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showing 10 items of 1034 documents
Direct adaptive tracking control for a class of pure-feedback stochastic nonlinear systems based on fuzzy-approximation
2014
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/462468 Open Access The problem of fuzzy-based direct adaptive tracking control is considered for a class of pure-feedback stochastic nonlinear systems. During the controller design, fuzzy logic systems are used to approximate the packaged unknown nonlinearities, and then a novel direct adaptive controller is constructed via backstepping technique. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood around …
New Objective Refraction Metric Based on Sphere Fitting to the Wavefront
2017
Purpose. To develop an objective refraction formula based on the ocular wavefront error (WFE) expressed in terms of Zernike coefficients and pupil radius, which would be an accurate predictor of subjective spherical equivalent (SE) for different pupil sizes.Methods. A sphere is fitted to the ocular wavefront at the center and at a variable distance,t. The optimal fitting distance,topt, is obtained empirically from a dataset of 308 eyes as a function of objective refraction pupil radius,r0, and used to define the formula of a new wavefront refraction metric (MTR). The metric is tested in another, independent dataset of 200 eyes.Results. For pupil radiir0≤2 mm, the new metric predicts the equ…
FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram
2020
Abstract Online detection and removal of eye blink (EB) artifacts from electroencephalogram (EEG) would be very useful in medical diagnosis and brain computer interface (BCI). In this work, approaches that combine unsupervised eyeblink artifact detection with empirical mode decomposition (EMD), and canonical correlation analysis (CCA), are proposed to automatically identify eyeblink artifacts and remove them in an online manner. First eyeblink artifact regions are automatically identified and an eyeblink artifact template is extracted via EMD, which incorporates an alternate interpolation technique, the Akima spline interpolation. The removal of eyeblink artifact components relies on the el…
Online detection and removal of eye blink artifacts from electroencephalogram
2021
Abstract The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, …
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
2018
International audience; The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error…
An Artificial Neural Network for 3D Localization of Brainstem Functional Lesions
2002
The human brainstem is a highly complex structure where even small lesions can give rise to a variety of symptoms and signs. Localizing the area of dysfunction within the brainstem is often a difficult task.To make localization easier, we have developed a neural net system, which uses 72 clinical and neurophysiological data inputs and displays it (using 5268 voxels) on a three-dimensional model of the human brainstem. The net was trained by means of a back-propagation algorithm, over a pool of 580 example-cases. Assessed on 200 test-cases, the net correctly localized 83.6% of the target voxels; furthermore the net correctly localized the lesion in 31/37 patients. Because our computer-assist…
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning
2020
Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as \(\epsilon \)-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approaches are fully explorative and exploitative without considering the underlying environment dynamics. Model-free RL works conceptually well in simulated environments, and empirical evidence suggests that trial and error lead to a near-opti…
Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site
2016
Thermal infrared (TIR) data are usually acquired at a coarser spatial resolution (CR) than visible and near infrared (VNIR). Several disaggregation methods have been recently developed to enhance the TIR spatial resolution using VNIR data. These approaches are based on the retrieval of a relation between TIR and VNIR data at CR, or training of a neural network, to be applied at the fine resolution afterward. In this work, different disaggregation methods are applied to the combination of two different sensors in the experimental test site of Barrax, Spain. The main objective is to test the feasibility of these techniques when applied to satellites provided with no TIR bands. Landsat and mod…
A spatially consistent downscaling approach for SMOS using an adaptive window
2017
The European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) is the first spaceborne mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improve the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm for SMOS is proposed to obtain high-resolution (HR) SM maps at 1 km (L4), from the ∼40 km native resolution of the instrument. This algorithm introduces the concept of a shape adaptive moving window as an improvement of the current semi-empirical downscaling approach at SMOS Barcelona Expert Center, based on the “universal triangle…
Operational forecasting of daily summer maximum and minimum temperatures in the Valencia Region
2013
Extreme-temperature events have a great impact on human society. Thus, knowledge of summer temperatures can be very useful both for the general public and for organizations whose workers operate in the open. An accurate forecasting of summer maximum and minimum temperatures could help to predict heatwave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The objective of this work is to evaluate the skill of the regional atmospheric and modelling system (RAMS) model in determining daily summer maximum and minimum temperatures in the Valencia Region. For this, we have used the real-time configuration of…