Search results for "ALGORITHM"
showing 10 items of 4887 documents
Illumination Correction on MR Images
2006
Objective. An important artifact corrupting Magnetic Resonance Images is the rf inhomogeneity, also called bias artifact. This anomaly produces an abnormal illumination fluctuation on the image, due to variations of the device magnetic field. This artifact is particularly strong on images acquired with a device specialized on upper and lower limbs due to their coil configuration. A method based on homomorphic filtering aimed to suppress this artifact was proposed by Guillemaud. This filter has two faults: it doesnt provide an indication about the cutoff frequency (cf) and introduces another illumination artifact on the edges of the foreground. This work is an improvement to this method because i…
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…
Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties
2019
International audience; In inverse problems, the use of an l(12) analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact while keeping information of interest contained in the biased solution. This is done through the use of refitting block penalties that only act on the co-support of the estimation. Based on an analysis of related works in the literature, we propose a new penalty that is well suited for refitting purposes. We also present an efficient algorithmic method to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good be…
Great Minds Think Alike? Spatial Search Processes Can Be More Idiosyncratic When Guided by More Accurate Information.
2022
Existing research demonstrates that pre-decisional information sampling strategies are often stablewithin a given person while varying greatly across people. However, it remains largely unknown whatdrives these individual differences, that is, why in some circumstances we collect information moreidiosyncratically. In this brief report, we present a pre-registered online study of spatial search. Usinga novel technique that combines machine-learning dimension reduction and sequence alignment algo-rithms, we quantify the extent to which the shape and temporal properties of a search trajectory areidiosyncratic. We show that this metric increases (trajectories become more idiosyncratic) when a p…
A new approach to portfolio selection based on forecasting
2023
In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio’s value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, whic…
A Bacterial-Based Algorithm to Simulate Complex Adaptative Systems
2014
Paper presented at the 13th International Conference on Simulation of Adaptive Behavior which took place at Castellón, Spain in 2014, July 22-25. Bacteria have demonstrated an amazing capacity to overcome envi-ronmental changes by collective adaptation through genetic exchanges. Using a distributed communication system and sharing individual strategies, bacteria propagate mutations as innovations that allow them to survive in different envi-ronments. In this paper we present an agent-based model which is inspired by bacterial conjugation of DNA plasmids. In our approach, agents with bounded rationality interact in a common environment guided by local rules, leading to Complex Adaptive Syste…
Constitutional Perspective of the Guarantees of Application of Artificial Intelligence: the Inescapable Protection of Fundamental Rights
2020
espanolLa Inteligencia Artificial tiene un innegable efecto en la sociedad actual, por lo que su estudio respecto de sus efectos juridicos deviene necesario. Y, en consecuencia, el modo en que se ven afectados los derechos fundamentales tiene especial importancia. De ahi que en el presente trabajo se estudie la influencia de los algoritmos en la determinacion de las resoluciones judi-ciales, sobre todo desde el punto de vista de como afectaria esta cuestion al derecho a la tutela judicial efectiva, reconocida como derecho fundamental en el articulo 24 de la Constitucion espanola. EnglishArtificial Intelligence has an undeniable effect on today’s society, so its study regarding its legal eff…
Abstract ID: 133 Fast and accurate 3D dose distribution computations using artificial neural networks
2017
In radiation therapy, the trade-off between accuracy and speed is the key of the algorithms used in Treatment Planning Systems (TPS). For photon beams, commercial solutions generally relies on analytic algorithms, biased Monte Carlo, or heavily parallelized Monte Carlo on Graphics Processing Units (GPU). Alternatively, we propose an algorithm using Artificial Neural Network (ANN) to compute the dose distributions resulting from ionizing radiations inside a phantom [1] , [2] . We present an evolution of this platform taking into account modulated field sizes and shapes, and various orientations of the beam to the phantom. Firstly, tomodensitometry-based phantoms are created to validate the d…
Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting
2019
This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used …
Tabu and Scatter Search for Artificial Neural Networks
2003
In this paper we address the problem of training multilayer feed-forward neural networks. These networks have been widely used for both prediction and classification in many different areas. Although the most popular method for training these networks is back propagation, other optimization methods such as tabu search or scatter search have been applied to solve this problem. This paper presents a new training algorithm based on the tabu search methodology that incorporates elements for search intensification and diversification by utilizing strategic designs where other previous approaches resort to randomization. Our method considers context and search information, as it is provided by th…