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EAGER: Synthesizing Signal Processing Functions with Biochemical Reactions

EAGER: Synthesizing Signal Processing Functions with Biochemical Reactions
EAGER:利用生化反应综合信号处理功能
批准号:
0946601
负责人:
Keshab Parhi
金额:
$20.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-08-01 至 2011-07-31

项目摘要

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中文摘要
翻译
该提案解决了在一个新的背景下的计算和信号处理操作的合成:蛋白质-蛋白质生物化学,其中输入和输出是不同类型蛋白质的数量。 该项目将证明生物化学可以实现简单而强大的数字信号处理(DSP)操作。 将实现像决策反馈均衡器这样的结构:给定蛋白质的输入量,系统自适应地自主决定是否提供药物。还将实施带通滤波:输出蛋白质的量是输入蛋白质的量的变化的频率的高度调谐函数。 将实现FFT等其他DSP功能。这种DSP功能的设计将在一个抽象的框架中进行研究,作为概念验证。(At这一次,研究将不会试图解决这些想法在体外或体内的实验应用)。 分析生物系统动力学的技术已经很成熟。 然而,合成计算与这种机制需要新的技术?和全新的思维方式 这一奋进的成功将在生物化学传感和药物输送等领域开辟许多机会。 该项目的一个重要目标是向广大受众传达跨学科研究的目标和动力。 将开发一个新的研究生课程,题为“电路,计算和生物学”,通过欧洲经委会系和明尼苏达大学新的生物医学信息学和计算生物学课程联合提供。
英文摘要
This proposal addresses the synthesis of computations and signal processing operations in a novel context: protein-protein biochemistry where the inputs and outputs are quantities of different types of proteins. The project will demonstrate that biochemistry can implement simple and powerful digital signal processing (DSP) operations. Constructs like and decision feedback equalizers will be implemented: given input quantities of proteins, the system performs a decision to deliver a drug or not, adaptively and autonomously. Also band-pass filtering will be implemented: the quantity of output protein is a highly-tuned function of the frequency of the changes in the quantities of input proteins. Other DSP functions such as FFTs will be implemented. The design of such DSP functions will be investigated in an abstract framework, as a proof of concept. (At this time, the research will not attempt to address the experimental application of these ideas in vitro or in vivo). Techniques for analyzing the dynamics of biological systems are well established. However, synthesizing computation with such mechanisms requires new techniques ? and an entirely new mindset. Success in this endeavor will open numerous opportunities in fields such as biochemical sensing and drug delivery. An important goal of the project is to communicate the goals and the impetus for interdisciplinary research to a wide audience. A new graduate-level course will be developed, titled "Circuits, Computation, and Biology" offered jointly through the ECE Department and the new Biomedical Informatics and Computational Biology Program at the University of Minnesota.
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Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption
  • 批准号:
    2243053
  • 项目类别:
    Standard Grant
  • 资助金额:
    $32.5万
  • 财政年份:
    2023
  • 负责人:
    Keshab Parhi
  • 依托单位:
Collaborative Research: SHF: Medium: TensorNN: An Algorithm and Hardware Co-design Framework for On-device Deep Neural Network Learning using Low-rank Tensors
  • 批准号:
    1954749
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2020
  • 负责人:
    Keshab Parhi
  • 依托单位:
SHF: Small: Collaborative Research: LDPD-Net: A Framework for Accelerated Architectures for Low-Density Permuted-Diagonal Deep Neural Networks
  • 批准号:
    1814759
  • 项目类别:
    Standard Grant
  • 资助金额:
    $27.5万
  • 财政年份:
    2018
  • 负责人:
    Keshab Parhi
  • 依托单位:
EAGER: Low-Energy Architectures for Machine Learning
  • 批准号:
    1749494
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.5万
  • 财政年份:
    2017
  • 负责人:
    Keshab Parhi
  • 依托单位:
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