In this repository, you will find some projects I have done for my classes at UBA (University of Buenos Aires) and NTNU (Norwegian University of Science and Technology).
"Cartas Enlazadas" (linked cards) was a one person project for the class "Algorithms and Data Structures II" at UBA. The goal was to model a ficticious game of the same name. In order to do that, we had to implement a generic doubly linked list in C++. We also had to make sure it didn't have any kind of memory leak using Valgrind. To run the test cases and check for memory leaks run the following commands in a Linux terminal:
g++ -g tests.cpp -o tests
valgrind --leak-check=full -v ./tests
This was a term assignment for the class "Engineering Distributed Real-time Systems" at NTNU. We worked in groups of 3, and the task was to build a distributed system that solved a specific problem, document the system and its development, and finally implement it using several Raspberry Pi as nodes. In our case, we decided to model a fire alarm system, using the Raspberry Pis as sensors.
In the first part of the project we had to make the specification and documentation using UML structure diagrams, sequence diagrams, activity diagrams and state machines of all the different parts of the system.
[Winter School 2014 - Final Project] (Final Project - ECI 2014 - Exploración visual de Big Data (spanish).pdf)
This was the final project for the 2014 intensive winter course "Big Data Architecture: Compute, Storage, Networking challenges" done at UBA. The topic was visual exploration of Big Data. I decided to analize the final match of the 2014 FIFA World Cup by using the data from the match that FIFA released on its website.
FIFA offered, among other thigs, tables of the ball passes distribution of Argentina and Germany. Using that data, I made graphs for both teams using each player as a node in a network and each pass as an edge connecting nodes. I then distributed the nodes reflecting the position of each player on the pitch and used the thickness of the arrows to represent the number of passes between nodes. This network-based analysis of a game allowed for a quantifiable representation of the style of each team, identified key players and highlighted potential weaknesses.