Abstract
Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12—237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming of a smartphone’s power budget. Such energy-efficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.
| Original language | English (US) |
|---|---|
| Journal | IEEE Journal of Solid-State Circuits |
| DOIs | |
| State | Accepted/In press - Oct 15 2015 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals. / Ren, Fengbo; Markovic, Dejan.
In: IEEE Journal of Solid-State Circuits, 15.10.2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals
AU - Ren, Fengbo
AU - Markovic, Dejan
PY - 2015/10/15
Y1 - 2015/10/15
N2 - Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12—237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming of a smartphone’s power budget. Such energy-efficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.
AB - Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12—237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming of a smartphone’s power budget. Such energy-efficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.
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UR - http://www.scopus.com/inward/citedby.url?scp=84944541790&partnerID=8YFLogxK
U2 - 10.1109/JSSC.2015.2480862
DO - 10.1109/JSSC.2015.2480862
M3 - Article
AN - SCOPUS:84944541790
JO - IEEE Journal of Solid-State Circuits
JF - IEEE Journal of Solid-State Circuits
SN - 0018-9200
ER -