Search results for "vector [form factor]"
showing 10 items of 770 documents
Nonlinear Dynamics Techniques for the Detection of the Brain Areas Using MER Signals
2008
A methodology for identifying brain areas from the brain MER signals (microelectrode recordings) is presented, which is based on a nonlinear feature set. We propose nonlinear dynamics measures such as correlation dimension, Hurst exponent and the largest Lyapunov exponent to characterize the dynamic structure. The MER records belong to the Polytechnical University of Valencia, 24 records for each zone (black substance, thalamus, subthalamus nucleus and uncertain area). The detection of each area using characteristics derived from complexity analysis was obtained through a classifier (support vector machine). The joint information between areas is remarkable and the best accuracy result was …
A Novel System for Multi-level Crohn’s Disease Classification and Grading Based on a Multiclass Support Vector Machine
2020
Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI…
Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution
2021
International audience; Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% acc…
Champs de vecteurs analytiques et champs de gradients
2002
A theorem of Łojasiewicz asserts that any relatively compact solution of a real analytic gradient vector field has finite length. We show here a generalization of this result for relatively compact solutions of an analytic vector field X with a smooth invariant hypersurface, transversally hyperbolic for X, where the restriction of the field is a gradient. This solves some instances of R. Thom's Gradient Conjecture. Furthermore, if the dimension of the ambient space is three, these solutions do not oscillate (in the sense that they cut an analytic set only finitely many times); this can also be applied to some gradient vector fields.
Learning the relevant image features with multiple kernels
2009
This paper proposes to learn the relevant features of remote sensing images for automatic spatio-spectral classification with the automatic optimization of multiple kernels. The method consists of building dedicated kernels for different sets of bands, contextual or textural features. The optimal linear combination of kernels is optimized through gradient descent on the support vector machine (SVM) objective function. Since a na¨ive implementation is computationally demanding, we propose an efficient model selection procedure based on kernel alignment. The result is a weight — learned from the data — for each kernel where both relevant and meaningless image features emerge after training. E…
Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion
2020
Recognition systems using multimodal biometrics attracts attention because they improve recognition efficiency and high-security level compared to the unimodal biometrics system. In this study, the authors present a secure multimodal biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion. The features extraction for FKP and FV are performed using transfer learning CNN architectures: AlexNet, VGG16, and ResNet50. The key step aims to …
Image Segmentation by Deep Community Detection Approach
2017
International audience; To address the problem of segmenting an image into homogeneous communities this paper proposes an efficient algorithm to detect deep communities in the image by maximizing at each stage a new centrality measure, called the local Fiedler vector centrality (LFVC). This measure is associated with the sensitivity of algebraic connectivity to node removals. We show that a greedy node removal strategy, based on iterative maximization of LFVC, has bounded performance loss relative to the optimal, but intractable, combinatorial batch removal strategy. A remarkable feature of this method is the ability to segments the image automatically into homogeneous regions by maximizing…
The origin of in-plane stresses in axially moving orthotropic continua
2016
In this paper, we address the problem of the origin of in-plane stresses in continuous, two-dimensional high-speed webs. In the case of thin, slender webs, a typical modeling approach is the application of a stationary in-plane model, without considering the effects of the in-plane velocity field. However, for high-speed webs this approach is insufficient, because it neglects the coupling between the total material velocity and the deformation experienced by the material. By using a mixed Lagrange–Euler approach in model derivation, the solid continuum problem can be transformed into a solid continuum flow problem. Mass conservation in the flow problem, and the behaviour of free edges in th…
A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks
2020
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on…
Input-Output Feedback Linearizing Control of Linear Induction Motor Taking into Consideration the End-Effects. Part II: Simulation and Experimental R…
2015
This is the second part of a paper, divided in two parts, dealing with the application of the input–output feedback linearization (FL) control technique to linear induction motors (LIMs). The first part has treated the theoretical formulation of the input–output feedback linearization control technique as to be applied to linear induction motors. This second part describes the set of tests, both in numerical simulations and experiments, performed to assess the validity of the control technique. In particular, it addresses the issues of the sensitivity of the FL control versus the LIM electrical parameters’ variations and the improvements achievable by considering the LIM dynamic end effects…