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==“Smart City” Course==
{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}


WS 2020/2021


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.
== Details ==
{{CourseDetails
|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=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta=MSc. Fabian Wölk, MSc. Weijun Wang, Dr. Tingting Yuan
|time=TBD
|place=TBD
|univz=[]
}}


Leading lecturer: Prof. Xiaoming Fu
==Announcement==


Teaching assistants: Fabian Wölk, Weijun Wang, Dr. Tingting Yuan


5-6 ECTS, 2 SWS
'''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.
'''


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==
==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.  
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:
The goal of this course is to:


-- Help students to further understand computer networks and data science knowledge.
* Help students to further understand computer networks and data science knowledge.


-- Help students to use computer science knowledge to build a practical AI system.
* 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.  
* Guide students to utilize knowledge to improve the performance of the system.  


In this course, each student (max. number 30) needs to:
In this course, each student (max. number 30) needs to:


-- Read state-of-art papers.
* Read state-of-art papers.


-- Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.
* 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.
* 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:
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)
* Depth camera (e.g. Intel RealSense D435)


-- On-Board-Computers (e.g. Raspberry Pi Zero)
* On-board computers (e.g. Raspberry Pi Zero)


-- Power Supply (e.g. EC Technology Powerbank)
* 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.
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 defined.
Further details are being defined.
==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 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==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | TBD
| TBD
|-
|}
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