Search results for "Mean square"
showing 10 items of 274 documents
JOINT TOPOLOGY LEARNING AND GRAPH SIGNAL RECOVERY VIA KALMAN FILTER IN CAUSAL DATA PROCESSES
2018
In this paper, a joint graph-signal recovery approach is investigated when we have a set of noisy graph signals generated based on a causal graph process. By leveraging the Kalman filter framework, a three steps iterative algorithm is utilized to predict and update signal estimation as well as graph topology learning, called Topological Kalman Filter or TKF. Similar to the regular Kalman filter, we first predict the a posterior signal state based on the prior available data and then this prediction is updated and corrected based on the recently arrived measurement. But contrary to the conventional Kalman filter algorithm, we have no information of the transition matrix and hence we relate t…
Probabilistic cross-validation estimators for Gaussian process regression
2018
Gaussian Processes (GPs) are state-of-the-art tools for regression. Inference of GP hyperparameters is typically done by maximizing the marginal log-likelihood (ML). If the data truly follows the GP model, using the ML approach is optimal and computationally efficient. Unfortunately very often this is not case and suboptimal results are obtained in terms of prediction error. Alternative procedures such as cross-validation (CV) schemes are often employed instead, but they usually incur in high computational costs. We propose a probabilistic version of CV (PCV) based on two different model pieces in order to reduce the dependence on a specific model choice. PCV presents the benefits from both…
Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring
2016
Abstract This paper presents an operational chain for high-resolution leaf area index (LAI) retrieval from multiresolution satellite data specifically developed for Mediterranean rice areas. The proposed methodology is based on the inversion of the PROSAIL radiative transfer model through the state-of-the-art nonlinear Gaussian process regression (GPR) method. Landsat and SPOT5 data were used for multitemporal LAI retrievals at high-resolution. LAI estimates were validated using time series of in situ LAI measurements collected during the rice season in Spain and Italy. Ground LAI data were collected with smartphones using PocketLAI, a specific phone application for LAI estimation. Temporal…
Crop Phenology Retrieval Through Gaussian Process Regression
2021
Monitoring crop phenology significantly assists agricultural managing practices and plays an important role in crop yield predictions. Multi-temporal satellite-based observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or deriving biophysical variables. This study presents a framework for automatic corn phenology characterization based on high spatial and temporal resolution time series. By using the Difference Vegetation Index (DVI) estimated from Sentinel-2 data over Iowa (US), independent phenological models were optimized using Gaussian Processes regression. Their respective performances were assessed based on simulated phenological indi…
Caracterización de las técnicas de nado a través del análisis de la aceleración 3D
2015
El objetivo de este estudio era caracterizar la aceleración en el dominio temporal y frecuencial en los cuatro estilos de nado. La muestra estuvo compuesta por 75 sujetos (39 del género masculino y 36 del género femenino), que realizaron cuatro repeticiones de 25 metros a máxima velocidad una cada uno de los estilos. Se utilizó un acelerómetro tri-axial con una sensibilidad de 10g y una precisión de 1000Hz, situado en la zona lumbar del nadador. Se seleccionaron los 6 segundos centrales del registro de aceleración. Las variables independientes fueron el estilo de nado y el género. En cuanto a las variables dependientes se analizaron las cíclicas (velocidad de nado, frecuencia de ciclo y lon…
Binding mode analysis of ABCA7 for the prediction of novel Alzheimer's disease therapeutics
2021
Graphical abstract
PENERAPAN METODE SINGLE MOVING AVERAGE DAN EXSPONENTIAL SMOOTHING PADA USAHA ASRIE MODESTA
2020
This study aims to (1) analyze the number of demands for batik products in the second period of 2018. (2) To analyze the most appropriate forecasting method. (3) To analyze the forecasting of the first period in 2019 using the selected forecasting method.
 This reseach uses primary data and secondary data with data collection techniques using interviews, observation, and documentation. The analysis used is Single Moving Averages and Exsponential Smoothing. 
 The results of research in forecasting demand for batik products in 2019 with the Single Moving Average method are 3,936 units with Mean Absolute Deviation (MAD) of 632.5 units and Mean Square Error (MSE) of 693,718 units. An…
Adaptive Kernel Learning for Signal Processing
2018
Adaptive filtering is a central topic in digital signal processing (DSP). By applying linear adaptive filtering principles in the kernel feature space, powerful nonlinear adaptive filtering algorithms can be obtained. This chapter introduces the wide topic of adaptive signal processing, and explores the emerging field of kernel adaptive filtering (KAF). In many signal processing applications, the problem of signal estimation is addressed. Probabilistic models have proven to be very useful in this context. The chapter discusses two families of kernel adaptive filters, namely kernel least mean squares (KLMS) and kernel recursive least‐squares (KRLS) algorithms. In order to design a practical …
A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time
2017
[EN] This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely…
Time course of human 40 Hz EEG activity accompanying P3 responses in an auditory oddball paradigm
1997
In order to quantify the time course of auditory P3-related gamma activity, root mean square (RMS) values were calculated from band-filtered (30-45 Hz) target and non-target responses in an auditory oddball experiment. Evoked (phase locked) gamma activity was evaluated from the time domain averages, whereas induced (not necessarily phase locked) activity was analyzed on the basis of single trials. Gamma RMS values were integrated across different time windows, namely the prestimulus, N50/P50, N100, pre P3, P3 and post P3 window. The single trial P3 window hereby was defined by a maximum amplitude criterion. In accordance with other studies, we found a pronounced increase of evoked gamma act…