Compressive Sensing

By Richard Baraniuk. 2008

2,548 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Biophysics, Computational Mechanics

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Citations in the corpus

Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, methodology): “This formulation is closely related to the compressive sensing framework, which allows for the sparse vector ξ to be determined from relatively few incoherent random measurements [12, 8, 9, 7, 2, 43].”
Learning to invert: Signal recovery via Deep Convolutional Networks (2017, background): “Compressive sensing (CS) [1–3] is a special case of this problem in which the signal has a sparse representation, i.e., there exists an N × N basis matrix Ψ = [ ψ 1 | ψ 2 | . . . | ψ N ] such that x = Ψs and only K ≪ N of the coefﬁcients s are Email: { ali.mousavi, richb } @rice.edu nonzero.”
Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR (2012, background): “The circles show the 90% detection rate positions with N ∈ [8, 20].”
Compressive data gathering for large-scale wireless sensor networks (2009, background): “This paper presents the first complete design to apply compressive sampling theory [13][4][7] to sensor data gathering for large-scale wireless sensor networks (WSNs), successfully addressing the two major challenges as outlined above.”
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, background): “For example, compressed sensing [31], [32], [33] provides convex algorithms to solve the combinatorial sparse signal reconstruction problem with high probability.”
Sensitivity to Basis Mismatch in Compressed Sensing (2010, background): “[1]–[3]) has created a great deal of enthusiasm among the signal processing community, as it suggests that reconstructing and even inverting high dimensional images (e.”
Sparse LMS for system identification (2009, methodology): “Motivated by LASSO [9] and recent progress in compressive sensing [10, 11, 12], we propose an alternative approach to i dentifying sparse systems using LMS filters.”
Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays (2012, positive): “With CS, there is no such overheads; the target signal x is blindly encoded as an M × 1 CS measurement vector, y [7]:”
Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling (2010, positive): “…that [7, Sec 3.3.4]: using the solution̂xS−TLS of (6) for a given multiplierλ > 0 and lettingµ := ‖x̂S−TLS‖1, the pertinent constraint isX1(µ) := {x ∈ Rn : ‖x‖1 ≤ µ}; and the equivalent constrained minimization problem is [cf. (6)] x̂S−TLS := arg min x∈X1(µ) f(x) , f(x) := ‖y −Ax‖22 1 + ‖x‖22 .”
Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics (2016, methodology): “SINDy uses sparse regression [57] for improved numerical robustness in noisy overdetermined problems, as opposed to earlier methods [58] that use compressed sensing [59]–[61].”
Adaptive Networks (2014, background): “The role of f ð w Þ is to ensure that the solution vector is sparse [161]–[163].”