Search results for "pattern"
showing 10 items of 4203 documents
Scenario of the Birth of Hidden Attractors in the Chua Circuit
2017
Recently it was shown that in the dynamical model of Chua circuit both the classical selfexcited and hidden chaotic attractors can be found. In this paper the dynamics of the Chua circuit is revisited. The scenario of the chaotic dynamics development and the birth of selfexcited and hidden attractors is studied. It is shown a pitchfork bifurcation in which a pair of symmetric attractors coexists and merges into one symmetric attractor through an attractormerging bifurcation and a splitting of a single attractor into two attractors. The scenario relating the subcritical Hopf bifurcation near equilibrium points and the birth of hidden attractors is discussed.
A fully adaptive multiresolution scheme for image processing
2007
A nonlinear multiresolution scheme within Harten's framework [A. Harten, Discrete multiresolution analysis and generalized wavelets, J. Appl. Numer. Math. 12 (1993) 153-192; A. Harten, Multiresolution representation of data II, SIAM J. Numer. Anal. 33 (3) (1996) 1205-1256] is presented. It is based on a centered piecewise polynomial interpolation fully adapted to discontinuities. Compression properties of the multiresolution scheme are studied on various numerical experiments on images.
A Parallel Approach for Statistical Texture Parameter Calculation
2014
This chapter focusses on the development of a new image processing technique for the processing of large and complex images, especially SAR images. We propose here a new and effective approach that outperforms the existing methods for the calculation of high order textural parameters. With a single processor, this approach is about \(256^{n-1}\) times faster than the co-occurrence matrix approach considered as classical, where \(n\) is the order of the textural parameter for a 256-gray scales image. In a parallel environment made of N processor, this performance can almost be multiply by the factor N. Our approach is based on a new modeling of textural parameters of a generic order \(n>1\) …
Exploration of neuronal and glial plasticity in the melanocortin system at the meal in a mouse model.
2017
In 2015, Nature published the largest pangenomic association study to date linking genetic variants to body mass index. This study highlighted the role of the central nervous system in vulnerability to obesity and supports an original concept that cerebral plasticity plays an important role in the control of energy balance. Thus, reduced cerebral plasticity capacities could lead to inadequate dietary behaviors, which would increase the risk of weight gain under caloric pressure. The anorectic neurons POMC and the orexigenic neurons AgRP of the melanocortin system, which control the energy balance, actually show synaptic plasticity properties in the adult brain. These phenomena are shown in …
Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval
2018
This paper introduces a feature selection method based on random forest -the Guided Regularized Random Forest (GRRF)- which can be used in classification and regression tasks. The method is based on the regularization of the information gain in the random forest nodes to obtain a subset of relevant and non-redundant features. The proposed method is used as a preliminary step In the process of retrieving biophysical parameters from a hyperspectral image. Preliminary experiments show that we can reduce the RMSE of the retrievals by around 7% for the Leaf Area Index and around 8% for the fraction of vegetation cover when compared to the results using random forest features.
Demultiplexing Visible and Near-Infrared Information in Single-Sensor Multispectral Imaging
2016
In this paper, we study a single-sensor imaging system that uses a multispectral filter array to spectrally sample the scene. Our system captures information in both visible and near-infrared bands of the electromagnetic spectrum. Due to manufacturing limitations, the visible filters in this system also transmit the NIR radiation. Similarly, visible light is transmitted by the NIR filter, leading to inaccurate mixed spectral measurements. We present an algorithm that resolves this issue by separating NIR and visible information. Our method achieves this goal by exploiting the correlation of multispectral images in both spatial and spectral domains. Simulation results show that the mean squa…
Therapeutic Drug Monitoring of Kidney Transplant Recipients Using Profiled Support Vector Machines
2007
This paper proposes a twofold approach for therapeutic drug monitoring (TDM) of kidney recipients using support vector machines (SVMs), for both predicting and detecting Cyclosporine A (CyA) blood concentrations. The final goal is to build useful, robust, and ultimately understandable models for individualizing the dosage of CyA. We compare SVMs with several neural network models, such as the multilayer perceptron (MLP), the Elman recurrent network, finite/infinite impulse response networks, and neural network ARMAX approaches. In addition, we present a profile-dependent SVM (PD-SVM), which incorporates a priori knowledge in both tasks. Models are compared numerically, statistically, and in…
A SYNTHETIC MEASURE FOR THE ASSESSMENT OF THE PROJECT PERFORMANCE
2009
The present paper aims to offer a synthetic project performance indicator (PPI) that aggregates two input parameters obtained by the Earned Value Analysis. The PPI is calculated by using a Fuzzy Inference System (FIS) able to single out a measure based on the input parameters, instead of formulating a mathematical model that could be a troublesome task whenever complex relations among the input variables exist. The purpose is to communicate the project performance to the stakeholders in a clear and complete way, for example, describing the PPI by means of contour lines.
Ranking of Brain Tumour Classifiers Using a Bayesian Approach
2009
This study presents a ranking for classifers using a Bayesian perspective. This ranking framework is able to evaluate the performance of the models to be compared when they are inferred from different sets of data. It also takes into account the performance obtained on samples not used during the training of the classifiers. Besides, this ranking assigns a prior to each model based on a measure of similarity of the training data to a test case. An evaluation consisting of ranking brain tumour classifiers is presented. These multilayer perceptron classifiers are trained with 1H magnetic resonance spectroscopy (MRS) signals following a multiproject multicenter evaluation approach. We demonstr…
Experimental nonlinear electrical reaction-diffusion lattice
1998
International audience; A nonlinear electrical reaction-diffusion lattice modelling the Nagumo equation is presented. It is shown that this system supports front propagation with a given velocity. This propagation is observed experimentally using a video acquisition system, and the measured velocity of the front is in perfect agreement with the theoretical prediction.