Smart city

From NET Wiki
Revision as of 21:22, 9 September 2020 by Xfu (talk | contribs) (Created page with "“Smart Cities” Course WS 2020/2021 Computer Networks Group, Institute of Computer Science, Universität Göttingen In collaboration with Göttinger Verkehrsbetriebe GmbH ...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

“Smart Cities” Course

WS 2020/2021

Computer Networks Group, Institute of Computer Science, Universität Göttingen In collaboration with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann)

Leading lecturer: Prof. Xiaoming Fu Teaching assistants: Fabian Wölk, Weijun Wang, Dr. Tingting Yuan

5-6 ECTS, 2 SWS

Course 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)

General Description

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 are being prepared.