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The implementation is based on a partially decentralized system architecture and statistical marginalization, and sampling-based measurement updates.
The implementation is based on so-called multi-spin coding techniques.
The implementation is based on Matlab and Easy Java Simulations (EJS).
The implementation is based on combining the power series expansion with a rational approximation.
The implementation is based on the finite element discretisation of a fuel cell distributed parameter model.
The implementation is based on CAPE-OPEN interfaces to allow a modular software design and easy extensions of the system.
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The implementation was based on the MVVM (Model, View a ViewModel) architectural pattern which is briefly described below.
The implementation was based on a fully connected test assignment, i.e., each node tests all others.
The evaluation of the extent of the implementation was based on a four stage model of success in simulation studies [ 24].
Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.
However, most of the implementations are based on software, due to the difficulties in performing real-time training of the output weights on hardware platforms.
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