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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.

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In this thesis, a two-word, three-word and four-word Mandarin phrases speech recognition system is studied and implemented. This system utilizes hidden Markov model, lexicon search strategy and tone recognition to select the initial phrase candidates and make the final decision. Experimental results indicate that using about one third of the total phrase population, 80%, 92% and 97% correct rates can be achieved for the 70,000 two-word, 24,000 three-word and 22,000 four-word phrases recognition problems respectively. Any spoken phrase can be found within 1 second, using a PC with Intel Celeron 2.4 GHz CPU and Red Hat Linux 9.0 operating system.

In this thesis, hidden Markov model, maximum likelihood ratio and lexicon search strategy are used to establish a Chinese resume inputting system. The resume contains five items: name introduction, gender, birth date, birth place and education. This system is developed using a PC with an Intel Pentium 1.6 GHz CPU and Red Hat Linux 9.0 operating system. For the speaker-dependent case, a resume can be completed within 45 seconds on the average.

The objective of this thesis is to design and implement a speech recognition system for one hundred thousand Chinese names. Mel frequency cepstrum coefficient, hidden Markov model and lexicon search strategy are utilized to choose the name candidates. Furthermore, a mandarin intonation technique is also incorporated into this system to increase the final speech recognition accuracy. The experimental results indicate that for the speaker dependent case, an 85% correct rate can be achieved by use of the proposed intonation classification scheme and the balanced monosyllable training database. The above correct rate has an increase of 8% over the previous method without using these two techniques. Under Redhat Linux 9.0 environment, a mandarin name can be recognized within 2 seconds by the use of a computer with Intel Celeron 2.4 GHz CPU.

Cloud computing provides business new working paradigm with the benefit of cost reduce and resource sharing. Tasks from different users may be performed on the same machine. Therefore, one primary security concern is whether user data is secure in cloud. On the other hand, hacker may facilitate cloud computing to launch larger range of attack, such as a request of port scan in cloud with virtual machines executing such malicious action. In addition, hacker may perform a sequence of attacks in order to compromise his target system in cloud, for example, evading an easy-to-exploit machine in a cloud and then using the previous compromised to attack the target. Such attack plan may be stealthy or inside the computing environment, so intrusion detection system or firewall has difficulty to identify it. The proposed detection system analyzes logs from cloud to extract the intensions of the actions recorded in logs. Stealthy reconnaissance actions are often neglected by administrator for the insignificant number of violations. Hidden Markov model is adopted to model the sequence of attack performed by hacker and such stealthy events in a long time frame will become significant in the state-aware model. The preliminary results show that the proposed system can identify such attack plans in the real network.