Collaborative Research: DDDAS-SMRP: Optimizing Signal and Image Processing in a Dynamic, Data-Driven Application System

合作研究：DDDAS-SMRP：在动态、数据驱动的应用系统中优化信号和图像处理

• 批准号: 0540154
• 负责人: Anna Gilbert
• 金额: $ 9万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-12-15 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540154&HistoricalAwards=false
• 关键词: Collaborative; Research; DDDAS; SMRP; Optimizing

This project will develop a dynamic, data-driven application system for signal and image processing under resource constraints. This system would lay a foundation for highly optimized implementations of fundamental signal and imaging processing computations that arise in many science and engineering problems, including image recognition, communications analysis, speech processing, querying, indexing, and retrieval from multimedia databases, and image segmentation of aerial, satellite, and astronomical images. The proposed multidisciplinary approach optimizes from algorithm specification, to mathematical representation, to software and hardware (FPGA) implementation, based on properties of data and unique requirements of the environment and the target hardware device. The novelty of the system is twofold: (1) it performs joint optimizationacross mathematical, software and hardware (system-on-a-chip FPGA) domains; and, (2) it is a dynamic, data-driven system in that signal-processing transforms are tailored to algorithm requirements and input signals, for reduced distortion and increased compression, and the system can be queried and steered during execution. Implementations are based on the best mathematical formulation of the problem coupled with automated selection of the best implementation among a space of alternatives, through the integration of models relating mathematical properties to implementationbehavior. Both hardware and software optimization are treated in a unified way. It is anticipated that with these methods the design-time will be decreased by two orders of magnitude or more, compared to implementations derived in a traditional way. Because the proposed system can explore a broad range of implementations that exceed the capabilities of a human designer, the implementations derived by the approach pursued in the project may even exhibit lower resource costs and higher performance. This research will provide a foundation for signal processing at all scales, providing key building blocks for engineers to build complex, distributed networks of adaptive signal processing sensors.

该项目将开发一个动态，数据驱动的应用程序系统，用于在资源约束下进行信号和图像处理。该系统将为在许多科学和工程问题中出现的基本信号和成像处理计算的高度优化的实现奠定基础，包括图像识别，通信分析，语音处理，查询，索引，索引和从多媒体数据库中的检索以及空中，卫星，卫星和天文学图像的图像分段。提出的多学科方法根据数据的属性以及环境的唯一要求和目标硬件设备优化了算法规范，数学表示，数学表示，软件和硬件（FPGA）实现。该系统的新颖性是双重的：（1）它执行关节优化的数学，软件和硬件（芯片上的芯片FPGA）域； （2）这是一个动态的，数据驱动的系统，因为信号处理转换是根据算法要求和输入信号量身定制的，以减少失真和增加压缩，并且可以在执行过程中查询和转向系统。实现是基于问题的最佳数学表述，并通过集成将数学属性与实现behavior相关的模型集成，并自动选择替代空间之间的最佳实现。硬件和软件优化均以统一的方式处理。可以预期，与传统方式得出的实现相比，使用这些方法将减少两个数量级或更多的数量级。由于拟议的系统可以探索超过人类设计师能力的广泛实施，因此项目中采用的方法得出的实现甚至可能显示出较低的资源成本和更高的性能。这项研究将为所有尺度上的信号处理提供基础，为工程师提供关键的构件，以建立自适应信号处理传感器的复杂，分布式网络。