A Mandarin phrase recognition system based on MFCC, LPC scaled excitation, vowel model, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. HMM, which is broadly used in speech recognition at present, is adopted in the main structure of recognition. In order to speed up the recognition time, we take advantage of stability of vowels in Mandarin and incorporate with vowel class recognition in our system. For the speaker-dependent case, a single Mandarin phrase recognition can be accomplished within 1 seconds on average in the laboratory environment.

In this dissertation work, a methodology is proposed to enable a robot to identify an object to be grasped and its intended grasp configuration while a human is teleoperating a robot towards the desired object. Based on the detected object and grasp configuration, the human is assisted in the teleoperation task. The environment is unstructured and consists of a number of objects, each with various possible grasp configurations. The identification of the object and the grasp configuration is carried out in real time, by recognizing the intention of the human motion. Simultaneously, the human user is assisted to preshape over the desired grasp configuration. This is done by scaling the components of the remote arm end-effector motion that lead to the desired grasp configuration and simultaneously attenuating the components that are in perpendicular directions. The complete process occurs while manipulating the master device and without having to interact with another interface. Intention recognition from motion is carried out by using Hidden Markov Model (HMM) theory. First, the objects are classified based on their shapes. Then, the grasp configurations are preselected for each object class. The selection of grasp configurations is based on the human knowledge of robust grasps for the various shapes. Next, an HMM for each object class is trained by having a skilled teleoperator perform repeated preshape trials over each grasp configuration of the object class in consideration. The grasp configurations are modeled as the states of each HMM whereas the projections of translation and orientation vectors, over each reference vector, are modeled as observations. The reference vectors are the ideal translation and rotation trajectories that lead the remote arm end-effector towards a grasp configuration. During an actual grasping task performed by a novice or a skilled user, the trained model is used to detect their intention. The output probability of the HMM associated with each object in the environment is computed as the user is teleoperating towards the desired object. The object that is associated with the HMM which has the highest output probability, is taken as the desired object. The most likely Viterbi state sequence of the selected HMM gives the desired grasp configuration. Since an HMM is associated with every object, objects can be shuffled around, added or removed from the environment without the need to retrain the models. In other words, the HMM for each object class needs to be trained only once by a skilled teleoperator. The intention recognition algorithm was validated by having novice users, as well as the skilled teleoperator, grasp objects with different grasp configurations from a dishwasher rack. Each object had various possible grasp configurations. The proposed algorithm was able to successfully detect the operator's intention and identify the object and the grasp configuration of interest. This methodology of grasping was also compared with unassisted mode and maximum-projection mode. In the unassisted mode, the operator teleoperated the arm without any assistance or intention recognition. In the maximum-projection mode, the maximum projection of the motion vectors was used to determine the intended object and the grasp configuration of interest. Six healthy and one wheelchair-bound individuals, each executed twelve pick-and-place trials in intention-based assisted mode and unassisted mode. In these trials, they picked up utensils from the dishwasher and laid them on a table located next to it. The relative positions and orientations of the utensils were changed at the end of every third trial. It was observed that the subjects were able to pick-and-place the objects 51% faster and with less number of movements, using the proposed method compared to the unassisted method. They found it much easier to execute the task using the proposed method and experienced less mental and overall workloads. Two able-bodied subjects also executed three preshape trials over three objects in intention-based assisted and maximum projection mode. For one of the subjects, the objects were shuffled at the end of the six trials and she was asked to carry out three more preshape trials in the two modes. This time, however, the subject was made to change their intention when she was about to preshape to the grasp configurations. It was observed that intention recognition was consistently accurate through the trajectory in the intention-based assisted method except at a few points. However, in the maximum-projection method the intention recognition was consistently inaccurate and fluctuated. This often caused to subject to be assisted in the wring directions and led to extreme frustration. The intention-based assisted method was faster and had less hand movements. The accuracy of the intention based method did not change when the objects were shuffled. It was also shown that the model for intention recognition can be trained by a skilled teleoperator and be used by a novice user to efficiently execute a grasping task in teleoperation.

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In service-oriented environments, services are put together in the form of a workflow with the aim of distributed problem solving. Capturing the execution details of the services' transformations is a significant advantage of using workflows. These execution details, referred to as provenance information, are usually traced automatically and stored in provenance stores. Provenance data contains the data recorded by a workflow engine during a workflow execution. It identifies what data is passed between services, which services are involved, and how results are eventually generated for particular sets of input values. Provenance information is of great importance and has found its way through areas in computer science such as: Bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is very limited as provenance systems started being developed for specific applications. Thus, applying learning and knowledge discovery methods to provenance data can provide rich and useful information on workflows and services. Therefore, in this work, the challenges with workflows and services are studied to discover the possibilities and benefits of providing solutions by using provenance data. A multifunctional architecture is presented which addresses the workflow and service issues by exploiting provenance data. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction. The specific contribution of the proposed architecture is its novelty in providing a basis for taking advantage of the previous execution details of services and workflows along with artificial intelligence and knowledge management techniques to resolve the major challenges regarding workflows. The presented architecture is application-independent and could be deployed in any area. The requirements for such an architecture along with its building components are discussed. Furthermore, the responsibility of the components, related works and the implementation details of the architecture along with each component are presented.

Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new frame-work based on hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified Expectation-Maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor-ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. The second part of the thesis is concerned with prediction of a deterministic response function y at some untried sites given values of y at a chosen set of design sites. The intended application is to computer experiments in which y is the output from a computer simulation and each design site represents a particular configuration of the input variables. A Bayesian version of the cubic spline method commonly used in numerical analysis is proposed, in which the random function that represents prior uncertainty about y is taken to be a specific stationary Gaussian process. An MCMC procedure is given for updating the prior given the observed y values. Simulation examples and a real data application are given to compare the performance of the Bayesian cubic spline with that of two existing methods.

Modern speech communication technologies expose users to perceptual quality degradations that were not experienced earlier with conventional telephone systems. Since perceived speech quality is a major contributor to the end user's perception of quality of service, speech quality estimation has become an important research field. In this dissertation, perceptual quality estimators are proposed for several emerging speech communication applications, in particular for i) wireless communications with noise suppression capabilities, ii) wireless-VoIP communications, iii) far-field hands-free speech communications, and iv) text-to-speech systems. First, a general-purpose speech quality estimator is proposed based on statistical models of normative speech behaviour and on innovative techniques to detect multiple signal distortions. The estimators do not depend on a clean reference signal hence are termed ``blind." Quality meters are then distributed along the network chain to allow for both quality degradations and quality enhancements to be handled. In order to improve estimation performance for wireless communications, statistical models of noise-suppressed speech are also incorporated. Next, a hybrid signal-and-link-parametric quality estimation paradigm is proposed for emerging wireless-VoIP communications. The algorithm uses VoIP connection parameters to estimate a base quality representative of the packet switching network. Signal-based distortions are then detected and quantified in order to adjust the base quality accordingly. The proposed hybrid methodology is shown to overcome the limitations of existing pure signal-based and pure link parametric algorithms. Temporal dynamics information is then investigated for quality diagnosis for hands-free speech communications. A spectro-temporal signal representation, where speech and reverberation tail components are shown to be separable, is used for blind characterization of room acoustics. In particular, estimators of reverberation time, direct-to-reverberation energy ratio, and reverberant speech quality are developed. Lastly, perceptual quality estimation for text-to-speech systems is addressed. Text- and speaker-independent hidden Markov models, trained on naturally produced speech, are used to capture normative spectral-temporal information. Deviations from the models, computed by means of a log-likelihood measure, are shown to be reliable indicators of multiple quality attributes including naturalness, fluency, and intelligibility. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-12-22 14:54:49.28

microRNAs (miRNAs) are small (20-23 nt), non-coding single stranded RNA molecules that play an important role in post-transcriptional regulation of protein-coding genes. miRNAs have been found in all animal lineages, and have been implicated as critical regulators during development in multiple species. The echinoderms, Strongylocentrotus purpuratus (sea urchin) and Patiria miniata (sea star) are excellent model organisms for studying development due to their well-characterized transcriptional gene networks, ease of working with their embryos in the laboratory and phylogenetic position as invertebrate deuterostomes. Literature on miRNAs in echinoderm embryogenesis is limited. It has been shown that RNAi genes are developmentally expressed and regulated in sea urchin embryos, but no study in the sea urchin has examined the expression of miRNAs. The goal of my work has been to study miRNA regulation in echinoderm developmental gene networks. I have identified developmentally regulated miRNAs in sea urchin and sea star embryos, using a combination of computational and wet lab experimental techniques. I developed a probabilistic model (named HHMMiR) based on hierarchical hidden Markov models (HHMMs) to classify genomic hairpins into miRNA precursors and random stem-loop structures. I then extended this model to make an efficient decoder by introduction of explicit state duration densities. We used the Illumina Genome Analyzer to sequence small RNA libraries in mixed stage population of embryos from one to three days after fertilization of S. purpuratus and P. miniata. We developed a computational pipeline for analysis of these miRNAseq data to reveal the miRNA populations in both species, and study their differential expression. We also used northern blots and whole mount in situ hybridization experimental techniques to study the temporal and spatial expression patterns of some of these miRNAs in sea urchin embryos. By knocking down the major components of the miRNA biogenesis pathway, we studied the global effects of miRNAs on embryo morphology and differentiation genes. The biogenesis genes selected for this purpose are the RNAse III enzyme, Dicer and Argonaute. Dicer is necessary for the processing of mature miRNAs from hairpin structures while Ago is a necessary part of the RISC (RNA interference silencing complex) assembly, which is required for the miRNA to hybridize to its target mRNA site. Knocking down these genes hinders normal development of the sea urchin embryo and leads to loss of the larval skeleton, a novel phenotype not seen in sea stars, as well as abnormal gastrulation. Comparison of differentiation gene marker expression between control and Ago knocked down sea urchin embryos shows interesting patterns of expansion and suppression of adjoining some embryonic territories, while ingression of larval skeletogenesis progenitors does not occur.

Understanding the behavior of pedestrians in road transportation is critical to maintain a safe en- vironment. Accidents on road transportation are one of the most common causes of death today. As autonomous vehicles start to become a standard in our society, safety on road transportation becomes increasingly important. Road transportation is a complex system with a lot of dierent factors. Identifying risky behaviors and preventing accidents from occurring requires better under- standing of the behaviors of the dierent persons involved. In this thesis the activities and behavior of a pedestrian is analyzed. Using sensor data from phones, eight dierent events of a pedestrian are classied using machine learning algorithms. Features extracted from phone sensors that can be used to model dierent pedestrian activities are identied. Current state of the art literature is researched to nd relevant machine learning algorithms for a classication model. Two models are implemented using two dierent machine learning algorithms: Articial Neural Network and Hid- den Markov Model. Two dierent experiments are conducted where phone sensor data is collected and classied using the models, achieving a classication accuracy of up to 93%.

O movimento dos lábios é um recurso visual relevante para a detecção da atividade de voz do locutor e para o reconhecimento da fala. Quando os lábios estão se movendo eles transmitem a idéia de ocorrências de diálogos (conversas ou períodos de fala) para o observador, enquanto que os períodos de silêncio podem ser representados pela ausência de movimentações dos lábios (boca fechada). Baseado nesta idéia, este trabalho foca esforços para detectar a movimentação de lábios e usá-la para realizar a detecção de atividade de voz. Primeiramente, é realizada a detecção de pele e a detecção de face para reduzir a área de extração dos lábios, sendo que as regiões mais prováveis de serem lábios são computadas usando a abordagem Bayesiana dentro da área delimitada. Então, a pré-segmentação dos lábios é obtida pela limiarização da região das probabilidades calculadas. A seguir, é localizada a região da boca pelo resultado obtido na pré-segmentação dos lábios, ou seja, alguns pixels que não são de lábios e foram detectados são eliminados, e em seguida são aplicados algumas operações morfológicas para incluir alguns pixels labiais e não labiais em torno da boca. Então, uma nova segmentação de lábios é realizada sobre a região da boca depois de aplicada uma transformação de cores para realçar a região a ser segmentada. Após a segmentação, é aplicado o fechamento das lacunas internas dos lábios segmentados. Finalmente, o movimento temporal dos lábios é explorado usando o modelo das cadeias ocultas de Markov (HMMs) para detectar as prováveis ocorrências de atividades de fala dentro de uma janela temporal. / Lips motion are relevant visual feature for detecting the voice active of speaker and speech recognition. When the lips are moving, they carries an idea of occurrence of dialogues (talk) or periods of speeches to the watcher, whereas the periods of silences may be represented by the absence of lips motion (mouth closed). Based on this idea, this work focus efforts to obtain the lips motion as features and to perform visual voice activity detection. First, the algorithm performs skin segmentation and face detection to reduce the search area for lip extraction, and the most likely lip regions are computed using a Bayesian approach within the delimited area. Then, the pre-segmentation of the lips is obtained by thresholding the calculated probability region. After, it is localized the mouth region by resulted obtained in pre-segmentation of the lips, i.e., some nonlips pixels detected are eliminated, and it are applied a simple morphological operators to include some lips pixels and non-lips around the mouth. Thus, a new segmentation of lips is performed over mouth region after transformation of color to enhance the region to be segmented. And, is applied the closing of gaps internal of lips segmented. Finally, the temporal motion of the lips is explored using Hidden Markov Models (HMMs) to detect the likely occurrence of active speech within a temporal window.

Made available in DSpace on 2014-06-11T19:26:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-08-01Bitstream added on 2014-06-13T18:54:08Z : No. of bitstreams: 1 tezza_rid_me_jabo.pdf: 2155126 bytes, checksum: 33a07381689b0811f980f19b4c0fc487 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Com o seqüenciamento completo do genoma do fitopatógeno Xanthomonas axonopodis pv. citri (Xac), em 2002, inúmeras possibilidades de estudo foram viabilizadas, dando margem à busca de novas formas de controle do cancro cítrico, baseadas em alvos moleculares. Estudos dessa natureza têm mostrado a existência de genes que somente são expressos quando a bactéria está se desenvolvendo in planta. Sabe-se que essa regulação é dependente da região promotora e sua identificação pode possibilitar avanços significativos na busca do controle dessa doença. Apesar do crescente avanço das técnicas experimentais in vitro em biologia molecular, identificar um número significante de promotores ainda é uma tarefa difícil e dispendiosa. Os métodos computacionais existentes enfrentam a falta de um número adequado de promotores conhecidos para identificar padrões conservados entre as espécies. Logo, um método para predizê-Ios em qualquer organismo procariótico ainda é um desafio. O Modelo Oculto de Markov é um modelo estatístico aplicável a muitas tarefas em biologia molecular. Entre elas, predição e mapeamento de seqüências promotoras no genoma de um procarioto. Neste trabalho, estudou-se o mapeamento in silico de promotores gênicos de todo o genoma da Xac e em 68% dos genes a localização de um promotor foi indicada. A análise comparativa com dados experimentais de proteômica mostrou que 72% das proteínas expressas identificaram-se com elementos desta lista de promotores, o que corresponde a 27% do total de genes do genoma. À partir destes dados foi possível levantar um rol de informações sobre estes candidatos a promotores incitando a novos estudos moleculares. / With the complete genome sequencing of the phytopathogen Xanthomonas axonopodis pv. Citri (Xac), in 2002, several study possibilities were made practical and then creating the search of new citrus canker control ways, based in molecular aims. This kind of studies has shown the genes existences that are only expressed when the bacteria are developing itself in plant. It has been known that this regulation is promoter region dependent and its identification can allow significant advances in the search of this disease control. Although increasing advance of in vitro experimental techniques in molecular biology, identifying a meaningful number of promoters is still a hard and expensive task. The existents computer science methods face the need of a proper number of known promoters to identify conserved patterns among the species. Therefore, a method to predict them in any prokaryote organism is still a challenge. The Hidden Markov Model (HMM) is a statistic model applicable in many tasks in molecular biology. Among them, prediction and mapping of the promoters sequences in prokaryotic genome. In this work, which has studied the genic promoters in silico mapping of the whole Xac genome, in 68% of the genes the promoter localization was indicated. The proteomic experimental data comparative analysis showed that 72% of the expressed proteins identified with elements from the promoters list, which corresponds 27% of the genome genes total. According to these data it was possible to generate an information roll about these promoters candidates inciting new molecular studies.