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This paper illustrates an architectural design of a novel variable input-feature correlated asynchronous sampling and time-encoded digitization approach for source compression and direct feature extraction from physiological signals. The complete architecture represents an analog-to-information (A2I) converter, designed for ultra-low-power mixed-signal(More)
This paper discusses the design of an asynchronous analog-to-digital converter targeted for low-power sensing applications. The asynchronous sampling scheme will save power because it only samples the input signal when it is changing. The idea of using an adaptive resolution to increase the maximum input frequency of the ADC is introduced. A prototype chip(More)
This paper describes a technique for digital error correction in pipelined analog-digital converters. It makes use of a slow, high resolution ADC in conjunction with an LMS algorithm to perform error correction in the background during normal conversion. The algorithm will be shown to correct for errors due to capacitor ratio mismatch, finite amplifier gain(More)
Wireless physiological sensors are often limited by energy consumption of the hardware. Power consumption is typically related to the amount of data being transmitted, conventionally the Nyquist rate which is twice the bandwidth of the signal. However, if the signals are sparse in a known basis, compressed sensing facilitates accurate reconstruction of data(More)