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On the other hand, an accurate curve fitting algorithm is not resource-effective for embedded platforms.
The voice-tag created by both algorithms is a set of phonetic strings that require very low storage, making them suitable for embedded platforms.
The phonetic decoder is the MLite++ ASR search engine, a Motorola proprietary HMM-based search engine for embedded platforms, with a phoneme loop grammar.
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The implementation was targeted for NVIDIA Jetson embedded platforms.
We find that ignoring energy considerations can lead to poor design choices, particularly for resource-constrained embedded platforms.
Limiting voice-tags to two or three dozen in this scenario may no longer be practical, though extending the number significantly in a traditional HMM-based voice-tag application could easily surpass the maximum memory consumption threshold for low-tier embedded platforms.
For this purpose, we consider the available XUP Xilinx Virtex-II Pro development embedded platforms for physical hardware implementations [32].
Linux-based OSes are available for the most important 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.
We present and evaluate a low-complexity algorithm to detect descending stairs and curbs of any shape, specifically designed for low-power real-time embedded platforms.
In Section 2 we describe the batch mode voice-to-phoneme conversion algorithm in particular and show how it meets the criteria of low computational complexity and memory consumption for a voice-tag application in embedded platforms.
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