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No / High levels of suspended solids are typically observed during the initial part of storms. Field evidence suggests that these suspended solids derive from the erosion of in-sewer sediment beds accumulated during dry and previous wet weather periods. Suspended sediment transport rate models within existing sewer network modeling tools have utilized inappropriate transport rate relationships developed mainly in fluvial environments. A process model that can account for the erosion of fine-grained highly organic in-sewer sediment deposits has been formulated. Values of parameters describing the increase in deposit strength with depth are required. These values are obtained using a genetic algorithm based calibration routine that ensures model simulations of suspended sediment concentrations that correspond to field data collected in a discrete length of sewer in Paris under known hydraulic event conditions. These results demonstrate the applicability of this modeling approach in simulating the magnitude and temporal distribution of suspended in-sewer sediment eroded by time varying flow. Further work is developing techniques to enable the application of this type of model at the network level.

The objective of this project is to develop a shape-based image analysis system, which allows classification and identification of holographic images of marine particles and plankton recorded by an underwater digital holographic camera. In order to achieve this goal, the first step is to extract shape regions of objects from images and to describe the regions by polygonal boundaries. After extraction of the polygonal boundary curve of an object, affine-invariant curve normalisation is implemented on the curve to reduce the influence of object shape deformations on object identification and classification. Six numeric features are then selected to describe shape properties of an object. Before these six shape features are used as a numeric interpretation of an object for image analysis, some processing of them is necessary, consisting of selecting the number of items in each feature and rescaling the selected feature vectors. Afterwards, Gaussian rescaling is adopted to rescale the feature data. Lastly, a shape-based image classification and identification system is built. The system contains two components: semi-automatic image classification (imCLASS) and automatic image identification (imIDENT). In imCLASS, an image retrieval method based on the support vector machine with a feedback mechanism has been developed. The function of imCLASS is to classify given images into different folders with the corresponding labels from the user. These labelled folders can be used to train the artificial neural network in imIDENT. A set of analyses of effects of the proposed methods in the process chain on image analysis are carried out. The whole performance of the system for classifying and identifying marine particles and plankton is also evaluated in terms of the time-cost and accuracy performance. In the end, some main conclusions are listed. The areas of weakness of the system are also highlighted for future work.

Thesis (M.Sc.)--Memorial University of Newfoundland, 2006. / Bibliography: leaves 162-184. Also available online.

Univ., Diss.--Göteborg, 2007. / Enth. außerdem 5 Zeitschriftenaufsätze.