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Our implementation is built atop Android's Camera2 API, which provides per-frame camera control and access to raw imagery, and is written in the Halide domain-specific language (DSL).
The proof of concept implementation is built on Oracle Java 1.8 and is tested on a HP Z230 workstation with 8 GB RAM and an Intel Xeon CPU (3.4 GHz).
IGVs data tiling implementation is built on a pyramidal data structure that can be described as follows.
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Our actual system implementation was built on the Linux OS (2.4.22 Kernel), the Orinoco IEEE 802.11b wireless card, and MIPL (mobile IPv6 for Linux), where the router advertisement and solicitation and binding update packets were adaptively modified and redefined to achieve the real implementation of the SIP-based MIP6-MANET integrated system.
The PCjr implementation was built by Jeff Par using similar technologies.
Our implementation was built on the LibSVM [ 9] library.
The implementation was built using CUDA, which offers better performance than OpenCL on NVIDIA GPUs (Weber et al., 2010).
This development has been a shot in the arm for AR developers, and there seems to be many implementations being built.
More recently, CNNs have been used for end-to-end discriminative text classification tasks involving the identification of high-level concepts prevalent throughout an entire document; however, most implementations are built on top of existing feature engineering and extraction methodologies.
The simulations and CHORUS implementations were built in-house and written in the Java 1.5 programming language.
While the applicability of the deductive approach is independent of any particular implementation, our prototype implementation, called BioDeducta, is built largely from existing components.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com