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The tracker APGL1 is developed by exploring the accelerated proximal gradient (APG) approach for sparse solution.
In this paper, a novel approach for sparse estimation is proposed.
In this paper, a new approach for sparse channel estimation of MIMO-OFDM systems based on compressed sensing has been presented.
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Previously reported approaches for sparse channel estimation can broadly be categorized into two types, namely the most significant tap (MST) detection and compressed channel sensing (CCS).
Many other approaches for sparse network recovery have been proposed.
An example with 300 states is provided to demonstrate the suitability of this approach for large, sparse systems.
In this paper we design and implement four parallel MST algorithms (three variations of Borůvka plus our new approach) for arbitrary sparse graphs that for the first time give speedup when compared with the best sequential algorithm.
We propose two different filtering schemes based on a new block-based combined approach, well suited for sparse adaptive algorithms.
This approach favoring searches for sparse and/or long regions, is sensitive to over-representation in multiple sequences, but is insensitive to positional context information within the same sequence.
There are two common approaches for storing sparse fingerprints.
28, 29 Thorough validation studies 30 show advantages of the PC-Algorithm compared to competing approaches 31– 35 especially for sparse graphs in terms of estimation quality (true and false discovery rates for edges) as well as computational speed.
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