Name
Abstract | ||
|---|---|---|
In a conventional bio-sensor, key signal features are acquired using Nyquist-rate analog-to-digital conversion without exploiting the typical bio-signal characteristic of sparsity in some domain (e.g., time, frequency, etc.). Compressed sensing (CS) is a signal processing paradigm that exploits this sparsity for commensurate power savings by enabling alias-free sub-Nyquist acquisition. In a severely energy constrained sensor, CS also eliminates the need for digital signal processing (DSP). A fully-integrated low-power CS analog front-end (CS-AFE) is described for an electrocardiogram (ECG) sensor. Switched-capacitor circuits are used to achieve high accuracy and low power. Implemented in 0.13 μm CMOS in 2×3 mm2, the prototype comprises a 384-bit Fibonacci-Galois hybrid linear feedback shift register and 64 digitally-selectable CS channels with a 6-bit C-2C MDAC/integrator and a 10-bit C-2C SAR ADC in each. Clocked at 2 kHz, the total power dissipation is 28 nW and 1.8 μW for one and 64 active channels, respectively. CS-AFE enables compressive sampling of bio-signals that are sparse in an arbitrary domain. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JSSC.2013.2284673 | Solid-State Circuits, IEEE Journal of |
Keywords | DocType | Volume |
CMOS integrated circuits,analogue-digital conversion,biomedical electronics,biosensors,circuit feedback,compressed sensing,electrocardiography,feature extraction,integrating circuits,low-power electronics,medical signal processing,shift registers,switched capacitor networks,wireless sensor networks,10-bit C-2C SAR ADC,6-bit C-2C MDAC-integrator,CMOS,ECG,Fibonacci-Galois hybrid linear feedback shift register,Nyquist-rate analog-to-digital conversion,alias-free subNyquist acquisition,arbitrary domain,biosignal characteristics,commensurate power savings,compressive sampling,conventional biosensor applications,digital signal processing,digitally-selectable compressed sensing channels,electrocardiogram sensor,frequency 2 kHz,fully-integrated low-power compressed sensing analog front-end,key signal features,power 1.8 muW,power 28 nW,power dissipation,prototype,severely energy constrained sensor,signal processing paradigm,sparsity,switched-capacitor circuits,Analog-to-digital converters,ECG,SAR ADC,analog-to-information converters,biomedical sensors,body-area networks,compressed sensing,compressive sampling,multiplying DAC,sub-Nyquist sampling,wavelets,wireless sensors | Journal | 49 |
Issue | ISSN | Citations |
2 | 0018-9200 | 32 |
PageRank | References | Authors |
1.23 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daibashish Gangopadhyay | 1 | 144 | 9.25 |
| Emily G. Allstot | 2 | 134 | 5.93 |
| Anna M. R. Dixon | 3 | 134 | 5.93 |
| Karthik Natarajan | 4 | 407 | 31.52 |
| Subhanshu Gupta | 5 | 65 | 10.52 |
| David J. Allstot | 6 | 473 | 80.41 |