Diode Updated 2025-07-16
Ideally can be thought of as a one-way ticket gate that only lets electrons go in one direction with zero resistance! Real devices do have imperfections however, so there is some resistance.
First they were made out of vacuum tubes, but later semiconductor diodes were invented and became much more widespread.
Diode bridge Updated 2025-07-16
Dipole antenna Updated 2025-07-16
Video 1.
Radio Wave Properties: Electric and Magnetic Dipole Antennae by Harvard Natural Sciences Lecture Demonstrations (2020)
Source. The dude lights bulbs on an antenna made of a single piece of copper, powered with EM radiation. Amazing.
Dirac equation Updated 2025-07-16
Adds special relativity to the Schrödinger equation, and the following conclusions come basically as a direct consequence of this!
Experiments not explained: those that quantum electrodynamics explains like:
See also: Dirac equation vs quantum electrodynamics.
The Dirac equation is a set of 4 partial differential equations on 4 complex valued wave functions. The full explicit form in Planck units is shown e.g. in Video 1. "Quantum Mechanics 12a - Dirac Equation I by ViaScience (2015)" at youtu.be/OCuaBmAzqek?t=1010:
Then as done at physics.stackexchange.com/questions/32422/qm-without-complex-numbers/557600#557600 from why are complex numbers used in the Schrodinger equation?, we could further split those equations up into a system of 8 equations on 8 real-valued functions.
Video 2.
PHYS 485 Lecture 14: The Dirac Equation by Roger Moore (2016)
Source.
TODO: in high level terms, why is QED more general than just solving the Dirac equation, and therefore explaining quantum electrodynamics experiments?
Also, is it just a bunch of differential equation (like the Dirac equation itself), or does it have some other more complicated mathematical formulation, as seems to be the case? Why do we need something more complicated than
The main high level insight seems to be that The Dirac equation does not work for more than one electron.
Disaccharide Updated 2025-07-16
Distribution (mathematics) Updated 2025-07-16
Generalize function to allow adding some useful things which people wanted to be classical functions but which are not,
It therefore requires you to redefine and reprove all of calculus.
For this reason, most people are tempted to assume that all the hand wavy intuitive arguments undergrad teachers give are true and just move on with life. And they generally are.
One notable example where distributions pop up are the eigenvectors of the position operator in quantum mechanics, which are given by Dirac delta functions, which is most commonly rigorously defined in terms of distribution.
Distributions are also defined in a way that allows you to do calculus on them. Notably, you can define a derivative, and the derivative of the Heaviside step function is the Dirac delta function.
Division Updated 2025-07-16
Discord (software) Updated 2025-07-16
Ciro Santilli's discord ID: cirosantilli#8921. See also: how to contact Ciro Santilli.
You gotta be born after the year 2000 to understand it.
This is becoming more and more popular as a group chat with channels and threads possibility as of 2020.
Very similar to Slack.
Not possible to anonymously join just one server without creating a new account? What's the point of servers then! www.reddit.com/r/discordapp/comments/6gmjl7/changing_nick_before_joining_a_new_server/ Oh, also nicks don't hide your username from the server in any way, you can get the original username by just clicking on the person's username.
No proper threaded discussion without creating new channels? As of 2022 there is kind of a way, but it was a bit obtuse.
As of 2022 they also have a school hub: support.discord.com/hc/en-us/articles/4406046651927-Discord-Student-Hubs-FAQ which auto creates groups by university email access. Good idea, and shows popularity amongst that user group.
Discrete Updated 2025-07-16
Something that is very not continuous.
Notably studied in discrete mathematics.
Discrete Fourier transform Updated 2025-07-16
Input: a sequence of complex numbers .
Output: another sequence of complex numbers such that:
Intuitively, this means that we are braking up the complex signal into sinusoidal frequencies:
  • : 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
and is the amplitude of each sine.
We use Zero-based numbering in our definitions because it just makes every formula simpler.
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.
Sample software implementations:
Figure 1.
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.
See sections: "Example 1 - N even", "Example 2 - N odd" and "Representation in terms of sines and cosines" of www.statlect.com/matrix-algebra/discrete-Fourier-transform-of-a-real-signal
The transform still has complex numbers.
Summary:
  • is real
Therefore, we only need about half of to represent the signal, as the other half can be derived by conjugation.
"Representation in terms of sines and cosines" from www.statlect.com/matrix-algebra/discrete-Fourier-transform-of-a-real-signal then gives explicit formulas in terms of .
Figure 1.
DFT of with 25 points
. Source at: numpy/fft_plot.py. This plot illustrates how the DFT of a real signal is symmetric around the middle point, and so only half of the transform points are needed to reconstruct the original signal. We also see how the phase of the sinusoids determines if their DFT components are real or imaginary.
This is the discrete logarithm problem where the group is a cyclic group.
In this case, the problem becomes equivalent to reversing modular exponentiation.
This computational problem forms the basis for Diffie-Hellman key exchange, because modular exponentiation can be efficiently computed, but no known way exists to efficiently compute the reverse function.
Disease Updated 2025-07-16

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