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被电子传感器所包围。对于诸如可穿戴和植入生物医学传感器之类的应用，对于不引人注目的微发行版，它们具有特殊的需求和机会，这些速度是自主运行的，就像大型合奏以绘制跨感兴趣的身体区域的体育活动一样。一个挑战是如何构建无线网络，从而从1000个单独的传感器中传输，接收和解码从微传感器群体传输，接收和解码到数据。一个粗略的类比是一个常见的射频标签，必须由单个收发器立即读取，其扭曲是在每个传感器位置的信号现在都会在时间和大小上变化。在这种情况下，脑部计算机界面提出了一个范式：如何通过自主脑植入的微传感器群体以高分辨率捕获神经元信号。在全球实验室中开发脑机界面开发的持续研究集中在许多方案上，在许多方案中，可以感受到在皮质中获得数千点的访问，以将大脑计算转化为有用的电子命令，例如用于预期的演讲。神经技术问题是三个方面：要毫不显着地记录大脑的电信号，将数据无线传输到人体外部接收器，并实时破译大量信号。许多动态环境的分布敏感性的案例的特征是事件的稀疏性，无论是自然还是人造系统，大脑中的神经元就是一个例子。提出的事件驱动的通信策略可以利用大脑作为灵感来有效地传播，准确检索和解释稀疏事件的稀疏事件。拟议的工作集中在一种构建大型无线微传感器射频网络的多合一方法上。外部收发器在向传感器提供无线电源的同时收集数据。每个传感器是一个亚毫米尺寸的硅系统在微芯片上，具有定制电路，设计用于“事件检测”，其中将随时间变化的传感器输入编码为一系列简短的“尖峰”。最近在所谓的动态视觉摄像机中出现了以脑启发的编码稀疏事件数据的方法。尖峰火车数据在芯片上转换为数字形式，并将其传输到一个常见的接收器。 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 proposals 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模拟。事件传感检测和无线通信方法非常适合用于分析的多感觉数据的神经形态计算方法。项目中的第三个关键要素。该团队将展示如何从由多达8000个微传感器组成的假设植入物中解码实际的大脑数据（从实际素数记录中的其他地方合成）。在效率和较短的延迟方面，神经形态计算似乎特别适合基于事件的数据。团队计划使用来自灵长类运动皮层的可用数据来展示如何对数千个神经元的无线信号进行解码以预测计划中的手臂和手动运动。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来评估。