Design and Exploration of Low-Power Analog to Information Conversion Based on Compressed Sensing

Abstract

Abstract-The long-standing analog-to-digital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures constraints are simply not manageable. Compressed sensing (CS) is a new emerging signal acquisition/compression paradigm that offers a striking alternative to traditional signal acquisition. Interestingly, by merging the sampling and compression steps, CS also removes a large part of the digital architecture and might thus considerably simplify analog-to-information (A2I) conversion devices. This so-called "analog CS," where compression occurs directly in the analog sensor readout electronics prior to analog-to-digital conversion, could thus be of great importance for applications where bandwidth is moderate, but computationally complex, and power resources are severely constrained. In our previous work (Mamaghanian, 2011), we quantified and validated the potential of digital CS systems for real-time and energy-efficient electrocardiogram compression on resource-constrained sensing platforms. In this paper, we review the state-of-the-art implementations of CS-based signal acquisition systems and perform a complete system-level analysis for each implementation to highlight their strengths and weaknesses regarding implementation complexity, performance and power consumption. Then, we introduce the spread spectrum random modulator pre-integrator (SRMPI), which is a new design and implementation of a CS-based A2I read-out system that uses spread spectrum techniques prior to random modulation in order to produce the low rate set of digital samples. Finally, we experimentally built an SRMPI prototype to compare it with state-of-the-art CS-based signal acquisition systems, focusing on critical system design parameters and constraints, and show that this new proposed architecture offers a compelling alternative, in particular for low power and computationally-constrained embedded systems.Index Terms-Analog-to-information, compressed sensing (CS), electrocardiogram (ECG) compression, low-power, random modulation pre-integrator (RMPI), spread spectrum, system-level design.

Energy Challenges for ICT

“…Predictable energy consumption is required to guarantee a particular battery life. To meet design goals within a small energy budget envelope, the design of each of the components is highly tailored to the targeted application (see e.g., [23,24] and Chapter 8 of Vol. 1 for design consideration of a wireless sensor node).…”

Power-efficient joint compressed sensing of multi-lead ECG signals

“…Conventionally, one would collect ECG samples at the Nyquist rate forming x and then compress it using non-linear digital compression techniques. CS offers a striking alternative by showing that you can collect roughly S samples using simple analog measurement waveforms, thus sensing/sampling and compressing at the same time (Analog CS) [4]. But in present work CS is done after ADC (digital CS), where digital samples vector x are available.…”

Neural signal compression using a minimum Euclidean or Manhattan distance cluster-based deterministic compressed sensing matrix

“…Currently, most of the designers use a type of random matrix as a sensing matrix in the system, such as the sub-Gaussian sensing matrix [7,15] or the random discrete Fourier transmission matrix [28]. However, the random matrix has disadvantages.…”

“…However, both of these methods cause distortion or loss of the data information. For example, in a neural spike-detection recorder, the data are obtained only in a time series or as an impulse signal but http not as the signal itself [15]. If the thresholds of the detection are not properly set, then the spikes cannot be detected.…”