Data representative of current designs and operating practices regarding emission of PM2.5 and PM2.5 precursors, especially organic compounds and aerosols, from stationary petroleum industry combustion sources is sparse.
Khan says she has "made every effort to verify" the prisoners' accounts, but her sources are sparse and most of them seem to be news media reports.
However, if the sources are sparse in the time domain, then the active sources and their amplitudes can be determined; this is equivalent to error correction codes.
In particular, if the sources are statistically independent random signals, independent component analysis (ICA) is effective, or if the sources are nonnegative, matrix factorisation is effective, or if the sources are sparse, a sparse component analysis can be effective.
These approaches could accurately separate the image mixtures using linear clustering when the linear clustering has less run time than super-plane clustering techniques, and the image sources are sparse enough [21].
When dealing with the underdetermined case, i.e., scenarios with more sources than sensors, methods usually associated with the idea of sparse component analysis (SCA) [1], which assume that the sources are sparse in some domain, are able to identify the mixing model or even, in some cases, perfectly separate the underlying sources [2].
A large number of methods for BI of underdetermined mixtures start from the assumption that the sources are sparse by nature (i.e., in its own domain such as the time domain) or could be made sparse in another domain (e.g., a transform domain).
In places where the sources are sparse and at each time instant, at most one of the sources has significant nonzero value, the columns of the mixing matrix may be calculated individually, which makes the solution to the underdetermined case possible.
However, information about their source parameters is sparse.
bits with probability p to be 1 and probability 1−p to be 0. When p≠0.5, the source is considered sparse or compressible.
Since the cardinality G of Ω, i.e., the number of grid points used to cover the spatial frequency domain, is much larger than the number of possible sources, then the vector x is sparse.
