STTR Phase I: Extended Analog Computer Development as a Digitally Configurable, High Speed, High Order, and Low Power Analog Matched Filter

STTR 第一阶段：扩展模拟计算机开发作为数字可配置、高速、高阶和低功耗模拟匹配滤波器

• 批准号: 1417062
• 负责人: Gregory Mattes
• 金额: $ 22.2万
• 依托单位: Analog Computing Solutions
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1417062&HistoricalAwards=false
• 关键词: STTR; Phase; Extended; Analog; Computer

The broader impact/commercial potential of this project lies in the ability of the Extended Analog Computer (EAC) to perform filtering operations not possible with current technology. The low power, near instantaneous filtering of complex analog signals, differentiates the EAC technology from DSP products currently in the marketplace. The development of prosthetics with advanced control characteristics is reaching a computational limit due to the need for neuromuscular waveform recognition and classification in the context of real-time, low power, small form factor computation. EACs promise to surmount these challenges, improving functionality of prosthetics and the quality of life for amputees. In 2010, the global prosthetics market was $3 billion and is expected to reach $4.5 billion by 2017, with myoelectric prosthetics representing a small, but growing fraction of this market. While the myoelectric prosthetics industry represents an initial customer base, this generic computing technology's financial upside can best be estimated by segmenting the multi-billion dollar market for digital signal processing and analog computing devices. The intent is to solve computing problems in niche markets within this broad potential marketplace.This Small Business Technology Transfer Research (STTR) Phase I project is focused on the development of the Extended Analog Computer (EAC) for application to myoelectric prosthetics. New myoelectric interface techniques, such as targeted muscle reinnervation (TMR) are simplifying the use of advanced multi-degree of freedom prosthetics by amputees. However, the dramatic increase in the number and density of electrode sites, and need to implant multi-electrode structures into targeted muscles will increase the signal processing requirements beyond the capacity of traditional mobile digital signal processing (DSP). The EAC is a radical departure from the digital computer, deriving its computational power by taking advantage of the intrinsic solutions to partial differential equations represented as an analog voltage manifold in space. The proposed research aims to implement automatic machine learning/training methods to automatically configure networks of EAC sheets in a radial basis function network (RBFN). The research also explores the effect of the geometry of the input and output points on the sheet to optimize them for the TMR application. Finally, a physical instantiation enabling stand-alone, real-time operation of an EAC-RBFN will be developed. Using recorded data from intramuscular electrode arrays, the performance of the EAC will be tested against standard DSP techniques.

该项目的更广泛的影响/商业潜力在于扩展模拟计算机（EAC）执行当前技术无法实现的滤波操作的能力。低功耗、近乎瞬时的复杂模拟信号滤波，使EAC技术区别于目前市场上的DSP产品。由于在实时、低功耗、小尺寸计算的背景下需要神经肌肉波形识别和分类，具有先进控制特性的假肢的发展正在达到计算极限。EACs承诺克服这些挑战，改善义肢的功能和截肢者的生活质量。2010年，全球假肢市场为30亿美元，预计到2017年将达到45亿美元，其中肌电假肢只占这个市场的一小部分，但正在增长。虽然肌电义肢行业代表了最初的客户群，但这种通用计算技术的财务优势可以通过分割数字信号处理和模拟计算设备的数十亿美元市场来最好地估计。其目的是在这个广阔的潜在市场中解决利基市场中的计算问题。这项小型企业技术转移研究（STTR）第一阶段项目的重点是开发应用于肌电假肢的扩展模拟计算机（EAC）。新的肌电界面技术，如定向肌肉神经再生（TMR），简化了截肢者对先进的多自由度假肢的使用。然而，电极位置的数量和密度的急剧增加，以及需要在目标肌肉中植入多电极结构，将增加信号处理的要求，超出了传统的移动数字信号处理（DSP）的能力。EAC与数字计算机完全不同，它通过利用空间中模拟电压流形表示的偏微分方程的固有解来推导其计算能力。提出的研究旨在实现自动机器学习/训练方法，在径向基函数网络（RBFN）中自动配置EAC片的网络。该研究还探讨了输入和输出点的几何形状对薄片的影响，以优化它们的TMR应用。最后，将开发一个能够独立实时操作EAC-RBFN的物理实例。使用肌内电极阵列记录的数据，EAC的性能将根据标准DSP技术进行测试。