In this study, video super-resolution using particle swarm optimization (PSO) is proposed to super-resolve low-resolution (LR) frames. The proposed super-resolution method consists of three main modules, i.e., supersampling, spatio-temporal classification, and frame fusion using PSO. In the proposed method, the LR frames are super-resolved to high-resolution frames through the fusion of four fu...

In instantaneous encephalogram or magnetoencephalogram (EEG/MEG) source analysis, ordinary least squares estimation (OLS) requires that the spatial noise is homoscedastic and uncorrelated over sensors. In spatio-temporal analysis OLS also requires that the noise is homoscedastic and uncorrelated in time (over samples). Generally, these assumptions are violated and, as a consequence, OLS can giv...

There are complex spatio-temporal relationships among cadastral entities. Cadastral spatio-temporal data model should not only describe the data structure of cadastral objects, but also express cadastral spatio-temporal relationships between cadastral objects. In the past, many experts and scholars have proposed a variety of cadastral spatio-temporal data models, but few of them concentrated on...

Many applications capture, or make use of, spatial data that changes over time. This requirement for effective and efficient spatio-temporal data management has given rise to a range of research activities relating to spatio-temporal data management. Such work has sought to understand, for example, the requirements of different categories of application, and the modelling facilities that are mo...

Background Among the proposals for joint disease mapping, the shared component model has become more popular. Another recent advance to strengthen inference of disease data has been the extension of purely spatial models to include time and space-time interaction. Such analyses have additional benefits over purely spatial models. However, only a few proposed spatio-temporal models could address...

Large-scale spatio-temporal data is crucial for testing the spatio-temporal pattern mining algorithms. In this paper we present a data set generator that can generate moving spatio-temporal instances, with evolving regions. The characteristics of data generated can be configured with parameters given to program and those parameters allow users to test the performance of spatio-temporal mining a...

In this paper we define a spatio-temporal model with location dependent parameters to describe temporal variation and spatial nonstationarity. We consider the prediction of observations at unknown locations using known neighbouring observations. Further we propose a local least squares based method to estimate the parameters at unobserved locations. The sampling properties of these estimators a...