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The objective of this thesis is to design and implement a speech inputting system for addresses in Taiwan,Mainland china and HongKong,The completed system has the capability to identify full census and posting addresses in Taiwan and full posting addresses in Peking¡BShanghai¡BTien-Jin and Chungchin of China¡CFor HongKong,a partial address system,including region/street name or school,hotal and other public location names,is implemented¡C In this thesis,Mel frequency cepstrum coefficient,Hidden Mavkov model and lexicon search strategy are applied to choose the initial address candidates¡FMandarin intonation classification technique is then used to increase the final correct rate,under speaker dependent case,a 90%correct rate can be reached by using a Intel Celeron 2.4GHz CPU and RedHat Linux 9.0 operating system¡CThe total address-inputting task can be completed within 3 seconds¡C

This thesis develops new hidden Markov models and applies them to financial market and macroeconomic time series. Chapter 1 proposes a probabilistic model of the return distribution with rich and heterogeneous intra-regime dynamics. It focuses on the characteristics and dynamics of bear market rallies and bull market corrections, including, for example, the probability of transition from a bear market rally into a bull market versus back to the primary bear state. A Bayesian estimation approach accounts for parameter and regime uncertainty and provides probability statements regarding future regimes and returns. A Value-at-Risk example illustrates the economic value of our approach. Chapter 2 develops a new efficient approach to model and forecast time series data with an unknown number of change-points. The key is assuming a conjugate prior for the time-varying parameters which characterize each regime and treating the regime duration as a state variable. Conditional on this prior and the time-invariant parameters, the predictive density and the posterior of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. In addition to the time-invariant structural change probability, one extension assumes the regime duration has a Poisson distribution. A new Markov Chain Monte Carlo sampler draws the parameters from the posterior distribution efficiently. The model is applied to Canadian inflation time series. Chapter 3 proposes an infinite dimension Markov switching model to accommodate regime switching and structural break dynamics or a combination of both in a Bayesian framework. Two parallel hierarchical structures, one governing the transition probabilities and another governing the parameters of the conditional data density, keep the model parsimonious and improve forecasts. This nonparametric approach allows for regime persistence and estimates the number of states automatically. A global identification algorithm for structural changes versus regime switching is presented. Applications to U.S. real interest rates and inflation compare the new model to existing parametric alternatives. Besides identifying episodes of regime switching and structural breaks, the hierarchical distribution governing the parameters of the conditional data density provides significant gains to forecasting precision.

This thesis develops new hidden Markov models and applies them to financial market and macroeconomic time series. Chapter 1 proposes a probabilistic model of the return distribution with rich and heterogeneous intra-regime dynamics. It focuses on the characteristics and dynamics of bear market rallies and bull market corrections, including, for example, the probability of transition from a bear market rally into a bull market versus back to the primary bear state. A Bayesian estimation approach accounts for parameter and regime uncertainty and provides probability statements regarding future regimes and returns. A Value-at-Risk example illustrates the economic value of our approach. Chapter 2 develops a new efficient approach to model and forecast time series data with an unknown number of change-points. The key is assuming a conjugate prior for the time-varying parameters which characterize each regime and treating the regime duration as a state variable. Conditional on this prior and the time-invariant parameters, the predictive density and the posterior of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. In addition to the time-invariant structural change probability, one extension assumes the regime duration has a Poisson distribution. A new Markov Chain Monte Carlo sampler draws the parameters from the posterior distribution efficiently. The model is applied to Canadian inflation time series. Chapter 3 proposes an infinite dimension Markov switching model to accommodate regime switching and structural break dynamics or a combination of both in a Bayesian framework. Two parallel hierarchical structures, one governing the transition probabilities and another governing the parameters of the conditional data density, keep the model parsimonious and improve forecasts. This nonparametric approach allows for regime persistence and estimates the number of states automatically. A global identification algorithm for structural changes versus regime switching is presented. Applications to U.S. real interest rates and inflation compare the new model to existing parametric alternatives. Besides identifying episodes of regime switching and structural breaks, the hierarchical distribution governing the parameters of the conditional data density provides significant gains to forecasting precision.

The objective of this thesis is to design and implement a speech recognition system for Mandarin names and phrases. This system utilizes Mel frequency cepstral coefficients, hidden Markov model and lexicon search strategy to select the phrase candidates. The experimental results indicate that for the speaker dependent case, a strategy incorporating overlapping frames and hybrid training can result in an improvement of 4%, 5%, 4% and 2% on the recognition rate for the Mandarin name, two-word, three-word and four-word phrase recognition systems respectively. Under Redhat Linux 9.0 operating system, any Mandarin name or phrase can be recognized within 2 seconds by a computer with Intel Celeron 2.4 GHz CPU.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / The introduction of network elements into telemetry systems brings a level of complexity that makes performance analysis difficult, if not impossible. Packet simulation is a well understood tool that enables performance prediction for network designs or for operational forecasting. Packet simulators must however be customized to incorporate aeronautical radio channels and other effects unique to the telemetry application. This paper presents a method for developing a Markov Model simulation for aeronautical channels for use in packet network simulators such as OPNET modeler. It shows how the Hidden Markov Model (HMM) and the Markov Model (MM) can be used together to first extract the channel behavior of an OFDM transmission for an aeronautical channel, and then effortlessly replicate the statistical behavior during simulations in OPENT Modeler. Results demonstrate how a simple Markov Model can capture the behavior of very complex combinations of channel and modulation conditions.

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Diabetes mellitus is a chronic disease in which the body does not produce sufficient insulin or in which the body has high insulin resistance thus making the regulation of blood glucose metabolism difficult. Currently, diabetes is still incurable. All patients need to well manage their blood glucose levels to reduce the risk of complications. This dissertation is comprised of two major studies. In diabetes type I, the blood glucose can only be managed by multiple daily injection of insulin. However, most patients tend to have difficulty in deciding the right amount of insulin dose. The first study is the development of a mathematical model of blood glucose levels, which leads to the development of a decision support system for diabetes type I using the Markov theory. In some type II and gestational diabetes, blood glucose can be managed by choosing diet properly and by exercising regularly. However, people tend to overestimate their activity levels. The second study describes the design and development of a wearable device based on the triaxial accelerometer that estimates the energy levels of normal daily physical activity with comparable accuracy to the gas analysis. This device development leads to two clinical studies. The first clinical study investigates whether the energy monitor could help people with diabetes in promoting and managing their daily activity and help to improve the glycosylated haemoglobin and body mass index. The second clinical study investigates whether the energy monitor could help pregnant women with gestational diabetes in managing their daily activity, blood glucose levels and body weight gain. This thesis also develops a telemedicine system to automate the data collection during the clinical trial period. The system would securely transmit all diabetes and energy data from the participants' home to a remote server. A key finding of this study was that a higher activity score results in smaller fluctuations in blood glucose levels between measurements in both diabetes and gestational diabetes subjects. This suggests that higher activity levels would make the management of diabetes more effective by reducing the fluctuation in blood glucose levels.

Diabetes mellitus is a chronic disease in which the body does not produce sufficient insulin or in which the body has high insulin resistance thus making the regulation of blood glucose metabolism difficult. Currently, diabetes is still incurable. All patients need to well manage their blood glucose levels to reduce the risk of complications. This dissertation is comprised of two major studies. In diabetes type I, the blood glucose can only be managed by multiple daily injection of insulin. However, most patients tend to have difficulty in deciding the right amount of insulin dose. The first study is the development of a mathematical model of blood glucose levels, which leads to the development of a decision support system for diabetes type I using the Markov theory. In some type II and gestational diabetes, blood glucose can be managed by choosing diet properly and by exercising regularly. However, people tend to overestimate their activity levels. The second study describes the design and development of a wearable device based on the triaxial accelerometer that estimates the energy levels of normal daily physical activity with comparable accuracy to the gas analysis. This device development leads to two clinical studies. The first clinical study investigates whether the energy monitor could help people with diabetes in promoting and managing their daily activity and help to improve the glycosylated haemoglobin and body mass index. The second clinical study investigates whether the energy monitor could help pregnant women with gestational diabetes in managing their daily activity, blood glucose levels and body weight gain. This thesis also develops a telemedicine system to automate the data collection during the clinical trial period. The system would securely transmit all diabetes and energy data from the participants' home to a remote server. A key finding of this study was that a higher activity score results in smaller fluctuations in blood glucose levels between measurements in both diabetes and gestational diabetes subjects. This suggests that higher activity levels would make the management of diabetes more effective by reducing the fluctuation in blood glucose levels.

No description available.