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The thesis discusses the variety of English that is spoken today in Brunei Darussalam and assesses its status as a 'New English'. Using a corpus of spoken data which was recorded and transcribed by the author, the thesis attempts to produce an empirically based linguistic description of the grammatical, lexical and discourse features found in spoken Brunei English and to discuss the ways in which these features differ from the equivalent features in Standard British English. The final part of the study is concerned with the pedagogical and language planning implications of recognizing the existence of a Bruneian variety of English, and with proposing an appropriate English language teaching model for the Bruneian education system.

This thesis addresses the problem of classifying an audio stream as either speech or music, an issue which is beginning to receive increasing attention due to its wide range of applications. Various techniques have been presented in last decade to discriminate between speech and music. However, their accuracy is still not sufficient since music can refer to a very broad class of signals due to the large number of musical instruments found in audio data. Performance can also be further compromised in noisy conditions, which are unavoidable in some practical situations. This thesis presents an analysis of feature extraction techniques and classifiers currently being used, followed by the proposal and evaluation of new features for improved classification. These include two novel cepstral features, delta cepstral energy and power spectrum deviation, along with amplitude and frequency modulation features. The modified group delay feature, initially proposed for speech recognition, is also investigated for speech and music discrimination. Experiments were performed using different sets of features, compared among themselves and with conventional MFCCs using error rate criteria and Detection Error Trade-off curves. It is shown that the proposed cepstral and modulation features result in an increase in the accuracy of the conventional MFCC based system. However, the modified group delay feature which has been shown to improve accuracy for speech classification problems, does not contribute much to the problem of speech and music discrimination. Among the ones presented here the optimum feature configuration, both modulation features with MFCC, resulted in overall error rate of 6.57% as compared to 7.43% for MFCC alone.

In two experiments the Weighted Salience Model (WSM) of conceptual combination was examined. Several of the hypotheses set forth in the WSM were evaluated, including the importance of salience of constituent features, differential interpretation strategies based on similarity, an initial reliance on the modifier as opposed to the head, and a context effect of salience reorganization. Results confirmed that the hierarchy of output dominance within constituent features was important in determining features in final combinations. Additionally, similar pairs were defined with property interpretations more frequently than were dissimilar pairs, and dissimilar pairs were defined with relation interpretations more frequently than were similar pairs. Context effects were demonstrated through the finding that target features were found more often in primed than unprimed pairs. The hypothesis of modifier superiority was not confirmed. These findings indicate that the WSM adds to the current understanding of conceptual combination through a reliance on output dominance and the importance of context. Despite these strengths, changes to the WSM may be necessary if future studies fail to support the importance of the modifier over the head noun.

Lignocellulose is a promising and valuable alternative energy source. Native lignocellulosic biomass has limited accessibility to cellulase enzyme due to structural features; therefore, pretreatment is an essential prerequisite to make biomass accessible and reactive by altering its structural features. The effects of substrate concentration, addition of cellobiase, enzyme loading, and structural features on biomass digestibility were explored. The addition of supplemental cellobiase to the enzyme complex greatly increased the initial rate and ultimate extent of biomass hydrolysis by converting the strong inhibitor, cellobiose, to glucose. A low substrate concentration (10 g/L) was employed to prevent end-product inhibition by cellobiose and glucose. The rate and extent of biomass hydrolysis significantly depend on enzyme loading and structural features resulting from pretreatment, thus the hydrolysis and pretreatment processes are intimately coupled because of structural features. Model lignocelluloses with various structural features were hydrolyzed with a variety of cellulase loadings for 1, 6, and 72 h. Glucan, xylan, and total sugar conversions at 1, 6, and 72 h were linearly proportional to the logarithm of cellulase loadings from approximately 10% to 90% conversion, indicating that the simplified HCH-1 model is valid for predicting lignocellulose digestibility. Carbohydrate conversions at a given time versus the natural logarithm of cellulase loadings were plotted to obtain the slopes and intercepts which were correlated to structural features (lignin content, acetyl content, cellulose crystallinity, and carbohydrate content) by both parametric and nonparametric regression models. The predictive ability of the models was evaluated by a variety of biomass (corn stover, bagasse, and rice straw) treated with lime, dilute acid, ammonia fiber explosion (AFEX), and aqueous ammonia. The measured slopes, intercepts, and carbohydrate conversions at 1, 6, and 72 h were compared to the values predicted by the parametric and nonparametric models. The smaller mean square error (MSE) in the parametric models indicates more satisfactorily predictive ability than the nonparametric models. The agreement between the measured and predicted values shows that lignin content, acetyl content, and cellulose crystallinity are key factors that determine biomass digestibility, and that biomass digestibility can be predicted over a wide range of cellulase loadings using the simplified HCH-1 model.

This research proposes a method to detect and classify the smoke particles of common household fires by analysing the image histogram features of smoke particles generated by Rayleigh scattered light. This research was motivated by the failure of commercially available photoelectric smoke detectors to detect smoke particles less than 100 nm in diameter, such as those in polyurethane (in furniture) fires, and the occurrence of false positives such as those caused by steam. Seven different types of particles (pinewood smoke, polyurethane smoke, steam, kerosene smoke, cotton wool smoke, cooking oil smoke and a test Smoke) were selected and exposed to a continuous spectrum of light in a closed particle chamber. A significant improvement over the common photoelectric smoke detectors was demonstrated by successfully detecting and classifying all test particles using colour histograms. As Rayleigh theory suggested, comparing the intensities of scattered light of different wavelengths is the best method to classify different sized particles. Existing histogram comparison methods based on histogram bin values failed to evaluate a relationship between the scattered intensities of individual red, green and blue laser beams with different sized particles due to the uneven particles movements inside the chamber. The current study proposes a new method to classify these nano-scale particles using the particle density independent intensity histograms feature; Maximum Value Index. When a Rayleigh scatter (particles that have the diameter which is less than one tenth of the incident wavelength) is exposed to a light with different wavelengths, the intensities of scattered light of each wavelength is unique according to the particle size and hence, a single unique maximum value index in the image intensity histogram can be detected. Each captured image in the video frame sequence was divided into its red, green and blue planes (single R, G, B channel arrays) and the particles were isolated using a modified frame difference method. Mean and the standard deviation of the Maximum Value Index of intensity histograms over predefined number of frames (N) were used to differentiate different types of particles. The proposed classification algorithm successfully classified all the monotype particles with 100% accuracy when N ≥ 100. As expected, the classifier failed to distinguish wood smoke from other monotype particles due to the rapid variation of the maximum value index of the intensity histograms of the consecutive images of the image sequence since wood smoke is itself a complex composition of many monotype particles such as water vapour and resin smoke. The results suggest that the proposed algorithm may enable a smoke detector to be safer by detecting a wider range of fires and reduce false alarms such as those caused by steam.

This study contributes a methodology and associated toolkit developed to allow users to experiment with the use of combined features in classification problems. Methods are provided for efficiently generating combined features from an original feature set, for efficiently selecting the most discriminating of these generated combined features, and for efficiently performing a preliminary comparison of the classification results when using the original features exclusively against the results when using the selected combined features. The potential benefit of considering combined features in classification problems is demonstrated by applying the developed methodology and toolkit to three sample data sets where the discovery of combined features containing new discriminating information led to improved classification results.

In two experiments the Weighted Salience Model (WSM) of conceptual combination was examined. Several of the hypotheses set forth in the WSM were evaluated, including the importance of salience of constituent features, differential interpretation strategies based on similarity, an initial reliance on the modifier as opposed to the head, and a context effect of salience reorganization. Results confirmed that the hierarchy of output dominance within constituent features was important in determining features in final combinations. Additionally, similar pairs were defined with property interpretations more frequently than were dissimilar pairs, and dissimilar pairs were defined with relation interpretations more frequently than were similar pairs. Context effects were demonstrated through the finding that target features were found more often in primed than unprimed pairs. The hypothesis of modifier superiority was not confirmed. These findings indicate that the WSM adds to the current understanding of conceptual combination through a reliance on output dominance and the importance of context. Despite these strengths, changes to the WSM may be necessary if future studies fail to support the importance of the modifier over the head noun.