This thesis is a practical project which involves moving images and paintings together as a medium that explores phenomena of contemporary China relating to personal identity, independence and its relationship with the traditional importance on collective groups, group centredness and interdependence. The project’s approach draws on sociological research on Western thought, values and beliefs naturally occurring in China since the “Open Door” policy as raw data to focus on the transition and transformation of contemporary Chinese individuality, and translates these data to form the concepts underpinning the metaphoric method of my artwork. Bubbles are the main visual symbols that metaphorically imply the incessantly transformable Chinese individuality and social cultural identity. My aim has been to portray this phenomenon through artistic practices on screens. By reflecting and engaging with moving images and paintings, underpinned by theoretical research and methods including data collecting, self-reflecting on data, practical manifestation and self-inquiry, I have attempted to unfold the phenomenon of contemporary Chinese individuality through my art practice. The thesis is composed as a creative work of moving images accompanied by an exegesis component. The moving image represents a nominal 80%, and the exegesis 20% of the final submission.

Bibliography: leaves 261-276. Electronic publication; full text available in PDF format; abstract in HTML format. This thesis is about the role that shop floor workers play in organisational change. In particular, it investigates the manner in which a distinct group of worker-level leaders and change agents affected the generation and implementation of change and helped to shape the change process in an organisation undergoing planned change. The data for the thesis were obtained from a three-year, longitudinal case-study of organisational change in a medium-sized automotive components manufacturer, Moving Metals Limited (MML). Electronic reproduction.[Australia] :Australian Digital Theses Program,2001.

Double Bind: Splitting identity and the body as an object is a research project consisting of studio-based practice presented mainly in video installation format. This work looks at hysterical symptoms as a performance of a body’s split identity. The project draws on the Lacanian theory of Mirror Stage which proposes that the self experienced by the subject, and the image of that self (represented in a mirror-like reflection, or an image) are different to each other, and the development of self-awareness as misrecognition of one’s self. As a conspicuous example of split body, Chapter One describes how the hysterical body, in clinical and artistic representation, is dissociated into multiple selves. In Chapter Two, I discuss some examples of contemporary performance artists who use themselves as subjects, but whose bodies become objects that do not portray the self. In the final chapter I explain how, in my video work, I objectify my own body and how I assess whether this is a mode of self-portraiture. / During the course of this research, I studied a wide range of medical resources and psychoanalytical literature, much of which employed visual illustration and documentation. For example, I have drawn inspiration from Jean-Martin Charcot’s photographic documents of female hysterics whom he treated as patients at the French hospital of La Salpêtrière in the late 19th century; in particular the figure of his most famous patient, known as Augustine. My research also involved studio-based investigation, such as experimentations with the performance of my own body in video format, and the contextual study of artistic and critical texts relating to contemporary media art. / The aim of this research is to demonstrate the ways in which my video performances split the body, creating an Other within one body that can be compared with the hysterical body of a patient, like Augustine, performing for her doctor. In this condition, I perform as the subject and the object of the gaze at the same time. My self-portrait is split in this way: it creates a body double, which I misrecognise as myself. But in doing so, I am both the director and the performer of the image. This is the double bind that my video work puts me into.

This study describes chick-a-dee calling behavior of Carolina chickadees (Poecile carolinensis) in the presence of “flying” predator models. Previous experimental research with black-capped chickadees (Poecile atricapilla) as well as Mexican chickadees (Poecile sclateri) suggested a relationship between the presence of predator stimuli and the note composition of chick-a-dee calls. Carolina chickadees were presented with two types of stimuli in field settings. These models were painted to resemble either a predatory sharp-shinned hawk (Accipiter striatus) or a blue jay (Cyanocitta cristata). Models consisted of pairs of five varying shapes, making ten models in all. A randomly chosen model was covered with a cloth and attached to a zip-line fastened to a tree about five meters high. During the procedure the cloth was removed and the model was released, causing it to “fly” to the opposite end of the zip line, which was attached at ground level. These trials were audio recorded at 20 field sites in eastern Tennessee from January - March 2010 and January - February 2011. Chickadees produced more C notes in response to hawk models than to blue jay models. Additionally, chickadees produced more A and C notes as well as more total notes during the post-exposure period than during the pre-exposure period for both the hawk and the blue jay conditions. The chickadees’ increased production of C notes in response to the hawk stimuli is consistent with previous research conducted on Mexican chickadees, potentially suggesting that the call structure of Carolina chickadees in threatening predator contexts is more similar to that of Mexican, rather than black-capped chickadees. Finally, chickadees increased production of A notes and C notes to the presence of both model types, providing support for the “Better Safe Than Sorry” strategy.

Résumé : Le travail réalisé au cours de cette thèse traite de la mise au point de méthodes d’estimation d’état robuste, avec deux domaines d’application en ligne de mire. Le premier concerne le positionnement sécuritaire en transport. L’objectif est de fournir la position et la vitesse du véhicule sous la forme d’intervalles avec un grand degré de confiance. Le second concerne la synthèse de capteurs logiciels pour les bioprocédés, et en particulier la reconstruction des concentrations de composants réactionnels à partir d’un nombre limité de mesures et d’un modèle mathématique interprétant le comportement dynamique de ces composants. L’objectif principal est de concevoir des algorithmes qui puissent fournir des estimations acceptables en dépit des incertitudes provenant de la mauvaise connaissance du système comme les incertitudes sur les paramètres du modèle ou les incertitudes de mesures. Dans ce contexte, plusieurs algorithmes ont été étudiés et mis au point. Ainsi, dans le cadre du positionnement de véhicule, la recherche s’est dirigée vers les méthodes robustes Hinfini et les méthodes par intervalles. Les méthodes Hinfini sont des méthodes linéaires prenant en compte une incertitude dans la modélisation et réalisant une optimisation min-max, c’est-à-dire minimisant une fonction de coût qui représente la pire situation compte tenu des incertitudes paramétriques. La contribution de ce travail concerne l’extension à des modèles faiblement non linéaires et l’utilisation d’une fenêtre glissante pour faire face à des mesures asynchrones. Les méthodes par intervalles développées ont pour but de calculer les couloirs de confiance des variables position et vitesse en se basant sur la combinaison d’intervalles issus des capteurs d’une part et sur l’utilisation conjointe d’un modèle dynamique et cinématique du véhicule d’autre part. Dans le cadre des capteurs logiciels pour bioprocédés, trois familles de méthodes ont été étudiées: le filtrage particulaire, les méthodes par intervalles et le filtrage par horizon glissant. Le filtrage particulaire est basé sur des méthodes de Monte-Carlo pour estimer la densité de probabilité conditionnelle de l’état connaissant les mesures. Un de ses principaux inconvénients est sa sensibilité aux erreurs paramétriques. La méthode développée s’applique aux bioprocédés et profite de la structure particulière des modèles pour proposer une version du filtrage particulaire robuste aux incertitudes des paramètres cinétiques. Des méthodes d’estimation par intervalles sont adaptées à la situation où les mesures sont disponibles à des instants discrets, avec une faible fréquence d’échantillonnage, en développant des prédicteurs appropriés. L’utilisation d’un faisceau de prédicteurs grâce à des transformations d’état et le couplage entre les prédicteurs avec des réinitialisations fréquentes permettent d’améliorer les résultats d’estimation. Enfin, une méthode basée sur le filtre à horizon glissant est étudiée en effectuant une optimisation min-max : la meilleure condition initiale est reconstruite pour le plus mauvais modèle. Des solutions sont aussi proposées pour minimiser la quantité de calculs. Pour conclure, les méthodes et résultats obtenus constituent un ensemble d’améliorations dans le cadre de la mise au point d’algorithmes robustes vis-à-vis des incertitudes. Selon les applications et les objectifs fixés, telle ou telle famille de méthodes sera privilégiée. Cependant, dans un souci de robustesse, il est souvent utile de fournir les estimations sous forme d’intervalles auxquels est associé un niveau de confiance lié aux conditions de l’estimation. C’est pourquoi, une des méthodes les plus adaptées aux objectifs de robustesse est représentée par les méthodes par intervalles de confiance et leur développement constituera un point de recherche futur. __________________________________________ Abstract : This thesis work is about the synthesis of robust state estimation methods applied to two different domains. The first area is dedicated to the safe positioning in transport. The objective is to compute the vehicle position and velocity by intervals with a great confidence level. The second area is devoted to the software sensor design in bioprocess applications. The component concentrations are estimated from a limited number of measurements and a mathematical model describing the dynamical behavior of the system. The main interest is to design algorithms which achieve estimation performance and take uncertainties into account coming from the model parameters and the measurement errors. In this context, several algorithms have been studied and designed. Concerning the vehicle positioning, the research activities have led to robust Hinfinity methods and interval estimation methods. The robust Hinfinity methods use a linear model taking model uncertainty into account and perform a min-max optimization, minimizing a cost function which describes the worst-case configuration. The contribution in this domain is an extension to some systems with a nonlinear model and the use of a receding time window facing with asynchronous data. The developed interval algorithms compute confidence intervals of the vehicle velocity and position. They use interval combinations by union and intersection operations obtained from sensors along with kinematic and dynamic models. In the context of bioprocesses, three families of state estimation methods have been investigated: particle filtering, interval methods and moving-horizon filtering. The particle filtering is based on Monte-Carlo drawings to estimate the posterior probability density function of the state variables knowing the measurements. A major drawback is its sensitivity to model uncertainties. The proposed algorithm is dedicated to bioprocess applications and takes advantage of the characteristic structure of the models to design an alternative version of the particle filter which is robust to uncertainties in the kinetic terms. Moreover, interval observers are designed in the context of bioprocesses. The objective is to extend the existing methods to discrete-time measurements by developing interval predictors. The use of a bundle of interval predictors thanks to state transformations and the use of the predictor coupling with reinitializations improve significantly the estimation performance. Finally, a moving-horizon filter is designed, based on a min-max optimization problem. The best initial conditions are generated from the model using the worst parameter configuration. Furthermore, additional solutions have been provided to reduce the computational cost. To conclude, the developed algorithms and related results can be seen as improvements in the design of estimation methods which are robust to uncertainties. According to the application and the objectives, a family may be favored. However, in order to satisfy some robustness criteria, an interval is preferred along with a measure of the confidence level describing the conditions of the estimation. That is why, the development of confidence interval observers represents an important topic in the future fields of investigation.

From the viewpoint of applications, conventional cameras are usually limited in their fields of view. The omni-directional camera has a full range in all directions, which gains the complete field of view. In the past, a moving object can be detected, only when the camera is static or moving with a known speed. If those methods are employed to mobile robots or vehicles, it will be difficult to determine the motion of moving objects observed by the camera. In this paper, we assume the omni-directional camera is mounted on a moving platform, which travels with a planar motion. The region of floor in the omni-directional image and the brightness constraint equation are applied to estimate the ego-motion. The depth information is acquired from the floor image to solve the problem that cannot be obtained by single camera systems. Using the estimated ego-motion, the optical flow caused by the floor motion can be computed. Therefore, comparing its direction with the direction of the optical flow on the image leads to detection of a moving object. Due to the depth information, even if the camera is in the condition that combining translational and rotational motions, a moving object can still be accurately identified.

Numerical simulations are presented for three dimensional viscous incompressible free shear flows. The numerical method is based on solving the vorticity equation using Vortex-In-Cell method. In this method, the vorticity field is discretized into a finite set of Lagrangian elements (particles) and the computational domain is covered by Eulerian mesh. Velocity field is computed on the mesh by solving Poisson equation. The solution proceeds in time by advecting the particles with the flow. Second order Adam-Bashford method is used for time integration. Exchange of information between Lagrangian particles and Eulerian grid is carried out using the M’4 interpolation scheme. The classical inviscid scheme is enhanced to account for stretching and viscous effects. For that matter, two schemes are used. The first one used periodic remeshing of the vortex particles along with fourth order finite difference approximation for the partial derivatives of the stretching and viscous terms. In the second scheme, derivatives are approximated by least squares polynomial. The novelty of this work is signified by using the moving least squares technique within the framework of the Vortex-in-Cell method and implementing it to a three dimensional temporal mixing layer. Comparisons of the mean flow and velocity statistics are made with experimental studies. The results confirm the validity of the present schemes. Both schemes also demonstrate capability to qualitatively capture significant flow scales, and allow gaining physical insight as to the development of instabilities and the formation of three dimensional vortex structures. The two schemes show acceptable low numerical diffusion as well.

Biofilm systems such as the moving bed biofilm reactor (MBBR) are finding increased application in wastewater treatment. One important process that governs MBBRs and yet is poorly understood is the rate of biofilm detachment. The detachment of cells from biofilm surfaces controls both the accumulation of biofilm and the quantity of biomass that is suspended in the bulk liquid phase. This changing balance of attached and suspended cells, in this thesis named the biosolids dynamics, can impact the efficacy of MBBRs. The goal of this research was to investigate how the biosolids dynamics are influenced by process changes relevant to applied wastewater treatment systems and suggest new routes to reactor design and optimization. To achieve this goal, the work addresses three separate but interconnected lines of inquiry. First, multivariate analysis (Principal Component Analysis, Partial Least Squares) was used to examine 2 years of historical data from an MBBR operating at a Canadian pulp mill in order to identify key process variables, perform process diagnostics, and act as a predictive tool. Secondly, the effect of calcium concentration on biofilm structure, microbiology and reactor performance was investigated in four laboratory-scale MBBRs operated at a range of calcium concentrations (1 to 300 mg/L Ca2+). It was found that above a threshold calcium concentration between 1-50 mg/L, MBBR biofilms were observed to be thicker with greater density, contain larger anoxic regions adjacent to the carrier substratum, have more proteinaceous EPS, and have altered microbial community structure. The results suggest an important role for calcium that should be considered in the design and operation of MBBRs. In the final line of inquiry, a diffusion-reaction biofilm model was adapted to represent the key processes of the MBBR. The model was found to simulate average trends observed in the lab-scale experiments allowing for quantification of the detachment rate. Transient periods of reactor starvation were also simulated by introducing a novel metabolic state function to account for down-regulation of metabolism as a result of starvation. This approach was found to accurately simulate starvation response when coupled with detachment expressions that were growth-dependant.

The objective of this thesis is to detect and identify moving objects in a video sequence. The currently available techniques for motion estimation can be broadly categorized into two main classes: block matching methods and optical flow methods.<p>This thesis investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas Kanade Optical Flow Algorithm has been used in this thesis for detection of moving objects in a video sequence. Derivatives of image brightness with respect to x-direction, y-direction and time t are calculated to solve the Optical Flow Constraint Equation. The algorithm produces results in the form of horizontal and vertical components of optical flow velocity, u and v respectively. This optical flow velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequences.<p>This method has been modified to include parameters such as neighborhood size and Gaussian pyramid filtering which improve the motion estimation process. The concept of Gaussian pyramids has been used to simplify the complex video sequences and the optical flow algorithm has been applied to different levels of pyramids. The estimated motion derived from the difference in the optical flow vectors for moving objects and stationary background has been used to segment the moving objects in the video sequences. A combination of erosion and dilation techniques is then used to improve the quality of already segmented content.<p>The Lucas Kanade Optical Flow Algorithm along with other considered parameters produces encouraging motion estimation and segmentation results. The consistency of the algorithm has been tested by the usage of different types of motion and video sequences. Other contributions of this thesis also include a comparative analysis of the optical flow algorithm with other existing motion estimation and segmentation techniques. The comparison shows that there is need to achieve a balance between accuracy and computational speed for the implementation of any motion estimation algorithm in real time for video surveillance.

The application of membrane bioreactors (MBRs) to wastewater treatment is increasing due to their ability to operate at high biomass concentrations and to deliver effluents of high quality. The major challenges associated with the application of MBRs is fouling which can shorten the useful life of the membrane, increase in the amount of energy consumed, and the cost for membrane cleaning. The main reasons for fouling are the deposition of solids as a cake layer, pore plugging by colloidal particles, adsorption of soluble compounds and biofouling. Fouling is a particular problem for activated sludge membrane bioreactors (AS-MBRs) since this process deals with liquors having a high concentration of total solids as well as dissolved compounds such as extracellular polymeric substances (EPS). The combination of a moving bed biofilm reactor and a membrane reactor (MBBR-MR) has significant potential. It may be considered as a compact wastewater treatment process which can compensate for the drawbacks of AS-MBRs. Readily biodegradable COD is removed in the MBBR while particulate matter is separated by the membrane. To further reduce the membrane fouling the effects of adding an intermediate coagulation stage was investigated critically on membrane fouling. The present study includes an overall assessment of the performance of a combined MBBR-MR system, based on the chemical oxygen demand (COD) removal efficiency and membrane fouling mechanism. The required test runs were conducted using pilot-scale MBBR and ultra filtration membrane. The pilot MBBR had a working volume of 1.8 m3 with a 60% carrier fill fraction. The MBBR was operated with loading rate of 78 ± 21 g/m2/d (HRT of 4 h). The ultra-filtration was spiral wound and composed of polyethersulfone (PES) with a pore size of 0.03 microns. The MBBR feed was obtained from a final treated wastewater effluent in a food processing plant located in SW Ontario. In this research, ferric chloride was also employed as a coagulant and influences of different coagulant doses and permeate fluxes on membrane fouling were studied. Based on the experimental results, it was found that the combination of MBBR with membrane filtration can produce a constant high quality permeate that is appropriate for water reuse purposes. The composition analysis of permeate showed that the stream is free of suspended solids and the average COD turns to 75 ± 25 mg/l. In addition, the MBBR had a SCOD removal of 76% ± 7% which is considered as a reasonable efficiency for a single reactor. Operating the membrane without adding coagulant caused rapid fouling in a short time period and the Trans Membrane Pressure (TMP) reached the maximum allowable pressure of 10 psi. However, addition of coagulant was found to decrease the fouling of the membrane as well as increasing the filtration time. The extent of the pre-coagulation effect on membrane fouling was found to strongly depend on the dosage of the coagulant and the MBBR effluent characteristics. A coagulant dose of 400 mg/l with a permeate flux of 7.6 LMH performed the best at reducing membrane fouling. Colloidal fouling was found to be a significant fouling mechanism at low coagulant dose (e.g. 200 mg/l), while cake formation appeared to be mainly responsible for fouling at higher coagulant doses. Permeate flux was found to have a significant effect on the fouling of the membrane. The presence of colloidal matters at low fluxes and TSS at higher fluxes were responsible for fouling of the membrane by blocking the pores and formation of the cake layer on the membrane surface, respectively. Then later addition of Dissolved Air Flotation (DAF) inside the factory had a noticeable effect on wastewater characteristics and consequently on fouling of the membrane. A 22% and 31% improvement in TCOD and TSS in the wastewater was observed leading to reduction in the fouling.