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This work presents high resolution and adaptable frequency measurement techniques for relaxation oscillator based capacitive sensors. The techniques rely on extremely simple hardware such as a 1-bit coarse voltage quantizer to generate a 1-bit data stream and subsequently determine frequencies with resolutions greater than 20 bits from the 1-bit data stream. The proposed readouts are based on two types of 1-bit hardware approaches with Nyquist sampling in one approach and oversampling in the other. The first approach employs a Nyquist rate sampled 1-bit comparator while the second approach relies on an oversampled 1-bit quantizer based delta-sigma modulator (DSM) as a hardware to generate the bit stream. High resolution frequency is estimated from the 1-bit data stream using parametric, non-parametric and compressed sensing approaches. We provide detailed study and measurement results comparing the performance of the proposed techniques for test inputs with different frequencies and amplitudes and multiple signal shapes including sine, square, triangular and sawtooth. We show that the 1-bit comparator along with the CS approach provides the best performance across all input amplitude, frequency and shapes. A capacitive water level sensor was implemented in a PCB and embedded in a conventional square wave relaxation oscillator to verify the proposed technique. Measurement results show that incremental SNRs can be achieved only by increasing the 1-bit data stream length without any change in the hardware thereby demonstrating the adaptability of the technique based on an application requirement. [ABSTRACT FROM AUTHOR]

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 very-large-scale integrated implementation. The device will be suitable for long-term wearable monitoring of physiological signals, such as electrocardiogram (ECG). We show representative case studies on QRS detection in an ECG signal utilizing the proposed A2I converter to prove the functionality of the design. Simulation results show large source compression in the ECG signal and more than 98% efficiency in the detection of the Q, R, and S waves for challenging ECG waveforms, all with extremely low-power and storage requirements. [ABSTRACT FROM PUBLISHER]