Smart city
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Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there. |
Details
| Workload/ECTS Credits: | 180h, 5-6 ECTS |
| Module: | M.Inf.1222 (Specialisation Computer Networks, 5 ECTS) or M.Inf.1129 (Social Networks and Big Data Methods, 5 ECTS) or M.Inf.1800 (Practical Course Advanced Networking, 6 ECTS) |
| Lecturer: | Prof. Xiaoming Fu |
| Teaching assistant: | MSc. Fabian Wölk, MSc. Weijun Wang, Dr. Tingting Yuan |
| Time: | TBD |
| Place: | TBD |
| UniVZ | [] |
Announcement
Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
General Description
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.
This course covers two aspects on Smart Cities in the context of public transport: event monitoring and passenger counting.
The goal of this course is to:
- Help students to further understand computer networks and data science knowledge.
- Help students to use computer science knowledge to build a practical AI system.
- Guide students to utilize knowledge to improve the performance of the system.
In this course, each student (max. number 30) needs to:
- Read state-of-art papers.
- Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.
- Learn how to analyze city public transport sensor data.
For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:
- Depth camera (e.g. Intel RealSense D435)
- On-board computers (e.g. Raspberry Pi Zero)
- Power supply (e.g. EC Technology Powerbank)
All these sub-systems in each bus will be combined to one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.
Further details will be given soon.
Prerequisites
- You are highly recommended to have completed a course on Data Science (e.g., "Data Science and Big Data Analytics" taught by Dr. Steffen Herbold or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
- Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries
Schedule
| When? | What? |
| TBD | TBD |