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Firstly, computing the inverse and determinant of the covariance matrices is complex, especially for large N c +N d.
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Biological matrices are complex; some may be difficult to obtain and available only in small amounts.
Since the food sample matrix is complex and pre-treatment is time-consuming, the traditional detection technology cannot meet the requirements of food testing.
The lignocellulosic matrix is complex and recalcitrant to conversion but research in biorefining is advancing rapidly and commercial facilities are expected in the near-term.
Additionally, for a given OFDM symbol, the optimization is limited to a single matrix-vector multiplication where matrix is complex valued, constant for a given system configuration and vector consists of data subcarriers symbols.
In general, the support is meant to be a simple isolation system, and the fact the stiffness matrix is complex and frequency-temperature dependent – due to its viscoelastic properties – forces the traditional methods to require an extremely long computing time, thus rendering them too time consuming in an optimization environment.
The structure of the mosquito and sand fly peritrophic matrix is complex and rearranges during the course of blood digestion [ 49].
Biomolecular signals within the native extracellular matrix are complex, with bioactive factors found in both soluble and sequestered states.
The contribution of MMPs to extracellular matrix remodeling is complex.
Matrix metalloprotein regulation is complex and occurs at the levels of both gene transcription and protein activation.
The computation of deconvolution for random matrices is more complex than the scalar case and it is explained in the following.
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