Another source is here.

The real reason is probably that in the past, if it realised that you were on a fast connection, it would turn into a supernode and use gigantic quantities of bandwidth.

The DSO Quad has digital channels as well, which is what I believe the grandparent to be alluding to.

Magnetics is a term sometimes used for all such components.

As you approach c, though, length and time dilation work in your favour. 1g (of force, since constant acceleration is not possible for obvious reasons) might produce diminishing returns from the earth's perspective with respect to speed, but from the traveller's perspective the distance from earth to the destination will diminish by an equivalent factor---such is my understanding, anyway.

If you're looking for an understanding of what the DFT computes, sure. But the OP was more interested in how it works than what it does, so all of the Matlab in the world won't do much good. If you try to actually write out the DFT matrix, you're overdoing it for this purpose---that it is possible to write the transformation from frequency-domain to time-domain as a multiplication by an invertible matrix is enough, since then the DFT is then just the solution of a system of linear equations.

You don't need a deep knowledge of linear algebra for the finite-length discrete-time case, just the basics that everyone studying maths/science/engineering gets taught in the first couple of months of university. The point that I was trying to make was that for finite-length discrete-time signals, all that you're really doing is solving a system of linear equations like every man and his dog can do after a bit of training---that your coefficients happen to be uniformly-sampled complex exponentials doesn't really matter until you start thinking about efficient implementation.

The introductory signal processing textbook that I have is Lathi's "Signal Processing and Linear Systems". If you don't want to see it from the signal processing side, then any book covering introductory PDEs should have some Fourier Series. Alternatively, have a look at this.

The distinction I suppose is between conclusions that one can work out by inspection or immediate application of theorem, rather than by taking a detour through intermediate steps. It's probably not quite so cut-and-dry, but that's how I tend to see things. Such turns of phrase are just about part of the jargon.

It almost functions as a punctuation mark to reassure the paranoid (which sometimes is practically everyone) that "no, this next bit really is what it looks like".

Sorry, I meant to say a real exponential. It's probably true for more than one as well, but that means more than one line of Octave code to check (or some kind of conscious thought).

Rereading your comment, I think I may have slightly misunderstood what you were saying. If you have produced a digital signal by sampling an analogue one (i.e. if you look at the values of a signal at various points) over a finite, the DFT (or the FFT as its most popular algorithm) will not tell you what the fundamental frequency is.

You can get an approximation by making assumptions about the nature of the signals that you sample, but this is not the best way to do it, and those assumptions are normally that the signal contains frequencies in a range only half the sample rate.

"Sit down and work out the maths" is really just code for "here's one I prepared earlier". If you're keen on this sort of thing, read a bit about solving systems of linear equations, and you will hopefully be able to look at the problem, exclaim "this is trivial!" and live forever happy in the knowledge that it is indeed theoretically soluble (as I tried to describe above) without having to concern ones self with the computational subtleties that suck all of the fun out of it.

MIT OCW have a set of videos on linear algebra if your curiosity is sufficient to justify chewing up some time on it. Probably some of the most useful partially-post-high-school maths that one can learn. Here is a free textbook on it.

In the DFT case the signal is merely a finite number of samples, so you can forget about the time-dependence and look at it as a long vector. If you sit down and work out the maths, you find that the vectors corresponding to the constant function and the various sinusoids are linearly independent.

All you're really doing then is solving a system of linear equations---they don't just have to be sinusoids, you could find the coefficients for any linearly independent set of functions. You could just as easily replace one of the sinusoids with an exponential function and still get coefficients out such that you could do your weighted sum of vectors and get back your original function.

The director's cut isn't by necessity worse---Blade Runner for instance had a better ending. Sometimes the studios will dumb something down, and one gets it back in this fashion.

Farnell have free next-day courier shipping for Australian orders; I'd suggest giving them a try if you haven't used them before.

Even if your software doesn't use it directly, more RAM means more disk cache.