EAGER-DynamicData: Collaborative: Exploiting the Dynamically Architectural Configurability for Compressed Sensing

EAGER-DynamicData:协作:利用压缩感知的动态架构可配置性

基本信息

  • 批准号:
    1462473
  • 负责人:
  • 金额:
    $ 4万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-09-15 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

Sensors or sensing systems are increasingly critical in a variety of applications including national security, surveillance monitoring and health care. Those systems should function with minimal hardware recourses, minimal communications and minimal computation overhead, and these efficiencies can dramatically improve the performance, reliability and usability, which can broaden the overall application scope of sensor systems. This EAGER project is to pursue preliminary results of dynamic configurability of architectural and circuit models in sensing systems, and the proposed research will have significant impacts on a range of sensing applications under the resource-constrained environment. For example, in large-scale sensor networks or implantable sensors, energy is tightly constrained. The ultimate goal of the research is to exploit the configurability and dynamics of sensing systems to improve the overall system efficiency. This project serves as an expedition to investigate the dynamically architectural sensing techniques and may open a new research direction of theory and practice in the signal acquisition. Upon the success of this project, a better performance-energy tradeoff in the sensing system will be obtained, which can further strengthen its advantage compared to other sampling techniques, and extend its application regime. To broaden the impacts of this project, PIs will disseminate the research results through multiple channels, including conference presentation, journal publication and open research material online. PIs also plan to integrate the research outcomes into the curriculum development and develop a new research seminar on related topics. The project will provide research opportunities for undergraduate students and researchers from underrepresented groups.Specifically, this EAGER project investigates the dynamic configurability of parameterized Compressed Sensing architecture. With the physical and architectural models, the Compressed Sensing architecture is flexible and provides a larger design/configuration space, and can adapt towards different signal structures and use conditions. The research work is expected to explore a deeper bound of the performance-energy by exploiting the architectural configurability with physical models. To this aim, a set of research tasks will be performed in this project, and the technical thrusts can be summarized from three aspects. First, the project will explore the configurability at both architectural- and circuit- levels in Compressed Sensing, incorporating signal structure variations. Multiple factors in the Compressed Sensing will be investigated. Second, by integrating physical models into the Compressed Sensing architecture, a larger design space will be discovered and defined. The benefit of the performance-energy trade-off will be demonstrated in the new space. Third, a set of novel algorithms will be developed for efficient configuration search in the design space. Several deterministic and heuristic strategies will be investigated in the project.
传感器或传感系统在包括国家安全、监视监测和医疗保健在内的各种应用中越来越重要。这些系统应该以最小的硬件资源、最小的通信和最小的计算开销来运行,并且这些效率可以显著地提高性能、可靠性和可用性,这可以拓宽传感器系统的整体应用范围。这个EAGER项目是追求传感系统中架构和电路模型的动态可配置性的初步结果,拟议的研究将对资源受限环境下的一系列传感应用产生重大影响。例如,在大规模传感器网络或植入式传感器中,能量受到严格限制。研究的最终目标是利用传感系统的可配置性和动态性来提高系统的整体效率。本课题是对动态建筑传感技术的探索,在信号采集的理论和实践上开辟了一个新的研究方向。该项目的成功将使传感系统获得更好的性能-能量平衡,从而进一步加强其相对于其他采样技术的优势,并扩展其应用范围。为扩大项目的影响,研究所将通过多种渠道传播研究成果,包括会议介绍、期刊出版和在线公开研究材料。研究所亦计划将研究成果纳入课程发展,并就相关课题举办新的研究研讨会。该项目将为本科生和研究人员提供研究机会。具体来说,EAGER项目研究了参数化压缩感知架构的动态可配置性。通过物理和架构模型,压缩感知架构是灵活的,并提供了更大的设计/配置空间,并且可以适应不同的信号结构和使用条件。本研究希望借由实体模型的建构,探索更深层次的效能-能量边界。为此,本项目将开展一系列的研究工作,其技术重点可以概括为三个方面。首先,该项目将探索压缩感知中架构和电路级别的可配置性,并结合信号结构变化。压缩感知中的多个因素将被研究。其次,通过将物理模型集成到压缩感知架构中,将发现和定义更大的设计空间。性能-能源权衡的好处将在新的空间中得到证明。第三,一组新的算法将开发有效的配置搜索在设计空间。本计画将探讨数种决定性与启发式策略。

项目成果

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Zhanpeng Jin其他文献

Towards EEG biometrics: pattern matching approaches for user identification
走向脑电图生物识别:用于用户识别的模式匹配方法
Thermodynamic assessment of the Al–Dy, Dy–Zr and Al–Dy–Zr systems
Al-Dy、Dy-Zr 和 Al-Dy-Zr 体系的热力学评估
  • DOI:
    10.1007/s11434-014-0192-y
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    H. Bo;Li;X. Xiong;Zhanpeng Jin
  • 通讯作者:
    Zhanpeng Jin
Experimental investigation and thermodynamic description of the Li–Si–Ni ternary system
Li·Si·Ni三元体系的实验研究及热力学描述
Optimization of the composition for synthesizing the high-Tc phase in Bi(Pb)SrCaCuO system
Bi(Pb)SrCaCuO体系中合成高温相的成分优化
  • DOI:
    10.1023/a:1004683626662
  • 发表时间:
    1999
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Huashan Liu;Li;Hao Yu;Yuelan Zhang;Zhanpeng Jin
  • 通讯作者:
    Zhanpeng Jin
Thermodynamic assessment of the Au-Ti system
Au-Ti 系统的热力学评估
  • DOI:
  • 发表时间:
    2001
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Weikun Luo;Zhanpeng Jin;Huashan Liu;Tao Wang
  • 通讯作者:
    Tao Wang

Zhanpeng Jin的其他文献

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{{ truncateString('Zhanpeng Jin', 18)}}的其他基金

TWC SBE: Medium: Collaborative: Brain Hacking: Assessing Psychological and Computational Vulnerabilities in Brain-based Biometrics
TWC SBE:媒介:协作:大脑黑客:评估基于大脑的生物识别技术中的心理和计算漏洞
  • 批准号:
    1840790
  • 财政年份:
    2018
  • 资助金额:
    $ 4万
  • 项目类别:
    Continuing Grant
TWC SBE: Medium: Collaborative: Brain Hacking: Assessing Psychological and Computational Vulnerabilities in Brain-based Biometrics
TWC SBE:媒介:协作:大脑黑客:评估基于大脑的生物识别技术中的心理和计算漏洞
  • 批准号:
    1564046
  • 财政年份:
    2016
  • 资助金额:
    $ 4万
  • 项目类别:
    Continuing Grant
TWC SBE: Small: Collaborative: Brain Password: Exploring A Psychophysiological Approach for Secure User Authentication
TWC SBE:小型:协作:大脑密码:探索安全用户身份验证的心理生理学方法
  • 批准号:
    1422417
  • 财政年份:
    2014
  • 资助金额:
    $ 4万
  • 项目类别:
    Standard Grant

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EAGER-DynamicData: Subspace Learning From Binary Sensing
EAGER-DynamicData:从二进制感知中学习子空间
  • 批准号:
    1833553
  • 财政年份:
    2018
  • 资助金额:
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    Standard Grant
EAGER-DynamicData: Generative Statistical Modeling for Dynamic and Distributed Data
EAGER-DynamicData:动态和分布式数据的生成统计建模
  • 批准号:
    1462230
  • 财政年份:
    2015
  • 资助金额:
    $ 4万
  • 项目类别:
    Standard Grant
EAGER-DynamicData: Collaborative Research: Data-driven morphing of parsimonious models for the description of transient dynamics in complex systems
EAGER-DynamicData:协作研究:数据驱动的简约模型变形,用于描述复杂系统中的瞬态动力学
  • 批准号:
    1462254
  • 财政年份:
    2015
  • 资助金额:
    $ 4万
  • 项目类别:
    Standard Grant
EAGER-DynamicData: A Scalable Framework for Data-Driven Real-Time Event Detection in Power Systems
EAGER-DynamicData:电力系统中数据驱动的实时事件检测的可扩展框架
  • 批准号:
    1462311
  • 财政年份:
    2015
  • 资助金额:
    $ 4万
  • 项目类别:
    Standard Grant
EAGER-DynamicData: Reducing Orbital Position Uncertainty with Ensembles of Upper Atmospheric Models
EAGER-DynamicData:利用高层大气模型集合降低轨道位置不确定性
  • 批准号:
    1462363
  • 财政年份:
    2015
  • 资助金额:
    $ 4万
  • 项目类别:
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Collaborative Research: EAGER-DynamicData: Machine Intelligence for Dynamic Data-Driven Morphing of Nodal Demand in Smart Energy Systems
合作研究:EAGER-DynamicData:智能能源系统中节点需求动态数据驱动变形的机器智能
  • 批准号:
    1462393
  • 财政年份:
    2015
  • 资助金额:
    $ 4万
  • 项目类别:
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EAGER- DynamicData: Novel Approaches for Optimization, Control, and Learning in Distributed Networks
EAGER-DynamicData:分布式网络中优化、控制和学习的新方法
  • 批准号:
    1462397
  • 财政年份:
    2015
  • 资助金额:
    $ 4万
  • 项目类别:
    Standard Grant
EAGER-DynamicData: Collaborative Research: Data-driven morphing of parsimonious models for the description of transient dynamics in complex systems
EAGER-DynamicData:协作研究:数据驱动的简约模型变形,用于描述复杂系统中的瞬态动力学
  • 批准号:
    1462241
  • 财政年份:
    2015
  • 资助金额:
    $ 4万
  • 项目类别:
    Standard Grant
Collaborative Research: EAGER-DynamicData: Machine Intelligence for Dynamic Data-Driven Morphing of Nodal Demand in Smart Energy Systems
合作研究:EAGER-DynamicData:智能能源系统中节点需求动态数据驱动变形的机器智能
  • 批准号:
    1462404
  • 财政年份:
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  • 资助金额:
    $ 4万
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EAGER-DynamicData: Subspace Learning From Binary Sensing
EAGER-DynamicData:从二进制感知中学习子空间
  • 批准号:
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