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David Bannach, Kai Kunze, Paul Lukowicz, and Oliver Amft. Distributed Modular Toolbox for Multi-modal Context Recognition. In ARCS 2006: Proceedings of the 19th International Conference on Architecture of Computing Systems., pp. 99–113, Springer, March 2006.
We present a GUI-based \em C++ toolbox that allows for building distributed, multi-modal context recognition systems by plugging together reusable, parameterizable components. The goals of the toolbox are to simplify the steps from prototypes to online implementations on low-power mobile devices, facilitate portability between platforms and foster easy adaptation and extensibility. The main features of the toolbox we focus on here are a set of parameterizable algorithms including different filters, feature computations and classifiers, a runtime environment that supports complex synchronous and asynchronous data flows, encapsulation of hardware-specific aspects including sensors and data types (e.g., int vs. float), and the ability to outsource parts of the computation to remote devices. In addition, components are provided for group-wise, event-based sensor synchronization and data labeling. We describe the architecture of the toolbox and illustrate its functionality on two case studies that are part of the downloadable distribution.
@INPROCEEDINGS{Bannach2006-P_ARCS,
author = {David Bannach and Kai Kunze and Paul Lukowicz and Oliver Amft},
title = {Distributed Modular Toolbox for Multi-modal Context Recognition},
booktitle = {ARCS 2006: Proceedings of the 19th International Conference on Architecture
of Computing Systems.},
year = {2006},
volume = {3894},
series = {Lecture Notes in Computer Science},
pages = {99--113},
month = {March},
publisher = {Springer},
abstract = {We present a GUI-based {\em C++} toolbox that allows for building
distributed, multi-modal context recognition systems by plugging
together reusable, parameterizable components. The goals of the toolbox
are to simplify the steps from prototypes to online implementations
on low-power mobile devices, facilitate portability between platforms
and foster easy adaptation and extensibility. The main features of
the toolbox we focus on here are a set of parameterizable algorithms
including different filters, feature computations and classifiers,
a runtime environment that supports complex synchronous and asynchronous
data flows, encapsulation of hardware-specific aspects including
sensors and data types (e.g., int vs.~float), and the ability to
outsource parts of the computation to remote devices. In addition,
components are provided for group-wise, event-based sensor synchronization
and data labeling. We describe the architecture of the toolbox and
illustrate its functionality on two case studies that are part of
the downloadable distribution.},
doi = {10.1007/11682127_8},
file = {Bannach2006-P_ARCS.pdf:Bannach2006-P_ARCS.pdf:PDF},
keywords = {context, processing, implementation},
owner = {oam},
timestamp = {2007/09/29}
}
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