No / A membrane-inspired evolutionary algorithm (MIEA) is a successful instance of a model linking membrane computing and evolutionary algorithms. This paper proposes the analysis of dynamic behaviors of MIEAs by introducing a set of population diversity and convergence measures. This is the first attempt to obtain additional insights into the search capabilities of MIEAs. The analysis is performed on the MIEA, QEPS (a quantum-inspired evolutionary algorithm based on membrane computing), and its counterpart algorithm, QIEA (a quantum-inspired evolutionary algorithm), using a comparative approach in an experimental context to better understand their characteristics and performances. Also the relationship between these measures and fitness is analyzed by presenting a tendency correlation coefficient to evaluate the importance of various population and convergence measures, which is beneficial to further improvements of MIEAs. Results show that QEPS can achieve better balance between convergence and diversity than QIEA, which indicates QEPS has a stronger capacity of balancing exploration and exploitation than QIEA in order to prevent premature convergence that might occur. Experiments utilizing knapsack problems support the above made statement.

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.

This dissertation offers my personal exploration of the loss of my father through the eyes of multiple selves. Utilizing an arts-inspired autoethnographic narrative case study approach, I detail my journey of meaning making as I explore my personal transitions and self-discovery in the face of my bereavement, while also uncovering the potential for growth and development within my relationship with my dad. Throughout this dissertation, I incorporate a variety of mediums to capture the essence of the experience of my filial bereavement. Through this synthesis of form, it is my goal to invite witnesses to enter my experience, to have the opportunity to explore a different way of knowing by being able to look through the eyes of my multiple experiencing selves and their presentation of emotion, thought, and behaviour. Through blending of genre, this study provides a unique way of exploring a lived experience. It is meant to provide a specific view of a broad topic from multiple angles. Though it is situated within my personal bereavement, a daughter’s loss of her father, and inevitably my story will demonstrate the cultural influence of my Canadian background, it also aims to touch on aspects of the universality of loss, of bereavement, and what it means to be alive.

Rapid prototyping tools exist in many fields of science and engineering, but are rare in biology especially not general tools that can handle the diversity and complexity of the many spatial and temporal scales in nature. In this thesis a general use, cell-based, middle-out biology emulation programming framework (outlining a programming paradigm) is presented, that enables biologists to emulate and use virtual biological systems of previously unimaginable complexity and potentially get results accurate enough to be used in research and ultimately, in clinical practice, such as diagnosis or operations. With this technology, virtual organisms can be created that are viable, fit and can be optimised for any task that arises. The tool, realised with a programming framework created for the C++ language is detailed and demonstrated through several examples of increasing complexity, namely several example organisms and a cancer emulation, showing both viable virtual organisms and usable experimental results.

The overall aim of the thesis has been to introduce Personal Mobile Grids (PMGrids) as a novel paradigm in grid computing that scales grid infrastructures to mobile devices and extends grid entities to individual personal users. In this thesis, architectural designs as well as simulation models for PM-Grids are developed. The core of any grid system is its resource scheduler. However, virtually all current conventional grid schedulers do not address the non-clairvoyant scheduling problem, where job information is not available before the end of execution. Therefore, this thesis proposes a honeybee inspired resource scheduling heuristic for PM-Grids (HoPe) incorporating a radical approach to grid resource scheduling to tackle this problem. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are initiated. Among the other main contributions are a comprehensive taxonomy of grid systems as well as a detailed analysis of the honeybee colony and its nectar acquisition process (NAP), from the resource scheduling perspective, which have not been presented in any previous work, to the best of our knowledge. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Comparisons with optimal values and worst bounds are conducted to gain a clear insight into HoPe behaviour, in terms of stability, throughput, turnaround time and speedup, under different running conditions of number of jobs and grid scales. Experimental results demonstrate the superiority of HoPe performance where it has successfully maintained optimum stability and throughput in more than 95% of the experiments, with HoPe achieving three times better than the OSH under extremely heavy loads. Regarding the turnaround time and speedup, HoPe has effectively achieved less than 50% of the turnaround time incurred by the OSH, while doubling its speedup in more than 60% of the experiments. These results indicate the potential of both PM-Grids and HoPe in realising futuristic grid visions. Therefore considering the deployment of PM-Grids in real life scenarios and the utilisation of HoPe in other parallel processing and high throughput computing systems are recommended.

A direction of arrival (DoA) estimation technique for broadband microwave signals is proposed using a single ultrawideband antenna. It is inspired by the sound source localization ability of a human auditory system using just one ear (monaural localization). By exploiting the incident angle-dependent frequency response of a wideband antenna, the DoA of a broadband microwave signal can be estimated. The DoA estimation accuracies are evaluated for two antenna configurations and microwave signals with different signal-to-noise ratios. Encouraging the DoA estimation performance of the proposed technique is demonstrated in both simulation and experiment.

Master of Science / Department of Clinical Sciences / Rose M. McMurphy / Hypoventilation (PaCO2 > 45 mmHg) and large P(A-a)O2 gradients due to V/Q mismatch and shunt, are common during isoflurane anesthesia in horses. A fraction of inspired oxygen < 50% has been shown to improve ventilation and decrease intra-operative atelectasis in humans and some animals. The study compared the effects of two different fractions of inspired oxygen, 50% versus > 95%, on ventilation, respiratory pattern, and P(A-a)O2 gradient in isoflurane anesthetized horses. Eight mature horses were sedated with IV xylazine (1.0 mg/kg) and anesthetized with diazepam (0.05 mg/kg) and ketamine (2.2 mg/kg) twice. Anesthesia was maintained with isoflurane (ET1.5 vol%) in either 50 or > 95% oxygen for 90 minutes. Both treatments were randomly assigned to each horse with a one week interval in between treatments. Horses were positioned in dorsal recumbency, connected to a preloaded circle breathing system and allowed to spontaneously ventilate. Measurements included inspiratory and expiratory peak flow and time, tidal volume, respiratory frequency, ETCO2, CO2, O2, PaO2, PaCO2, pH, SaO2, heart rate, and arterial blood pressure. Calculated values included PAO2, P(A-a)O2, P(A-a)O2 rate of change, and physiologic dead space. FiO2 of 50% resulted in a lower PaO2, SaO2, PAO2, and P(A-a)O2. No significant change in PaCO2, ventilatory pattern, or any remaining measured variables was observed (p<0.05). The use of 50% oxygen and nitrogen as the carrier gas did not significantly change the ventilatory characteristics or improve oxygenation in isoflurane anesthetized horses. Repeatable respiratory rhythms characteristics were observed for horses while inspiring 50% and > 95% oxygen. A high A-a oxygen gradient with an equal rate of change overtime was still observed during both treatments.

L’étude du fonctionnement de notre système nerveux et des mécanismes sensoriels a mené à la création de capteurs événementiels. Ces capteurs ont un fonctionnement qui retranscrit les atouts de nos yeux et oreilles par exemple. Cette thèse se base sur la recherche de méthodes bio-inspirés et peu coûteuses en énergie permettant de traiter les données envoyées par ces nouveaux types de capteurs. Contrairement aux capteurs conventionnels, nos rétines et cochlées ne réagissent qu’à l’activité perçue dans l’environnement sensoriel. Les implémentations de type « rétine » ou « cochlée » artificielle, que nous appellerons capteurs dynamiques, fournissent des trains d’évènements comparables à des impulsions neuronales. La quantité d’information transmise est alors étroitement liée à l’activité présentée, ce qui a aussi pour effet de diminuer la redondance des informations de sortie. De plus, n’étant plus contraint à suivre une cadence d’échantillonnage, les événements créés fournissent une résolution temporelle supérieure. Ce mode bio-inspiré de retrait d’information de l’environnement a entraîné la création d’algorithmes permettant de suivre le déplacement d’entité au niveau visuel ou encore reconnaître la personne parlant ou sa localisation au niveau sonore, ainsi que des implémentations d’environnements de calcul neuromorphiques. Les travaux que nous présentons s’appuient sur ces nouvelles idées pour créer de nouvelles solutions de traitement. Plus précisément, les applications et le matériel développés s’appuient sur un codage temporel de l’information dans la suite d'événements fournis par le capteur. / Studying how our nervous system and sensory mechanisms work lead to the creation of event-driven sensors. These sensors follow the same principles as our eyes or ears for example. This Ph.D. focuses on the search for bio-inspired low power methods enabling processing data from this new kind of sensor. Contrary to legacy sensors, our retina and cochlea only react to the perceived activity in the sensory environment. The artificial “retina” and “cochlea” implementations we call dynamic sensors provide streams of events comparable to neural spikes. The quantity of data transmitted is closely linked to the presented activity, which decreases the redundancy in the output data. Moreover, not being forced to follow a frame-rate, the created events provide increased timing resolution. This bio-inspired support to convey data lead to the development of algorithms enabling visual tracking or speaker recognition or localization at the auditory level, and neuromorphic computing environment implementation. The work we present rely on these new ideas to create new processing solutions. More precisely, the applications and hardware developed rely on temporal coding of the data in the spike stream provided by the sensors.

The cellular nature of many biological materials, providing them with low density, high strength and high toughness, have fascinated many researchers in the field of botany and structural biology since at least one century. Bamboo, sponges, trabecular bone, tooth and honeybee combs are only few examples of natural materials with cellular architecture. It has been widely recognised that the geometric and mechanical characteristics of the microscopic building blocks play a fundamental role on the behavior observed at the macroscale. Up to date, many efforts have been devoted to the analysis of cellular materials with empty cells to predict the structure-property relations that link the macroscopic properties to the mechanics of their underlying microstructure. Surprisingly, notwithstanding the great advantages of the composite solutions in nature, in the literature a limited number of investigations concern cellular structures having the internal volumes of the cells filled with fluids, fibers or other bulk materials as commonly happens in biology. In particular, a continuum model has not been derived and explicit formulas for the effective elastic constants and constitutive relations are currently not available. To provide a contribution in this limitedly explored research area, this thesis describes the mathematical formulation and modelling technique leading to explicit expressions for the macroscopic elastic constants and stress-strain relations of biologically inspired composite cellular materials. Two examples are included. The first deals with a regular hexagonal architecture inspired by the biological parenchyma tissue. The second concerns a mutable cellular structure, composed by mutable elongated hexagonal cells, inspired by the hygroscopic keel tissue of the ice plant Delosperma nakurense. In both cases, the predicted results are found to be in very good agreement with the available data in the literature. Then, by taking into account the benefits offered by the complex hierarchical organisation of many natural systems, the attention is focused on the potential value of adding structural hierarchy into two-dimensional composite cellular materials having a self-similar hierarchical architecture, in the first case, and different levels with different cell topologies, in the second. In contrast to the traditional cellular materials with empty cells, the analysis reveals that, in the cell-filled configuration, introducing levels of hierarchy leads to an improvement in the specific stiffness. Finally, to offer concrete and relevant tools to engineers for developing future generations of materials with enhanced performance and unusual functionalities, a novel strategy to obtain a honeycomb with mutable cells is proposed. The technique, based on the ancient Japanese art of kirigami, consists in creating a pattern of cuts into a flat sheet of starting material, which is then stretched to give a honeycomb architecture. It emerges a vast range of effective constants that the so-called kirigami honeycomb structures can be designed with, just by changing the value of the applied stretch.

A Clusterização de dados em grupos oferece uma maneira de entender e extrair informações relevantes de grandes conjuntos de dados. A abordagem em relação a aspectos como a representação dos dados e medida de similaridade entre clusters, e a necessidade de ajuste de parâmetros iniciais são as principais diferenças entre os algoritmos de clusterização, influenciando na qualidade da divisão dos clusters. O uso cada vez mais comum de grandes conjuntos de dados aliado à possibilidade de melhoria das técnicas já existentes tornam a clusterização de dados uma área de pesquisa que permite inovações em diferentes campos. Nesse trabalho é feita uma revisão dos métodos de clusterização já existentes, e é descrito um novo método de clusterização de dados baseado na identificação de comunidades em redes complexas e modelos computacionais inspirados biologicamente. A técnica de clusterização proposta é composta por duas etapas: formação da rede usando os dados de entrada; e particionamento dessa rede para obtenção dos clusters. Nessa última etapa, a técnica de otimização por nuvens de partículas é utilizada a fim de identificar os clusters na rede, resultando em um algoritmo de clusterização hierárquico divisivo. Resultados experimentais revelaram como características do método proposto a capacidade de detecção de clusters de formas arbitrárias e a representação de clusters com diferentes níveis de refinamento. / DAta clustering is an important technique to understand and to extract relevant information in large datasets. Data representation and similarity measure adopted, and the need to adjust initial parameters, are the main differences among clustering algorithms, interfering on clusters quality. The crescent use of large datasets and the possibility to improve existing techniques make data clustering a research area that allows innovation in different fields. In this work is made a review of existing data clustering methods, and it is proposed a new data clustering technique based on community dectection on complex networks and bioinspired models. The proposed technique is composed by two steps: network formation to represent input data; and network partitioning to identify clusters. In the last step, particle swarm optimization technique is used to detect clusters, resulting in an hierarchical clustering algorithm. Experimental results reveal two main features of the algorithm: the ability to detect clusters in arbitrary shapes and the ability to generate clusters with different refinement degrees