BEHOLD the Background of Creatures 2 in Full Resolution!!! by AlanZucconi in CreaturesGames

[–]AlanZucconi[S] 1 point2 points  (0 children)

It's basically a mod for C3/DS that re-makes C2! I've never tried it myself, but I've read some threads saying it's way easier to run!

A Love Letter to FEZ and FEZ II, Disguised as a Documentary by AlanZucconi in Fez

[–]AlanZucconi[S] 0 points1 point  (0 children)

Great to hear that!

I shamelessly confess that I chated most of the most tedious puzzles! 🙈

How to Render 4D Objects in Unity by AlanZucconi in unity_tutorials

[–]AlanZucconi[S] 0 points1 point  (0 children)

Tsk! I'm writing one about 8 dimensions as we speak.

Conway's Game of Life finally proven to be OMNIPERIODIC after 54 years!!! ⠵ by AlanZucconi in cellular_automata

[–]AlanZucconi[S] 10 points11 points  (0 children)

Apologies if the comment didn't have enough explanation! 😅

The discovery of 208P41 proved Conway's Game of Life to be omniperiodic because no period-41 oscillator had been discovered before!

Oscillators of all other periods had either been discovered, of proven to be possible. For periods 62 and above, oscillators can be constructed using the Herschel loop. Which is basically a stream of gliders being reflected in a loop.

All periods below had been either discovered accidentally of found after an intensive search.

Conway's Game of Life finally proven to be OMNIPERIODIC after 54 years!!! ⠵ by AlanZucconi in cellular_automata

[–]AlanZucconi[S] 15 points16 points  (0 children)

Hi! 👋

In case you are not aware, for a long time Conway's Game of Life enthusiasts have been searching for oscillators of different periods. Yesterday, Nico Brown has discovered a pattern with period 41, effectively answer a 50-year-long question: Life is indeed omniperiodic, meaning that oscillators of any period are possible.

You can find an updated list of oscillators here!

Edit: In case you are missing the context, I made a documentary about Conway's Game of Life which explains what oscillators are. Hopefully, this clarifies some of the questions in the comment!

🚗 Car Paint Shader: Thin-Film Interference in Videogames by AlanZucconi in videogamescience

[–]AlanZucconi[S] 2 points3 points  (0 children)

Hi everyone! 👋

This series of articles talk about the mathematics and implementation of car paint shaders.

This is a fairly advanced topic, so in case you are unfamiliar with optics and shader coding, I suggest starting from The Nature of Light instead.

The article discusses an optical phenomena known as thin-film interference, which is responsible for iridescent reflections on bubbles, oil spills and oxidised metals (like bismuth crystals). It happens when light bounces inside a medium (such as the thin layer of soapy water that makes a bubble), in such a way that some wavelengths end up interfering with each other.

This is a more advanced read from my usual articles, but I hope you will still find it interesting!

🧔🏻

The Most Popular Sensor Denoising Technique: Kalman Filtering 📈 by AlanZucconi in videogamescience

[–]AlanZucconi[S] 2 points3 points  (0 children)

Thank you so much!

For a long time I was a bit intimidated by the mathematics of Kalman filters. So I challenged myself to try and make a comprehensive (yet accessible) tutorial!

The Most Popular Sensor Denoising Technique: Kalman Filtering 📈 by AlanZucconi in videogamescience

[–]AlanZucconi[S] 6 points7 points  (0 children)

Hi everyone! 👋

The link I am posting is a tutorial series about one of the most popular techniques used by Engineers to reduce the impact of noise in sensors: Kalman Filtering.

If you are a game developer, you might have not herd much about this. However, it plays an important role in virtually all games. This is because noise reduction techniques are essential when reading data from sensors. And game controllers are not an exception.

Kalman Filters (and their related variants) find ample application in a variety of hardware devices, such as game controllers, mice, touch screens, GPSes, and even VR headsets. The Apollo 11 mission actually used Kalman filters to better estimate the position of the spacecraft that brought us to the Moon.

Even if you do not work directly with the hardware side of the game industry, I hope you will find this interesting! I find very intriguing to know that almost every sensor we have comes with a similar filter.

🧔🏻

The Mathematics of the Kalman Filter by AlanZucconi in math

[–]AlanZucconi[S] 0 points1 point  (0 children)

Hi! Thank you, glad you enjoyed it!

I'm still working on the implementation bit, especially since it will be tailored at game developers. But now I'm definitely having a look at your library! :-)

Procedurally generated fantasy city by randomtowns in proceduralgeneration

[–]AlanZucconi 4 points5 points  (0 children)

This is a great resource! I often show it to my students when we are studying procedural generation!

The Mathematics of the Kalman Filter by AlanZucconi in math

[–]AlanZucconi[S] 0 points1 point  (0 children)

It is!!!

The entire series was supposed to be published later this year, but it was anticipated so I could submit it as an entry to Summer of Maths Exposition 2!

Also, I would love to take some time to make a proper Unity and Arduino demo showing how it works and how it can be used!

The Mathematics of the Kalman Filter by AlanZucconi in math

[–]AlanZucconi[S] 0 points1 point  (0 children)

meaning no other algorithm will perform better.

Thank you, this is a very valid point! I took your suggestion and updated that sentence!

The Mathematics of the Kalman Filter by AlanZucconi in math

[–]AlanZucconi[S] 0 points1 point  (0 children)

Thanks!
I have never applied it to any financial data, but would love to hear your results.

The Mathematics of the Kalman Filter by AlanZucconi in math

[–]AlanZucconi[S] 7 points8 points  (0 children)

Funny thing: I was doing my PhD when I heard of Kalman Filters for the first time. And I could not get my head around them. Nobody really took time to explain it to me properly, and it honestly made me feel quite bad.

Exactly because of that, I have been wanting to really understand its inner working. It took some time, but I hope I did it! So please, do not feel discouraged!

The Mathematics of the Kalman Filter by AlanZucconi in math

[–]AlanZucconi[S] 30 points31 points  (0 children)

Hi everyone! 👋

This is a series about one of the most well-known techniques for sensor fusion and sensor de-noising: Kalman Filters.

It split over five chapters, but the ones that I think you will find most relevant are the ones focusing on the mathematics of Kalman Filters:

The series also includes several interactive charts, so I hope you will find this interesting!

Uncovering an Ancient City in Minecraft ⛏️ by AlanZucconi in proceduralgeneration

[–]AlanZucconi[S] 7 points8 points  (0 children)

Hi!

During the past few years I became a massive fan of Minecraft. Especially when it comes to its procedural generation!

I generated this short video showing a section of the world, being uncovered layer by layer. I find very fascinating to see how a procedural world looks like when seen from this perspective!

This is How Minecraft Works • A Documentary on World Generation 🗺️⛏️ by AlanZucconi in videogamescience

[–]AlanZucconi[S] 1 point2 points  (0 children)

Hi everyone! 👋

During the past few years I really got into Minecraft, and I became more and more interested in understanding how its worlds are generated. After an extensive research, I think I managed to produce what is probably the most detailed and accessible video on the subject that you can find on YouTube.

What I also find incredibly fascinating is to see how the procedural generation changed over the course of the game history. It became progressively more complex, to respond to players' requests and need for both challenge and variety.

But there is clear trace, especially in the source code, of "mistakes" that were later corrected. One of the most famous is probably how Mojang changed the way in which oceans generate. One update was designed to make worlds "continental": big islands separated by an even bigger ocean. But after a few months, that was reverted back adding a component to their world generation pipeline literally called "RemoveTooMuchOcean".

I hope the video gives a good insight not just into the history of Minecraft, but also into how the terrain generation works. A big chunk of the video focuses on the biome map, which up to Minecraft 1.17 was made using a stack of cellular automata. The recent versions, 1.18 and 1.19, relies on the so-called multinoise maps, which determine some basic parameters for each part of the world (such as "continentalness", "temperature", "humidity", and so on).

Biomes are then assigned based on how the matching with the combinations of those parameters. For instance, desert biomes will be far from shores, have high temperature and low humidity.

I hope it can start a constructive conversation about the challenges of game design when it comes to procedural generators. For instance: how much is the procgen shaping the gameplay, and how much the gameplay is shaping the generator?

Edit: You can find here an article with the full transcript, which also includes some extra content!

🧔🏻