Search results for "SMOOTH"
showing 10 items of 710 documents
Firing characteristics of vestibular nuclei neurons in the alert monkey after bilateral vestibular neurectomy
1992
After destruction of the peripheral vestibular system which is not activated by moving large-field visual stimulation, not only labyrinthine-ocular reflexes but also optokinetic-ocular responses related to the "velocity storage" mechanism are abolished. In the normal monkey optokinetic-ocular responses are reflected in sustained activity changes of central vestibular neurons within the vestibular nuclei. To account for the loss of optokinetic responses after labyrinthectomy, inactivation of central vestibular neurons consequent on the loss of primary vestibular activity is assumed to be of major importance. To test this hypothesis we recorded the neural activity within the vestibular nuclea…
A robust blind 3-D mesh watermarking based on wavelet transform for copyright protection
2019
Nowadays, three-dimensional meshes have been extensively used in several applications such as, industrial, medical, computer-aided design (CAD) and entertainment due to the processing capability improvement of computers and the development of the network infrastructure. Unfortunately, like digital images and videos, 3-D meshes can be easily modified, duplicated and redistributed by unauthorized users. Digital watermarking came up while trying to solve this problem. In this paper, we propose a blind robust watermarking scheme for three-dimensional semiregular meshes for Copyright protection. The watermark is embedded by modifying the norm of the wavelet coefficient vectors associated with th…
Optimized Kernel Entropy Components
2016
This work addresses two main issues of the standard Kernel Entropy Component Analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis. In this work, we propose an extension of the KECA method, named Optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular…
A Robust Blind 3-D Mesh Watermarking Technique Based on SCS Quantization and Mesh Saliency for Copyright Protection
2019
Due to the recent demand of 3-D meshes in a wide range of applications such as video games, medical imaging, film special effect making, computer-aided design (CAD), among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased in the last decade. Nowadays, the majority of robust 3-D watermarking approaches have mainly focused on the robustness against attacks while the imperceptibility of these techniques is still a serious challenge. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and scalar Costa scheme (SCS) for Copyright protection is proposed. The watermark is embedded by quantifying the vertex n…
Modeling temporal treatment effects with zero inflated semi-parametric regression models: The case of local development policies in France
2017
International audience; A semi-parametric approach is proposed to estimate the variation along time of the effects of two distinct public policies that were devoted to boost rural development in France over a similar period of time. At a micro data level, it is often observed that the dependent variable, such as local employment, does not vary along time, so that we face a kind of zero inflated phenomenon that cannot be dealt with a continuous response model. We introduce a conditional mixture model which combines a mass at zero and a continuous response. The suggested zero inflated semi-parametric statistical approach relies on the flexibility and modularity of additive models with the abi…
Conditional particle filters with diffuse initial distributions
2020
Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in statistical applications. We propose a simple but generally applicable auxiliary variable method, which can be used together with the CPF in order to perform efficient inference with diffuse initial distributions. The method only requires simulatable Markov transitions that are reversible with respect to the initial distribution, which can be improper. We focus in particular on random-walk type transitions which are reversible with respect to a uniform init…
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer
2023
Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifie…
PANORMUS-SPH. A new Smoothed Particle Hydrodynamics solver for incompressible flows
2015
Abstract A new Smoothed Particle Hydrodynamics (SPH) solver is presented, fully integrated within the PANORMUS package [7] , originally developed as a Finite Volume Method (FVM) solver. The proposed model employs the fully Incompressible SPH approach, where a Fractional Step Method is used to make the numerical solution march in time. The main novelty of the proposed model is the use of a general and highly flexible procedure to account for different boundary conditions, based on the discretization of the boundary surfaces with a set of triangles and the introduction of mirror particles with suitable hydrodynamic properties. Both laminar and turbulent flows can be solved (the latter using t…
Robotic assistance for industrial sanding with a smooth approach to the surface and boundary constraints
2021
[EN] Surface treatment operations, such as sanding, deburring, finishing, grinding, polishing, etc. are progressively becoming more automated using robotic systems. However, previous research in this field used a completely automatic operation of the robot system or considered a low degree of human-robot interaction. Therefore, to overcome this issue, this work develops a truly synergistic cooperation between the human operator and the robot system to get the best from both. In particular, in the application developed in this work the human operator provides flexibility, guiding the tool of the robot system to treat arbitrary regions of the workpiece surface; while the robot system provides…
Tracking of Quantized Signals Based on Online Kernel Regression
2021
Kernel-based approaches have achieved noticeable success as non-parametric regression methods under the framework of stochastic optimization. However, most of the kernel-based methods in the literature are not suitable to track sequentially streamed quantized data samples from dynamic environments. This shortcoming occurs mainly for two reasons: first, their poor versatility in tracking variables that may change unpredictably over time, primarily because of their lack of flexibility when choosing a functional cost that best suits the associated regression problem; second, their indifference to the smoothness of the underlying physical signal generating those samples. This work introduces a …