文件名称:Compressive-Sensing-for-Signal-Ensembles
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Compressive sensing (CS) is a new approach to simultaneous sensing and compression
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
basis. The CS literature has focused almost exclusively on problems involving single
signals in one or two dimensions. However, many important applications involve distributed
networks or arrays of sensors. In other applications, the signal is inherently
multidimensional and sensed progressively along a subset of its dimensions examples
include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity
models for signal ensembles that exploit both intra- and inter-signal correlation
structures. Joint sparsity models enable a reduction in the total number of compressive
mea-Compressive sensing (CS) is a new approach to simultaneous sensing and compression
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
basis. The CS literature has focused almost exclusively on problems involving single
signals in one or two dimensions. However, many important applications involve distributed
networks or arrays of sensors. In other applications, the signal is inherently
multidimensional and sensed progressively along a subset of its dimensions examples
include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity
models for signal ensembles that exploit both intra- and inter-signal correlation
structures. Joint sparsity models enable a reduction in the total number of compressive
mea
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
basis. The CS literature has focused almost exclusively on problems involving single
signals in one or two dimensions. However, many important applications involve distributed
networks or arrays of sensors. In other applications, the signal is inherently
multidimensional and sensed progressively along a subset of its dimensions examples
include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity
models for signal ensembles that exploit both intra- and inter-signal correlation
structures. Joint sparsity models enable a reduction in the total number of compressive
mea-Compressive sensing (CS) is a new approach to simultaneous sensing and compression
that enables a potentially large reduction in the sampling and computation
costs for acquisition of signals having a sparse or compressible representation in some
basis. The CS literature has focused almost exclusively on problems involving single
signals in one or two dimensions. However, many important applications involve distributed
networks or arrays of sensors. In other applications, the signal is inherently
multidimensional and sensed progressively along a subset of its dimensions examples
include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity
models for signal ensembles that exploit both intra- and inter-signal correlation
structures. Joint sparsity models enable a reduction in the total number of compressive
mea
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