MIT 6.300 — Lecture 2

Sinusoids and Series

§ Lecture: Convergence

Here is the graph of the Fourier series of a square wave, truncated to its first hundred terms.

Notice how the discontinuity of the square wave presents itself in its Fourier series. It turns out that the 9%9\% error shown above never goes away, even as arbitrarily many terms are added!

Theorem. (Gibbs' Phenomenon) Suppose a (sufficiently well-behaved) signal f(t)f(t) has a “step discontinuity” at t=t0t = t_0; that is,

L=lim⁡t→t0−f(t)≠lim⁡t→t0+f(t)=R.L = \lim_{t \to t_0^-} f(t) \neq \lim_{t \to t_0^+} f(t) = R.

Furthermore, let fK(t)f_K(t) denote the partial sum of the first KK terms of the Fourier series for f(t)f(t). Then the following hold:

In particular, the Fourier series of a discontinuous function converges pointwise, but not uniformly.

The exact value of the 9%9\% constant is 1π∫0πsin⁡(t)t dt−12≈0.08949\dfrac{1}{\pi} \displaystyle \int_0^\pi \dfrac{\sin(t)}{t} \, \mathrm{d}t - \dfrac{1}{2} \approx 0.08949.

§ Lecture: Transforming Fourier Series

Problem. Given the Fourier coefficients (c0,ck,dk)(c_0, c_k, d_k) for f(t)f(t), determine the Fourier coefficients of f(2t)f(2t).

Solution: The only difference between f(2t)f(2t) and f(t)f(t) is in their fundamental frequencies. The Fourier coefficients do not change.

Remark. Think of the Fourier coefficients as describing the quality of a signal. A piano and a trumpet playing 440 Hz will have different qualities, but a piano playing 440 Hz and a piano playing 880 Hz will not (assuming ideal conditions).

Problem. Given the Fourier coefficients (c0,ck,dk)(c_0, c_k, d_k) of f(t)f(t), determine the Fourier coefficients of f(t−T/2)f(t - T/2).

Solution: Just note the following (recalling that ω0=2πT\omega_0 = \frac{2\pi}{T} always):

cos⁡(kω0(t−T/2))=(−1)kcos⁡(kω0t)   ∥   sin⁡(kω0(t−T/2))=(−1)ksin⁡(kω0t).\cos\left(k \omega_0 \left(t - T/2\right)\right) = (-1)^k \cos(k \omega_0 t) ~~~ \parallel ~~~ \sin\left(k \omega_0 \left(t - T/2\right)\right) = (-1)^k \sin(k \omega_0 t).

Thus, odd-indexed coefficients are negated, and all other coefficients remain unchanged.

§ Recitation: Symmetry in Fourier Series

Recall from Lecture 1 that it's easy to compute the Fourier series of an even or odd function.

Theorem. (Everything is Even/Odd) Every function f(t)f(t) can be expressed as the sum of an even function and an odd function.

f(t)=(f(t)+f(−t)2)⏟even+(f(t)−f(−t)2)⏟oddf(t) = \underbrace{\left(\dfrac{f(t) + f(-t)}{2}\right)}_{\text{even}} + \underbrace{\left(\dfrac{f(t) - f(-t)}{2}\right)}_{\text{odd}}

Proof: The proof is in the statement of the theorem itself.   ■\blacksquare

In 6.300, “symmetric” and “antisymmetric” mean “even” and “odd”, respectively—and “asymmetric” means neither.

Thus, if an asymmetric signal can be easily expressed as the sum of a symmetric and antisymmetric signal, then computing its Fourier series might be easier to do in two steps…