A new approach to the problem of modelling and predicting respiration motion has been implemented. This is a dual-component model, which describes the respiration motion as a non-periodic time series superimposed onto a periodic waveform. A periodic autoregressive moving average algorithm has been used to define a mathematical model of the periodic and non-periodic components of the respiration...

Women's basal body temperature (BBT) shows a periodic pattern that associates with menstrual cycle. Although this fact suggests a possibility that daily BBT time series can be useful for estimating the underlying phase state as well as for predicting the length of current menstrual cycle, little attention has been paid to model BBT time series. In this study, we propose a state-space model that...

Building a reliable forecast of solar activity is long-standing problem that requires to accurately describe past and current global dynamics. However, synoptic observations magnetic fields subsurface flows became available relatively recently. In this paper, we present an investigation effects short observational data series on accuracy cycle prediction. This analysis performed using the annua...

Sequencing batch reactor (SBR) technology has found many applications in industrial wastewater treatment in recent years. The aim of this study was to determine the optimal time for a cycle of the sequencing batch reactor (SBR) and evaluate the performance of a SBR for petrochemical wastewater treatment in that cycle time. The reactor was operated with a suspended biomass configuration under ae...

The online observation of a construction site and processes bears significant advantage to all business sector. BIM is the combination of a 3D model of the project and a project-planning program which improves the project planning model by up to 6D (Adding Time, Cost and Material Information dimensions to the model). RFID technology is an appropriate information synchronization tool between the...

AbstrAct The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that ...