2024-04: got two backpacks "for free" with the Lenovo reward points from buying the Lenovo ThinkPad P14s gen4 amd, not bad, that was already cheap and now I got some extra swag:The sport backpack has a fatal flaw: no strap to hold laptop in place, wo it just tumbles back and forth as you walk.
- Lenovo Select Targus 16" Sport Backpack: www.lenovo.com/gb/en/p/accessories-and-software/cases-and-bags/backpacks/gx41l44751
- Lenovo Select Targus 16" Mobile Elite Backpack: www.lenovo.com/gb/en/p/accessories-and-software/cases-and-bags/backpacks/gx41l44752. Measured weight: 1070 g
Output: another sequence of complex numbers such that:Intuitively, this means that we are braking up the complex signal into sinusoidal frequencies:and is the amplitude of each sine.
- : is kind of magic and ends up being a constant added to the signal because
- : sinusoidal that completes one cycle over the signal. The larger the , the larger the resolution of that sinusoidal. But it completes one cycle regardless.
- : sinusoidal that completes two cycles over the signal
- ...
- : sinusoidal that completes cycles over the signal
Motivation: similar to the Fourier transform:In particular, the discrete Fourier transform is used in signal processing after a analog-to-digital converter. Digital signal processing historically likely grew more and more over analog processing as digital processors got faster and faster as it gives more flexibility in algorithm design.
- compression: a sine would use N points in the time domain, but in the frequency domain just one, so we can throw the rest away. A sum of two sines, only two. So if your signal has periodicity, in general you can compress it with the transform
- noise removal: many systems add noise only at certain frequencies, which are hopefully different from the main frequencies of the actual signal. By doing the transform, we can remove those frequencies to attain a better signal-to-noise
Sample software implementations:
- numpy.fft, notably see the example: numpy/fft.py
DFT of with 25 points
. This is a simple example of a discrete Fourier transform for a real input signal. It illustrates how the DFT takes N complex numbers as input, and produces N complex numbers as output. It also illustrates how the discrete Fourier transform of a real signal is symmetric around the center point.Can be seen as a cheap form of DNA sequencing that only test for a few hits. Some major applications:
- gene expression profiling
- single-nucleotide polymorphism: specificity is high enough to detect snips
MOOCs are a bad idea. We don't want to simply map the pre-computer classroom to the Internet. The Internet allows, and requires, fundamentally new ways to do things. More like Stack Overflow/Wikipedia. More like OurBigBook.com.
Most of these are going to be Whole-genome sequencing of some model organism:en.wikipedia.org/wiki/Whole_genome_sequencing#History lists them all. Basically th big "firsts" all happened in the 1990s and early 2000s.
- 2003: Human Genome Project (3 Gbp)
Docker is good.
As a lightweight virtualization however, it does break more often than full proper virtualization like QEMU after some updates.
Not "Yt" because that is already "Yttrium". God.
The Story of Light by Bell Labs (2015)
Source. Gives some ideas of the history of fiber optics. Features: Herwig Kogelnik.Fiber optics fundamentals by Shaoul Ezekiel
. Source. 2008 at MIT. Theory and demonstration.- youtu.be/0DCrIAxEv_Y?t=560:Terefore, the 1.5 micrometer window truly is the minimum.
- on smaller wavelengths, loss is due to Rayleigh scattering
- on longer wavelengths, loss is due to material absorption
When Ciro finally understood that this is a play on Larry Page's name (of course it is, typical programmer/academic humor stuff), his mind blew.
There are unlisted articles, also show them or only show them.