Prediction of 3-dimensional coverage surface area of the femoral head in hip dysplasia through conventional computed tomography

BACKGROUND: Assessment of 3-dimensional (3D) femoral head coverage is critical in evaluating, preoperative planning, and treating hip dysplasia. PURPOSE: To (1) propose a mathematical model to establish 3D femoral head coverage using conventional computed tomography (CT), (2) determine the correlation of 2D parameters with 3D coverage, and (3) characterize the patterns of dysplasia based on 3D morphology. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: We identified 30 patients (n = hips) with symptomatic dysplasia and 30 patients (n = hips) without dysplasia. Patients with dysplastic hips were matched with regard to sex, age, and body mass index to those with nondysplastic hips. Preoperative CTs were analyzed using 3D software, and 3D femoral head surface area coverage (FHSAC; in %) was assessed in 4 quadrant zones: anteromedial, anterolateral, posteromedial, and posterolateral. To assess lateral coverage of the femoral head, we introduced the anterolateral femoral head coverage angle (ALFC) and the posterolateral femoral head coverage angle (PLFC). RESULTS: Reduced femoral head coverage was more pronounced in dysplastic versus nondysplastic hips in the anterolateral quadrant (18% vs 40.7%, respectively) and posterolateral quadrant (35.8% vs 56.9%, respectively) ( CONCLUSION: The ALFC and The PLFC were strongly correlated with 3D lateral FHSAC and were able to predict 3D coverage accurately

Multi-Robot 3D Coverage of Unknown Terrains

International audienceIn this paper we study the problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. In such a mission, the robots should simultaneously accomplish two objectives: firstly, to make sure that the overall terrain is visible by the team and, secondly, that the distance between each point in the terrain and one of the robots is as small as possible. These two objectives should be efficiently fulfilled given the physical constraints and limitations imposed at the particular coverage application (i.e., obstacle avoidance, limited sensor capabilities, etc). As the terrain's morphology is unknown and it can be quite complex and non-convex, standard multi-robot coordination and control algorithms are not applicable to the particular problem treated in this paper. In order to overcome such a problem, a new approach that is based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated in this paper. Both rigorous mathematical arguments and extensive simulations on unknown terrains establish that the proposed approach provides an efficient methodology that can easily incorporate any particular constraints and quickly and safely navigate the robots to an arrangement that optimizes surveillance coverage

Theoretical Findings and Measurements on Planning a UHF RFID System inside a Room

This paper investigates the problem of improving the identification performance of a UHF RFID system inside a room. We assume static reader, passive tags and availability of commodity antennas. A ray-tracing propagation model is developed that includes multipath in 3D space. It is found that careful selection of reader antenna placement and tilting must be performed to control destructive interference effects. Furthermore, 3D coverage performance gains on the order of 10% are observed by implementing tags’ diversity. A device that successfully manipulates destructive interference is introduced. All theoretical findings are verified by measurements. Finally, a method to perform propagation measurements with commodity RFID hardware is demonstrated

Coverage and Connectivity in Three-Dimensional Networks

Most wireless terrestrial networks are designed based on the assumption that the nodes are deployed on a two-dimensional (2D) plane. However, this 2D assumption is not valid in underwater, atmospheric, or space communications. In fact, recent interest in underwater acoustic ad hoc and sensor networks hints at the need to understand how to design networks in 3D. Unfortunately, the design of 3D networks is surprisingly more difficult than the design of 2D networks. For example, proofs of Kelvin's conjecture and Kepler's conjecture required centuries of research to achieve breakthroughs, whereas their 2D counterparts are trivial to solve. In this paper, we consider the coverage and connectivity issues of 3D networks, where the goal is to find a node placement strategy with 100% sensing coverage of a 3D space, while minimizing the number of nodes required for surveillance. Our results indicate that the use of the Voronoi tessellation of 3D space to create truncated octahedral cells results in the best strategy. In this truncated octahedron placement strategy, the transmission range must be at least 1.7889 times the sensing range in order to maintain connectivity among nodes. If the transmission range is between 1.4142 and 1.7889 times the sensing range, then a hexagonal prism placement strategy or a rhombic dodecahedron placement strategy should be used. Although the required number of nodes in the hexagonal prism and the rhombic dodecahedron placement strategies is the same, this number is 43.25% higher than the number of nodes required by the truncated octahedron placement strategy. We verify by simulation that our placement strategies indeed guarantee ubiquitous coverage. We believe that our approach and our results presented in this paper could be used for extending the processes of 2D network design to 3D networks.Comment: To appear in ACM Mobicom 200