Search results for "Linear"
showing 10 items of 7165 documents
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
2008
The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detectio…
Experimental validation for spectrum cartography using adaptive multi-kernels
2017
This paper validates the functionality of an algorithm for spectrum cartography, generating a radio environment map (REM) using adaptive radial basis functions (RBF) based on a limited number of measurements. The power at all locations is estimated as a linear combination of different RBFs without assuming any prior information about either power spectral densities (PSD) of the transmitters or their locations. The RBFs are represented as centroids at optimized locations, using machine learning to jointly optimize their positions, weights and Gaussian decaying parameters. Optimization is performed using expectation maximization with a least squares loss function and a quadratic regularizer. …
Krill herd algorithm-based neural network in structural seismic reliability evaluation
2018
ABSTRACTIn this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Alg…
Advanced computation in cardiovascular physiology: New challenges and opportunities
2021
Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes’ may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a speci…
Perspectives and reflections on teaching linear algebra
2020
Abstract This paper presents ‘expert opinions’ on what should be taught in a first-year linear algebra course at university; the aim is to gain a generic picture and general guiding principles for such a course. Drawing on a Delphi method, 14 university professors—called ‘experts’ in this study—addressed the following questions: What should be on a first-year linear algebra undergraduate course for engineering and/or mathematics students? How could such courses be taught? What tools (if any) are essential to these two groups of students? The results of the investigation, these experts’ opinions, mainly concern what should be in a linear algebra course (e.g. problem-solving and applications)…
On the application of the generalized means to construct multiresolution schemes satisfying certain inequalities proving stability
2021
Multiresolution representations of data are known to be powerful tools in data analysis and processing, and they are particularly interesting for data compression. In order to obtain a proper definition of the edges, a good option is to use nonlinear reconstructions. These nonlinear reconstruction are the heart of the prediction processes which appear in the definition of the nonlinear subdivision and multiresolution schemes. We define and study some nonlinear reconstructions based on the use of nonlinear means, more in concrete the so-called Generalized means. These means have two interesting properties that will allow us to get associated reconstruction operators adapted to the presence o…
Computational issues in fitting joint frailty models for recurrent events with an associated terminal event.
2020
Abstract Background and objective: Joint frailty regression models are intended for the analysis of recurrent event times in the presence of informative drop-outs. They have been proposed for clinical trials to estimate the effect of some treatment on the rate of recurrent heart failure hospitalisations in the presence of drop-outs due to cardiovascular death. Whereas a R-software-package for fitting joint frailty models is available, some technical issues have to be solved in order to use SASⓇ 1 software, which is required in the regulatory environment of clinical trials. Methods: First, we demonstrate how to solve these issues by deriving proper likelihood-decompositions, in particular fo…
Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology
2021
[EN] Background and objective: Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies directly affect this region. In order to characterize the left ventricle, it is necessary to extract its volume. In this work we present a neural network architecture that is capable of directly estimating the left ventricle volume in short axis cine Magnetic Resonance Imaging in the end-diastolic frame and provide a segmentation of the region which is the basis of the volume calculation, thus offering explain-ability to the estimated value. Methods: The network was des…
Intertwining operators between different Hilbert spaces: connection with frames
2009
In this paper we generalize a strategy recently proposed by the author concerning intertwining operators. In particular we discuss the possibility of extending our previous results in such a way to construct (almost) isospectral self-adjoint operators living in different Hilbert spaces. Many examples are discussed in details. Many of them arise from the theory of frames in Hilbert spaces, others from the so-called g-frames.
A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
2014
Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, gras…