Add to ORKGAbstract. In this paper, a binary sparse observation matrix for compressive sensing is deterministically constructed via a pseudo-random sequence generated by the sub-shift mapping of finite type on the chaotic symbolic space. Analysis and experimental results demonstrate the proposed matrix’s simplification can be regarded as a reliable method and is usable in compressive sensing applications
In An asymptotic result on compressed sensing matrices, a new construction for com-pressed sensing m...
(RIP) are central to the emerging theory of compressive sensing (CS). Initial results in CS establis...
Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low...
Abstract—Compressive sensing is a new methodology to cap-ture signals at sub-Nyquist rate. To guaran...
Abstract: Compressive sensing is a new sampling theory to capture signals at sub-Nyquist rate. To gu...
As an emerging field for sampling paradigms, compressive sensing (CS) can sample and represent signa...
Abstract—Compressive Sensing (CS) is a new sampling theory which allows signals to be sampled at sub...
In this paper, a novel paradigm of constructing measurement matrices is proposed for compressive sen...
Compressive sensing uses simultaneous sensing and compression to provide an efficient image acquisit...
These notes give a mathematical introduction to compressive sensing focusing on recovery using `1-mi...
International audienceCompressive sensing is a new methodology to cap- ture signals at sub-Nyquist r...
Compressive sensing achieves effective dimensionality reduc-tion of signals, under a sparsity constr...
Compressed Sensing concerns a new class of linear data acquisition protocols that are more efficient...
Recent developments at the intersection of algebra and optimization theory—by the name of compressed...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
In An asymptotic result on compressed sensing matrices, a new construction for com-pressed sensing m...
(RIP) are central to the emerging theory of compressive sensing (CS). Initial results in CS establis...
Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low...
Abstract—Compressive sensing is a new methodology to cap-ture signals at sub-Nyquist rate. To guaran...
Abstract: Compressive sensing is a new sampling theory to capture signals at sub-Nyquist rate. To gu...
As an emerging field for sampling paradigms, compressive sensing (CS) can sample and represent signa...
Abstract—Compressive Sensing (CS) is a new sampling theory which allows signals to be sampled at sub...
In this paper, a novel paradigm of constructing measurement matrices is proposed for compressive sen...
Compressive sensing uses simultaneous sensing and compression to provide an efficient image acquisit...
These notes give a mathematical introduction to compressive sensing focusing on recovery using `1-mi...
International audienceCompressive sensing is a new methodology to cap- ture signals at sub-Nyquist r...
Compressive sensing achieves effective dimensionality reduc-tion of signals, under a sparsity constr...
Compressed Sensing concerns a new class of linear data acquisition protocols that are more efficient...
Recent developments at the intersection of algebra and optimization theory—by the name of compressed...
Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using ...
In An asymptotic result on compressed sensing matrices, a new construction for com-pressed sensing m...
(RIP) are central to the emerging theory of compressive sensing (CS). Initial results in CS establis...
Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low...