Uni Bremen: VAK 04-326-FT-041
Category: Lecture+Lesson, 4 SWS
Master Course
ECTS: 6, Winter Semester
University of Bremen
Lecturer: PD Dr. Stefan Bosse
File | Version | Description |
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miss2k.script.pdf | 04.02.20 | Skript |
miss2k.html | 11.12.19 | Foliensatz Komplett |
miss2k0.html | 23.10.19 | Foliensatz Modul 0 |
miss2kA.html | 23.10.19 | Foliensatz Modul A |
miss2kB.html | 23.10.19 | Foliensatz Modul B |
miss2kC.html | 23.10.19 | Foliensatz Modul C |
miss2kD.html | 11.12.19 | Foliensatz Modul D |
miss2kE.html | 08.01.20 | Foliensatz Modul E |
miss2kF.html | 19.12.19 | Foliensatz Modul F |
miss2kG.html | 19.12.19 | Foliensatz Modul G |
miss2kH.html | 19.12.19 | Foliensatz Modul H |
miss2kI.html | 19.12.19 | Foliensatz Modul I |
miss2kJ.html | 19.12.19 | Foliensatz Modul J |
miss2kK.html | 19.12.19 | Foliensatz Modul K |
miss2kR.html | 19.12.19 | Foliensatz Modul R |
File | Version | Description |
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tutorial1.html | 12.11.19 | Interaktives Lua Tutorial mit Übung |
tutorial3.html | 23.12.19 | Sensordatenverarbeitung und Messtechnik (Kann derzeit nur Online ausgeführt werden!) |
File | Description |
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lua_tutorial.pdf | Lua Tutorial |
File | Version | Description |
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dhrystoneX.html | 30.10.19 | Dhrystone Benchmark (requires Browser) |
jystone.js | 30.10.19 | Dhrystone Benchmark (requires nodejs) |
pystone.py | 30.10.19 | Dhrystone Benchmark (requires python) |
lystone.lua | 30.10.19 | Dhrystone Benchmark (requires luajit/lua/lvm) |
dhry | 7.11.19 | Dhrystone Benchmark (native C), Linux x86 |
luaweb.html | 1.1.12 | LuaOS WEB API (requires Browser) |
luaos | 1.2.4 | LuaOS (requires lvm) |
lvm | 1.1.7 (05.19) | Lua Virtual Machine (lvm), Linux x86 |
lvm | 1.1.7 (05.19) | Lua Virtual Machine (lvm), Linux x64 |
lvm | 1.1.7 (05.19) | Lua Virtual Machine (lvm), SunOS 11.3 x86 |
lvm.exe | 1.1.7 (05.19) | Lua Virtual Machine (lvm), Windows x86 |
Firefox-31.0 | 31.0 | Firefox Portable, Linux x86 |
Participation in the event is intended to provide students with interdisciplinary system-oriented access to the modeling, design and application of material-embedded or material-applied sensory systems that, due to their technical implementation and application, place special demands on data processing Understanding the overall system (including aspects of materials science and technologies). These new sensory materials are found eg. B. in robotics (cognition) or in production technology for material monitoring application.
Def.: Sensory materials consist of a carrier material, that cab represent a mechanically supporting structure, and from embedded sensor networks that integrate not only sensors but also electronics for sensor signal processing, data processing, communication, and communication and power supply networks.
The following competences should be acquired: