Christiaan2

Christiaan2

Geek Repo

Github PK Tool:Github PK Tool

Christiaan2's repositories

Arduino_Oscilloscope

Oscilloscope able to monitor different variables with a frequency of max 250Hz. Uses MATLAB to plot the data in real-time and saves a log file containing the data for later processing. The data comes from an Arduino.

Language:C++Stargazers:4Issues:1Issues:0

APiE_ImageAnalysis

Apply some image analysis techniques using Matlab

Language:MATLABStargazers:0Issues:2Issues:0

Arduino_BatteryCharger

Program to charge a NiCd battery with a constant current. Charge current can be set in the Arduino sketch. The charge process can be monitored in real-time using Matlab. Schematics are included.

Language:C++Stargazers:0Issues:1Issues:0

Arduino_Robot

Program to control a robot using an Arduino Uno. The robot is propelled by two DC motors and equipped with an ultra-sonic sensor so that it can avoid obstacles. Speed and amount of rotation of both motors is measured using IR-encoders. The motors are controlled via PID.

Language:C++Stargazers:0Issues:1Issues:0

Carvana-Image-Masking-Challenge---Submission-1

U-net implementation (depth = 6). The network was trained with a resolution of 640x960 (RGB) and data-augmentation was used during training. Training was stopped until dice coefficient on validation set (20% of total train data) didn't improve anymore. Best weights are used. Dice score: 0.99547 (private leaderboard)

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

OurVisualMicroTest2

A small test to use Visual Studio to code an Arduino.

Language:CStargazers:0Issues:1Issues:0
Language:PythonStargazers:0Issues:2Issues:0

Test_speed_of_different_datagenerators

Colab-notebook used to test the speed of different datagenerators. Training time and GPU utilization is measured for different datagenerators with real-time data-augmentation. A U-net model is trained using the carvana-image-masking-challenge dataset.

Language:Jupyter NotebookStargazers:0Issues:1Issues:0

Tornado-Webserver

This repository is intended to get started with IoT applications. It contains a website where inputs can be monitored and outputs can be changed. Website will be updated in real-time via websocket-protocol. Backend is written in Python using Tornado.

Language:PythonStargazers:0Issues:1Issues:0