Search results for "kernel"
showing 10 items of 357 documents
A DGS gesture dictionary for modelling on mobile devices
2017
ABSTRACTInteractive or Dynamic Geometry System (DGS) is a tool that help to teach and learn geometry using a computer-based interactive environment. Traditionally, the interaction with DGS is based on keyboard and mouse events where the functionalities are accessed using a menu of icons. Nevertheless, recent findings suggest that such a traditional model of interaction has a steep learning curve and is inadequate to develop DGS for devices with multi-touch screens. Thus, we propose a new interaction model for DGS based on a gesture dictionary which enables the construction and manipulation of geometric objects without the need of accessing a menu of icons. The dictionary is divided into thr…
Large-scale random features for kernel regression
2015
Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical retrieval of bio-geo-physical parameters. The RKS method allows to approximate a kernel matrix with a set of random bases sampled from the Fourier domain. We extend their use to other bases, such as wavelets, stumps, and Walsh expansions. We show that kernel regression is now possible for data…
Spectral density estimation for stationary stable random fields
1995
International audience
Cloud masking and removal in remote sensing image time series
2017
Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clo…
Influence of convolution filtering on coronary plaque attenuation values: observations in an ex vivo model of multislice computed tomography coronary…
2007
Attenuation variability ( measured in Hounsfield Units, HU) of human coronary plaques using multislice computed tomography (MSCT) was evaluated in an ex vivo model with increasing convolution kernels. MSCT was performed in seven ex vivo left coronary arteries sunk into oil followingthe instillation of saline (1/infinity) and a 1/50 solution of contrast material ( 400 mgI/ml iomeprol). Scan parameters were: slices/ collimation, 16/0.75 mm; rotation time, 375 ms. Four convolution kernels were used: b30f-smooth, b36f-medium smooth, b46f-medium and b60f-sharp. An experienced radiologist scored for the presence of plaques and measured the attenuation in lumen, calcified and noncalcified plaques …
Efficient Kernel Cook's Distance for Remote Sensing Anomalous Change Detection
2021
Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about the characterization of distributions, detection of anomalies, extreme events, and changes. One useful tool to detect multivariate anomalies is the celebrated Cook's distance. Instead of assuming a linear relationship, we present a novel kernelized version of the Cook's distance to address anomalous change detection in remote sensing images. Due to the large computational burden involved in the direct kernelization, and the lack of out-…
Performance of power control in inter-cell interference coordination for frequency reuse
2010
To mitigate inter-cell interference in 3G evolution systems, a novel inter-cell interference coordination scheme called soft fractional frequency reuse is proposed in this article, which enables to improve the data rate in cell-edge. On this basis, an inter-cell power control is presented for the inter-cell interference coordination, and the inter-cell balanced signal to interference plus noise ratio (SINR) among users is established for power allocation, which enables mitigation of inter-cell interference. Especially, the power control is based on a novel exponential kernel equation at higher convergence speed than the traditional arithmetic kernel equations. Numerical results show that th…
A brief overview on the numerical behavior of an implicit meshless method and an outlook to future challenges
2015
In this paper recent results on a leapfrog ADI meshless formulation are reported and some future challenges are addressed. The method benefits from the elimination of the meshing task from the pre-processing stage in space and it is unconditionally stable in time. Further improvements come from the ease of implementation, which makes computer codes very flexible in contrast to mesh based solver ones. The method requires only nodes at scattered locations and a function and its derivatives are approximated by means of a kernel representation. A perceived obstacle in the implicit formulation is in the second order differentiations which sometimes are eccesively sensitive to the node configurat…
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval
2018
The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromised because of the extremely high spectral dimensionality and the high number of variables to be predicted simultaneously across the atmospheric column. All this poses a challenge for selecting and studying optimal models and processing schemes. Earlier work has shown non-linear models such as kernel methods and neural networks perform w…
L'évolution de l'occupation humaine : l'analyse spatiale exploratoire des données.Le problème de l'incertitude et de l'hétérogénéité des données en a…
2011
The analysis of the evolution of the settlement requires a lot of methodological questioning. Despite a frequent use of the spatial analysis methods, few works return to their methodological problems. This article proposes to make a comparison and a discussion around two exploratory statistical methods (the K function of Ripley and the Kernel Density Estimation). It seems that scale, quality and quantity of input data, are three essential parameters to be taken into account so as to lead a spatial analysis in archaeology. In intrinsic way, archaeological data are non homogeneous. For that purpose, our article proposes a multi scalar approach integrating the non homogeneousness character of …