ACME Volume 2 2018

The trickiest part of this section for me was following all the notation. It made sense, but I had to think about it sometimes to understand what was going on. I like learning another way to estimate functions using by projecting them onto an orthonormal basis. It makes the Fourier transform seem less magical and more functional. And it makes me wonder, what other useful bases could we use?

I don't understand the last "Vista" in the section. I'm not sure how you can recreate a non-periodic function from just the relatively few points k*pi/n for k in the integers. But that is pretty amazing. I liked understanding how antialiasing works. It was satisfying seeing the material from the other class apply so specifically.

I understood the math for this section, but I'm not quite sure I understand what a convolution is doing in real life. How exactly does it "average data locally"? I'm not used to learning math with applications in mind. It is interesting to have them combined.

I don't quite understand Figure 8.4.3. It looks like the blue signal is the same on the two pictures but the red is different, but the caption says it should be the other way. I'm guessing the caption is wrong? This section wasn't too bad because I had done the Discrete Fourier Series lab today. So it helped the lab make more sense. So I don't have anything to add.

It took me a while to figure out why we used D(t-s) instead of D(t). I like Fourier series and the way they decompose waves.

The part of this section I am shakiest on is remembering the complex/trig stuff. But it makes sense. I like Fourier series. I like how many different fields they apply to.