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In Section 4, we see how the 1-D modal retrieval problem may be addressed using sparse approximations and how the multigrid approach can be applied.
(ii) We show how the 1-D modal retrieval problem can be addressed using sparse estimation approach by building a dictionary whose atoms are calculated by sampling the modal function over a 2-D grid (frequency and damping factor) in order to obtain all possible modes combinations.
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In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation.
This has given rise to the problem of sparse signal recovery using the MMV model[31] to analyze joint row sparsity, as shown in Figure 4 and which will be addressed below.
This transformation can be computed efficiently using sparse-matrix techniques.
Blind source separation by sparse decomposition has been addressed by Zibulevsky and Pearlmutter [178] for both over-determined/exactly-determined and underdetermined systems using the maximum a posteriori approach.
In this paper, the design of highly synchronizable, sparse and robust dynamical networks is addressed.
Both sparse and full optimal preamble designs were addressed.
The method, proposed in this paper, addresses these points using sparse response surface and a relevant criterion for results accuracy.
Dispersion of sparse (low solids content) agglomerates by hydrodynamic shear is addressed both experimentally and theoretically.
Sahnoun et al. [8] address multi-dimensional modal estimation using sparse estimation techniques in combination with an efficient multigrid approach.
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