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Electromagnetic imaging has been a powerful technique in various
civil and military applications across medical imaging, geophysics,
and space exploration. The Nyquist-Shannon theory has formed the
basis for processing the signals in such systems. The advent of
Compressive Sensing techniques has enabled
low-dimension-model-based techniques to be used to break many of
the bottlenecks of the earlier technologies.
Low-dimensional-model-based electromagnetic imaging remains at its
early stage, and many important issues relevant to practical
applications need to be carefully investigated. In particular, this
is the era of big data with booming electromagnetic sensing, by
which massive data are being collected for retrieving very detailed
information of probed objects. This monograph gives an overview of
the low-dimensional models of structure signals, along with its
relevant theories and low-complexity algorithms of signal recovery.
It further reviews the recent advancements of
low-dimensional-model-based electromagnetic imaging in various
applied areas. It is a comprehensive introduction for researchers
and engineers wishing to understand the state-of-the-art of
electromagnetic imaging.
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