Thesis (M.S.)--State University of New York at Binghamton, Dept. of Electrical & Computer Engineering, 2006. / Includes bibliographical references.

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Model Predictive Control (MPC) refers to a class of algorithms that optimize the future behavior of the plant subject to operational constraints [46]. The merits of the class algorithms include its ability to handle imposed hard constraints on the system and perform on-line optimization. This thesis investigates design and implementation of continuous time model predictive control using Laguerre polynomials and extends the design ap- proaches proposed in [43] to include intermittent predictive control, as well as to include the case of the nonlinear predictive control. In the Intermittent Predictive Control, the Laguerre functions are used to describe the control trajectories between two sample points to save the com- putational time and make the implementation feasible in the situation of the fast sampling of a dynamic system. In the nonlinear predictive control, the Laguerre polynomials are used to describe the trajectories of the nonlinear control signals so that the reced- ing horizon control principle are applied in the design with respect to the nonlinear system constraints. In addition, the thesis reviews several Quadratic Programming methods and compares their performances in the implementation of the predictive control. The thesis also presents simulation results of predictive control of the autonomous underwater vehicle and the water tank.

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This PhD dissertation comprises four essays on forecasting financial markets with unsupervised predictive analytics techniques, most notably time series extrapolation methods and artificial neural networks. Key objectives of the research were reproducibility and replicability, which are fundamental principles in management science and, as such, the implementation of all of the suggested algorithms has been fully automated and completely unsupervised in R. As with any predictive analytics exercise, computational intensiveness is a significant challenge and criterion of performance and, thus, both forecasting accuracy and uncertainty as well as computational times are reported in all essays. Multiple horizons, multiple methods and benchmarks and multiple metrics are employed as dictated by good practice in empirical forecasting exercises. The essays evolve in nature as each one is based on the previous one, testing one more condition as the essays progress, outlined in sequence as follows: which method wins overall in a very extensive evaluation over five frequencies (yearly, quarterly, monthly, weekly and daily data) over 18 time series of stocks with the biggest capitalization from the FTSE 100, over the last 20 years (first essay); the impact of horizon in this exercise and how this promotes different winners for different horizons (second essay); the impact of using uncertainty in the form of maximum-minimum values per period, despite still being interested in forecasting the mean expected value over the next period; and introducing a second variable capturing all other aspects of the behavioural nature of the financial environment – the trading volume – and evaluating whether this improves forecasting performance or not. The whole endeavour required the use of the High Performance Computing Wales (HPC Wales) for a significant amount of time, incurring computational costs that ultimately paid off in terms of increased forecasting accuracy for the AI approaches; the whole exercise for one series can be repeated on a fast laptop device (i7 with 16 GB of memory). Overall (forecasting) horses for (data) courses were once again proved to perform best, and the fact that one method cannot win under all conditions was once more evidenced. The introduction of uncertainty (in terms of range for every period), as well as volume as a second variable capturing environmental aspects, was beneficial with regard to forecasting accuracy and, overall, the research provided empirical evidence that predictive analytics approaches have a future in such a forecasting context. Given this was a predictive analytics exercise, focus was placed on forecasting levels (monetary values) and not log-returns; and out-of-sample forecasting accuracy, rather than causality, was a primary objective, thus multiple regression models were not considered as benchmarks. As in any empirical predicting analytics exercise, more time series, more artificial intelligence methods, more metrics and more data can be employed so as to allow for full generalization of the results, as long as all of these can be fully automated and forecast unsupervised in a freeware environment – in this thesis that being R.

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Recently, for the new demands of nowadays multimedia system, such as video interaction, the MPEG-4 standard has been designed. In MPEG-4, because of those new demands of nowadays multimedia system the video stream can be divided into several video object planes ( VOPs ). Those VOPs can be separately encoded, stored, or transmitted. VOP is the basic interactive unit in MPEG-4 video stream, how to automatically or semi-automatically separate appropriate VOPs from an image sequence has become one of the most important issues for an MPEG-4 system, which is also the goal of this proposal. However, MPEG-4 does not provide concrete techniques for VOP extraction. Nonetheless, it is very difficult to extract VOPs, thus the preprocessing used to decompose sequences into VOPs becomes an important issue for an MPEG-4 system, which is also the goal of this thesis. In this thesis, we will develop techniques for segmenting images contained in an image sequence, which can separate two or more image segments ( or regions ) from MPEG-4 test image sequences, and those image segments can be coded as MPEG-4 VOPs. First, we utilize the feature of wavelet to improve the change detection, such that we can obtain a better result of the moving object edge by improved change detection. Second, we use an edge-based method for tracking boundary which is using the canny edge detection and the connected edge component labeling to label those edges. Third, we can combine those two information to obtain a more complete boundary by extracting moving object edges. Although we catch all the edges which is detected on the location of the true boundary, it usually occurs some gaps on which we catch. Because it sometimes will not have a clear boundary, we have to find some method to complete these gaps. Therefore, we propose a multi-level prediction scheme to complete the gaps between the disjoint edges of the boundary we caught by extending the edges on the predictive direction. Final, we use a simple connecting operation for the little gaps (distance=1 or 2). That will make the result more close and smooth. Experimental results for several test sequences show that this novel automatic video segmentation algorithm can give a more accurate object masks.

Predictive vegetation mapping was employed to predict the distribution of vegetation communities and physiognomies in the portion of the Scandinavian mountains in Sweden. This was done to address three main research questions: (1) what environmental variables are important in structuring vegetation patterns in the study area? (2) how well does a classification tree predict the composition of mountain vegetation in the study area using the chosen environmental variables for the study? and (3) are vegetation patterns better predicted at higher levels of physiognomic aggregation? Using GIS, a spatial dataset was first developed consisting of sampled points across the full geographic range of the study area. The sample contained existing vegetation community data as the dependent variable and various environmental data as the independent variables thought to control or correlate with vegetation distributions. The environmental data were either obtained from existing digital datasets or derived from Digital Elevation Models (DEMs). Utilizing classification tree methodology, three model frameworks were developed in which vegetation was increasingly aggregated into higher levels of physiognomic organization. The models were then pruned, and accuracy statistics were obtained. Results indicated that accuracy improved with increasing aggregation of the dependent variable. The three model frameworks were then applied to the Abisko portion of the study area in northwestern Sweden to produce predictive maps which were compared to the current vegetation distribution. Compositional patterns were critically analyzed in order to: (1) assess the ability of the models to correctly classify general vegetation patterns at the three levels of physiognomic classification, (2) address the extent to which three specific ecological relationships thought to control vegetation distribution in this area were manifested by the model, and (3) speculate as to possible sources of error and factors affecting accuracy of the models.