up in the measurement bandwidth is small, for instance. But now, let's say that the signal is a bit wider band or lower SNR or you're uncertain of its exact frequency, then the shape of the filter starts to make a big difference. Now, let’s look at a system where there’s some decimation involved - any decimation raises the prospect of “out of band signals” aliasing into the post decimation passband. Now, all of a sudden, the filtering before the decimator starts to become more important. And the number of bits you have to carry starts being more important. It actually took a fair amount of work to *prove* that a system I was working on a) accurately measured the signal (in the presence of other large signals) b) that there weren’t numerical issues causing the strong signal to show up in the low level signal filter bins c) that the measured noise floor matched the expectation ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
Hoi Mattia, On Mon, 27 Nov 2017 23:04:56 +0100 Mattia Rizzi wrote: > >To make the point a bit more clear. The above means that noise with > > a PSD of the form 1/f^a for a>=1 (ie flicker phase, white frequency > > and flicker frequency noise), the noise (aka random variable) is: > > 1) Not independently distributed > > 2) Not stationary > > 3) Not ergodic > > I think you got too much in theory. If you follow striclty the statistics > theory, you get nowhere. > You can't even talk about 1/f PSD, because Fourier doesn't converge over > infinite power signals. This is true. But then the Fourier transformation integrates time from minus infinity to plus infinity. Which isn't exactly realistic either. The power in 1/f noise is actually limited by the age of the universe. And quite strictly so. The power you have in 1/f is the same for every decade in frequency (or time) you go. The age of the universe is about 1e10 years, that's roughly 3e17 seconds, ie 17 decades of possible noise. If we assume something like a 1k carbon resistor you get something around of 1e-17W/decade of noise power (guestimate, not an exact calculation). That means that resistor, had it been around ever since the universe was created, then it would have converted 17*1e-17 = 2e-16W of heat into electrical energy, on average, over the whole liftime of the universe. That's not much :-) > In fact, you are not allowed to take a realization, make several fft and > claim that that's the PSD of the process. But that's what the spectrum > analyzer does, because it's not a multiverse instrument. Well, any measurement is an estimate. > Every experimentalist suppose ergodicity on this kind of noise, otherwise > you get nowhere. Err.. no. Even if you assume that the spectrum tops off at some very low frequency and does not increase anymore, ie that there is a finite limit to noise power, even then ergodicity is not given. Ergodicity breaks because the noise process is not stationary. And assuming so for any kind of 1/f noise would be wrong. Attila Kinali -- The bad part of Zurich is where the degenerates throw DARK chocolate at you. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
Hi, On 11/27/2017 07:37 PM, Attila Kinali wrote: Moin Ralph, On Sun, 26 Nov 2017 21:33:03 -0800 Ralph Devoe wrote: The issue I intended to raise, but which I'm not sure I stated clearly enough, is a conjecture: Is least-square fitting as efficient as any of the other direct-digital or SDR techniques? You stated that, yes, but it's well hidden in the paper. Least-square fitting done right is very efficient. A good comparison would illustrate that, but it is also expected. What does differ is how well adapted different approaches is. If the conjecture is true then the SDR technique must be viewed as one several equivalent algorithms for estimating phase. Note that the time deviation for a single ADC channel in the Sherman and Joerdens paper in Fig. 3c is about the same as my value. This suggests that the conjecture is true. Yes, you get to similar values, if you extrapolate from the TDEV data in S&J Fig3c down to 40µs that you used. BUT: while S&J see a decrease of the TDEV consistend with white phase noise until they hit the flicker phase noise floor at about a tau of 1ms, your data does not show such a decrease (or at least I didn't see it). There is a number of ways to do this. There is even a number of ways that least square processing can be applied. The trouble with least square estimators is that you do not maintain the improvement for longer taus, and the paper PDEV estimator does not either. That motivated me to develop a decimator method for phase, frequency and PDEV that extends in post-processing, which I presented last year. Other criticisms seem off the mark: Several people raised the question of the filter factor of the least-square fit. First, if there is a filtering bias due to the fit, it would be the same for signal and reference channels and should cancel. Second, even if there is a bias, it would have to fluctuate from second to second to cause a frequency error. Bob answered that already, and I am pretty sure that Magnus will comment on it as well. Both are better suited than me to go into the details of this. Yes, see my comment. Least square estimator for phase and frequency applies a linear ramp weighing on phase samples or parabolic curve weighing on frequency samples. These filter, and the bandwidth of the filter depends on the sample count and time between samples. As sample count increases, the bandwidth goes way down. Third, the Monte Carlo results show no bias. The output of the Monte Carlo system is the difference between the fit result and the known MC input. Any fitting bias would show up in the difference, but there is none. Sorry, but this is simply not the case. If I undestood your simulations correctly (you give very little information about them), you used additive Gaussian i.i.d noise on top of the signal. Of course, if you add Gaussian i.i.d noise with zero mean, you will get zero bias in a linear least squares fit. But, as Magnus and I have tried to tell you, noises we see in this area are not necessarily Gauss i.i.d. Only white phase noise is Gauss i.i.d. Most of the techniques we use in statistics implicitly assume Gauss i.i.d. Go back to the IEEE Special issue on time and frequency from february 1966 you find a nice set of articles. In there is among others David Allans article on 2-sample variance that later became Allans variance and now Allan variance. Another article is the short but classic write up of another youngster, David Leeson, which summarize a model for phase noise generation which we today refer to the Leeson model. To deeper appreciate the Leeson model, check out the phase-noise book of Enrico Rubiola, which gives you some insight. If you want to make designs, there is more to it, so several other papers needs to be read, but here you just need to understand that you get 3 or 4 types of noises out of an oscillator, and the trouble with them is that noise does not converge like your normal textbook on statistics would make you assume. The RMS estimator on your frequency estimation does not converge, in fact it goes astray and vary with the amount of samples. This was already a known problem, but the solution came with Dave Allans paper. It in fact includes a function we later would refer to a bias function that depends on the number of samples taken. This motivates the conversion from one M sample variance to a 2-sample variance and a N sample variance to a 2-sample variance such that they can be compared. The bias function varies with the number of samples and the dominant noise-form. The noiseforms are strange and their action on statistics is strange. You need to understand how they interact with your measurement tool, and that well, in the end you need to test all noiseforms. Attila says that I exaggerate the difficulty of programming an FPGA. Not so. At work we give experts 1-6 months for a new FPGA design. We recently ported some code from a Spartan 3 to a Spartan 6. Months
Re: [time-nuts] Allan variance by sine-wave fitting
I'm talking about flicker noise processes 2017-11-27 23:04 GMT+01:00 Mattia Rizzi : > Hi, > > >To make the point a bit more clear. The above means that noise with > a PSD of the form 1/f^a for a>=1 (ie flicker phase, white frequency > and flicker frequency noise), the noise (aka random variable) is: > 1) Not independently distributed > 2) Not stationary > 3) Not ergodic > > I think you got too much in theory. If you follow striclty the statistics > theory, you get nowhere. > You can't even talk about 1/f PSD, because Fourier doesn't converge over > infinite power signals. > In fact, you are not allowed to take a realization, make several fft and > claim that that's the PSD of the process. But that's what the spectrum > analyzer does, because it's not a multiverse instrument. > Every experimentalist suppose ergodicity on this kind of noise, otherwise > you get nowhere. > > cheers, > Mattia > > 2017-11-27 22:50 GMT+01:00 Attila Kinali : > >> On Mon, 27 Nov 2017 19:37:11 +0100 >> Attila Kinali wrote: >> >> > X(t): Random variable, Gauss distributed, zero mean, i.i.d (ie PSD = >> const) >> > Y(t): Random variable, Gauss distributed, zero mean, PSD ~ 1/f >> > Two time points: t_0 and t, where t > t_0 >> > >> > Then: >> > >> > E[X(t) | X(t_0)] = 0 >> > E[Y(t) | Y(t_0)] = Y(t_0) >> > >> > Ie. the expectation of X will be zero, no matter whether you know any >> sample >> > of the random variable. But for Y, the expectation is biased to the last >> > sample you have seen, ie it is NOT zero for anything where t>0. >> > A consequence of this is, that if you take a number of samples, the >> average >> > will not approach zero for the limit of the number of samples going to >> infinity. >> > (For details see the theory of fractional Brownian motion, especially >> > the papers by Mandelbrot and his colleagues) >> >> To make the point a bit more clear. The above means that noise with >> a PSD of the form 1/f^a for a>=1 (ie flicker phase, white frequency >> and flicker frequency noise), the noise (aka random variable) is: >> >> 1) Not independently distributed >> 2) Not stationary >> 3) Not ergodic >> >> >> Where 1) means there is a correlation between samples, ie if you know a >> sample, you can predict what the next one will be. 2) means that the >> properties of the random variable change over time. Note this is a >> stronger non-stationary than the cyclostationarity that people in >> signal theory and communication systems often assume, when they go >> for non-stationary system characteristics. And 3) means that >> if you take lots of samples from one random process, you will get a >> different distribution than when you take lots of random processes >> and take one sample each. Ergodicity is often implicitly assumed >> in a lot of analysis, without people being aware of it. It is one >> of the things that a lot of random processes in nature adhere to >> and thus is ingrained in our understanding of the world. But noise >> process in electronics, atomic clocks, fluid dynamics etc are not >> ergodic in general. >> >> As sidenote: >> >> 1) holds true for a > 0 (ie anything but white noise). >> I am not yet sure when stationarity or ergodicity break, but my guess >> would >> be, that both break with a=1 (ie flicker noise). But that's only an >> assumption >> I have come to. I cannot prove or disprove this. >> >> For 1 <= a < 3 (between flicker phase and flicker frequency, including >> flicker >> phase, not including flicker frequency), the increments (ie the difference >> between X(t) and X(t+1)) are stationary. >> >> Attila Kinali >> >> >> -- >> May the bluebird of happiness twiddle your bits. >> >> ___ >> time-nuts mailing list -- time-nuts@febo.com >> To unsubscribe, go to https://www.febo.com/cgi-bin/m >> ailman/listinfo/time-nuts >> and follow the instructions there. >> > > ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
Hi, >To make the point a bit more clear. The above means that noise with a PSD of the form 1/f^a for a>=1 (ie flicker phase, white frequency and flicker frequency noise), the noise (aka random variable) is: 1) Not independently distributed 2) Not stationary 3) Not ergodic I think you got too much in theory. If you follow striclty the statistics theory, you get nowhere. You can't even talk about 1/f PSD, because Fourier doesn't converge over infinite power signals. In fact, you are not allowed to take a realization, make several fft and claim that that's the PSD of the process. But that's what the spectrum analyzer does, because it's not a multiverse instrument. Every experimentalist suppose ergodicity on this kind of noise, otherwise you get nowhere. cheers, Mattia 2017-11-27 22:50 GMT+01:00 Attila Kinali : > On Mon, 27 Nov 2017 19:37:11 +0100 > Attila Kinali wrote: > > > X(t): Random variable, Gauss distributed, zero mean, i.i.d (ie PSD = > const) > > Y(t): Random variable, Gauss distributed, zero mean, PSD ~ 1/f > > Two time points: t_0 and t, where t > t_0 > > > > Then: > > > > E[X(t) | X(t_0)] = 0 > > E[Y(t) | Y(t_0)] = Y(t_0) > > > > Ie. the expectation of X will be zero, no matter whether you know any > sample > > of the random variable. But for Y, the expectation is biased to the last > > sample you have seen, ie it is NOT zero for anything where t>0. > > A consequence of this is, that if you take a number of samples, the > average > > will not approach zero for the limit of the number of samples going to > infinity. > > (For details see the theory of fractional Brownian motion, especially > > the papers by Mandelbrot and his colleagues) > > To make the point a bit more clear. The above means that noise with > a PSD of the form 1/f^a for a>=1 (ie flicker phase, white frequency > and flicker frequency noise), the noise (aka random variable) is: > > 1) Not independently distributed > 2) Not stationary > 3) Not ergodic > > > Where 1) means there is a correlation between samples, ie if you know a > sample, you can predict what the next one will be. 2) means that the > properties of the random variable change over time. Note this is a > stronger non-stationary than the cyclostationarity that people in > signal theory and communication systems often assume, when they go > for non-stationary system characteristics. And 3) means that > if you take lots of samples from one random process, you will get a > different distribution than when you take lots of random processes > and take one sample each. Ergodicity is often implicitly assumed > in a lot of analysis, without people being aware of it. It is one > of the things that a lot of random processes in nature adhere to > and thus is ingrained in our understanding of the world. But noise > process in electronics, atomic clocks, fluid dynamics etc are not > ergodic in general. > > As sidenote: > > 1) holds true for a > 0 (ie anything but white noise). > I am not yet sure when stationarity or ergodicity break, but my guess would > be, that both break with a=1 (ie flicker noise). But that's only an > assumption > I have come to. I cannot prove or disprove this. > > For 1 <= a < 3 (between flicker phase and flicker frequency, including > flicker > phase, not including flicker frequency), the increments (ie the difference > between X(t) and X(t+1)) are stationary. > > Attila Kinali > > > -- > May the bluebird of happiness twiddle your bits. > > ___ > time-nuts mailing list -- time-nuts@febo.com > To unsubscribe, go to https://www.febo.com/cgi-bin/ > mailman/listinfo/time-nuts > and follow the instructions there. > ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
On Mon, 27 Nov 2017 19:37:11 +0100 Attila Kinali wrote: > X(t): Random variable, Gauss distributed, zero mean, i.i.d (ie PSD = const) > Y(t): Random variable, Gauss distributed, zero mean, PSD ~ 1/f > Two time points: t_0 and t, where t > t_0 > > Then: > > E[X(t) | X(t_0)] = 0 > E[Y(t) | Y(t_0)] = Y(t_0) > > Ie. the expectation of X will be zero, no matter whether you know any sample > of the random variable. But for Y, the expectation is biased to the last > sample you have seen, ie it is NOT zero for anything where t>0. > A consequence of this is, that if you take a number of samples, the average > will not approach zero for the limit of the number of samples going to > infinity. > (For details see the theory of fractional Brownian motion, especially > the papers by Mandelbrot and his colleagues) To make the point a bit more clear. The above means that noise with a PSD of the form 1/f^a for a>=1 (ie flicker phase, white frequency and flicker frequency noise), the noise (aka random variable) is: 1) Not independently distributed 2) Not stationary 3) Not ergodic Where 1) means there is a correlation between samples, ie if you know a sample, you can predict what the next one will be. 2) means that the properties of the random variable change over time. Note this is a stronger non-stationary than the cyclostationarity that people in signal theory and communication systems often assume, when they go for non-stationary system characteristics. And 3) means that if you take lots of samples from one random process, you will get a different distribution than when you take lots of random processes and take one sample each. Ergodicity is often implicitly assumed in a lot of analysis, without people being aware of it. It is one of the things that a lot of random processes in nature adhere to and thus is ingrained in our understanding of the world. But noise process in electronics, atomic clocks, fluid dynamics etc are not ergodic in general. As sidenote: 1) holds true for a > 0 (ie anything but white noise). I am not yet sure when stationarity or ergodicity break, but my guess would be, that both break with a=1 (ie flicker noise). But that's only an assumption I have come to. I cannot prove or disprove this. For 1 <= a < 3 (between flicker phase and flicker frequency, including flicker phase, not including flicker frequency), the increments (ie the difference between X(t) and X(t+1)) are stationary. Attila Kinali -- May the bluebird of happiness twiddle your bits. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
[time-nuts] Some more info on the 5065A optical unit
Bob, The cardboard is impregnated with the foam insulation goop and then sealed under the foam layer. It is totally sealed off from any environmental effects. Cheers, Corby ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
Moin Ralph, On Sun, 26 Nov 2017 21:33:03 -0800 Ralph Devoe wrote: > The issue I intended to raise, but which I'm not sure I stated clearly > enough, is a conjecture: Is least-square fitting as efficient as any of the > other direct-digital or SDR techniques? You stated that, yes, but it's well hidden in the paper. > Is the resolution of any > direct-digital system limited by (a) the effective number of bits of the > ADC and (b) the number of samples averaged? Thanks to Attila for reminding > me of the Sherman and Joerdens paper, which I have not read carefully > before. In their appendix Eq. A6 they derive a result which may or may not > be related to Eq. 6 in my paper. They are related, but only accidentally. S&J derive a lower bound for the Allan variance from the SNR. You try to derive the lower bound for the Allan variance from the quantization noise. That you end up with similar looking formulas comes from the fact that both methods have a scaling in 1/sqrt(X) where X is the number of samples taken. though S&J use the number of phase estimates, while you use the number of ADC samples. While related, they are not the same. And you both have a scaling of 1/(2*pi*f) to get from phase to time. You will notice that your formla contains a 2^N term, with N being the number of bits, but which you derive from the SNR (ENOB). It's easy to show that the SNR due to quantization noise is proportional to size of an LSB, ie. SNR ~ 2^N. If we now put in all variables and substitute 2^N by SNR will see: S&J: sigma >= 1/(2*pi*f) * sqrt(2/(SNR*N_sample)) (note the inequality!) Yours: sigma ~= 1/(2*pi*f) * 1/SNR * sqrt(1/M) (up to a constant) Note three differences: 1) S&J scales with 1/sqrt(SNR) while yours scales with 1/SNR 2) S&J have a tau depndence implicit in the formula due to N_sample, you do not. 3) S&J is a lower bound, yours an approximation (or claims to be). > If the conjecture is true then the SDR > technique must be viewed as one several equivalent algorithms for > estimating phase. Note that the time deviation for a single ADC channel in > the Sherman and Joerdens paper in Fig. 3c is about the same as my value. > This suggests that the conjecture is true. Yes, you get to similar values, if you extrapolate from the TDEV data in S&J Fig3c down to 40µs that you used. BUT: while S&J see a decrease of the TDEV consistend with white phase noise until they hit the flicker phase noise floor at about a tau of 1ms, your data does not show such a decrease (or at least I didn't see it). > Other criticisms seem off the mark: > > Several people raised the question of the filter factor of the least-square > fit. First, if there is a filtering bias due to the fit, it would be the > same for signal and reference channels and should cancel. Second, even if > there is a bias, it would have to fluctuate from second to second to cause > a frequency error. Bob answered that already, and I am pretty sure that Magnus will comment on it as well. Both are better suited than me to go into the details of this. > Third, the Monte Carlo results show no bias. The output > of the Monte Carlo system is the difference between the fit result and the > known MC input. Any fitting bias would show up in the difference, but there > is none. Sorry, but this is simply not the case. If I undestood your simulations correctly (you give very little information about them), you used additive Gaussian i.i.d noise on top of the signal. Of course, if you add Gaussian i.i.d noise with zero mean, you will get zero bias in a linear least squares fit. But, as Magnus and I have tried to tell you, noises we see in this area are not necessarily Gauss i.i.d. Only white phase noise is Gauss i.i.d. Most of the techniques we use in statistics implicitly assume Gauss i.i.d. To show you that things fail in quite interesting way assume this: X(t): Random variable, Gauss distributed, zero mean, i.i.d (ie PSD = const) Y(t): Random variable, Gauss distributed, zero mean, PSD ~ 1/f Two time points: t_0 and t, where t > t_0 Then: E[X(t) | X(t_0)] = 0 E[Y(t) | Y(t_0)] = Y(t_0) Ie. the expectation of X will be zero, no matter whether you know any sample of the random variable. But for Y, the expectation is biased to the last sample you have seen, ie it is NOT zero for anything where t>0. A consequence of this is, that if you take a number of samples, the average will not approach zero for the limit of the number of samples going to infinity. (For details see the theory of fractional Brownian motion, especially the papers by Mandelbrot and his colleagues) A PSD ~ 1/f is flicker phase noise, which usually starts to be relevant in our systems for sampling times between 1µs (for high frequency stuff) and 1-100s (high stability oscillators and atomic clocks). Unfortunately, the Allan deviation does not discern between white phase noise and flicker phase noise, so it's not possible to see in your plots where flicker noise becomes relevant (that's
Re: [time-nuts] Some more info on the 5065A optical unit
Hi If the coil form really is plain old cardboard. I wonder what the impact of humidity is? With the heat rise and all the packing, it’s going to be a pretty indirect effect. It might be an issue on a unit that had been stored powered off for a number of years. Straight cardboard *is* an issue on RF coils in humidity. Bob > On Nov 27, 2017, at 12:34 PM, cdel...@juno.com wrote: > > Made some measurements on a 5065A optical unit. > > -There are 3 layers in the magnetic shield. > -There is about 1/8" of insulation between the > outer and middle, and between the middle and inner shields. > -The C-field winding is wound on a cardboard form that > fits tightly into the inner shield. > -There is almost 1/2" of foamed insulation between the C-field > winding and the cell and oven forms and windings. > > Per Poul-Hennings data a 3.25 deg C change of the outer shield > will result in a 1 deg C change in the C-field coil. > > This equates to a 3.4X10-14 frequency shift when not using > active current drive. > > If the outer shield is actively held to plus/minus 1 deg C then > the shift will be held within plus/minus 1.05X10-14 as long as > the ambient temperature stays within the regulating range of the > PC liquid cooler. > > Cheers, > > Corby > > ___ > time-nuts mailing list -- time-nuts@febo.com > To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts > and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
[time-nuts] Some more info on the 5065A optical unit
Made some measurements on a 5065A optical unit. -There are 3 layers in the magnetic shield. -There is about 1/8" of insulation between the outer and middle, and between the middle and inner shields. -The C-field winding is wound on a cardboard form that fits tightly into the inner shield. -There is almost 1/2" of foamed insulation between the C-field winding and the cell and oven forms and windings. Per Poul-Hennings data a 3.25 deg C change of the outer shield will result in a 1 deg C change in the C-field coil. This equates to a 3.4X10-14 frequency shift when not using active current drive. If the outer shield is actively held to plus/minus 1 deg C then the shift will be held within plus/minus 1.05X10-14 as long as the ambient temperature stays within the regulating range of the PC liquid cooler. Cheers, Corby ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
[time-nuts] HP 5065A - Some OP-Amp, FB and teflon stand off questions...
Ulf, Be careful if you are going to duplicate the new style A9 for use in a chassis that was built for the old style! On the old style chassis one of the resistors is mounted on the rear of the front panel loop switch. This resistor is mounted on the A9 module for the new style chassis. Which teflon feedthrus? The ones for the DC connections to the metal modules? Cheers, Corby ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Allan variance by sine-wave fitting
Hi, On 11/27/2017 04:05 PM, Bob kb8tq wrote: Hi On Nov 27, 2017, at 12:33 AM, Ralph Devoe wrote: Here's a short reply to the comments of Bob, Attila, Magnus, and others. Thanks for reading the paper carefully. I appreciate it. Some of the comments are quite interesting, other seem off the mark. Let's start with an interesting one: The issue I intended to raise, but which I'm not sure I stated clearly enough, is a conjecture: Is least-square fitting as efficient as any of the other direct-digital or SDR techniques? Is the resolution of any direct-digital system limited by (a) the effective number of bits of the ADC and (b) the number of samples averaged? Thanks to Attila for reminding me of the Sherman and Joerdens paper, which I have not read carefully before. In their appendix Eq. A6 they derive a result which may or may not be related to Eq. 6 in my paper. If the conjecture is true then the SDR technique must be viewed as one several equivalent algorithms for estimating phase. Note that the time deviation for a single ADC channel in the Sherman and Joerdens paper in Fig. 3c is about the same as my value. This suggests that the conjecture is true. Other criticisms seem off the mark: Several people raised the question of the filter factor of the least-square fit. First, if there is a filtering bias due to the fit, it would be the same for signal and reference channels and should cancel. Errr … no. There are earlier posts about this on the list. The *objective* of ADEV is to capture noise. Any filtering process rejects noise. That is true in DMTD and all the other approaches. Presentations made in papers since the 1970’s demonstrate that it very much does not cancel out or drop out. It impacts the number you get for ADEV. You have thrown away part of what you set out to measure. It's obvious already in David Allan's 1966 paper. It's been verified and "re-discovered" a number of times. You should re-read what I wrote, as it gives you the basic hints you should be listening to. Yes, ADEV is a bit fussy in this regard. Many of the other “DEV” measurements are also fussy. This is at the heart of why many counters (when they estimate frequency) can not be used directly for ADEV. Any technique that is proposed for ADEV needs to be analyzed. For me it's not fuzzy, or rather, the things I know about these and their coloring is one thing and the things I think is fuzzy is the stuff I haven't published articles on yet. The point here is not that filtering makes the measurement invalid. The point is that the filter’s impact needs to be evaluated and stated. That is the key part of the proposed technique that is missing at this point. The traditional analysis is that the bandwidth derives from the nyquist frequency of sampling, as expressed in David own words when I discussed it last year "We had to, since that was the counters we had". Staffan Johansson of Philips/Fluke/Pendulum wrote a paper on using linear regression, which is just another name for least square fit, frequency estimation and it's use in ADEV measurements. Now, Prof. Enrico Rubiola realized that something was fishy, and it indeed is, as the pre-filtering with fixed tau that linear regression / least square achieves colors the low-tau measurements, but not the high-tau measurements. This is because the frequency sensitivity of high tau ADEVs becomes so completely within the passband of the pre-filter that it does not care, but for low tau the prefiltering dominates and produces lower values than it should, a biasing effect. He also realized that the dynamic filter of MDEV, where the filter changes with tau, would be interesting and that is how he came about to come up with the parabolic deviation PDEV. Now, the old wisdom is that you need to publish the bandwidth of the pre-filtering of the channel, or else the noise estimation will not be proper. Look at the Allan Deviation Wikipedia article for a first discussion on bias functions, they are all aspects of biasing of various forms of processing. The lesson to be learned here is that there is a number of different ways that you can bias your measurements such that your ADEV values will no longer be "valid" to correctly performed ADEV, and thus the ability to compare them to judge levels of noise and goodness-values is being lost. I know it is a bit much to take in at first, but trust me that this is important stuff. So be careful about wielding "of the mark", this is the stuff that you need to be careful about that we kindly try to advice you on, and you should take the lesson when it's free. Cheers, Magnus Bob Second, even if there is a bias, it would have to fluctuate from second to second to cause a frequency error. Third, the Monte Carlo results show no bias. The output of the Monte Carlo system is the difference between the fit result and the known MC input. Any fitting bias would show up in the difference, but there
Re: [time-nuts] Allan variance by sine-wave fitting
Hi > On Nov 27, 2017, at 12:33 AM, Ralph Devoe wrote: > > Here's a short reply to the comments of Bob, Attila, Magnus, and others. > Thanks for reading the paper carefully. I appreciate it. Some of the > comments are quite interesting, other seem off the mark. Let's start with > an interesting one: > > The issue I intended to raise, but which I'm not sure I stated clearly > enough, is a conjecture: Is least-square fitting as efficient as any of the > other direct-digital or SDR techniques? Is the resolution of any > direct-digital system limited by (a) the effective number of bits of the > ADC and (b) the number of samples averaged? Thanks to Attila for reminding > me of the Sherman and Joerdens paper, which I have not read carefully > before. In their appendix Eq. A6 they derive a result which may or may not > be related to Eq. 6 in my paper. If the conjecture is true then the SDR > technique must be viewed as one several equivalent algorithms for > estimating phase. Note that the time deviation for a single ADC channel in > the Sherman and Joerdens paper in Fig. 3c is about the same as my value. > This suggests that the conjecture is true. > > Other criticisms seem off the mark: > > Several people raised the question of the filter factor of the least-square > fit. First, if there is a filtering bias due to the fit, it would be the > same for signal and reference channels and should cancel. Errr … no. There are earlier posts about this on the list. The *objective* of ADEV is to capture noise. Any filtering process rejects noise. That is true in DMTD and all the other approaches. Presentations made in papers since the 1970’s demonstrate that it very much does not cancel out or drop out. It impacts the number you get for ADEV. You have thrown away part of what you set out to measure. Yes, ADEV is a bit fussy in this regard. Many of the other “DEV” measurements are also fussy. This is at the heart of why many counters (when they estimate frequency) can not be used directly for ADEV. Any technique that is proposed for ADEV needs to be analyzed. The point here is not that filtering makes the measurement invalid. The point is that the filter’s impact needs to be evaluated and stated. That is the key part of the proposed technique that is missing at this point. Bob > Second, even if > there is a bias, it would have to fluctuate from second to second to cause > a frequency error. Third, the Monte Carlo results show no bias. The output > of the Monte Carlo system is the difference between the fit result and the > known MC input. Any fitting bias would show up in the difference, but there > is none. > > Attila says that I exaggerate the difficulty of programming an FPGA. Not > so. At work we give experts 1-6 months for a new FPGA design. We recently > ported some code from a Spartan 3 to a Spartan 6. Months of debugging > followed. FPGA's will always be faster and more computationally efficient > than Python, but Python is fast enough. The motivation for this experiment > was to use a high-level language (Python) and preexisting firmware and > software (Digilent) so that the device could be set up and reconfigured > easily, leaving more time to think about the important issues. > > Attila has about a dozen criticisms of the theory section, mostly that it > is not rigorous enough and there are many assumptions. But it is not > intended to be rigorous. This is primarily an experimental paper and the > purpose of the theory is to give a simple physical picture of the > surprizingly good results. It does that, and the experimental results > support the conjecture above. > The limitations of the theory are discussed in detail on p. 6 where it is > called "... a convenient approximation.." Despite this the theory agrees > with the Monte Carlo over most of parameter space, and where it does not is > discussed in the text. > > About units: I'm a physicist and normally use c.g.s units for > electromagnetic calculations. The paper was submitted to Rev. Sci. Instr. > which is an APS journal. The APS has no restrictions on units at all. > Obviously for clarity I should put them in SI units when possible. > > Ralph > KM6IYN > ___ > time-nuts mailing list -- time-nuts@febo.com > To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts > and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
