Chris Bore <chris.bore@gmail.com> wrote: < I repeatedly come across objections to using complex numbers for DSP. < I wonder if there is a good way that I can explain without struggling? < The current case concerns ultrasound, where the measurement is of < pressure (on a transducer). My processing uses complex numbers, which < naturally arise when I demodulate the signal. The question I am asked < is, why are complex numbers necessary when the signal itself seems to < be real-valued? It took me a while to understand, or at least believe that I do... < My answer (one of them) is that the signal is modelled using complex < exponentials (Fourier analysis) and so this is the natural way to < handle those quantities. But often, a measurement of < 'amplitude' (instantaneous, real-valued, pressure) is desired, and < this seems to be a real-valued quantity. My argument here is that this < is modelled as the sum of two complex exponentials, contra-rotating < (one with +ve and one with -ve frequency), to produce a resultant that < happens to have zero imaginary part. It is the counter-rotating part that is important. More on that below. < So far so clear. But we can also derive Foruier transforms that are < based on sums of sine and cosine functions - each of which seem to be < real-valued quantities, and so this bypasses the complex < implementation. Wrong argument. The Fourier sine and cosine transforms are fundamentally different. If you want a pure real transform, there is the Hartley transformsform. The fundamental difference is in the boundary conditions used. < My argument here is that this is the same as complex < numbers, only in a different form where the complex arithmetic is done < explicitly by the way the sine and cosine terms are added and < multiplied, and where amplitude/phase replace real/imaginary in a < different arithmetic. But my current disputee convincingly suggests < that the complex representation is therefore unnecessary. < Two questions: < 1) am I correct in saying that the sine+cosine transform simply < duplicates complex arithmetic by inventing a sort of phase/amplitude < arithmetic? The sine and cosine transform are different. The important thing is that an exponential has a phase/amplitude and direction. (An analogy to standing waves will be important here.) < 2) is there a simple and convincing argument to explain this if < it is the case? When working with waves, there are always two important quantities but often we measure only one. Current and voltage in electronics, pressure and velocity in acoustics. The complex value conveniently keeps two quantities together. If you measure the voltage as a function of time on a wire, or pressure in an air column, you may find it sinusoidal, but you don't know which way (or both ways) the wave is moving. For that you need to know the current or air velocity. If you want to describe propagating waves without complex numbers, you need to keep two unrelated quantities. With the assumption of uniform media and discontinuous boundaries it is easy to keep one complex value for each wave. (Two for waves going in both directions, possibly with different amplitude.) For non-uniform media, it might be that the best way is to keep both quantities and not use complex numbers. OK, now for a story that this reminds me of. In a lecture demonstration in my undergrad physics class, the lecturer was showing the analogy between waves in a transmission line and acoustic waves in an air column. At the end, the answer came out wrong. It seems that an open end cable is analogous to a closed end air column, but that wasn't good enough. For the next lecture, the same demonstration equipment was out, but this time with a current probe on the oscilloscope. It seems that it is easier to measure voltage than current, and easier to measure air pressure than air velocity. The wave equations are symmetric between voltage/current and pressure/velocity but people are not. < One further issue. If I am interested in 'amplitude', then for example < if I average two numbers of equal amplitude and opposite phase, then < if I use a complex representation the 'average' amplitude is zero, < whereas if I average only their amplitudes then the average is equal < to either of the original amplitudes. Clearly the second case is not < the same measure as the first, but is there an easy explanation as to < why and what it measures? You might look at the explanations of SWR, and standing waves, in radio transmission. That might be the most common case where two waves of different amplitude going in opposite direction are being measured. I believe, though, that is an important part of ultrasound, too. The Fourier sine and cosine transform are for the case where you have equal amplitude waves going in each direction. That is, the boundary conditions are amplitude is zero at the end (sine) or derivative is zero at the end (cosine). The Fourier exponential transform, along with the Hartley transform, have periodic boundary conditions. That is, the signal and its derivative have the same value at each end. -- glen

# Complex versus real numbers

Started by ●August 25, 2009

Reply by ●August 25, 20092009-08-25

Reply by ●August 25, 20092009-08-25

glen herrmannsfeldt wrote:> Chris Bore <chris.bore@gmail.com> wrote:...> < So far so clear. But we can also derive Foruier transforms that are > < based on sums of sine and cosine functions - each of which seem to be > < real-valued quantities, and so this bypasses the complex > < implementation. > > Wrong argument. The Fourier sine and cosine transforms are > fundamentally different. If you want a pure real transform, > there is the Hartley transformsform. The fundamental difference > is in the boundary conditions used.I think this is a misapprehension on your part. I believe that Chris meant a single transform given in terms of sines and cosines (rectangular coordinates) rather than complex exponentials (polar coordinates). ... Jerry -- Engineering is the art of making what you want from things you can get. �����������������������������������������������������������������������

Reply by ●August 25, 20092009-08-25

Jerry Avins <jya@ieee.org> wrote: < glen herrmannsfeldt wrote: <> Chris Bore <chris.bore@gmail.com> wrote: < ... <> < So far so clear. But we can also derive Foruier transforms that are <> < based on sums of sine and cosine functions - each of which seem to be <> < real-valued quantities, and so this bypasses the complex <> < implementation. <> Wrong argument. The Fourier sine and cosine transforms are <> fundamentally different. If you want a pure real transform, <> there is the Hartley transformsform. The fundamental difference <> is in the boundary conditions used. < I think this is a misapprehension on your part. I believe that Chris < meant a single transform given in terms of sines and cosines < (rectangular coordinates) rather than complex exponentials (polar < coordinates). Maybe. It took me a while (until the explanation in Numerical Recipes) to understand the difference. Mentioning sine and transform in the same sentence seems too close to a Fourier sine transform for me, though. Hopefully the rest of the explanation helps. -- glen

Reply by ●August 26, 20092009-08-26

"Jerry Avins" <jya@ieee.org> wrote in message news:S4Ukm.284435$Ta5.144832@newsfe15.iad...> > ..... To calculate sqrt(a^2+b^2) on a slide rule, experienced users > calculate a/sin(atan(a/b)).Wow! No matter how much maths ("math" to the Yanks) you have under your belt, there's always yet another fascinating insight lurking just around the corner!

Reply by ●August 26, 20092009-08-26

On Tue, 25 Aug 2009 05:28:00 -0700 (PDT), Chris Bore <chris.bore@gmail.com> wrote: [Snipped by Lyons] Hi Chris, I clearly understand why you ask your good questions. If I find myself trying to explain why complex numbers are so often used in DSP, I generally say something like: * "Out of convenience. Euler's equations allow us to represent a real signal as the sum of positive- and negative-frequency complex exponentials if it suits our purpose (if it's useful for some reason to do so). And when thinking about a complex exponential, Euler allows us to it as a combination (orthogonal, that is) of two real functions (a special combination of a real sine and a real cosine.) At that point I usually mumble something about Euler's equations being a kind of "Rosetta Stone" that allows us to translate back and forth between real and complex representations--whichever suits our fancy. * Complex (quadrature) signal representation is wildly useful in accurately measuring the instantaneous amplitude, phase, or frequency of a signal. (For amplitude, phase, and frequency demodulation.) * When we perform spectrum analysis in DSP, our spectral results are in the form of complex numbers. That's because the DFT computes both spectral magnitudes ***and*** the relative phase between spectral components. * For pencil & paper analysis, complex (quadrature) signal representation is often more convenient. If you ask me what is the form of the product of two sinusoids, I always have scramble around to find a trig identity from some math book. It's easy for me to determine the product of two complex exponentials (it's merely the sum of exponents). * I usually end my explanation with, "We use complex numbers because that's the way God wants it to be.">One further issue. If I am interested in 'amplitude', then for example >if I average two numbers of equal amplitude and opposite phase, then >if I use a complex representation the 'average' amplitude is zero, >whereas if I average only their amplitudes then the average is equal >to either of the original amplitudes. Clearly the second case is not >the same measure as the first, but is there an easy explanation as to >why and what it measures?Your last topic points out an issue that may cause us problems in our discussion--semantics. (Jerry Avins alluded to this.) I've always thought that "amplitude" meant the difference between a real number and zero. And an "amplitude" value can be a positive or a negative real-only quantity. And I've always thought that the word "magnitude" meant a positive value only. For example, a single complex number can be described by a real "magnitude" value and a real phase value. In any case, your questions about averaging two numbers is thought provoking. If the numbers each have an amplitude and a phase, then the two numbers *must* be complex. And it seems to me that averaging two complex numbers only has meaning if we average the numbers' real and imaginary parts separately. This topic seems to be closely related to the material in a blog, called "The Nature of Circles", by Peter Kootsookos (our own Dr. K) at: http://www.dsprelated.com/showarticle/57.php Chris, you might take a look at that blog. See Ya, [-Rick-]

Reply by ●August 26, 20092009-08-26

On Aug 25, 8:28�am, Chris Bore <chris.b...@gmail.com> wrote:> I repeatedly come across objections to using complex numbers for DSP. > I wonder if there is a good way that I can explain without struggling? > > The current case concerns ultrasound, where the measurement is of > pressure (on a transducer). My processing uses complex numbers, which > naturally arise when I demodulate the signal. The question I am asked > is, why are complex numbers necessary when the signal itself seems to > be real-valued? > > My answer (one of them) is that the signal is modelled using complex > exponentials (Fourier analysis) and so this is the natural way to > handle those quantities. But often, a measurement of > 'amplitude' (instantaneous, real-valued, pressure) is desired, and > this seems to be a real-valued quantity. My argument here is that this > is modelled as the sum of two complex exponentials, contra-rotating > (one with +ve and one with -ve frequency), to produce a resultant that > happens to have zero imaginary part. > > So far so clear. But we can also derive Foruier transforms that are > based on sums of sine and cosine functions - each of which seem to be > real-valued quantities, and so this bypasses the complex > implementation. My argument here is that this is the same as complex > numbers, only in a different form where the complex arithmetic is done > explicitly by the way the sine and cosine terms are added and > multiplied, and where amplitude/phase replace real/imaginary in a > different arithmetic. But my current disputee convincingly suggests > that the complex representation is therefore unnecessary. > > Two questions: > > 1) am I correct in saying that the sine+cosine transform simply > duplicates complex arithmetic by inventing a sort of phase/amplitude > arithmetic? > > 2) is there a simple and convincing argument to explain this if it is > the case? > > One further issue. If I am interested in 'amplitude', then for example > if I average two numbers of equal amplitude and opposite phase, then > if I use a complex representation the 'average' amplitude is zero, > whereas if I average only their amplitudes then the average is equal > to either of the original amplitudes. Clearly the second case is not > the same measure as the first, but is there an easy explanation as to > why and what it measures? > > Thanks, > > Chris > ===================== > Chris Bore > BORES Signal Processingwww.bores.comChris, The blunt and honest answer for your client (although probably one you can't give him in person) is that just because he doesn't understand something, doesn't mean he should fear it. This is a possibly a psychological argument, not a technical one. Will he be upset to discover that the electricity he uses to toast his bread in the morning came out of a nuclear power plant, for which the theoretical physicist used complex values to model the reactions? This is the same kind of reaction I had from an old boss for whom the digital electronics concept of "state machine" was the devil's work... too complex for him to understand, and he got worried every time I described a solution in terms of one. "Couldn't you just use gates and flip-flops instead?", he suggested... I kid you not. I look at your sine+cosine versus complex number problem through a more abstract lens. If you get enough *raw information* about a problem, and there's a solution, you can get the solution from your information. But once you fold, merge, lose, destroy enough of your raw information, you can't get the right solution any more. In very general terms, it's like having enough dimensions, or degrees of freedom, or sampling rate. Below a certain point, you can't solve the problem anymore. It's like thinking you have a very large random matrix (lots of entropy, lots of information to solve your problem) but then discovering it really only has rank 2. In universal algebra, this notion is captured by the idea of a universal arrow (function) through which you can factor any other arrow. The kernel of your universal arrow function must refine the kernel of your original function in order for you to be able to factor through it. This kernel refinement captures the level of "raw information" needed. Not the best description but it's how I think of it. Using sine and cos transforms separately, you're adding the same amount of degrees of freedom as you would generate using the complex- valued transform. And because they're orthogonal, you are sure they capture all the information in the complex output. If they didn't, it would be like a matrix of lower rank (as an analogy). You can think of these two representations as isomorphic in the sense that, by defining enough rules about recombining the separated results, you can still get your original solution. But they're certainly not *equal* - as has been pointed out, the root of x^2+1=0 is certainly not any real combination of sines and cosines - it's a very unique mathematical object which *is*, in its heart and soul, a complex number. You're not really "duplicating" complex arithmetic so much as coming up with an "socially acceptable" form which can be translated to-and- from the complex numbers that you *really* use to solve your problem. But because it's "socially acceptable", it prevents the peasants from rising up and burning you at the stake. Just my 2 cents. - Kenn

Reply by ●August 26, 20092009-08-26

On 26 Aug, 14:59, sleeman <kennheinr...@sympatico.ca> wrote:> The blunt and honest answer for your client (although probably one you > can't give him in person) is that just because he doesn't understand > something, doesn't mean he should fear it. This is a possibly a > psychological argument, not a technical one....> ... got worried every time I > described a solution in terms of one. "Couldn't you just use gates and > flip-flops instead?", he suggested... �I kid you not.This was the exact reason why my improved passive sonar never caught on: The people who needed to know were too hung up in familiar but irrelevant semantics and terminology. Everybody who had tried to solve the same problem before me had been limited by the (artificial) limitations by the semantics they used. So when I disregarded the 'tradition' I found the solution very quickly (as I recall, in a couple of afternoons, inbetween other work), but since everybody else were unable to communicate the subject without resorting to traditional semantics, no one were able to understand what I had done. Of course, my solution could not be expressed in the 'old' semantics; if it had, somebody would have found it decades before me.> You're not really "duplicating" complex arithmetic so much as coming > up with an "socially acceptable" form which can be translated to-and- > from the complex numbers that you *really* use to solve your problem. > But because it's "socially acceptable", it prevents the peasants from > rising up and burning you at the stake.This is an important point: People who are unaware of the role of semantics will instinctively react to the effect that "so you think I am stupid?!" if you point out the details to them - as has been amply demonstrated in a different thread here over the past few days. Do not underestimate the effects of the psychological blow it is to find out that one needs to learn very basic stuff. In my experience this happens especially with people who are in way over their heads, and who somehow sense - but do not understand! - that they are. People who understand that they are in deep, ask for *help*, not for extra manhours to get the job done, and appreciate the help they get. Such people also tend to be keen on learning form you along the way. Just be very cautios and play your cards carefully. When it comes to people, always expect the worst. Rune

Reply by ●August 26, 20092009-08-26

On Aug 26, 2:59 pm, sleeman <kennheinr...@sympatico.ca> wrote: On Aug 25, 8:28 am, Chris Bore <chris.b...@gmail.com> wrote:> Using sine and cos transforms separately, you're adding the > same amount of degrees of freedom as you would generate > using the complex- valued transform. And because they're > orthogonal, you are sure they capture all the information > in the complex output. If they didn't, it would be like a > matrix of lower rank (as an analogy). You can think of > these two representations as isomorphic in the sense that, > by defining enough rules about recombining the separated > results, you can still get your original solution.Right, but the sine/cosine basis is not just some arbitrary basis related to the complex exponential basis by an arbitrary unitary transformation. The sine and cosine functions of a given frequency span the same 2d subspace (with real coefficients) as the complex exponential of that frequency and its negative (with conjugate coefficients). These subspaces are the smallest subspaces preserved by translations and reflections (time reversal). illywhacker;

Reply by ●August 26, 20092009-08-26

On Aug 25, 2:28�pm, Chris Bore <chris.b...@gmail.com> wrote:> Two questions: > > 1) am I correct in saying that the sine+cosine transform simply > duplicates complex arithmetic by inventing a sort of phase/amplitude > arithmetic?Sort of. Sine and cosine functions are linear combinations of a complex exponential of the same frequency and its complex conjugate. They therefore span the same set of functions. Two things need to be explained: what is so special about complex exponentials paired with their complex conjugates, and why are these particular linear combinations important? See below. The real importance of complex exponentials is that they are preserved, up to a factor, by translations. Indeed, they are the only such functions that are also bounded. So if you want to have a representation of a function/signal that behaves simply under translations, then you are pretty much forced to use Fourier transforms. If you now also allow reflections (i.e. time reversal in the 1d case), then starting from a complex exponential, you generate its complex conjugate. These two functions, as frequency chnages, give a set of 2d subspaces that are the smallest subspaces preserved by translations and reflections. But for each frequency, any two linearly independent linear combinations of these two will do just as well in spannign the subspace. What makes sine and cosine special is that they are real. Thus, for real signals, one can ensure real coeffiicents.> 2) is there a simple and convincing argument to explain this if it is > the case?No. It is highly unlikely that you can explain in a simple way (i.e. without giving a lecture course) why complex numbers are useful, to someone who does not realize why complex numbers are useful. On the other hand, I suppose that if they understand calculus, you could show them what I just stated. Take a translation by a distance t; which functions are preserved by this for all t? Start with a function f. Translate it by t, i.e. f(x + t) is the translated function evaluated at x. Demand that f(x + t) = a(t) f(x) for some function a(t), i.e. the function is preserved by translations. Now it may be obvious that f has to be an exponential at this point, but if not, differentiate with respect to t and set t = 0: f'(x) = a'(0) f(x) . This is a differential equation whose solution is: f(x) = B exp(a'(0) x) + C for any B and C. For it to be bounded, a'(0) must be imaginary, giving you complex exponentials. The original equation (*) is stronger than the differential equation, and fixes C = 0. Normalization fixes B. illywhacker;

Reply by ●August 26, 20092009-08-26

Rick Lyons <R.Lyons@_bogus_ieee.org> wrote: (snipped even more) < * "Out of convenience. Euler's equations allow < us to represent a real signal as the sum of < positive- and negative-frequency complex exponentials < if it suits our purpose (if it's useful for some reason < to do so). And when thinking about a complex exponential, < Euler allows us to it as a combination < (orthogonal, that is) of two real functions (a special < combination of a real sine and a real cosine.) < At that point I usually mumble something about Euler's < equations being a kind of "Rosetta Stone" that allows < us to translate back and forth between real and complex < representations--whichever suits our fancy. I wouldn't mind if you read and commented on my previous post in this thread, anyway... < * Complex (quadrature) signal representation is wildly < useful in accurately measuring the instantaneous < amplitude, phase, or frequency of a signal. (For amplitude, < phase, and frequency demodulation.) < * When we perform spectrum analysis in DSP, our < spectral results are in the form of complex numbers. < That's because the DFT computes both spectral magnitudes < ***and*** the relative phase between spectral components. Yes. Maybe even more emphasis on this one. Where waves are concerned, there are usually two physical quantities involved, such as voltage and current. We usually only measure one, but both are important, as is the relative phase of the two. < * For pencil & paper analysis, complex (quadrature) signal < representation is often more convenient. If you ask me < what is the form of the product of two sinusoids, I always < have scramble around to find a trig identity from some < math book. It's easy for me to determine the product < of two complex exponentials (it's merely the sum of < exponents). That might be enough, but I believe in wave problems there is more. < * I usually end my explanation with, "We use complex < numbers because that's the way God wants it to be." This comes out especially in quantum mechanics. Is the wave function really complex, or is it just easier that way? In EE, one usually want the voltage to be real. That isn't so obvious in QM. (snip) < I've always thought that "amplitude" meant the difference < between a real number and zero. And an "amplitude" value < can be a positive or a negative real-only quantity. < And I've always thought that the word "magnitude" < meant a positive value only. For example, a single < complex number can be described by a real "magnitude" < value and a real phase value. I believe that, at least as used in physics, amplitude includes phase. That may be for lack of enough words. There are descriptions like: "For coherent signals, add the amplitude, for incoherent signals, add the intensity." Leaving out the question of partial coherence, phase must be considered in the "add amplitude" statement. < In any case, your questions about averaging two numbers < is thought provoking. If the numbers each have an amplitude < and a phase, then the two numbers *must* be complex. And it I would say that they also could be sine or cosine with a phase term, but much easier if complex. < seems to me that averaging two complex numbers only has < meaning if we average the numbers' real and imaginary < parts separately. This topic seems to be closely related < to the material in a blog, called "The Nature of Circles", < by Peter Kootsookos (our own Dr. K) at: -- glen