Multivariate count data are found in a variety of fields. For modeling such data, one may consider the multivariate Poisson distribution. Overdispersion is a problem when modeling the data with the multivariate Poisson distribution. Therefore, in this thesis we propose a new multivariate Poisson hidden Markov model based on the extension of independent multivariate Poisson finite mixture models, as a solution to this problem. This model, which can take into account the spatial nature of weed counts, is applied to weed species counts in an agricultural field. The distribution of counts depends on the underlying sequence of states, which are unobserved or hidden. These hidden states represent the regions where weed counts are relatively homogeneous. Analysis of these data involves the estimation of the number of hidden states, Poisson means and covariances. Parameter estimation is done using a modified EM algorithm for maximum likelihood estimation. <p>We extend the univariate Markov-dependent Poisson finite mixture model to the multivariate Poisson case (bivariate and trivariate) to model counts of two or three species. Also, we contribute to the hidden Markov model research area by developing Splus/R codes for the analysis of the multivariate Poisson hidden Markov model. Splus/R codes are written for the estimation of multivariate Poisson hidden Markov model using the EM algorithm and the forward-backward procedure and the bootstrap estimation of standard errors. The estimated parameters are used to calculate the goodness of fit measures of the models.<p>Results suggest that the multivariate Poisson hidden Markov model, with five states and an independent covariance structure, gives a reasonable fit to this dataset. Since this model deals with overdispersion and spatial information, it will help to get an insight about weed distribution for herbicide applications. This model may lead researchers to find other factors such as soil moisture, fertilizer level, etc., to determine the states, which govern the distribution of the weed counts.

Noise-like activity (NLA) refers to spontaneous subthreshold fluctuations in membrane potential. In this thesis, we examine the role that synaptic channel fluctuations play in contributing to NLA by comparing a detailed biophysical model to experimental data from whole-intact hippocampal interneurons. To represent the contribution from synaptic channel fluctuations, we switch the synapses in the model from traditional to Markovian formalisms and demonstrate statistically relevant increases the standard deviation; power-law scaling exponent; and power spectral density in the 5-100 Hz and 1-5 kHz ranges. However, while synaptic channel fluctuations have a definite effect, we found that they were significantly more subtle than the synaptic response to network activity. This indicates that synaptic channel fluctuations do indeed play a significant role in subthreshold noise, but, overall, synaptic NLA is dominated by the synaptic response to presynaptic network activity.

Noise-like activity (NLA) refers to spontaneous subthreshold fluctuations in membrane potential. In this thesis, we examine the role that synaptic channel fluctuations play in contributing to NLA by comparing a detailed biophysical model to experimental data from whole-intact hippocampal interneurons. To represent the contribution from synaptic channel fluctuations, we switch the synapses in the model from traditional to Markovian formalisms and demonstrate statistically relevant increases the standard deviation; power-law scaling exponent; and power spectral density in the 5-100 Hz and 1-5 kHz ranges. However, while synaptic channel fluctuations have a definite effect, we found that they were significantly more subtle than the synaptic response to network activity. This indicates that synaptic channel fluctuations do indeed play a significant role in subthreshold noise, but, overall, synaptic NLA is dominated by the synaptic response to presynaptic network activity.

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<p>Large-scale, in vitro vaccine screening is an expensive and slow process, while rational vaccine design is faster and cheaper. As opposed to the emperical ways to design vaccines in biology laboratories, rational vaccine design models the structure of vaccines with computational approaches. Building an effective predictive computer model requires extensive knowledge of the process or phenomenon being modelled. Given current knowledge about the steps involved in immune system responses, computer models are currently focused on one or two of the most important and best known steps; for example: presentation of antigens by major histo-compatibility complex (MHC) molecules. In this step, the MHC molecule selectively binds to some peptides derived from antigens and then presents them to the T-cell. One current focus in rational vaccine design is prediction of peptides that can be bound by MHC.<p>Theoretically, predicting which peptides bind to a particular MHC molecule involves discovering patterns in known MHC-binding peptides and then searching for peptides which conform to these patterns in some new antigenic protein sequences. According to some previous work, Hidden Markov models (HMMs), a machine learning technique, is one of the most effective approaches for this task. Unfortunately, for computer models like HMMs, the number of the parameters to be determined is larger than the number which can be estimated from available training data.<p>Thus, heuristic approaches have to be developed to determine the parameters. In this research, two heuristic approaches are proposed. The rst initializes the HMM transition and emission probability matrices by assigning biological meanings to the states. The second approach tailors the structure of a fully connected HMM (fcHMM) to increase specicity. The effectiveness of these two approaches is tested on two human leukocyte antigens(HLA) alleles, HLA-A*0201 and HLAB* 3501. The results indicate that these approaches can improve predictive accuracy. Further, the HMM implementation incorporating the above heuristics can outperform a popular prole HMM (pHMM) program, HMMER, in terms of predictive accuracy.

Multivariate count data are found in a variety of fields. For modeling such data, one may consider the multivariate Poisson distribution. Overdispersion is a problem when modeling the data with the multivariate Poisson distribution. Therefore, in this thesis we propose a new multivariate Poisson hidden Markov model based on the extension of independent multivariate Poisson finite mixture models, as a solution to this problem. This model, which can take into account the spatial nature of weed counts, is applied to weed species counts in an agricultural field. The distribution of counts depends on the underlying sequence of states, which are unobserved or hidden. These hidden states represent the regions where weed counts are relatively homogeneous. Analysis of these data involves the estimation of the number of hidden states, Poisson means and covariances. Parameter estimation is done using a modified EM algorithm for maximum likelihood estimation. <p>We extend the univariate Markov-dependent Poisson finite mixture model to the multivariate Poisson case (bivariate and trivariate) to model counts of two or three species. Also, we contribute to the hidden Markov model research area by developing Splus/R codes for the analysis of the multivariate Poisson hidden Markov model. Splus/R codes are written for the estimation of multivariate Poisson hidden Markov model using the EM algorithm and the forward-backward procedure and the bootstrap estimation of standard errors. The estimated parameters are used to calculate the goodness of fit measures of the models.<p>Results suggest that the multivariate Poisson hidden Markov model, with five states and an independent covariance structure, gives a reasonable fit to this dataset. Since this model deals with overdispersion and spatial information, it will help to get an insight about weed distribution for herbicide applications. This model may lead researchers to find other factors such as soil moisture, fertilizer level, etc., to determine the states, which govern the distribution of the weed counts.

This thesis investigates the design and implementation strategies for a Spanish speech recognition system. It utilizes the speech features of the 242 common Spanish mono-syllables as the major training and recognition methodology. A training database of twelve utterances per mono-syllable is established by applying Spanish pronunciation rules. These twelve utterances are collected through reading six rounds of the same mono-syllable with two different tones. The first pronounced pattern has high pitch of tone one, while the second one has falling pitch of tone four. Mel-frequency cepstral coefficients, linear predictive cepstral coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the AMD Sempron Processor 2800+ with 1.6GHz clock rate personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 86% can be reached for a 4217 Spanish phrase database. The average computation time for each phrase is about 1.5 seconds.

This thesis investigates the design and implementation strategies for a French speech recognition system. It utilizes the speech features of the 425 common French mono-syllables as the major training and recognition methodology. A training database is established by reading each mono-syllable 12 times in 6 rounds. Every mono-syllable is consecutively read twice with different tones. The first pronounced pattern has high pitch of tone 1,while the second one has falling pitch of tone 4. Mel-frequency cepstrum coefficients, linear predictive cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the AMD Athlon xp 2800+ with clock rate 2.2GHz personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 86% can be reached for a 3850 French phrase database. The average computation time for each phrase is about 1.5 seconds.

This thesis investigates the design and implementation strategies for a German speech recognition system. It utilizes the speech features of the 434 common German mono-syllables as the major training and recognition methodology. A training database is established by reading each mono-syllable 12 times in 6 rounds. Every mono-syllable is consecutively read twice with different tones. The first pronounced pattern has high pitch of tone 1, while the second one has falling pitch of tone 4. Mel-frequency cepstral coefficients, linear predictive cepstral coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the AMD Athlon X2-240 with 2.8 GHz clock rate personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 84% can be reached for a 3900 German phrase database. The average computation time for each phrase is within 1 second.