IEEE Trans Biomed Circuits Syst. 2011 Dec;5(6):546-54. doi: 10.1109/TBCAS.2011.2176726.
This paper presents a voltage-scalable digital signal processing system designed for the use in a wireless sensor node (WSN) for ambulatory monitoring of biomedical signals. To fulfill the requirements of ambulatory monitoring, power consumption, which directly translates to the WSN battery lifetime and size, must be kept as low as possible. The proposed processing platform is an event-driven system with resources to run applications with different degrees of complexity in an energy-aware way. The architecture uses effective system partitioning to enable duty cycling, single instruction multiple data (SIMD) instructions, power gating, voltage scaling, multiple clock domains, multiple voltage domains, and extensive clock gating. It provides an alternative processing platform where the power and performance can be scaled to adapt to the application need. A case study on a continuous wavelet transform (CWT)-based heart-beat detection shows that the platform not only preserves the sensitivity and positive predictivity of the algorithm but also achieves the lowest energy/sample for ElectroCardioGram (ECG) heart-beat detection publicly reported today.
本文提出了一种可扩展电压的数字信号处理系统，专为用于无线传感器节点（WSN）中进行生物医学信号的动态监测而设计。为了满足动态监测的要求，必须尽可能降低功耗，因为功耗直接决定了 WSN 电池的寿命和尺寸。所提出的处理平台是一个事件驱动的系统，具有以节能的方式运行不同复杂程度的应用程序的资源。该架构使用有效的系统分区来实现占空比、单指令多数据 (SIMD) 指令、电源门控、电压缩放、多个时钟域、多个电压域和广泛的时钟门控。它提供了一种替代的处理平台，其功率和性能可以进行调整以适应应用需求。基于连续小波变换 (CWT) 的心跳检测的案例研究表明，该平台不仅保留了算法的灵敏度和正预测性，而且还实现了当今公开报道的心电图 (ECG) 心跳检测的最低能量/样本。
