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Liu et al. designed a compressive data collection for WSN.
The compressive data gathering improves the energy efficiency among the sensors.
The papers [11 13] proposed a novel compressive data collection scheme.
A compressive data collection scheme for WSNs is presented in [39].
While the center station needs to decode these compressive data streams to recover the raw image or video.
Hence the static tensile and compressive data provide the base-line reference.
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Ebrahimi et al. proposed a compressive data-gathering approach based on the random projection.
Compressive sensing theory can sample and compress data simultaneously which provide a new approach for big data classification.
The compressive phase takes a protein sequence database and produces a compressed data structure amenable to the search step described in Section 2.2.
Compressive sensing (CS) [1] theory provides a new way to sample and compress data.
We introduce a compressive algorithm, CaBLASTP, along with an implementation that allows direct computation on the compressed data.
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