It is well established that acquiring large amounts of range data with vision sensors can quickly lead to important data management challenges where processing capabilities become saturated and pre-empt full usage of the information available for autonomous systems to make educated decisions. While sub-sampling offers a naïve solution for reducing dataset dimension after acquisition, it does not capitalize on the knowledge available in already acquired data to selectively and dynamically drive the acquisition process over the most significant regions in a scene, the latter being generally characterized by variations in depth and surface shape in the context of 3D imaging. This thesis discusses the development of two formal improvement measures, the first based upon surface meshes and Ordinary Kriging that focuses on improving scene accuracy, and the second based upon probabilistic occupancy grids that focuses on improving scene coverage. Furthermore, three selection processes to automatically choose which locations within the field of view of a range sensor to acquire next are proposed based upon the two formal improvement measures. The first two selection processes each use only one of the proposed improvement measures. The third selection process combines both improvement measures in order to counterbalance the parameters of the accuracy of knowledge about the scene and the coverage of the scene. The proposed algorithms mainly target applications using random access range sensors, defined as sensors that can acquire depth measurements at a specified location within their field of view. Additionally, the algorithms are applicable to the case of estimating the improvement and point selection from within a single point of view, with the purpose of guiding the random access sensor to locations it can acquire. However, the framework is developed to be independent of the range sensing technology used, and is validated with range data of several scenes acquired from many different sensors employing various sensing technologies and configurations. Furthermore, the experimental results of the proposed selection processes are compared against those produced by a random sampling process, as well as a neural gas selective sensing algorithm.

It is well established that acquiring large amounts of range data with vision sensors can quickly lead to important data management challenges where processing capabilities become saturated and pre-empt full usage of the information available for autonomous systems to make educated decisions. While sub-sampling offers a naïve solution for reducing dataset dimension after acquisition, it does not capitalize on the knowledge available in already acquired data to selectively and dynamically drive the acquisition process over the most significant regions in a scene, the latter being generally characterized by variations in depth and surface shape in the context of 3D imaging. This thesis discusses the development of two formal improvement measures, the first based upon surface meshes and Ordinary Kriging that focuses on improving scene accuracy, and the second based upon probabilistic occupancy grids that focuses on improving scene coverage. Furthermore, three selection processes to automatically choose which locations within the field of view of a range sensor to acquire next are proposed based upon the two formal improvement measures. The first two selection processes each use only one of the proposed improvement measures. The third selection process combines both improvement measures in order to counterbalance the parameters of the accuracy of knowledge about the scene and the coverage of the scene. The proposed algorithms mainly target applications using random access range sensors, defined as sensors that can acquire depth measurements at a specified location within their field of view. Additionally, the algorithms are applicable to the case of estimating the improvement and point selection from within a single point of view, with the purpose of guiding the random access sensor to locations it can acquire. However, the framework is developed to be independent of the range sensing technology used, and is validated with range data of several scenes acquired from many different sensors employing various sensing technologies and configurations. Furthermore, the experimental results of the proposed selection processes are compared against those produced by a random sampling process, as well as a neural gas selective sensing algorithm.

Silicone polymers and network-materials have proven extremely useful in a variety of applications owing to their superb properties when compared to carbon-based polymers. Polysiloxanes containing functional groups other than simple alkyl moieties have allowed for further manipulations of pendant groups along the polymer backbone leading to a greater range of possible chemical transformations, as well as changes in physical/interfacial properties. One aspect of functional polymers that has yet to be explored with respect to primarily silicone-based systems is that of stimuli-responsive materials. In order for this unique application to work, silicones must be functionalized with a group or groups that can influence the polymer’s properties based on that group’s response to specific external stimuli. Boronic acids represent one such group, wherein the most common stimuli used to affect changes in ionization state and solubility are pH and diol-binding. Boronic acids are also capable of forming weak hydrogen-bonded dimers with other boronic acids, and dynamic covalent bonds with Lewis bases. It is proposed that the covalent attachment of boronic acids and their derivatives onto silicones could lead to stimuli-responsive silicone materials. Herein, the synthesis of silicone-boronic acids and their protected boronic esters is described. The simple two-step method involving boronic acid protection followed by hydrosilylation has led to a variety of molecules differing in molecular weight and three-dimensional geometry through the use of commercially available hydride-functional silicones. Initial results regarding saccharide binding selectivity and the impacts on silicone solubility are provided. The unique interfacial behaviour of silicone-boronic esters and their propensity to form self-assembled, crosslinked films at an air/water interface are also reported. Using several different diol protecting groups and a variety of aqueous sub-phases, the mechanism for changes in physical properties as well as crosslinking were revealed. Finally, the production of new thermoplastic silicone elastomers from silicone-boronic esters and amine-containing molecules is discussed. The Lewis acid/Lewis base complexation that occurs between nitrogen and boron can provide enough strength to produce robust, yet recyclable, silicone elastomers without the use of catalyst or solvent. Elastomers can be easily dissolved and reformed through the introduction and removal of a mono-functional Lewis base. The impact of crosslink density, controlled by the quantities and molecular weights of each polymer component used, on physical characteristics is reported. / Thesis / Doctor of Philosophy (PhD)