Thesis studied correlations between swallowing accelerometry parameters and anthropometrics in 50 healthy participants. Anthropometrics include: age, gender, weight, height, body fat percent, neck circumference and mandibular length. Dual-axis swallowing signals, from a biaxial accelerometer were obtained for 5-saliva and 10-water (5-wet and 5-wet chin-tuck) swallows per participant. Two patient-independent automatic segmentation algorithms using discrete wavelet transforms of swallowing sequences segmented: 1) saliva/wet swallows and 2) wet chin-tuck swallows. Extraction of swallows hinged on dynamic thresholding based on signal statistics. Canonical correlation analysis was performed on sets of anthropometric and swallowing signal variables including: variance, skewness, kurtosis, autocorrelation decay time, energy, scale and peak-amplitude. For wet swallows, significant linear relationships were found between signal and anthropometric variables. In superior-inferior directions, correlations linked weight, age and gender to skewness and signal-memory. In anterior-posterior directions, age was correlated with kurtosis and signal-memory. No significant relationship was observed for dry and wet chin-tuck swallowing

Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%.

[Abstract]Market segmentation has been identified in the tourism marketing literature as an effective tool that can be used by management to meet the needs of a market more efficiently and effectively. It has been widely used by researchers seeking to develop tourism segments. Historically, most segmentation studies have developed tourism profiles at a destination using 1) a visitor questionnaire survey developed from studies in the literature or 2) secondary data. Very little research has taken a stakeholder approach to destination segmentation, despite many authors arguing the importance of utilising a stakeholder approach for destination management and marketing purposes. This research proposes a two-step approach to destination segmentation. It details this approach using one Australian destination. The two-step approach to segmentation begins with firstly understanding how tourism stakeholders at a destination segment their market before surveying tourists for the purposes of identifying segments. In contrast to previous tourist-focused segmentation studies, the two-step approach recommended and detailed in this study considers both tourism stakeholders and tourists. The segments devised from the proposed approach are then compared and contrasted with segments currently utilised by the destination marketing organisation (DMO). Step one involved interviewing 14 tourism stakeholders to determine how they segment the market. Based on these findings, a questionnaire survey was developed and data was collected from 852 tourists. This represented step two of the approach. Three tourism segments were identified through cluster analysis. Only one of these three segments was comparable with the segments defined by the DMO. The other two were not considered in the DMO segmentation. These segments represented over half of all tourists in the sample. Based on the sample in this study, the DMO segments target less than a quarter of the types of tourists visiting the destination. Contributions to theory and practice were identified. Firstly, it was recognised that different tourism stakeholders attract different tourists, not all stakeholders segment their market, and some stakeholders do not adhere to segments targeted by the DMO which may lead to an inconsistent message in the market. Secondly, the two-step approach is a new method incorporating a stakeholder view, which gives a more holistic view and a richer description for the segments obtained when compared with academic and practitioner segmentation approaches. The two-step approach can be utilised at other tourist destinations. The two-step approach to segmentation is capable of assisting tourism marketers to target more of the tourists frequenting the destination. This study suggests that many dollars may be wasted targeting tourists that are not likely to travel to the destination and not targeting those who would. Future research should be conducted at alternative destinations to further the understanding of the recommended two-step approach to segmentation.

This thesis presents a new approach to the visualization of audio files that simultaneously illustrates general audio properties and the component sounds that comprise a given input file. New audio segmentation and classification methods are reported that outperform existing methods. In order to visualize audio files, the audio is segmented (separated into component sounds) and then classified in order to select matching archetypal images or video that represent each audio segment and are used as templates for the visualization. Each segment's template image or video is then subjected to image processing filters that are driven by audio features. One visualization method reported represents heterogeneous audio files as a seamless image mosaic along a time axis where each component image in the mosaic maps directly to a discovered component sound. The second visualization method, video texture mosaics, builds on the ideas developed in time mosaics. A novel adaptive video texture generation method was created by using acoustic similarity detection to produce a resultant video texture that more accurately represents an audio file. Compared with existing visualization methods such as oscilloscopes and spectrograms, both approaches yield more accessible illustrations of audio files and are more suitable for casual and non expert users.

No description available.

This thesis presents a new approach to the visualization of audio files that simultaneously illustrates general audio properties and the component sounds that comprise a given input file. New audio segmentation and classification methods are reported that outperform existing methods. In order to visualize audio files, the audio is segmented (separated into component sounds) and then classified in order to select matching archetypal images or video that represent each audio segment and are used as templates for the visualization. Each segment's template image or video is then subjected to image processing filters that are driven by audio features. One visualization method reported represents heterogeneous audio files as a seamless image mosaic along a time axis where each component image in the mosaic maps directly to a discovered component sound. The second visualization method, video texture mosaics, builds on the ideas developed in time mosaics. A novel adaptive video texture generation method was created by using acoustic similarity detection to produce a resultant video texture that more accurately represents an audio file. Compared with existing visualization methods such as oscilloscopes and spectrograms, both approaches yield more accessible illustrations of audio files and are more suitable for casual and non expert users.

No description available.

Diss. Stockholm : Handelshögskolan, 2005.

Thesis (Ph.D.) - Carleton University, 2005. / Includes bibliographical references (p. 127-139). Also available in electronic format on the Internet.

Thesis (doctoral)--Göteborgs universitet, 1999. / Includes bibliographical references.