Dynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing

TL;DR

This paper introduces a dynamic predictive sampling ADC that adaptively selects sampling points for sparse signals, significantly reducing data and power consumption in ECG monitoring.

Contribution

A novel dynamic predictive sampling ADC design that reduces data throughput and power usage by non-uniform sampling based on signal prediction.

Findings

Achieves a data compression factor of 6.17

Realizes a power saving factor of 31%

Operates at 1 kHz sampling rate with 10-bit resolution

Abstract

This paper presents a dynamic predictive sampling (DPS) based analog-to-digital converter (ADC) that provides a non-uniform sampling of input analog continuous-time signals. The processing unit generates a dynamic prediction of the input signal using two prior-quantized samplings to compute digital values of an upper threshold and a lower threshold. The digital threshold values are converted to analog thresholds to form a tracking window. A comparator compares the input analog signal with the tracking window to determine if the prediction is successful. A counter records timestamps between the unsuccessful predictions, which are the selected sampling points for quantization. No quantization is performed for successfully predicted sampling points so that the data throughput and power can be saved. The proposed circuits were designed as a 10-bit ADC using 0.18 micro CMOS process sampling at 1 kHz. The results show that the proposed system can achieve a data compression factor of 6.17 and a power saving factor of 31% compared to a Nyquist rate SAR ADC for ECG monitoring.