Results 21 to 30 of about 10,846,082 (297)
Signal Recovery From Unlabeled Samples [PDF]
IEEE Transactions on Signal Processing, 2017 In this paper, we study the recovery of a signal from a set of noisy linear projections (measurements), when such projections are unlabeled, that is, the correspondence between the measurements and the set of projection vectors (i.e., the rows of the measurement matrix) is not known a priori.
Saeid Haghighatshoar, Giuseppe Caire
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Recovery ... reporting overview; Connecticut recovery; CT recovery ... progress
, 2010 Updated quarterly; Began in 2009.; Title from home page last modified 10/29/2009 (viewed Oct. 29, 2009).; At head of title: State of Connecticut.; "On October 1, state agencies that have received Recovery Act funds began reporting to the federal ...
core +5 more sources
Stable signal recovery from incomplete and inaccurate measurements [PDF]
, 2005 Suppose we wish to recover a vector x0 β βπ (e.g., a digital signal or image) from incomplete and contaminated observations y = A x0 + e; A is an π Γ π matrix with far fewer rows than columns (π βͺ π) and e is an error term.
E. Candès, J. Romberg, T. Tao
semanticscholar +1 more source
Signal Recovery on Incoherent Manifolds [PDF]
IEEE Transactions on Information Theory, 2012 Suppose that we observe noisy linear measurements of an unknown signal that can be modeled as the sum of two component signals, each of which arises from a nonlinear sub-manifold of a high dimensional ambient space. We introduce SPIN, a first order projected gradient method to recover the signal components.
Chinmay Hegde, Richard G. Baraniuk
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Jointly Sparse Signal Recovery and Support Recovery via Deep Learning With Applications in MIMO-Based Grant-Free Random Access [PDF]
IEEE Journal on Selected Areas in Communications, 2020 In this article, we investigate jointly sparse signal recovery and jointly sparse support recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in many applications in communications and signal processing.
Ying Cui, Shuaichao Li, Wanqing Zhang
semanticscholar +1 more source
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 Compressed sensing (CS) MRI relies on adequate under-sampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sampling patterns for these k-space coefficients has received significant ...
I. Huijben +2 more
semanticscholar +1 more source
Micromachines, 2023 The effects of optical sampling pulse power, RF power, and electronic back-end bandwidth on the performance of time- and wavelength-interleaved photonic analog-to-digital converter (PADC) with eight-channel 41.6 GHz pulses have been experimentally ...
Junli Qi +10 more
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Consistent Sampling and Signal Recovery [PDF]
IEEE Transactions on Signal Processing, 2007 An attractive formulation of the sampling problem is based on the principle of a consistent signal reconstruction. The requirement is that the reconstructed signal is indistinguishable from the input in the sense that it yields the exact same measurements. Such a system can be interpreted as an oblique projection onto a given reconstruction space.
(A. Hirabayashi), εΉ³ζ ζ, Unser, M.
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Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery [PDF]
IEEE Transactions on Signal Processing, 2020 Sparse signal recovery or compressed sensing can be formulated as certain sparse optimization problems. The classic optimization theory indicates that the Newton-like method often has a numerical advantage over the classic gradient method for nonlinear ...
Nan Meng, Yun-Bin Zhao
semanticscholar +1 more source
Methods of Spline Functions in Solving Problems of Telecommunication and Information Technologies
Proceedings of the International Conference on Applied Innovations in IT, 2023 The solution to problems of telecommunications and information technologies using spline approximation and spline extrapolation based on real and complex spline functions is considered.
Irina Strelkovskaya +2 more
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