A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data.

The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects.

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result.

The Power of Big Data and Data Analytics for AMI Data: A Case Study.

In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI).

Optimized Gaussian model for non-uniform heating compensation in pulsed thermography.

This paper presents a new, to the best of our knowledge, methodology for the thermal compensation of background heating in thermograms of composites. The technique analyzes the spatial data of the thermal images obtained from a pulsed thermography inspection and automatically calculates the optimal parameters of a predefined objective function. These parameters are obtained by curve fitting using the least squares method and model the temperature distribution of the image background using the proposed objective function.

Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks.

The success of many projects linked to the management and planning of water resources depends mainly on the quality of the climatic and hydrological data that is provided. Nevertheless, the missing data are frequently found in hydroclimatic variables due to measuring instrument failures, observation recording errors, meteorological extremes, and the challenges associated with accessing measurement areas. Hence, it is necessary to apply an appropriate fill of missing data before any analysis.