EAGER: Computer Architectures and Algorithms for Adaptive Human Computer Interfaces
EAGER:自适应人机界面的计算机架构和算法
基本信息
- 批准号:1143995
- 负责人:
- 金额:$ 15万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-09-01 至 2013-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The ease of use of a human computer interface depends critically on the latency of the system and its ability to adapt to the environment. Differences in lighting, visual appearance and user behavior can significantly alter the input data. Furthermore, the reaction time must be less than 100 milliseconds to appear instantaneous to the user. Achieving high accuracy demands a system that adapts to these changing characteristics while processing a significant amount of data in a short amount of time.We propose a computer architecture for adaptive real-time signal processing systems that combines a general purpose processor with custom hardware. The custom hardware performs the low-level, high-throughput signal processing on the raw signals and feeds them to the processor which performs the high level signal processing and decision making. The processor also executes machine learning algorithms that change the parameters of the low-level processing to adapt them to the current statistical properties of the data.This project will develop a human-computer interface based on audio and video sensors that allows a user to interact with the computer through gestures and voice alone. This requires research advances in computer architecture, embedded systems, signal processing, machine learning and human-computer interaction. The major research challenge is in the integration of knowledge from the different areas to create a functional system. This system will serve as a prototype for novel human computer interactions and will be a foundation for future collaboration between the different fields.
人机界面的易用性主要取决于系统的延迟及其适应环境的能力。照明、视觉外观和用户行为的差异会显著改变输入数据。 此外,反应时间必须小于100毫秒,以使用户立即看到。 实现高精度需要一个系统,能够适应这些不断变化的特性,同时处理大量的数据在很短的时间内,我们提出了一种计算机体系结构的自适应实时信号处理系统,结合了通用处理器与自定义硬件。定制硬件对原始信号执行低级别、高吞吐量的信号处理,并将它们馈送到执行高级别信号处理和决策的处理器。处理器还执行机器学习算法,改变底层处理的参数,使其适应数据的当前统计特性。该项目将开发基于音频和视频传感器的人机界面,允许用户仅通过手势和语音与计算机进行交互。 这需要在计算机体系结构、嵌入式系统、信号处理、机器学习和人机交互方面取得研究进展。主要的研究挑战是整合来自不同领域的知识,以创建一个功能系统。该系统将作为新型人机交互的原型,并将成为未来不同领域之间合作的基础。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Yoav Freund其他文献
span class="sans-serif"StreaMRAK/span a streaming multi-resolution adaptive kernel algorithm
- DOI:
10.1016/j.amc.2022.127112 - 发表时间:
2022-08-01 - 期刊:
- 影响因子:3.400
- 作者:
Andreas Oslandsbotn;Željko Kereta;Valeriya Naumova;Yoav Freund;Alexander Cloninger - 通讯作者:
Alexander Cloninger
Yoav Freund的其他文献
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{{ truncateString('Yoav Freund', 18)}}的其他基金
RI: Medium: Quantifying and utilizing confidence in machine learning
RI:中:量化和利用机器学习的信心
- 批准号:
1162581 - 财政年份:2012
- 资助金额:
$ 15万 - 项目类别:
Standard Grant
RI-Small: Learning from data of low intrinsic dimension
RI-Small:从低内在维度的数据中学习
- 批准号:
0812598 - 财政年份:2008
- 资助金额:
$ 15万 - 项目类别:
Continuing Grant
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