The Cycle Atlanta project aims to create sensor systems that allow a bike to "see" its environment and collect data as a participatory effort to help the City of Atlanta make informed decisions about biking infrastructures. The team from Atlanta's Data Science for Social Good 2017 Summer program particularly worked on (1) reverse engineering all the prototypes developed separately by the LMC6650 team and consolidating all sensors into two microcontoller units (Raspberry Pi and Arduino) using the Master-Slave architecture; (2) identifying hardware/software bugs/flaws and refining systems iteratively; (3) implementing failure-tolerence techniques; (4) classifying environmental factors (to detect semantci-level objects) after collecting data using GoPro, voice recorders, and sensors; and (5) visualizing and analyzing the collected data using 3D models and machine learning techniques.
- System Overview
- Hardware Systems (Electrical Devices and Circuits)
- Software Systems (Server Configurations and Scripts)
- Cases Design
- Data Visualization (3D)
- Data Analysis
- Backlog (Issues and Solutions)
- Dr. Kari Watkins
- Mariam Asad
- Bijan Tehrani
- Raj Lal
- Michael Vogel
- Bradley Rittenhouse
LMC6650 students designed a custom PCB board for gas sensors, developed the Air Quality module and a data collection server on Raspberry Pi using RestAPIs, and implemented the proximity/serface quality sensor functionalities using Rapsberry Pi and Arduino.
- Raspberry Pi
- Arduino Uno
- Matrix One Creator
- Anker 26800 mAh portable power bank
- Knowles BU21771 contact microphone
- Dimension ST768 for 3D printing
- Air Quality team: Orianna Ott , Lorina Navarro , Reuben Fishback
- Proximity team: Sharon Ang , Aastha Jalan , Nikhil Soraba
- Surface Quality team: Nene Igietseme , Kristin Hare , Shruti Dalvi , Jayanth Mohana Krishna