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Show me some more numbers

Started by Cedron June 4, 2015
[...snip...]
> >That first sentence makes no sense. You can test your generator and your >data however you want. That's any algorithm tester's responsibility.
That's
>why it is common to use well tested and documented generators. That's
also
>why seeded generators are used so that anyone else can regenerate the
data
>to test if they wish.
Here is how the PRG is seeded: //--- Seed the random number generator srand( (int) time( NULL ) ); Here is the routine: //============================================================================ double PseudoGaussian( double argNoiseLevel ) { if( argNoiseLevel == 0.0 ) return 0.0; double theStretch = 11.23; // 7.94; double theShift = theStretch * 0.5; double theResize = theStretch / (double) RAND_MAX; double theSum = 0.0; for( int t = 0; t < 10; t++ ) { theSum += (double) random() * theResize - theShift; } return argNoiseLevel * theSum * 0.10; } //============================================================================ Feel free to test away. If you want my source code for the signal testing, provide an email address and I will send it to you.
> >When generating independent finite sequences of AWGN there is an
expected
>variance in mean known as the 'expected error in mean'. There is an >expected error in variance known as the 'expected error in variance'. A
generator
>that produces independent sequences with a consistently larger or >consistently smaller mean than the expected error in mean is broken and
should be
>fixed or replaced and retested. A generator that produces independent >sequences with consistently larger or consistently smaller error in
variance than
>the expected error in mean is broken and should be fixed or replaced and >retested. >
See what I said in my response to Steve Pope.
>Mean and variance are not the only statistics of a generator that can
be
>used to evaluate the correctness of a generator of AWGN. There are
similar
>expected errors in skew and kurtosis for example. >
Fancy words for the third and fourth moments. Do you know what term follows in this sequence: Position, velocity, acceleration, ?????. You can measure your distribution in umpteen ways, the mean and RMS is how it is specified. All I claimed was "near Gaussian", not AWGN, the routine fits that description, does it not?
>Real implementations of algorithms operate on finite data sets that do
not
>have an expected mean of zero or expected error in variance of zero and >are not properly tested by cooked data. > >If you think smaller expected error is needed, get it by fixing your >broken generator, if that is the problem or increasing the size of the
data set,
>not cooking the data. > >Dale B. Dalrymple
I'm made a comparison test to test the various formulas, nitpicking about whether the noise was USDA prime or not is a side issue. If you really want to talk about the quality of PRGs, go talk to the encryption folks. How about the formulas themselves? I have provided a 3 Bin Real signal formula, and its derivation. I have provided a 3 Bin and a 2 Bin formulas for a Complex signal, without providing their derivation. I have derived a 2 Bin Real signal formula, that I have not provided, nor its derivation. All these formulas are exact in the noiseless case. It turns out my 3 Bin Complex signal formula is equivalent to Candan's 2013. I wouldn't have known that without these tests. As a result I now have an alternative derivation of Candan's 2013 formula that I think is much cleaner and neater than his. So I ask again, of the three formulas I have provided, have you, oh experienced one, ever seen them before? I know it really irks you for me to say that window functions don't help with frequency determination, so I have asked you to provide a window/formula combination that can beat my results. So far, nothing. The Gaussian window/parabolic formula is not exact in the discrete case and has already fallen short in Julien's, and my, testing. You'll need to provide another one. I derived my 2 Bin Real signal formula in response to Martin Vicanek's paper. My solution is singular, his is a one parameter family, thus more comprehensive. In his paper he says: "Clearly, we can use this freedom to our advantage and make an optimum choice of e.g. a and b for best signal to noise ratio." I have corresponded with him and am trying to help him solve this. He is very sharp. I am also trying to figure out where my solution is on his continuum in useful terms. Neither are trivial problems. It may turn out that mine is the optimum, or he might find a better answer. I'm rooting for the latter. All are exact in the noiseless case. Finally, I am wondering where you stand on the discussion Steve Pope and I are having. Is it better to use one set of noise patterns for all the run trials, or should each one get its own? Ced --------------------------------------- Posted through http://www.DSPRelated.com
Cedron <103185@DSPRelated> wrote:

>In this test, for what I am trying to show, I don't really think it >matters. Our discussion on whether to use a single set of noise patterns >for all runs or give each run a fresh pattern is independent of the >particular noise model used, agreed?
Yes, we were discussing that, but more recently you introduced the idea of "re-centering" and "re-scaling" the noise so I was responding (or reacting) to those comments. Smeve
>Cedron <103185@DSPRelated> wrote: > >>In this test, for what I am trying to show, I don't really think it >>matters. Our discussion on whether to use a single set of noise
patterns
>>for all runs or give each run a fresh pattern is independent of the >>particular noise model used, agreed? > >Yes, we were discussing that, but more recently you introduced the idea >of "re-centering" and "re-scaling" the noise so I was responding >(or reacting) to those comments. > > >Smeve
Yes, I did within the context of the discussion. At issue was how to differentiate the effects of the noise from the effects of the formulas in the results. Centering and rescaling would help with that at the cost of being less realistic. I do appreciate your commentary. I also appreciate you discussing the real issues involved rather than appealing to convention or authority. Ced --------------------------------------- Posted through http://www.DSPRelated.com
Cedron <103185@DSPRelated> wrote:

>>Cedron <103185@DSPRelated> wrote:
>>>Our discussion on whether to use a single set of noise patterns >>>for all runs or give each run a fresh pattern is independent of the >>>particular noise model used, agreed?
>>Yes, we were discussing that, but more recently you introduced the idea >>of "re-centering" and "re-scaling" the noise so I was responding >>(or reacting) to those comments.
>Yes, I did within the context of the discussion. At issue was how to >differentiate the effects of the noise from the effects of the formulas in >the results.
Okay
>Centering and rescaling would help with that at the cost of >being less realistic.
I think that's a pretty bad direction to go in. Separately from that question, and back to your original point, consider the following pair of experiments: 1) Evaluate by simulation one frequency-estimation algorithm at one frequency/phase input to the DFT at one SNR, say SNR1. Use a set of runsize (say runsize = 10,000) noise patterns from a N(0,1) generator. Reduce the sim results to a bias/standard deviation pair. Observe that the results have converged because increasing the runsize (with additional noise patterns, say to 50,000 total) does not materially affect the reduced results. 2) Now evaluate the same frequency-estimation algorithm at the same frequency/phase input to the DFT at a different SNR, say SNR2. Do you use the same 10,000 noise patterns, or a different 10,000? If you use the same patterns, starting with the same 10,000 patterns as in case 1) and confirm that this has converged by increasing the runsize to 50,000 (while still using the same additional 40,000 patterns as used in case 1) you then know that any unexpected results are the result of algorithm behavior. Whereas if you instead use a different 10,000 patterns, and see unexpected reduced results at that point, you do not yet know for certain that the unexpected results are due to the algorithm behavior, or due to having changed the noise patterns. You are more dependent on increasing the runsize to be able to make this distinction. So I say you have a better ability to distinguish algorithm behavior from the effects of possibly pathological noise noise patterns if you do not change the noise patterns between experiment 1) and experiment 2). However, opinions on this are bound to vary, and could depend on the surrounding scenario and requirements, so I am not preaching this as gospel; just that is seems logical and scientific to me. (For similar reasons, and for repeatability, you want to use seeded noise, and not randomized noise, as mentioned by other contributors to this thread.) Steve
>Cedron <103185@DSPRelated> wrote: > >>>Cedron <103185@DSPRelated> wrote: > >>>>Our discussion on whether to use a single set of noise patterns >>>>for all runs or give each run a fresh pattern is independent of the >>>>particular noise model used, agreed? > >>>Yes, we were discussing that, but more recently you introduced the
idea
>>>of "re-centering" and "re-scaling" the noise so I was responding >>>(or reacting) to those comments. > >>Yes, I did within the context of the discussion. At issue was how to >>differentiate the effects of the noise from the effects of the formulas
in
>>the results. > >Okay > >>Centering and rescaling would help with that at the cost of >>being less realistic. > >I think that's a pretty bad direction to go in. >
I wouldn't call it good or bad. What you are in essence doing is shortcutting using a much larger runsize. The purpose of a larger runsize is to get the distributions closer to the ideal.
>Separately from that question, and back to your original point, >consider the following pair of experiments: > >1) Evaluate by simulation one frequency-estimation algorithm at one >frequency/phase input to the DFT at one SNR, say SNR1. Use a set of >runsize (say runsize = 10,000) noise patterns from a N(0,1) generator. >Reduce the sim results to a bias/standard deviation pair. Observe that >the results have converged because increasing the runsize (with >additional noise patterns, say to 50,000 total) does not materially >affect the reduced results. >
In my response, I'm going to ignore the precision limitation of large runsizes I seem to have found, not that it is insignificant, but it is not germane to the principles being discussed. Now you have stepped off the presumption that one set of results is sufficient. My recommendation was if you were going to to 50,000 runs, do five 10,000 runs so you have five sets of results to compare. You can calculate the overall average and standard deviation from the five sets.
>2) Now evaluate the same frequency-estimation algorithm at the same >frequency/phase input to the DFT at a different SNR, say SNR2. > >Do you use the same 10,000 noise patterns, or a different 10,000? > >If you use the same patterns, starting with the same 10,000 patterns >as in case 1) and confirm that this has converged by increasing the >runsize to 50,000 (while still using the same additional 40,000 patterns
>as used in case 1) you then know that any unexpected results are the >result of algorithm behavior. >
I think there is an underlying assumption of linearity in this argument. I have been careful to say that the increase is the standard deviations seems to be roughly proportional (linear) to the RMS of the noise. This comes from the analytical view where VB(Z+E)/V(Z+E) = VBZ/VZ + (Misc terms)E + H.O.T. Where H.O.T = Higher Order Terms. At some point, when E gets larger, the H.O.T terms become significant.
>Whereas if you instead use a different 10,000 patterns, and see
unexpected
>reduced results at that point, you do not yet know for certain that the >unexpected results are due to the algorithm behavior, or due to having >changed the noise patterns. You are more dependent on increasing >the runsize to be able to make this distinction. > >So I say you have a better ability to distinguish algorithm behavior from
>the effects of possibly pathological noise noise patterns if you do not >change the noise patterns between experiment 1) and experiment 2). >
I see what you are saying, but we are also discussing whether the same pattern should be used row by row as well. If you look at the bias of the 2 Bin Complex formula applied to a real signal, it is fairly complicated. Now it shows up in the noiseless case so you know it is not due to the noise. Suppose though that some how your formula reacted to noise in a biased way. If so, having multiple sets of noise as I suggested would show that bias, and that bias would appear no matter what noise patterns were thrown against it. However, if the bias appears under the circumstance you describe at SNR1, and then reappears at SNR2, you still can't distinguish whether is was the noise or the formula creating it. If the bias appears with totally different noise sets, you can conclude, but not be certain, that it was due to the formula.
>However, opinions on this are bound to vary, and could depend on the >surrounding scenario and requirements, so I am not preaching this >as gospel; just that is seems logical and scientific to me. >
Being open minded seems to be a quality lacking among many of the posters here, there is plenty of gospel preached. For what I have been trying to show about the behavior of the formulas, I think either method would suffice. I will code it your way and post the results, we can take the discussion from there.
>(For similar reasons, and for repeatability, you want to use seeded
noise,
>and not randomized noise, as mentioned by other contributors to this >thread.) > >Steve
I posted my code in my response to that other contributor. I do indeed use a seeded PRG. You are kind to call him a contributor, I see him just as a heckler. He lost a considerable amount of credibility when he said: "For the same signal duration, increasing the sample frequency and transform size will separate the two components of the real signal and reduce the "self interference" of a real signal with a complex estimation algorithm." Ced --------------------------------------- Posted through http://www.DSPRelated.com
Cedron <103185@DSPRelated> wrote:

>>>Centering and rescaling would help with that at the cost of >>>being less realistic.
>>I think that's a pretty bad direction to go in.
>I wouldn't call it good or bad. What you are in essence doing is >shortcutting using a much larger runsize. The purpose of a larger runsize >is to get the distributions closer to the ideal.
But it's then no longer N(0,1) noise. That to me is a very big deal.
>>consider the following pair of experiments:
>>1) Evaluate by simulation one frequency-estimation algorithm at one >>frequency/phase input to the DFT at one SNR, say SNR1. Use a set of >>runsize (say runsize = 10,000) noise patterns from a N(0,1) generator. >>Reduce the sim results to a bias/standard deviation pair. Observe that >>the results have converged because increasing the runsize (with >>additional noise patterns, say to 50,000 total) does not materially >>affect the reduced results.
>In my response, I'm going to ignore the precision limitation of large >runsizes I seem to have found, not that it is insignificant, but it is not >germane to the principles being discussed.
Agreed.
>Now you have stepped off the presumption that one set of results is >sufficient. My recommendation was if you were going to to 50,000 runs, do >five 10,000 runs so you have five sets of results to compare. You can >calculate the overall average and standard deviation from the five sets.
This is equivalent, or almost equivalent. In any case, for anything other than a quick evaluation, there needs to be a rational way of concluding that your runsize is large enough to get to statistically accurate results. So I think we're in agreeent here.
>>2) Now evaluate the same frequency-estimation algorithm at the same >>frequency/phase input to the DFT at a different SNR, say SNR2.
>>Do you use the same 10,000 noise patterns, or a different 10,000?
>>If you use the same patterns, starting with the same 10,000 patterns >>as in case 1) and confirm that this has converged by increasing the >>runsize to 50,000 (while still using the same additional 40,000 patterns > >>as used in case 1) you then know that any unexpected results are the >>result of algorithm behavior.
>I think there is an underlying assumption of linearity in this argument.
Almost, sort of. There's an assumption that "unexpected results" need to be investigated. As you suggest, it might be "unexpected" if the trend vs. SNR of the standard deviation in the reduced results does not track the RMS level of the noise as you change SNR. But this is algorithm- dependent, even investigation-dependent, so in the above I did not specifically define this as what is "unexpected". But at a higher level, I am suggesting the hypothesis is of the form: "Does the unexpected behavior [however that is defined] result form an insufficiency in the algorithm, or does it result from outliers in the noise patterns?" This is, I think, an attempt to formalize (slightly) the type of distinctions you and I are discussing.
>I have been careful to say that the increase is the standard deviations >seems to be roughly proportional (linear) to the RMS of the noise. This >comes from the analytical view where > >VB(Z+E)/V(Z+E) = VBZ/VZ + (Misc terms)E + H.O.T. > >Where H.O.T = Higher Order Terms.
>At some point, when E gets larger, the H.O.T terms become significant.
Yes, this is a very good level of detail for understanding how it is behaving.
>>[snip] >>So I say you have a better ability to distinguish algorithm behavior from >>the effects of possibly pathological noise noise patterns if you do not >>change the noise patterns between experiment 1) and experiment 2).
> I see what you are saying, but we are also discussing whether the same > pattern should be used row by row as well.
Yes, we've discussed both.
>If you look at the bias of the 2 Bin Complex formula applied to a real >signal, it is fairly complicated. Now it shows up in the noiseless case >so you know it is not due to the noise. Suppose though that some how your >formula reacted to noise in a biased way. If so, having multiple sets of >noise as I suggested would show that bias, and that bias would appear no >matter what noise patterns were thrown against it. However, if the bias >appears under the circumstance you describe at SNR1, and then reappears at >SNR2, you still can't distinguish whether is was the noise or the formula >creating it. If the bias appears with totally different noise sets, you >can conclude, but not be certain, that it was due to the formula.
I see what you are saying also.
>>However, opinions on this are bound to vary, and could depend on the >>surrounding scenario and requirements, so I am not preaching this >>as gospel; just that is seems logical and scientific to me.
>Being open minded seems to be a quality lacking among many of the posters >here, there is plenty of gospel preached. For what I have been trying to >show about the behavior of the formulas, I think either method would >suffice. I will code it your way and post the results, we can take the >discussion from there. > >>(For similar reasons, and for repeatability, you want to use seeded >noise, >>and not randomized noise, as mentioned by other contributors to this >>thread.)
>I posted my code in my response to that other contributor. I do indeed >use a seeded PRG.
Good, I'll take a look at your code when I have a chance. Steve
On Thursday, June 11, 2015 at 9:06:35 AM UTC-7, Cedron wrote:
...
> Being open minded seems to be a quality lacking among many of the posters > here, there is plenty of gospel preached. For what I have been trying to > show about the behavior of the formulas, I think either method would > suffice. I will code it your way and post the results, we can take the > discussion from there.
Everyone has a right to an opinion. Everyone has the opportunity for their opinions to be knowledgeable. Some people think that their opinions on topics they are not knowledgeable about are as significant as their opinions on topics they are well practiced in. Other people who disagree with that are mistaken as being closed-minded. There has been a lot of "gospel" practiced by those here. People have pointed you to some of that practice, but you have mistaken it for haystacks.
> > >(For similar reasons, and for repeatability, you want to use seeded > noise, > >and not randomized noise, as mentioned by other contributors to this > >thread.) > > > >Steve > > I posted my code in my response to that other contributor. I do indeed > use a seeded PRG.
So I take it that you believe that the code you posted is responsive to the issues for which generator seeding is used. The code that performs the seeding is: ------------------------------------------- Here is how the PRG is seeded: //--- Seed the random number generator srand( (int) time( NULL ) ); -------------------------------------------
> > You are kind to call him a contributor, I see him just as a heckler. He > lost a considerable amount of credibility when he said: "For the same > signal duration, increasing the sample frequency and transform size will > separate the two components of the real signal and reduce the "self > interference" of a real signal with a complex estimation algorithm." > > Ced
Yes, that statement of mine was in error. The "sample frequency and" should be removed. Fortunately you were able to correct it and to understand the intent of the sentence and the point of the intended change. In your noise generation you used "time(NULL)" as a seed. This serves to make the actual seed value used impossible to determine by both the original coder and everyone else. That makes it impossible for anyone to repeat the run, which is the purpose of the seeding. Was this your intent? Was this a simple lapse, a failure to understand noise generators or a failure to comprehend the the basic principles of testing algorithms with noise? How do you think hiding the seed value contributes to the discussion? Dale B. Dalrymple
dbd  <d.dalrymple@sbcglobal.net> wrote:

>On Thursday, June 11, 2015 at 9:06:35 AM UTC-7, Cedron wrote:
>> Pope wrote
>>> (For similar reasons, and for repeatability, you want to use seeded >>> noise, and not randomized noise, as mentioned by other contributors >>> to this thread.)
>> I posted my code in my response to that other contributor. I do indeed >> use a seeded PRG.
>So I take it that you believe that the code you posted is responsive to >the issues for which generator seeding is used. The code that performs >the seeding is: > >------------------------------------------- >Here is how the PRG is seeded: >//--- Seed the random number generator > > srand( (int) time( NULL ) ); >-------------------------------------------
In my terminology, this is "randomized", and I think we all agree, inappropriate for most purposes in comm system performance simulations including the simulations at hand. The "randomize" terminology dates way back from BASIC. I do not know if anyone uses the term in this way anymore. There are times, when implementing a comm system, you actually do want to randomize some parameter. A good example is the random backoff in a CSMA algorithm. If you use a deterministic algorithm to generate the backoffs, there is the possiblity of two nodes being by chance at the same point in the deterministic sequence, and always colliding from then on. I was faced with this situation once when designing a packet radio. I didn't have a really good source of randomized values for the CSMA backoffs, but I ultimately utilized a combination of the counter updated by the timer interrupt, and the most recent return address on the stack, and so far as I was able to determine, this was sufficient. More critical are cryptographic requirements for random keys. The first few versions of PGP tried to randomize but the methods were not sufficient, such that for the first five years or so of PGP's existence it was easily cracked despite the strength of the underlying crypto algorithms themselves. Oops. Steve
> >There has been a lot of "gospel" practiced by those here. People have >pointed you to some of that practice, but you have mistaken it for
haystacks.
>
Forests and trees. Let's step up a level. I derived and presented three frequency determination formulas. On the most difficult one, I have written a blog article detailing its derivation. It is my opinion that the real significance of these formulas is theoretical. It seems to be your opinion, and many here, that theoretical isn't very important, it's practicality that matters. So I wrote a testing program to demonstrate to you "real worlders" that the formulas work, and work well. Now you are just quibbling about was the demonstration program up to your testing conventions. Guess what? It doesn't matter. Either my formulas exist in "the gospel" or they don't. As I have explained, I have searched for them without finding them. I have asked experts and been directed to literature, although covering the same topic, don't include them. So you have seen my formulas. Have you seen them elsewhere? Never mind how I tested them. Julien and Jacobsen have both tested them independently and their results are similar to mine. You have not questioned their testing methodology, I presume then it meets your standards. The formulas work well, they ought to be in "the gospel". I have asked you specifically as an advocate of window functions, to provide one that outperforms my formulas. You have not. Until you can, I don't see any point in jumping through your hoops. I asked your opinion on the issue that Steve Pope and I are discussing. You have not, no big deal, it isn't an obligation. But since you profess such expertise on proper testing, I would think it would be an easy one for you to answer.
> >So I take it that you believe that the code you posted is responsive to >the issues for which generator seeding is used. The code that performs
the
>seeding is: > >------------------------------------------- >Here is how the PRG is seeded: >//--- Seed the random number generator > > srand( (int) time( NULL ) ); >------------------------------------------- >
The PRG is a single seeded generator. The seeding code could easily be replaced with an input value or hard coded. As per my discussion with Steve Pope, I prefer many different patterns, thus I test with it as stated. You don't have my testing code anyway, so repeatability is a moot issue. Guess what? The results don't change much run after run.
>> >> You are kind to call him a contributor, I see him just as a heckler.
He
>> lost a considerable amount of credibility when he said: "For the same >> signal duration, increasing the sample frequency and transform size
will
>> separate the two components of the real signal and reduce the "self >> interference" of a real signal with a complex estimation algorithm." >> >> Ced > >Yes, that statement of mine was in error. The "sample frequency and" >should be removed. Fortunately you were able to correct it and to
understand the
>intent of the sentence and the point of the intended change. >
It still wouldn't be correct. The phrase that needs to be removed is "For the same signal duration". In order to increase resolution, you need to increase the duration. This is independent of the sample count. On the other hand, the sample frequency, which for a fixed duration corresponds to the sample count is inextricably linked to the transform size.
>In your noise generation you used "time(NULL)" as a seed. This serves to >make the actual seed value used impossible to determine by both the
original
>coder and everyone else. That makes it impossible for anyone to repeat
the
>run, which is the purpose of the seeding. Was this your intent? Was this
a
>simple lapse, a failure to understand noise generators or a failure to >comprehend the the basic principles of testing algorithms with noise? How
do
>you think hiding the seed value contributes to the discussion? > >Dale B. Dalrymple
The encryption folks would laugh at that statement. Again all this talk about proper randomization, Steve and my discussion, repeatability, proper testing, is all besides the point. As far as I can tell I have come up with a major innovation. Rather than trying to shoot it down, you should be exploring it. That's what I mean by being closed minded. Not open to the possibility that an improved method exists. Many of the people here, in which I include you, seem to behave as if their doctrine needs to be defended rather than seeking out new innovations. This is more akin to how a religious organization functions than an academic one. I stand by my heckler characterization. I'm tired of it. Ced --------------------------------------- Posted through http://www.DSPRelated.com
>dbd <d.dalrymple@sbcglobal.net> wrote: > >>On Thursday, June 11, 2015 at 9:06:35 AM UTC-7, Cedron wrote: > >>> Pope wrote > >>>> (For similar reasons, and for repeatability, you want to use seeded >>>> noise, and not randomized noise, as mentioned by other contributors >>>> to this thread.) > >>> I posted my code in my response to that other contributor. I do
indeed
>>> use a seeded PRG. > >>So I take it that you believe that the code you posted is responsive to >>the issues for which generator seeding is used. The code that performs >>the seeding is: >> >>------------------------------------------- >>Here is how the PRG is seeded: >>//--- Seed the random number generator >> >> srand( (int) time( NULL ) ); >>------------------------------------------- > >In my terminology, this is "randomized", and I think we all agree, >inappropriate for most purposes in comm system performance simulations >including the simulations at hand. > >The "randomize" terminology dates way back from BASIC. I do not know if >anyone uses the term in this way anymore. > >There are times, when implementing a comm system, you actually >do want to randomize some parameter. A good example is the >random backoff in a CSMA algorithm. If you use a deterministic >algorithm to generate the backoffs, there is the possiblity >of two nodes being by chance at the same point in the deterministic >sequence, and always colliding from then on. > >I was faced with this situation once when designing a packet >radio. I didn't have a really good source of randomized values for >the CSMA backoffs, but I ultimately utilized a combination of the counter
>updated by the timer interrupt, and the most recent return address on >the stack, and so far as I was able to determine, this was sufficient. > >More critical are cryptographic requirements for random keys. The >first few versions of PGP tried to randomize but the methods were >not sufficient, such that for the first five years or so of PGP's >existence it was easily cracked despite the strength of the >underlying crypto algorithms themselves. Oops. > > >Steve
Just like the precision limitation, I don't think the seeding of the PRG, or even the noise type, is germane to the issue of whether the same noise pattern should be used for each row/level, of the test to better determine the behavior of the formulas. There are many tricks to using computer state variables to reseed PRGs on the fly so any encryption based on them can be cracked. You have mentioned some of them. This is a different topic suitable for a different newsgroup. Sorry, I don't have the program recoded yet for pattern reuse. Ced --------------------------------------- Posted through http://www.DSPRelated.com