Co-channel interference limits the capacity of cellular systems, reduces the throughput of wireless local area networks, and is the major hurdle in deployment of high altitude communication platforms. It is also a problem for systems operating in unlicensed bands such as the 2.4 GHz ISM band and for narrowband systems that have been overlaid with spread spectrum systems. In this work we have developed model-based techniques for the demodulation of co-channel MSK signals. It is shown that MSK signals can be written in the linear model form, hence a minimum variance unbiased (MVU) estimator exists that satisfies the Cramer-Rao lower bound (CRLB) with equality. This framework allows us to derive the best estimators for a single-user and a two-user case. These concepts can also be extended to wideband signals and it is shown that the MVU estimator for Direct Sequence Spread Spectrum signals is in fact a decorrelator-based multiuser detector. However, this simple linear representation does not always exist for continuous phase modulations. Furthermore, these linear estimators require perfect channel state information and phase synchronization at the receiver, which is not always implemented in wireless communication systems. To overcome these shortcomings of the linear estimation techniques, we employed an autoregressive modeling approach. It is well known that the AR model can accurately represent peaks in the spectrum and therefore can be used as a general FM demodulator. It does not require knowledge of the exact signal model or phase synchronization at the receiver. Since it is a non-coherent reception technique, its performance is compared to that of the limiter discriminator. Simulation results have shown that model-based demodulators can give significant gains for certain phase and frequency offsets between the desired signal and an interferer. / Master of Science

Parametric uncertainty is one of many possible causes of divergence for the Kalman filter. Frequently, state estimation errors caused by imperfect model parameters are reduced by including the uncertain parameters as states (i.e., augmenting the state vector). For many situations, this not only improves the state estimates, but also improves the accuracy and precision of the parameters themselves. Unfortunately, not all filters benefit from this augmentation due to computational restrictions or because the parameters are poorly observable. A parameter with low observability (e.g., a set of high order gravity coefficients, a set of camera offsets, lens calibration controls, etc.) may not acquire enough measurements along a particular trajectory to improve the parameter's accuracy, which can cause detrimental effects in the performance of the augmented filter. The problem is then how to reduce the dimension of the augmented state vector while minimizing information loss. This dissertation explored possible implementations of reduced-order filters which decrease computational loads while also minimizing state estimation errors. A theoretically rigorous approach using the ?consider" methodology was taken at discrete time intervals were explored for linear systems. The continuous dynamics, discretely measured (continuous-discrete) model was also expanded for use with nonlinear systems. Additional techniques for reduced-order filtering are presented including the use of additive process noise, an alternative consider derivation, and the minimum variance reduced-order filter. Multiple simulation examples are provided to help explain critical concepts. Finally, two hardware applications are also included to show the validity of the theory for real world applications. It was shown that the minimum variance consider Kalman filter (MVCKF) is the best reduced-order filter to date both theoretically and through hardware and software applications. The consider method of estimation provides a compromise between ignoring parameter error and completely accounting for it in a probabilistic sense. Based on multiple measures of optimality, the consider filtering framework can be used to account for parameter error without directly estimating any or all of the parameters. Furthermore, by accounting for the parameter error, the consider approach provides a rigorous path to improve state estimation through the reduction of both state estimation error and with a consistent variance estimate. While using the augmented state vector to estimate both states and parameters may further improve those estimates, the consider estimation framework is an attractive alternative for complex and computationally intensive systems, and provides a well justified path for parameter order reduction.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / High data rate communication between airborne vehicles and ground stations over the bandwidth constrained Aeronautical Telemetry channel is attributed to the development of bandwidth efficient Advanced Range Telemetry (ARTM) waveforms. This communication takes place over a multipath channel consisting of two components - a line of sight and one or more ground reflected paths which result in frequency selective fading. We concentrate on the ARTM SOQPSKTG transmit waveform suite and decode information bits using the reduced complexity Viterbi algorithm. Two different methodologies are proposed to implement reduced complexity Viterbi decoders in multipath channels. The first method jointly equalizes the channel and decodes the information bits using the reduced complexity Viterbi algorithm while the second method utilizes the minimum mean square error equalizer prior to applying the Viterbi decoder. An extensive numerical study is performed in comparing the performance of the above methodologies. We also demonstrate the performance gain offered by our reduced complexity Viterbi decoders over the existing linear receiver. In the numerical study, both perfect and estimated channel state information are considered.

Este trabalho tem como analisa principal a estratégia de hedge para o exportador de café nas principais regiões brasileiras, utilizando o modelo tradicional de hedge de variância mínima para a receita. São propostas quatro estratégias: sem hedge, hedge de preço do café, hedge de câmbio e hedge simultâneo de preço do café e câmbio. Chega-se à conclusão que a estratégia de hedge simultâneo de preços e câmbio é mais efetiva em diminuir a variância da receita do produtor em relação a outras estratégias analisadas. A redução do risco de taxa de câmbio, em conjunto com o risco de preços é importante para a gestão estratégica dos exportadores de commodities. / This thesis aims to analyze the hedging strategies for coffee export in the main Brazilian regions, using the traditional model of minimum variance hedge. In this way, four hedging strategies were proposed: no hedge, hedge coffee prices, exchange hedge and hedge simultaneous coffee prices and exchange rates. The result show that the hedging strategy of simultaneous price and exchange is more effective in reducing the variance of revenue producer comparing with other strategies analyzed. Reducing the risk of exchange rate, together with the price risk is important for the strategic management of commodity exporters.

In this study, the use of modified genetic algorithms (MGA) in the parameterization of the Transmission Mechanisms is facilitated. The new algorithm is proposed from the genetic algorithm with some additional strategies, and yields a faster convergence and a more accurate search. Firstly, this near-optimum search technique, MGA-based ID method, is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to compare with the LMS (Least mean-squares) method and GA method. Then, this proposed algorithm is applied to the identification of the Transmission Mechanisms of DC motor. The parameters of the friction force and DC motor are estimated in a single identification experiment. It is also shown that this technique is capable of identifying the whole transmission system. Finally, the Minimum Variance Controller (MVC) is taken to track the desired speed trajectory and then a comparison to the conventional digital PID controller is shown. Experiment results are included to demonstrate the excellent performance of the MVC.

In this thesis we search for optimal hedging strategy in stock index futures markets by providing a comprehensive comparison of variety types of models in the related literature. We concentrate on the strategy that minimizes portfolio risk, i.e., minimum variance hedge ratio (MVHR) estimated from a range of time series models with different assumptions of market volatility. There are linear regression models assuming time-invariant volatility; GARCH-type models capturing time-varying volatility, Markov regime switching (MRS) regression models assuming state-varying volatility, and MRS-GARCH models capturing both time-varying and state-varying volatility. We use both Maximum Likelihood Estimation (MLE) and Bayesian Gibbs-Sampling approach to estimate the models with four commonly used index futures contracts: S&P 500, FTSE 100, Nikkei 225 and Hang Seng index futures. We apply risk reduction and utility maximization criterions to evaluate hedging performance of MVHRs estimated from these models. The in-sample results show that the optimal hedging strategy for the S&P 500 and the Hang Seng index futures contracts is the MVHR estimated using the MRS-OLS model, while the optimal hedging strategy for the Nikkei 225 and the FTSE 100 futures contracts is the MVHR estimated using the Asymmetric-Diagonal-BEKK-GARCH and the Asymmetric-DCC-GARCH model, respectively. As in the out-of sample investigation, the time-varying models such as the BEKK-GARCH models especially the Scalar-BEKK model outperform those state-varying MRS models in majority of futures contracts in both one-step- and multiple-step-ahead forecast cases. Overall the evidence suggests that there is no single model that can consistently produce the best strategy across different index futures contracts. Moreover, using more sophisticated models such as MRS-GARCH models provide some benefits compared with their corresponding single-state GARCH models in the in-sample case but not in the out-of-sample case. While comparing with other types of models MRS-GARCH models do not necessarily improve hedging efficiency. Furthermore, there is evidence that using Bayesian Gibbs-sampling approach to estimate the MRS models provides investors more efficient hedging strategy compared with the MLE method.

Este trabalho tem como analisa principal a estratégia de hedge para o exportador de café nas principais regiões brasileiras, utilizando o modelo tradicional de hedge de variância mínima para a receita. São propostas quatro estratégias: sem hedge, hedge de preço do café, hedge de câmbio e hedge simultâneo de preço do café e câmbio. Chega-se à conclusão que a estratégia de hedge simultâneo de preços e câmbio é mais efetiva em diminuir a variância da receita do produtor em relação a outras estratégias analisadas. A redução do risco de taxa de câmbio, em conjunto com o risco de preços é importante para a gestão estratégica dos exportadores de commodities. / This thesis aims to analyze the hedging strategies for coffee export in the main Brazilian regions, using the traditional model of minimum variance hedge. In this way, four hedging strategies were proposed: no hedge, hedge coffee prices, exchange hedge and hedge simultaneous coffee prices and exchange rates. The result show that the hedging strategy of simultaneous price and exchange is more effective in reducing the variance of revenue producer comparing with other strategies analyzed. Reducing the risk of exchange rate, together with the price risk is important for the strategic management of commodity exporters.

Volatile prices of commodities relate to financial risk faced by individuals or economic subjects exposed to them. One way to minimize the impact of change in market price is to use its hedging by futures contracts. The optimal hedge ratio estimation (ratio between units of spot and futures contracts) is the focus of this study. Its objective is to compare hedge ratios based on minimum variance methodology using three methods - OLS, ARCH and GARCH, by measuring their hedging effectiveness using variance and value at risk reduction. The results differ across commodities, however several conclusions can be made. The ARCH-based hedge ratios do not perform significantly worse than the GARCH-based hedge ratios. The same estimation method can be used for assets having similar returns development and a well performing hedge can be expected. Results of hedge ratios of strongly correlated assets estimated by different methods tend to have very similar values to one another and to the related correlation coefficient. More generally, the best performing hedge ratios are those having very similar values to correlation between spot and futures 1-day returns.

This thesis aims to construct an optimal portfolio and model as well as forecast its volatility. The performance of the optimal portfolio is then compared to two benchmarks, namely, an equally weighted portfolio and the market index SP 500. The volatility is estimated by employing two GARCH-type models known as standard GARCH, and GJR-GARCH. The GJR-GARCH outperformed its counterpart in terms of Log-likelihood, AIC, and BIC. The forecast performance is compared based on two statistical errors, root mean squared error, and mean absolute error. The optimal portfolio outperformed its counterparts in both statistical errors. Moreover, standard GARCH gave lower statistics than GJR-GARCH. These empirical results are of important significance to portfolio management and risk management processes.

License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition. / Master of Science