MRI: Aquisition of Research Computational Equipment

MRI：采购研究计算设备

• 批准号: 0215583
• 负责人: Patrick O'Leary
• 金额: $ 9.58万
• 依托单位: University of Alaska Anchorage Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0215583&HistoricalAwards=false
• 关键词: MRI; Aquisition; Research; Computational; Equipment

EIA-0215583Patrick O'LearyKenrick MockUniversity of Alaska AnchorageMRI: Acquisition of Research Computational EquipmentThis proposal from an EPCoR state, aiming at performing collaborative research on computational and visualization aspects of various application areas, targets interactive computational environments and intelligent systems; more specifically, parallel processing, scientific visualization, and intelligent systems. The work, requiring a high performance infrastructure, supports complex computing and the ability to visualize, store, and easily retrieve large data sets. The advancement of research techniques again requires significantly improved distributed computing and visualization capabilities. The computational steering tools require analysis involving consistency, latency, scalability, and perturbation costs. Research activities in an Interactive Computational Environment (ICE) involve: 1. Seamless Monitoring and Steering of High-Performance Distributed Computations, 2. Realistic Simulators, and 3. Computational models.The intelligent systems component explores information systems (new techniques and applications for machine learning, e.g., user profiles), information relevance and retrieval, efficient methods for human-computer interaction, and multi-agent research.

EIA-0215583 Patrick O‘LearyKenrick MockUniversity of Alaska AndrageMRI：收购研究计算设备这个提案来自EPCOR州，旨在对各种应用领域的计算和可视化方面进行协作研究，目标是交互式计算环境和智能系统；更具体地说，并行处理、科学可视化和智能系统。这项工作需要高性能的基础设施，支持复杂的计算以及可视化、存储和轻松检索大型数据集的能力。研究技术的进步再次要求显著提高分布式计算和可视化能力。计算导向工具需要分析一致性、延迟、可伸缩性和扰动成本。交互式计算环境(ICE)中的研究活动包括：1.高性能分布式计算的无缝监控和引导，2.逼真的模拟器，以及3.计算模型。智能系统组件探索信息系统(机器学习的新技术和应用，例如，用户简档)、信息相关性和检索、人机交互的有效方法，以及多代理研究。