We present a novel, hybrid parallel continuous collision detection (HPCCD) method that exploits the availability of multi-core CPU and GPU architectures. HPCCD is based on a bounding volume hierarchy (BVH) and selectively performs lazy reconstructions. Our method works with a wide variety of deforming models and supports selfcollision detection. HPCCD takes advantage of hybrid multi-core architectures -using the general-purpose CPUs to perform the BVH traversal and culling while GPUs are used to perform elementary tests that reduce to solving cubic equations. We propose a novel task decomposition method that leads to a lock-free parallel algorithm in the main loop of our BVH-based collision detection to create a highly scalable algorithm. By exploiting the availability of hybrid, multi-core CPU and GPU architectures, our proposed method achieves more than an order of magnitude improvement in performance using four CPU-cores and two GPUs, compared to using a single CPU-core. This improvement results in an interactive performance, up to 148 fps, for various deforming benchmarks consisting of tens or hundreds of thousand triangles.
We propose a novel, multi-resolution method to efficiently perform large-scale cloth simulation. Our cloth simulation method is based on a triangle-based energy model constructed from a cloth mesh. We identify that solutions of the linear system of cloth simulation are smooth in certain regions of the cloth mesh and solve the linear system on those regions in a reduced solution space. Then we reconstruct the original solutions by performing a simple interpolation from solutions computed in the reduced space. In order to identify regions where solutions are smooth, we propose simplification metrics that consider stretching, shear, and bending forces, as well as geometric collisions. Our multi-resolution method can be applied to many existing cloth simulation methods, since our method works on a general linear system. In order to demonstrate benefits of our method, we apply our method into four large-scale cloth benchmarks that consist of tens or hundreds of thousands of triangles. Because of the reduced computations, we achieve a performance improvement by a factor of up to one order of magnitude, with a little loss of simulation quality.
The purpose of this project was to understand the nature of an architect's professional power. The central questions were: (1) What is the impact of specialized knowledge on the professional autonomy of architects in general? and (2) What are the relationships between task complexity, specialized knowledge, and the professional autonomy of healthcare architects in particular? To answer these questions, this research utilized interviews and focus groups. Focus groups provided in-depth knowledge on a sub-question: How do real-world situations restrict or reinforce the professional autonomy of healthcare architects? The interviews on this sub-question were project-specific to help gain an understanding of the impact that healthcare design complexity and research utilization have on practice and professional autonomy. Two main relationships were discovered from the interviews and focus groups. One was the relationship between the context of healthcare design complexity and the culture of healthcare design practice. The other was the relationship between changing professional attitudes and the consequences of changes in the profession.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.