Browsing by Author "Quek, Francis"

Now showing 1 - 5 of 5
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    An Application-Oriented Approach for Accelerating Data-Parallel Computation with Graphics Processing Unit
    Ponce, Sean; Jing, Huang; Park, Seung In; Khoury, Chase; Quek, Francis; Cao, Yong (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009-03-01)
    This paper presents a novel parallelization and quantitative characterization of various optimization strategies for data-parallel computation on a graphics processing unit (GPU) using NVIDIA's new GPU programming framework, Compute Unified Device Architecture (CUDA). CUDA is an easy-to-use development framework that has drawn the attention of many different application areas looking for dramatic speed-ups in their code. However, the performance tradeoffs in CUDA are not yet fully understood, especially for data-parallel applications. Consequently, we study two fundamental mathematical operations that are common in many data-parallel applications: convolution and accumulation. Specifically, we profile and optimize the performance of these operations on a 128-core NVIDIA GPU. We then characterize the impact of these operations on a video-based motion-tracking algorithm called vector coherence mapping, which consists of a series of convolutions and dynamically weighted accumulations, and present a comparison of different implementations and their respective performance profiles.
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    The Catchment Feature Model: A Device for Multimodal Fusion and a Bridge between Signal and Sense
    Quek, Francis (2004-09-18)
    The catchment feature model addresses two questions in the field of multimodal interaction: how we bridge video and audio processing with the realities of human multimodal communication, and how information from the different modes may be fused. We argue from a detailed literature review that gestural research has clustered around manipulative and semaphoric use of the hands, motivate the catchment feature model psycholinguistic research, and present the model. In contrast to “whole gesture” recognition, the catchment feature model applies a feature decomposition approach that facilitates cross-modal fusion at the level of discourse planning and conceptualization. We present our experimental framework for catchment feature-based research, cite three concrete examples of catchment features, and propose new directions of multimodal research based on the model.
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    The Effects of Finger-Walking in Place (FWIP) on Spatial Knowledge Acquisition in Virtual Environments
    Kim, Ji-Sun; Gracanin, Denis; Quek, Francis (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009-12-01)
    Spatial knowledge, necessary for efficient navigation, comprises route knowledge (memory of landmarks along a route) and survey knowledge (overall representation like a map). Virtual environments (VEs) have been suggested as a power tool for understanding some issues associated with human navigation, such as spatial knowledge acquisition. The Finger-Walking-in-Place (FWIP) interaction technique is a locomotion technique for navigation tasks in immersive virtual environments (IVEs). The FWIP was designed to map a human’s embodied ability overlearned by natural walking for navigation, to finger-based interaction technique. Its implementation on Lemur and iPhone/iPod Touch devices was evaluated in our previous studies. In this paper, we present a comparative study of the joystick’s flying technique versus the FWIP. Our experiment results show that the FWIP results in better performance than the joystick’s flying for route knowledge acquisition in our maze navigation tasks.
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    Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling
    Park, Seung In; Cao, Yong; Watson, Layne T.; Quek, Francis (Department of Computer Science, Virginia Polytechnic Institute & State University, 2012)
    Though the GPGPU concept is well-known in image processing, much more work remains to be done to fully exploit GPUs as an alternative computation engine. This paper investigates the computation-to-core mapping strategies to probe the efficiency and scalability of the robust facet image modeling algorithm on GPUs. Our fine-grained computation-to-core mapping scheme shows a significant performance gain over the standard pixel-wise mapping scheme. With in-depth performance comparisons across the two different mapping schemes, we analyze the impact of the level of parallelism on the GPU computation and suggest two principles for optimizing future image processing applications on the GPU platform.
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    Supporting Learning through Spatial Information Presentations in Virtual Environments
    Ragan, Eric Dennis (Virginia Tech, 2013-06-11)
    Though many researchers have suggested that 3D virtual environments (VEs) could provide advantages for conceptual learning, few studies have attempted to evaluate the validity of this claim. While many educational VEs share the challenge of providing learners with information within 3D spaces, few researchers have investigated what approaches are used to help learn new information from 3D spatial representations. It is not understood how well learners can take advantage of 3D layouts to help understand information. Additionally, although complex arrangements of information within 3D space can potentially allow for large amounts of information to be presented within a VE, accessing this information can become more difficult due to the increased navigational challenges. Complicating these issues are details regarding display types and interaction devices used for educational applications. Compared to desktop displays, more immersive VE systems often provide display features (e.g., stereoscopy, increased field of view) that support improved perception and understanding of spatial information. Additionally, immersive VE often allow more familiar, natural interaction methods (e.g., physical walking or rotation of the head and body) to control viewing within the virtual space. It is unknown how these features interact with the types of spatial information presentations to affect learning. The research presented in this dissertation investigates these issues in order to further the knowledge of how to design VEs to support learning. The research includes six studies (five empirical experiments and one case study) designed to investigate how spatial information presentations affect learning effectiveness and learner strategies. This investigation includes consideration for the complexity of spatial information layouts, the features of display systems that could affect the effectiveness of spatial strategies, and the degree of navigational control for accessing information. Based on the results of these studies, we created a set of design guidelines for developing VEs for learning-related activities. By considering factors of virtual information presentation, as well as those based on the display-systems, our guidelines support design decisions for both the software and hardware required for creating effective educational Ves.