Robust compressive sensing of sparse signals: a review
Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many
applications, robust estimators are needed. Among the scenarios in which robust …
applications, robust estimators are needed. Among the scenarios in which robust …
Wristband-type driver vigilance monitoring system using smartwatch
Studies have presented that the driver vigilance level has serious implication in the
causation of road accidents. This paper focuses on integrating both the vehicle-based …
causation of road accidents. This paper focuses on integrating both the vehicle-based …
An overview of robust compressive sensing of sparse signals in impulsive noise
While compressive sensing (CS) has traditionally relied on l 2 as an error norm, a broad
spectrum of applications has emerged where robust estimators are required. Among those …
spectrum of applications has emerged where robust estimators are required. Among those …
Robust subspace tracking with missing entries: The set-theoretic approach
S Chouvardas, Y Kopsinis… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
In this paper, an Adaptive Projected Subgradient Method (APSM) based algorithm for robust
subspace tracking is introduced. A properly chosen cost function is constructed at each time …
subspace tracking is introduced. A properly chosen cost function is constructed at each time …
Noise estimation and type identification in natural scene and medical images using deep learning approaches
The image enhancement for the natural images is the vast field where the quality of the
images degrades based on the capturing and processing methods employed by the …
images degrades based on the capturing and processing methods employed by the …
[PDF][PDF] Robust algorithms for linear and nonlinear regression via sparse modeling methods: theory, algorithms and applications to image denoising
G Papageorgiou - 2016 - pergamos.lib.uoa.gr
The task of robust regression is of particular importance in signal processing, statistics and
machine learning. Ordinary estimators, such as the Least Squares (LS) one, fail to achieve …
machine learning. Ordinary estimators, such as the Least Squares (LS) one, fail to achieve …