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Infiltration facilities are source control mechanisms that are implemented in urban developments with reduced natural permeable surfaces. Despite the development of design criteria for infiltration facilities, these systems continue to fail due to headloss development, overflow, or chemical breakthrough. The limited research on the long-term performance of these systems has emphasized the role of physical filtration mechanisms within porous media filters to address concerns surrounding system failure, namely filter clogging. A continuous macroscopic depth filtration model was developed to investigate the clogging potential of the underlying sand filter. This continuous model furthers the understanding of temporal and spatial changes in system performance for the development of more appropriate design criteria and more suitable maintenance regimes. The characterization of long-term system performance by defining three different failure modes and a probabilistic approach comprises a comprehensive methodology by considering several performance criteria rather than assuming that one criterion dictates the overall system performance.

Infiltration facilities are source control mechanisms that are implemented in urban developments with reduced natural permeable surfaces. Despite the development of design criteria for infiltration facilities, these systems continue to fail due to headloss development, overflow, or chemical breakthrough. The limited research on the long-term performance of these systems has emphasized the role of physical filtration mechanisms within porous media filters to address concerns surrounding system failure, namely filter clogging. A continuous macroscopic depth filtration model was developed to investigate the clogging potential of the underlying sand filter. This continuous model furthers the understanding of temporal and spatial changes in system performance for the development of more appropriate design criteria and more suitable maintenance regimes. The characterization of long-term system performance by defining three different failure modes and a probabilistic approach comprises a comprehensive methodology by considering several performance criteria rather than assuming that one criterion dictates the overall system performance.

The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented.

Activated carbons have been modif fed using reactive chemicals to produce adsorbents of enhanced hydrophobic character which will also be resistant to surface oxidation that results from exposure to humid air ("ageing"). The intention was that modification would not disrupt the carbon pore structure. The adsorptive properties of the modified carbons have been investigated using probe molecules Including nitrogen, water, hexane, and chloropicrin, and the ageing characteristics of the carbons, and the factors controlling the adsorption of a model hydrophobic vapour from high humidity air have been studied. Directly fluorinated carbons were unstable, probably due to weakly adsorbed fluorine. Treatment of these adsorbents with other chemicals indicated the potential of the technique for Introducing specific functional groups onto the carbon surface. Carbons modified using selective fluorinating reagents (hexafluoropropene and 1,1-difluoroethene) were more hydrophobic, and adsorbed hydrophobic vapours more efficiently from humid air in comparison to controls. These adsorbents aged, but at a reduced rate in comparison to control carbon. Carbons modified using chlorinating reagents (carbonyl chloride and chlorine) and treated with solvents to remove adsorbed reagent and/or reaction products were of improved hydrophobic character, and adsorbed hydrophobic vapours from humid air at least as efficiently as the control samples. More importantly, these carbons offered resistance to ageing effects. A study of the factors controlling the efficiency with which hydrophobic vapours; are adsorbed from humid air revealed that the surface chemistry of the carbon is important, but that under typical conditions of use, filter performance was limited by the rate at which water displaced by the organic vapour could be carried away by the airstream. The results illustrate that filters containing chemically modified activated carbon offer advantages when volatile hydrophobic contaminant vapours are present, and where ageing effects are an important mechanism by which filtration efficiency is degraded.

No description available.

No description available.

Image classification using data from neuromorphic vision sensors is a challenging task that affects the use of dynamic vision sensor cameras in real- world environments. One impeding factor is noise in the neuromorphic event stream, which is often generated by the dynamic vision sensors themselves. This means that effective noise filtration is key to successful use of event- based data streams in real-world applications. In this paper we harness two feature representations of neuromorphic vision data in order to apply conventional frame-based image tools on the neuromorphic event stream. We use a standard noise filter to evaluate the effectiveness of noise filtration using a popular dataset converted to neuromorphic vision data. The two feature representations are the best-of-class standard Histograms of Averaged Time Surfaces (HATS) and a simpler grid matrix representation. To evaluate the effectiveness of the noise filter, we compare classification accuracies using various noise filter windows at different noise levels by adding additional artificially generated Gaussian noise to the dataset. Our performance metrics are reported as classification accuracy. Our results show that the classification accuracy using frames generated with HATS is not significantly improved by a noise filter. However, the classification accuracy of the frames generated with the more traditional grid representation is improved. These results can be refined and tuned for other datasets and may eventually contribute to on- the- fly noise reduction in neuromorphic vision sensors. / Händelsekameror är en ny typ av kamera som registrerar små ljusförändringar i kamerans synfält. Sensorn som kameran bygger på är modellerad efter näthinnan som finns i våra ögon. Näthinnan är uppbyggd av tunna lager av celler som omvandlar ljus till nervsignaler. Eftersom synsensorer efterliknar nervsystemet har de getts namnet neuromorfiska synsensorer. För att registrera små ljusförändringar måste dessa sensorer vara väldigt känsliga vilket även genererar ett elektroniskt brus. Detta brus försämrar kvalitén på signalen vilket blir en förhindrande faktor när dessa synsensorer ska användas i praktiken och ställer stora krav på att hitta effektiva metoder för brusredusering. Denna avhandling undersöker två typer av digitala framställningar som omvandlar signalen ifrån händelsekameror till något som efterliknar vanliga bilder som kan användas med traditionella metoder för bildigenkänning. Vi undersöker brusreduseringens inverkan på den övergripande noggrannhet som uppnås av en artificiell intelligens vid bildigenkänning. För att utmana AIn har vi tillfört ytterligare normalfördelat brus i signalen. De digitala framställningar som används är dels histogram av genomsnittliga tidsytor (eng. histograms of averaged time surfaces) och en matrisrepresentation. Vi visar att HATS är robust och klarar av att generera digitala framställningar som tillåter AIn att bibehålla god noggrannhet även vid höga nivåer av brus, vilket medför att brusreduseringens inverkan var försumbar. Matrisrepresentationen gynnas av brusredusering vid högre nivåer av brus.