458 research outputs found

    A research on a reconfigurable hypar structure for architectural applications

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    Thesis (Master)--İzmir Institute of Technology, Architecture, İzmir, 2013Includes bibliographical references (leaves: 102-108)Text in English; Abstract: Turkish and Englishxii, 108 leavesKinetic design strategy is a way to obtain remarkable applications in architecture. These kinetic designs can offer more advantages compared to conventional ones. Basic knowledge of different disciplines is necessary to generate kinetic designs. In other words, interdisciplinary studies are critical. Therefore, architect's knowledge must be wide-ranging in order to increase novel design approaches and applications. The resulting rich hybrid products increase the potential of the disciplines individually. Research on kinetic structures shows that the majority of kinetic structures are deployable. However, deployable structures can only be transformed from a closed compact configuration to a predetermined expanded form. The motivation of the present dissertation is generating a novel 2 DOF 8R reconfigurable structure which can meet different hyperbolic paraboloid surfaces for architectural applications. In order to obtain this novel structure; the integration between the mechanism science and architecture is essential. The term reconfigurable will be used in the present dissertation to describe deployable structures with various configurations. The novel reconfigurable design utilizes the overconstrained Bennett linkage and the production principals of ruled surfaces. The dissertation begins with a brief summary of deployable structures to show their shortcomings and their lack of form flexibility. Afterward, curved surfaces, basic terms in mechanisms and overconstrained mechanisms were investigated. Finally, a proposed novel mechanism which is inspired from the basic design principles of Bennett linkage and the fundamentals of ruled surfaces are explained with the help of kinematic diagrams and models

    EXTENDING CONVOLUTION THROUGH SPATIALLY ADAPTIVE ALIGNMENT

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    Convolution underlies a variety of applications in computer vision and graphics, including efficient filtering, analysis, simulation, and neural networks. However, convolution has an inherent limitation: when convolving a signal with a filter, the filter orientation remains fixed as it travels over the domain, and convolution loses effectiveness in the presence of deformations that change alignment of the signal relative to the local frame. This problem metastasizes when attempting to generalize convolution to domains without a canonical orientation, such as the surfaces of 3D shapes, making it impossible to locally align signals and filters in a consistent manner. This thesis presents a unified framework for transformation-equivariant convolutions on arbitrary homogeneous spaces and 2D Riemannian manifolds called extended convolution. This approach is based on the the following observation: to achieve equivariance to an arbitrary class of transformations, we only need to consider how the positions of points as seen in the frames of their neighbors deform. By defining an equivariant frame operator at each point with which we align the filter, we correct for the change in the relative positions induced by the transformations. This construction places no constraints on the filters, making extended convolution highly descriptive. Furthermore, the framework can handle arbitrary transformation groups, including higher-dimensional non-compact groups that act non-linearly on the domain. Critically, extended convolution naturally generalizes to arbitrary 2D Riemannian manifolds as it does not need a canonical coordinate system to be applied. The power and utility of extended convolution is demonstrated in several applications. A unified framework for image and surface feature descriptors called Extended Convolution Histogram of Orientations (ECHO) is proposed, based on the optimal filters maximizing the response of the extended convolution at a given point. Using the generalization of extended convolution to surface vector fields, state-of-the-art surface convolutional neural networks (CNNs) are constructed. Last, we move beyond rotations and isometries and use extended convolution to design spherical CNNs equivariant to Mobius transformations, representing a first step toward conformally-equivariant surface networks

    Automated visual inspection for the quality control of pad printing

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    Pad printing is used to decorate consumer goods largely because of its unique ability to apply graphics to doubly curved surfaces. The Intelpadrint project was conceived to develop a better understanding of the process and new printing pads, inks and printers. The thesis deals primarily with the research of a printer control system including machine vision. At present printing is manually controlled. Operator knowledge was gathered for use by an expert system to control the process. A novel local corner- matching algorithm was conceived to effect image segmentation, and neuro-fuzzy techniques were used to recognise patterns in printing errors. Non-linear Finite Element Analysis of the rubber printing-pad led to a method for pre-distorting artwork so that it would print undistorted on a curved product. A flexible, more automated printer was developed that achieves a higher printing rate. Ultraviolet-cured inks with improved printability were developed. The image normalisation/ error-signalling stage in inspection was proven in isolation, as was the pattern recognition system

    Courbure discrète : théorie et applications

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    International audienceThe present volume contains the proceedings of the 2013 Meeting on discrete curvature, held at CIRM, Luminy, France. The aim of this meeting was to bring together researchers from various backgrounds, ranging from mathematics to computer science, with a focus on both theory and applications. With 27 invited talks and 8 posters, the conference attracted 70 researchers from all over the world. The challenge of finding a common ground on the topic of discrete curvature was met with success, and these proceedings are a testimony of this wor

    Defects and defect engineering in Soft Matter

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    Self-organization in heterogeneous biological systems

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    Self-organization is an ubiquitous and fundamental process that underlies all living systems. In cellular organisms, many vital processes, such as cell division and growth, are spatially and temporally regulated by proteins -- the building blocks of life. To achieve this, proteins self-organize and form spatiotemporal patterns. In general, protein patterns respond to a variety of internal and external stimuli, such as cell shape or inhomogeneities in protein activity. As a result, the dynamics of intracellular pattern formation generally span multiple spatial and temporal scales. This thesis addresses the underlying mechanisms that lead to the formation of heterogeneous patterns. The main themes of this work are organized into three parts, which are summarized below. The first part deals with the general problem of mass-conserving reaction-diffusion dynamics in spatially non-uniform systems. In section 1 of chapter II, we study the dynamics of the E. coli Min protein system -- a paradigmatic model for pattern formation. More specifically, we consider a setup with a fixed spatial heterogeneity in a control parameter, and show that this leads to complex multiscale pattern formation. We develop a coarse-graining approach that enables us to explain and reduce the dynamics to the "hydrodynamic variables'' at large length and time scales. In another project, we consider a system where spatial heterogeneities are not imposed externally, but self-generated by the dynamics via a mechanochemical feedback loop between geometry and reaction-diffusion system (section 2 of chapter II). We show that the resulting dynamics can be explained from the phase-space geometry of the reaction-diffusion system. The second part focuses on how patterns in realistic cell geometries are controlled by shape and biochemical cues. We examine axis selection of PAR polarity patterns in C. elegans, where we show that spatial variations in the bulk-surface ratio and a tendency of the system to minimize the pattern interface yield robust long-axis polarization of PAR protein patterns (section 1 of chapter III). In a second project, we develop a theoretical model that explains the localization of the B. subtilis Min protein system (section 2 of chapter 3). We show that a biochemical cue -- which acts as a template for pattern formation -- guides and stabilizes Min patterns. In the third part, we study the coupling between lipid membranes and curvature-generating proteins. We demonstrate that myosin-VI motor proteins cooperatively bind to saddle-shaped regions of lipid membranes, and thereby induce large-scale membrane remodeling (section 1 of chapter IV). To understand the dynamics, we develop a coarse-grained geometric model and show that the emergence of regular spatial structures can be explained by a "push-pull'' mechanism: protein binding destabilizes the membrane shape at all length scales, and this is counteracted by line tension. Inspired by this system, we then investigate a general model for the dynamics of growing protein-lipid interfaces (section 2 of chapter IV). A key feature of the model is that the protein binding kinetics is explicitly coupled to the morphology of the interface. We show that such a coupling leads to turbulent dynamics and a roughening transition of the interface that is characterized by universal scaling behaviour

    Study and Development of Techniques for 3D Dental Identification

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    Ph.DDOCTOR OF PHILOSOPH
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