AHLAWAT
RAVEDM or Rgb Audio Visualizing Electro Dancable Music 4x4x4 (I tried my best to fit it into the title) It is a 4x4x4 Green LED cube as of now. I couldn't make RGB possible as of now due to the high price of data transfer LEDS, and my inability to control 28 (8 for each color and 4 for ground) pins with PWM. Even MEGA has 12 PWMs as far as I can remember (fact check this), I would require 2 Megas and one UNO atleast or some other solution. I explained this project in the github page below.
This project involves the re-creation of the classic Chrome Dinosaur game using
python and making AI play the game. We have used Supervised Learning as well as
Genetic Learning in parts to train the model.
We first built a standalone game on python using the help of pygame so that we
could visually and manually play the game in order to collect training data.
We then concatenated all the csv file(training data) into one file and fed this
to the train_simple_model.py, which is a very basic CNN model that
trains over the given data and saves the best model in a .pth file (say,
best_model.pth).
This concludes the Supervised Learning part.
We them passed this best_model.pth file to the RL model,
Genetic_Model.py that uses Genetic algorithms such as - mutation, population
After spending the better part of the last 2 months ricing my laptop, I am finally done and satisfied — and I love my laptop too much now.
The Linux iceberg was no joke but I’m happy I crashed into it.
System details:
Distro: Arch
Desktop Environment: Hyprland Tiling Manager
Display Manager (login): WarGames
Side-bar: Eww bar
Bluetooth: Bluetuith (TUI)
Spotify: spotifyd + spotify_player + Glava
Browser: Firefox with DuckDuckGo
Text Editor: VS Code
Credit to @Hayaugh for his rice, and his video which I used as a template for the rice (it's mostly his with a few modifications by me).
Also credit to Maud-Lin on r/unixporn for the Minecraft GRUB theme: Minegrub World.
I use Arch btw.
SUUUIIIIIIIIIIIIIII
This project, part of the Krittika Summer Project at Krittika - The Astronomy Club, IIT Bombay,
focused on simulating and analyzing the formation and evolution of binary black holes.
We analyzed binary system interactions such as Roche lobe overflow, stellar mergers, and common envelope evolution.
Using Compact Object Mergers: Population Astrophysics and Statistics (COMPAS), a rapid stellar/binary
population synthesis code, we simulated over 100,000 stellar structures and Compact Binary Objects
(binary black holes and neutron stars).
To optimize performance, we parallelized simulations by splitting runs through batch processing,
which reduced large-scale runtime by nearly 90%.
We illustrated stellar evolution and compact object formation with scatter plots, Hertzsprung–Russell (HR) diagrams,
and chirp mass distributions.
Additionally, we investigated the evolution of gravitational wave emissions from binary systems,
rigorously comparing simulated data with real observations from LIGO–Virgo. By iteratively adjusting
parameters, we achieved an accuracy match of 29%.
This project involved building a CNN-RNN based audio classifier using the PyTorch library to identify
song patterns from feature vectors.
We reconstructed Mel-frequency cepstral coefficient (MFCC) files into .wav audio files for
manual classification and validation.
A dataset of 116 MFCC samples was analyzed using heatmaps, PCA, scatter plots, and elbow curves to
understand feature distribution.
We also constructed a custom-labeled dataset of 180 external songs, achieving a training accuracy of
over 90%.
The model was trained on an NVIDIA P100 GPU using the Adam optimizer and Cross-Entropy loss functions.
As part of the DS109: Introduction to Design course under instructor Swati Agarwal,
I created a stop-motion animation by illustrating over 50 detailed sketches, showcasing strong artistic skills.
The animation was compiled, edited, and refined using Adobe Premiere Pro along with various online
resources, demonstrating advanced proficiency in video editing and post-production workflows.