Brain computer interface (BCI) systems measure brain signal and translate it into control commands in an attempt to mimic specific human thinking activities. In recent years, many researchers have shown their interests in BCI systems, which has resulted in many experiments and applications. However, most methods are just based on a specific selected dataset or a typical feature. As a result, there are questions about whether some methods generalise well on other datasets. Therefore, the major motivation of this thesis is to compare various features and classifiers described in the literature. Pattern recognition is considered as the core part of a BCI system in our research. In this thesis, a number of different features and classifiers are compared in terms of classification accuracy and computation time. The studied features are: time series waveform, autoregressive (AR) components, spectral components; these are used with different classifiers: such as template matching, nearest neighbour, linear discriminant analysis (LDA), Bayesian statistical and fuzzy logic decision classifiers. In order to assess and compare these different features and classifiers, an extensive investigation was carried out on a public dataset (imagined left or right hand movement) from an international BCI competition and the results are reported in this thesis. The classification was done in a continuous fashion, to match a real time application. In this process, the average and best accuracy, as well as the computation time, were analysed and compared. The results showed that most classifiers achieved very high accuracies and short computation times for most features. A BCI experiment based on imagined left or right hand movement was carried out at the University of Adelaide and some investigations on the data from this experiment are discussed. The result shows that the selected classifiers can work well with this new dataset without much additional preprocessing or modifications. Finally, this thesis culminates with some conclusions based on our research, and discusses some further potential work. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1415396 / Thesis (M.Eng.Sc.) - University of Adelaide, School of Electrical and Electronic Engineering, 2010

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaf 19). Also available on the World Wide Web.

In this dissertation, I study the distribution of number in Azeri within the Exo- Skeletal model of Borer (2005a). I adopt the Exo-Skeletal model's assumption that number marking is a syntactic rather than a lexical process. Following Borer (2005a), I assume that, in order to be counted, nouns need to be individuated by means of a functional category Div. In Borer's model, plural markers and classifiers are argued to be generated in DivP. However, unlike Borer, I propose that the plural marker in Azeri is not an individuator. Instead, it solely marks plurality. Under my proposal, individuation in Azeri is morphologically null. Moreover, I argue that classifiers do not belong to the category of individuators either and their function is to unitize the individuated object. Therefore, I consider classifiers in Azeri to be generated on a cluster head where they contribute to a group formation process. The generation of the plural marker and the classifier on heads other than division derives the conclusion that the individuation in Azeri is morphologically null. Furthermore, I investigate the interpretation of number in the verbal domain, i.e. in TP, in the presence of the viewpoint aspect in both telic and atelic contexts. I argue that the singular interpretation of the Azeri bare noun is linked to the projection of AspQ, where the specific interpretation of the bare noun arises under the effect of the perfective aspect. The presence of AspQ yields a telic interpretation of the event structure, and the DP in the specifier of AspQ is the subject of quantity (Borer, 2005a). Moreover, according to Borer, number ambiguous nouns are generated in atelic structures where AspQ is absent. In this case, the DP does not have to be the subject of quantity and the availability of quantity on the DP remains optional. Nevertheless, (non-)specific interpretation of the noun in telic and atelic contexts in Azeri, I argue to be due to the impact of the viewpoint aspect.

Made available in DSpace on 2016-08-29T15:33:14Z (GMT). No. of bitstreams: 1 tese_4163_.pdf: 479699 bytes, checksum: 04f01a137084c0859b4494de6db8b3ac (MD5) Previous issue date: 2011-02-23 / Classificadores do tipo máquina de vetores de suporte (SVM) são atualmente considerados uma das técnicas mais poderosas para se resolver problemas de classificação com duas classes. Para aumentar o desempenho alcançado por classificadores SVM individuais, uma abordagem bem estabelecida é usar uma combinação de SVMs, a qual corresponde a um conjunto de classificadores SVMs que são, simultaneamente, individualmente precisos e coletivamente divergentes em suas decisões. Este trabalho propõe uma abordagem para se criar combinações de SVMs, baseada em um processo de três estágios. Inicialmente, são usadas execuções complementares de uma busca baseada em algoritmos genéticos (GEFS), com o objetivo de investigar globalmente o espaço de características para definir um conjunto de subconjuntos de características. Em seguida, para cada um desses subconjuntos de características definidos, uma SVM que usa parâmetros otimizados é construída. Por fim, é empregada uma busca local com o objetivo de selecionar um subconjunto otimizado dessas SVMs, e assim formar a combinação de SVMs que é finalmente produzida. Os experimentos foram realizados num contexto de detecção de defeitos em máquinas industriais. Foram usados 2000 exemplos de sinais de vibração de moto bombas instaladas em plataformas de petróleo. Os experimentos realizados mostram que o método proposto para se criar combinação de SVMs apresentou um desempenho superior em comparação a outras abordagens de classificação bem estabelecidas.

This thesis aims at investigate the feasibility of classifying the soprano singing voice type using a single layer neural network trained with the FIR-ELM algorithm after that the monaural auditory mixture has been segmented with the Harmonic, Percussive and Residual, HPR, decomposition algorithm, previously introduced by Driedger et al.Two different decomposition structures has been evaluated both based on the same HPR decomposition technique. Firstly one single layer that only take advantage of the result of the more pure harmonic and the more pure percussive components of the signal. Secondly, one multilayer structure that further decompose both the harmonic and the percussive components but also takes into account the components that can not be clearly categorized as neither harmonic or percussive components, these are the residual components. The result of the classification was up to 98.5 $\%$ after using these segmentation techniques, this shows that it is feasibly to classify the singing voice type soprano in an monaural source recorded in a non-professional environment using the FIR-ELM algorithm.

This masters thesis deals with algorithms for learning SVM classifiers on hardware systems and their implementation in FPGA. There are basics about classifiers and learning. Two learning algorithms are introduced SMO algorithm and one hardware-friendly algorithm.

No description available.

This thesis provides a syntactic and semantic analysis of bare arguments and classifiers in Thai as well as accounting for its nominal word order. Adopting the Nominal Mapping Parameter (Chierchia 1998), it is argued that Thai nouns are names of kinds. Kinds are of type <s,e>, which are allowed to appear without overt determiners in argument position. For this reason, Thai nouns cannot directly combine with a quantifier without the help of a classifier. The study shows that Thai arguments behave like English bare arguments (bare plurals and mass nouns) in that they exhibit scopelessness and can be interpreted with different meanings such as weak indefinite, generic and kind interpretations. Unlike English bare arguments, the Thai counterparts may also have a definite interpretation. This is because Thai lacks an overt definite determiner. In addition, the thesis provides a unified analysis for the occurrence of Thai classifiers in different contexts. It is assumed that a classifier occurs in a quantified context to provide a portion of a kind (Krifka 1995, Chierchia 1998). The thesis further proposes that a classifier occurs in a non-quantified context where there is no overt numeral when the noun phrase is specific. A specific noun phrase includes those appearing with a demonstrative, the numeral ‘one’ or a modifier. As for the word order within the nominal domain, it is proposed that the noun, although merged at the bottom of the Specific Phrase underlyingly, always appears in the initial position to check an uninterpretable nominal feature in the Specific head.

Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.

This thesis provides a syntactic and semantic analysis of bare arguments and classifiers in Thai as well as accounting for its nominal word order. Adopting the Nominal Mapping Parameter (Chierchia 1998), it is argued that Thai nouns are names of kinds. Kinds are of type <s,e>, which are allowed to appear without overt determiners in argument position. For this reason, Thai nouns cannot directly combine with a quantifier without the help of a classifier. The study shows that Thai arguments behave like English bare arguments (bare plurals and mass nouns) in that they exhibit scopelessness and can be interpreted with different meanings such as weak indefinite, generic and kind interpretations. Unlike English bare arguments, the Thai counterparts may also have a definite interpretation. This is because Thai lacks an overt definite determiner. In addition, the thesis provides a unified analysis for the occurrence of Thai classifiers in different contexts. It is assumed that a classifier occurs in a quantified context to provide a portion of a kind (Krifka 1995, Chierchia 1998). The thesis further proposes that a classifier occurs in a non-quantified context where there is no overt numeral when the noun phrase is specific. A specific noun phrase includes those appearing with a demonstrative, the numeral ‘one’ or a modifier. As for the word order within the nominal domain, it is proposed that the noun, although merged at the bottom of the Specific Phrase underlyingly, always appears in the initial position to check an uninterpretable nominal feature in the Specific head.