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But their computing complexities limit their application on embedded platforms.
Finally, we benchmark our detector on embedded platforms to measure the execution time and the power consumption.
However, these high-quality imaging algorithms are computationally demanding and difficult to implement in real-time, especially on embedded platforms with limited computing resources.
We describe two voice-to-phoneme conversion algorithms for speaker-independent voice-tag creation specifically targeted at applications on embedded platforms.
Before discussing the overall outcomes, we present a benchmarking of our algorithm on embedded platforms, specifically with regard to the resolution.
Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy.
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Multiprocessor systems on chips (MPSoCs) are envisioned as the future of embedded platforms such as game-engines, smart-phones and palmtop computers.
On the other hand, an accurate curve fitting algorithm is not resource-effective for embedded platforms.
Secondly, as opposed to the Bumblebee library that is proprietary, we can easily port Konolige's implementation on our embedded platforms.
The proposed implementation emphasizes the portability of our algorithm on either single or multi-core embedded platforms for on-line mosaicing onboard 3D scanning devices.
This paper describes Abhainn, a rapid implementation methodology and toolsuite for translating an algorithmic expression of the system to a working solution on FPGA-centric embedded platforms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com