Search results for "Data set"
showing 10 items of 154 documents
Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems
2021
AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface car…
Clustering categorical data: A stability analysis framework
2011
Clustering to identify inherent structure is an important first step in data exploration. The k-means algorithm is a popular choice, but K-means is not generally appropriate for categorical data. A specific extension of k-means for categorical data is the k-modes algorithm. Both of these partition clustering methods are sensitive to the initialization of prototypes, which creates the difficulty of selecting the best solution for a given problem. In addition, selecting the number of clusters can be an issue. Further, the k-modes method is especially prone to instability when presented with ‘noisy’ data, since the calculation of the mode lacks the smoothing effect inherent in the calculation …
Dimension Estimation in Two-Dimensional PCA
2021
We propose an automated way of determining the optimal number of low-rank components in dimension reduction of image data. The method is based on the combination of two-dimensional principal component analysis and an augmentation estimator proposed recently in the literature. Intuitively, the main idea is to combine a scree plot with information extracted from the eigenvectors of a variation matrix. Simulation studies show that the method provides accurate estimates and a demonstration with a finger data set showcases its performance in practice. peerReviewed
Artificial neural networks for predicting dorsal pressures on the foot surface while walking
2012
In this work, artificial neural networks (ANNs) are proposed to predict the dorsal pressure over the foot surface exerted by the shoe upper while walking. A model that is based on the multilayer perceptron (MLP) is used since it can provide a single equation to model the exerted pressure for all the materials used as shoe uppers. Five different models are produced, one model for each one of the four subjects under study and an overall model for the four subjects. The inputs to the neural model include the characteristics of the material and the positions during a whole step of 14 pressure sensors placed on the foot surface. The goal is to find models with good generalization capabilities, (…
Single imputation method of missing values in environmental pollution data sets
2006
Abstract Missing data represent a general problem in many scientific fields above all in environmental research. Several methods have been proposed in literature for handling missing data and the choice of an appropriate method depends, among others, on the missing data pattern and on the missing-data mechanism. One approach to the problem is to impute them to yield a complete data set. The goal of this paper is to propose a new single imputation method and to compare its performance to other single and multiple imputation methods known in literature. Considering a data set of PM 10 concentration measured every 2 h by eight monitoring stations distributed over the metropolitan area of Paler…
Determination of quality parameters of beers by the use of attenuated total reflectance-Fourier transform infrared spectroscopy
2005
Abstract The estimation of important quality parameters of beers, such as original and real extracts and alcohol content, has been evaluated by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) using a partial least square (PLS) calibration approach. Two sample populations, one consisting of 24 samples and other of 21 samples, obtained from the Spanish market and covering different types of beer were used. The first set was used for building and validating the model, whereas the second, measured 6 months after, was used for evaluating its robustness. The spectral range and the size of the calibration set and its suitability for building the PLS model have been …
Towards a new generation of high-resolution meteorological input data for small-scale hydrologic modeling
2011
Summary Current and future challenges of hydrologic sciences are to accurately predict and assess climate-driven impacts on water resources for the relevant scales of planning. However, process-based small-scale hydrologic modeling is data demanding and large uncertainties exist in data-sparse areas. The aim of our study was to test the applicability of the COSMO-DE analysis data (COSMO-DE-A) for hydrologic modeling. COSMO-DE-A data are a new meteorological data set with high temporal and spatial resolution that originates from the German Weather Service data assimilation system using the COSMO-DE weather prediction model. We collected field parameters in a small (10 km 2 ) mountainous catc…
<title>Methodology for quantitative analysis of scaling effects in multiresolution datasets acquired with airborne sensors flying at different …
2001
Scaling issues are always playing a critical role in most studies based on remote sensing data. The process of getting quantitative scaling information from raw multi-resolution images is not trivial, and many aspects must be taken very carefully into consideration. To get a better picture about the role of spatial resolution, we conducted a series of flights in summer 1997, in several test sites over Spain and Portugal. In order to minimize the time of acquisition (to get minimal changes in atmospheric status and solar illumination) we used three flight altitude levels, that produced images with 1.25 m, 3 m and 12 m resolutions. The main steps in our methodology are: a) Geometrical registr…
'Dual' Gravity: Using Spatial Econometrics to Control for Multilateral Resistance
2010
We propose a quantity-based `dual' version of the gravity equation that yields an estimating equation with both cross-sectional interdependence and spatially lagged error terms. Such an equation can be concisely estimated using spatial econometric techniques. We illustrate this methodology by applying it to the Canada-U.S. data set used previously, among others, by Anderson and van Wincoop (2003) and Feenstra (2002, 2004). Our key result is to show that controlling directly for spatial interdependence across trade flows, as suggested by theory, significantly reduces border effects because it captures `multilateral resistance'. Using a spatial autoregressive moving average specification, we …
Measurement error in schooling data: the OECD case
2003
The effect of human capital is difficult to estimate in cross-country analysis due to measurement errors in schooling data. Using data for OECD countries over the period 1960–1990, the reliability of the widely used Barro and Lee's data set and also the new De la Fuente and Domenech's data set is analysed. Results show that both suffer from measurement errors, but the latter seems to reflect the rate of growth of schooling more accurately, especially when taking long differences.