Collaborative Research: Large-Scale Wireless RF Networks of Microchip Sensors

合作研究：微芯片传感器的大规模无线射频网络

• 批准号: 2322600
• 负责人: Arto Nurmikko
• 金额: $ 38.36万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322600&HistoricalAwards=false
• 关键词: Collaborative; Research; Large; Scale; Wireless

The world around us is increasingly surrounded by electronic sensors. For applications such as wearable and implantable biomedical sensors there is a particular need and opportunity for unobtrusive microdevices which operate autonomously as large ensembles to map physiological activity across a body area of interest. A challenge is how to construct a wireless network whereby data from a microsensor population is transmitted, received, and decoded to unravel data, say from 1000 individual sensors. A rough analogy is that of a population of common radio-frequency tags which must be read at once by a single transceiver - with the twist that signals at each sensor location will now vary both in time and in magnitude. A brain-computer interface suggests a paradigm in this context: how to capture neuronal signals at high resolution by a population of autonomous brain implanted microsensors. Ongoing research for development of brain-machine interfaces in laboratories worldwide is focused on a number of schemes where access to thousands of points in the cortex is sought to translate brain computations to useful electronic commands e.g. for intended speech. The neurotechnology problem is three-fold: to record electrical signals from the brain unobtrusively, to transmit the data wirelessly to a body external receiver, and to decipher the multitude of signals in real time. Many cases of distributed sensing of a dynamical environment are characterized by sparsity of events whether in nature or man-made systems, neurons in the brain being an example. The proposed event-driven communication strategy enables the efficient transmission, accurate retrieval, and interpretation of sparse events across a network of thousands of wireless microsensors – using the brain as an inspiration. The proposed work is focused on an all-in-one approach to build a large scale wireless microsensor radio-frequency network. An external transceiver collects data while supplying wireless power to the sensors. Each sensor is a sub-millimeter size silicon system-on-microchip with custom circuitry designed for ‘event detection’ where time-varying sensor inputs are encoded as a series of short ‘spikes’. The brain-inspired method of encoding data from sparse events has emerged recently in so- called dynamic vision cameras. Spike train data are converted into digital form on chip and transmitted to one common receiver. Since only the event-driven spikes are transmitted through the network, the bandwidth of the communication system can be utilized very efficiently enabling a large population of sensors to be incorporated into the network.The team proposes to build a microsensor system and demonstrate low-error rate and efficient asynchronous, encoded wireless transmission in the laboratory using fabricated microchips, and to show extended applicability to thousands of nodes though simulations.Importantly, the event-sensing detection and wireless communication approach is quite suited for a neuromorphic computational approach for analyzing multisensory data; the third key element in the project. The team will show how to decode actual brain data (synthesized elsewhere from actual primate brain recordings) from a hypothetical implant composed of up to 8000 microsensors. Neuromorphic computing appears particularly suited decoding event-based data in terms of efficiency and short latency. Using available data from the primate motor cortex the team plans to show how to decode wireless signals from thousands of neurons for the prediction of planned arm and hand movement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

我们周围的世界越来越多地被电子传感器所包围。对于诸如可穿戴和可植入生物医学传感器之类的应用，特别需要和机会的是不引人注目的微型设备，其作为大的集合体自主操作以映射感兴趣的身体区域上的生理活动。一个挑战是如何构建一个无线网络，通过该网络传输、接收和解码来自微传感器群体的数据，以解开来自1000个单独传感器的数据。一个粗略的类比是，一群普通的射频标签必须由一个收发器一次读取-与扭曲，在每个传感器位置的信号现在将在时间和幅度上都不同。脑机接口在这方面提出了一个范例：如何通过一群自主的大脑植入微传感器以高分辨率捕获神经元信号。世界各地的实验室正在进行的脑机接口开发研究集中在一些方案上，其中试图访问皮层中的数千个点，以将大脑计算转换为有用的电子命令，例如用于预期的语音。神经技术的问题有三个方面：不引人注目地记录来自大脑的电信号，将数据无线传输到身体外部的接收器，以及在真实的时间内破译大量的信号。动态环境的分布式感知的许多情况的特征在于事件的稀疏性，无论是在自然还是人造系统中，大脑中的神经元就是一个例子。所提出的事件驱动的通信策略能够在数千个无线微传感器的网络中有效传输，准确检索和解释稀疏事件-使用大脑作为灵感。提出的工作集中在一个多合一的方法来建立一个大规模的无线微传感器射频网络。外部收发器收集数据，同时向传感器提供无线电力。每个传感器都是一个亚毫米大小的硅微芯片系统，具有为“事件检测”而设计的定制电路，其中时变传感器输入被编码为一系列短“尖峰”。最近，在所谓的动态视觉相机中出现了一种大脑启发的方法，即从稀疏事件中编码数据。尖峰序列数据在芯片上转换为数字形式，并传输到一个公共接收器。由于只有事件驱动的尖峰信号通过网络传输，通信系统的带宽可以非常有效地利用，使大量的传感器被纳入网络。该团队建议建立一个微传感器系统，并在实验室中使用制造的微芯片演示低错误率和高效的异步编码无线传输，重要的是，事件感知检测和无线通信方法非常适合用于分析多传感器数据的神经形态计算方法;这是该项目的第三个关键要素。该团队将展示如何从一个由多达8000个微传感器组成的假想植入物中解码实际的大脑数据（从实际灵长类动物大脑记录的其他地方合成）。神经形态计算在效率和短延迟方面似乎特别适合解码基于事件的数据。利用灵长类动物运动皮层的可用数据，该团队计划展示如何解码来自数千个神经元的无线信号，以预测计划中的手臂和手部运动。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。