University of Extremadura

About: University of Extremadura is a education organization based out in Badajoz, Spain. It is known for research contribution in the topics: Population & Hyperspectral imaging. The organization has 7856 authors who have published 18299 publications receiving 396126 citations. The organization is also known as: Universidad de Extremadura.

From the Internet of Things to the Internet of People

Abstract: There's growing interest in developing applications for the Internet of Things. Such applications' main objective is to integrate technology into people's everyday lives, to be of service to them en masse. The form in which this integration is implemented, however, still leaves much room for improvement. Usually, the user must set parameters within the application. When the person's context changes, they have to manually reconfigure the parameters. What was meant to be a commodity in an unforeseen situation then becomes extra noise. This article describes a reference architecture that improves how people are integrated with the IoT, with smartphones doing the connecting. The resulting integration opens the way to new IoT scenarios supporting evolution towards the Internet of People.

The Maunder minimum (1645–1715) was indeed a grand minimum: A reassessment of multiple datasets

Abstract: Aims. Although the time of the Maunder minimum (1645–1715) is widely known as a period of extremely low solar activity, it is still being debated whether solar activity during that period might have been moderate or even higher than the current solar cycle #24. We have revisited all existing evidence and datasets, both direct and indirect, to assess the level of solar activity during the Maunder minimum. Methods. We discuss the East Asian naked-eye sunspot observations, the telescopic solar observations, the fraction of sunspot active days, the latitudinal extent of sunspot positions, auroral sightings at high latitudes, cosmogenic radionuclide data as well as solar eclipse observations for that period. We also consider peculiar features of the Sun (very strong hemispheric asymmetry of the sunspot location, unusual differential rotation and the lack of the K-corona) that imply a special mode of solar activity during the Maunder minimum. Results. The level of solar activity during the Maunder minimum is reassessed on the basis of all available datasets. Conclusions. We conclude that solar activity was indeed at an exceptionally low level during the Maunder minimum. Although the exact level is still unclear, it was definitely lower than during the Dalton minimum of around 1800 and significantly below that of the current solar cycle #24. Claims of a moderate-to-high level of solar activity during the Maunder minimum are rejected with a high confidence level.

The classroom practice of preservice teachers and their conceptions of teaching and learning science

Abstract: The present article describes research carried out with four student teachers of primary and secondary science education The preservice teachers' conceptions of the learning and teaching of science were analyzed and compared with their classroom practice when teaching science lessons The data gathering procedures included a questionnaire and interviews, both analyzed by means of cognitive maps, and classroom observations during the participants' practice teaching The results did not allow a general correspondence to be established between preservice teachers' conceptions about teaching and learning science and their classroom behavior The implications of the research for teacher education are discussed © 1998 John Wiley & Sons, Inc Sci Ed82:197–214, 1998

Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery

Abstract: Anomaly detection is an active topic in hyperspectral imaging, with many practical applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses the covariance matrix and mean vector to represent background signals, assuming that the background information adjusts to a multivariate normal distribution. However, in general, real images present very complex backgrounds. As a result, in many situations, the background information cannot be properly modeled. An important reason is that that background samples often contain also anomalous pixels and noise, which lead to a high false alarm rate. Therefore, the characterization of the background is essential for successful anomaly detection. In this paper, we develop two novel approaches: weighted-RXD (W-RXD) and linear filter-based RXD (LF-RXD) aimed at improving background in RXD-based anomaly detection. By reducing the weight of the anomalous pixels or noise signals and increasing the weight of the background samples, W-RXD can provide better estimations of the background information. In turn, LF-RXD uses the probability of each pixel as background to filter wrong anomalous or noisy instances. Our experimental results, intended to analyze the performance of the newly developed anomaly detectors, indicate that the proposed approaches achieve good performance when compared with other classic approaches for anomaly detection in the literature.