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Benjamin Paaßen / median_distance_glvq
GNU General Public License v3.0 or laterMedian Distance Generalized Learning Vector Quantization is a classification algorithm for distance data where data is represented by prototypes and classification is done by assigning the label of the closest prototype.
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Analysis of cost data provided by Copernicus by comparing it to OpenAPC.
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AMiRo / AMiRo-BLT
GNU General Public License v3.0 onlyThe bootloader toolchain for the microcontroller based modules of the Autonomous Mini Robot (AMiRo). It comprises the code for the microcontrollers as well as two according flashing tools and defines a system synchronization protocol (SSSP).
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AMiRo / AMiRo-OS
OtherThe operating system for the Autonomous Mini Robot (AMiRo). It is based on ChibiOS, but enhances it for more flexible use.
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AMiRo / AMiRo-LLD
OtherThis is a compilation of low-level drivers for the various periphery of the Autonomous Mini Robot (AMiRo) platform.
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AMiRo / µRT
OtherA lightweight real-time middleware with built-in system failure detection.
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AMiRo / AMiRo-Apps
OtherA collection of applications and configurations to be used in combination with AMiRo-OS and μRtWare.
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Cord Wiljes / rdm-course-survey-old
Creative Commons Attribution 4.0 InternationalUpdated -
FFPiH / uebung2017-10
BSD 3-Clause "New" or "Revised" LicenseUpdated -
FFPiH / uebung2017-4
BSD 3-Clause "New" or "Revised" LicenseRecord syntax, Lenses, Quickcheck, Dungeon crawler...
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FFPiH / uebung2017-9
BSD 3-Clause "New" or "Revised" LicenseYesod - Web development mit Haskell und Shakespear
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FFPiH / uebung2017_3
BSD 3-Clause "New" or "Revised" LicenseUpdated -
FFPiH / uebung2017_2
BSD 3-Clause "New" or "Revised" LicenseUpdated -
FFPiH / uebung2017_1
BSD 3-Clause "New" or "Revised" LicenseUpdated -
FFPiH / Vorlesung2016
Creative Commons Attribution Non Commercial Share Alike 4.0 InternationalFolien und Übungsaufgaben für unsere Vorlesung
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Folien der Vorlesung "Fortgeschrittene Funktionale Programmierung in Haskell"
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ECCB 2018 Tutorial #13: Modern and scalable tools for efficient analysis of very large metagenomic datasets. Please refer to http://eccb18.org/tutorial-13/ and https://eccb18-metagenomics-tutorial.readthedocs.io for details.
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