Results 41 to 50 of about 10,846,082 (297)
Dilated POCS: Minimax Convex Optimization
IEEE Access, 2023 Alternating projection onto convex sets (POCS) provides an iterative procedure to find a signal that satisfies two or more convex constraints when the sets intersect.
Albert R. Yu +6 more
doaj +1 more source
Stable signal recovery in compressed sensing with a structured matrix perturbation [PDF]
, 2012 The sparse signal recovery in the standard compressed sensing (CS) problem requires that the sensing matrix be known a priori. Such an ideal assumption may not be met in practical applications where various errors and fluctuations exist in the sensing ...
Zai Yang +5 more
core +1 more source
Recovery ... reporting overview; Connecticut recovery; CT recovery ... progress
, 2011 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 +3 more sources
Lipschitz Learning for Signal Recovery
CoRR, 2019 We consider the recovery of signals from their observations, which are samples of a transform of the signals rather than the signals themselves, by using machine learning (ML). We will develop a theoretical framework to characterize the signals that can be robustly recovered from their observations by an ML algorithm, and establish a Lipschitz ...
Hong Jiang 0002 +2 more
openaire +2 more sources
Methods for early recognition of OFDM data [PDF]
Компьютерная оптика, 2020 A technique of early recognition (recovery) of data transmitted using OFDM technology by an incompletely received signal is considered. Theoretically, this approach is able to increase the speed of information transfer, as well as the resistance of the ...
Ruslan Yuzkiv +3 more
doaj +1 more source
Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
doaj +1 more source
Joint Channel Estimation and Signal Recovery in RIS-Assisted Multi-User MISO Communications [PDF]
IEEE Wireless Communications and Networking Conference, 2020 Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency ...
Li Wei +5 more
semanticscholar +1 more source
Trainable ISTA for Sparse Signal Recovery [PDF]
IEEE Transactions on Signal Processing, 2018 In this paper, we propose a novel sparse signal recovery algorithm called the trainable iterative soft thresholding algorithm (TISTA). The proposed algorithm consists of two estimation units: a linear estimation unit and a minimum mean squared error ...
Daisuke Ito +2 more
semanticscholar +1 more source
Non-Bayesian Estimation Framework for Signal Recovery on Graphs [PDF]
IEEE Transactions on Signal Processing, 2020 Graph signals arise from physical networks, such as power and communication systems, or as a result of a convenient representation of data with complex structure, such as social networks.
T. Routtenberg
semanticscholar +1 more source
Sampling and Recovery of Graph Signals [PDF]
, 2018 The aim of this chapter is to give an overview of the recent advances related to sampling and recovery of signals defined over graphs. First, we illustrate the conditions for perfect recovery of bandlimited graph signals from samples collected over a selected set of vertexes. Then, we describe some sampling design criteria proposed in the literature to
Paolo Di Lorenzo +2 more
openaire +3 more sources