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{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} | |||
Computer Networks | == 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=[] | |||
}} | |||
==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== | ==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 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: | 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: | 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. | 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=}} | |||
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|{{Hl2}} |'''When?''' | |||
|{{Hl2}} |'''What?''' | |||
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| align="right" | TBD | |||
| TBD | |||
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