The predominant model of the human hip is a mechanical ball-and-socket joint. This description has two key implications: that the motion of the hip is purely rotational, and that the rigid articulating geometry of the hip is a sphere-on-sphere contact. Since the widespread adoption of this model, in the late 1960s, there has however been a persistent thread of literature suggesting that the articulating geometry of the hip is aspherical. The recent widespread availability of three-dimensional medical imaging now makes it possible to empirically assess the applicability of the predominant model. For this research dissertation, two arthritic groups were examined: patients either had primary early-life osteoarthritis of the hip, or hip dysplasia with secondary osteoarthritis. Computed tomography scans, taken as part of routine preoperative preparation, served as the source data for this work. The scans were manually segmented to produce 3D models of the bones of the hip, which were further refined to isolate the bony articular surfaces. These surfaces were fit to general ellipsoids and to spheres, the latter being the ball-and-socket model. The arthritic hips examined had comparable fitting accuracy for both ellipsoids and spheres; however, sixteen of nineteen hips formed geometrically incompatible ball-and-socket joints. The dysplastic hips examined had a notable difference in fitting accuracy, with ellipsoids being a statistically significantly better fit to the hip geometry. The ellipsoid shapes in all cases were aspherical, and in each population formed a statistically significantly aspherical group. There were no trends relating the ellipsoid shapes of bones of an individual joint, nor were there practical differences between the ellipsoid shapes between the two populations. Despite patient groups not being controlled for age, sex, or race, and accounting for typical manual segmentation errors, these results suggest that the hip is aspherically shaped. Thus, the geometric foundation of the ball-and-socket motion may be unsupported, and the conventional kinematic description of the hip may be called into question. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2011-09-28 09:41:29.114

Shape optimization has become an important tool in industry to minimize weight and generate new designs. At the same time, companies are turning to CAD-centric design strategies where robust parametric CAD models are used to generate new designs and part-families of current designs, as well as the tooling and manufacturing procedures. However, due to its complexity, the optimal topology results are often discarded or recreated by hand into a CAD model. From a design stand point, the results can be improved with the use of manufacturing constraints on the shape optimization process. These constraints improve the manufacturability based on common manufacturing practices. Even with these improvements, the process of converting topology results to CAD can cost substantial amounts of time and money. This thesis proposes a method of semi-automatically recognizing the voids, created during the shape optimization process, with parametric features based on CAD geometry construction. These parametric features are based on sets of cross-sectional shapes and spine rules to create solid objects. These features are then sent to the CAD part file via programming APIs that exist in the software packages. By recognizing features usable to the CAD systems, the voids can be characterized in the CAD model using robust dimensional constraints. This allows for the CAD model approximation to represent the topology optimization results with dimensional values from simpler shapes. Size optimization can then be applied to optimize the approximating model and regain any fidelity loss in the analytic model. Test cases created with and without manufacturing constraints show considerable promise in a proof-of-concept scenario. These tests utilize the topology optimization software HyperMesh from Altair and the CAD package NX 4.0 from Siemens (formerly UGS). The voids from shape optimization in these tests are recognized inside of HyperMesh, fit with a simple parametric feature, and created in the part model using the Open C API in NX.

There is growing interest in the application of high-resolution radars in autonomous vehicles due to their affordability and high angular resolution. However, the azimuth ambiguity caused by the large physical distance between radar antennas relative to the signal wavelength is a challenge for its application. The problem of multiple extended target tracking using high-resolution radar measurements with azimuth ambiguity is considered. A novel pseudo-3D assignment (P3DA) method based on the pseudo measurement set (PMS) is proposed to resolve the azimuth ambiguity. This method can resolve mono (single) and split (duplicated) azimuth ambiguities common in extended target tracking. The Lagrangian relaxation based on a flexible search (LR-FS) algorithm is proposed to solve the P3DA-PMS problem efficiently. Simulation and experiment results show that the proposed algorithm outperforms conventional methods that do not address the azimuth ambiguity of extended target tracking. Since data association with only one data frame will lose information about target evolution and cannot change an association later based on subsequent measurements, a novel two-step multiframe assignment method is proposed to resolve split and azimuth ambiguity separately. In the first step, the split ambiguity is resolved by the PMS-to-PMS association, resulting in a merged PMS (MPMS). In the second step, the azimuth ambiguity is resolved by the Track-to-MPMS association. Numerical results show that the proposed method performs better than the P3DA-PMS-based method. The vehicles tracking with high-resolution radars need to provide information about their orientation and shape to achieve lidar-like performance. Due to self-occlusion, the L-shape model is frequently utilized to depict the structure of a typical vehicle. Since the measurement accuracy of high-resolution radars is not as high as that of lidars, radar measurement noise cannot be ignored. Moreover, as a side effect of using large wavelengths, multiple measurements may be produced per time step due to multipath effects. As a result, more outliers and inliers can be generated in high-resolution radar measurements. A novel lognormal likelihood-aided L-shape model is proposed to approximate the distribution of high-resolution radar measurements of vehicles. Numerical results evaluated on simulation data and the KITTI dataset show that the proposed algorithm achieves smaller orientation and position errors and larger generalized intersection over union (GIoU) compared to existing L-shape fitting algorithms for lidar measurements. / Dissertation / Doctor of Philosophy (PhD)