Smart city: Difference between revisions
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“Smart Cities” Course | ==“Smart Cities” Course== | ||
WS 2020/2021 | WS 2020/2021 | ||
Computer Networks Group, Institute of Computer Science, Universität Göttingen | 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. | ||
Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) | |||
Leading lecturer: Prof. Xiaoming Fu | Leading lecturer: Prof. Xiaoming Fu | ||
Teaching assistants: Fabian Wölk, Weijun Wang, Dr. Tingting Yuan | Teaching assistants: Fabian Wölk, Weijun Wang, Dr. Tingting Yuan | ||
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==General Description== | ==General Description== | ||
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 | Further details are being defined. | ||
Revision as of 20:24, 9 September 2020
“Smart Cities” Course
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.
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 defined.