Real property law

The aim of this book is to provide the student and/or practitioner with a straightforward outline of some of the primary elements underlying the recognition and regulation of real property.

Comparison of a self-processed EM3000 multibeam echosounder dataset with a QTC view habitat mapping and a sidescan sonar imagery, Tamaki Strait, New Zealand

A methodology for automatically processing the data files from an EM3000 multibeam echosounder (Kongsberg Maritime, 300 kHz) is presented. Written in MatLab, it includes data extraction, bathymetry processing, computation of seafloor local slope, and a simple correction of the backscatter along-track banding effect. The success of the latter is dependent on operational restrictions, which are also detailed. This processing is applied to a dataset acquired in 2007 in the Tamaki Strait, New Zealand. The resulting maps are compared with a habitat classification obtained with the acoustic ground-discrimination software QTC View linked to a 200-kHz single-beam echosounder and to the imagery from a 100-kHz sidescan sonar survey, both performed in 2002. The multibeam backscatter map was found to be very similar to the sidescan imagery, quite correlated to the QTC View map on one site but mainly uncorrelated on another site. Hypotheses to explain these results are formulated and discussed. The maps and the comparison to prior surveys are used to draw conclusions on the quality of the code for further research on multibeam benthic habitat mapping.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.