Sparse polynomial chaos expansions: Literature survey and benchmark
Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
Compressive sensing in electromagnetics-a review
Several problems arising in electromagnetics can be directly formulated or suitably recast for
an effective solution within the compressive sensing (CS) framework. This has motivated a …
an effective solution within the compressive sensing (CS) framework. This has motivated a …
A hybrid prognostics approach for estimating remaining useful life of rolling element bearings
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in
reducing costly unplanned maintenance and increasing the reliability, availability, and safety …
reducing costly unplanned maintenance and increasing the reliability, availability, and safety …
Rethinking Bayesian learning for data analysis: The art of prior and inference in sparsity-aware modeling
Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range
Capacity estimation is essential for battery health management during the whole lifecycle.
The data-driven technique has shown advanced performance in battery capacity estimation …
The data-driven technique has shown advanced performance in battery capacity estimation …
Non-Bayesian activity detection, large-scale fading coefficient estimation, and unsourced random access with a massive MIMO receiver
In this paper, we study the problem of user activity detection and large-scale fading
coefficient estimation in a random access wireless uplink with a massive MIMO base station …
coefficient estimation in a random access wireless uplink with a massive MIMO base station …
Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling
Battery health monitoring and management is of extreme importance for the performance
and cost of electric vehicles. This paper is concerned with machine-learning-enabled battery …
and cost of electric vehicles. This paper is concerned with machine-learning-enabled battery …
A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution
Histopathology diagnosis is based on visual examination of the morphology of histological
sections under a microscope. With the increasing popularity of digital slide scanners …
sections under a microscope. With the increasing popularity of digital slide scanners …
[PDF][PDF] Dlib-ml: A machine learning toolkit
DE King - The Journal of Machine Learning Research, 2009 - jmlr.org
There are many excellent toolkits which provide support for developing machine learning
software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library …
software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library …
BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease
Alzheimer's disease (AD), the most common form of dementia, shares many aspects of
abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based …
abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based …