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This paper proposes a triple-mode discrete-time incremental analog-to-digital converter (IADC) employing successive approximation register (SAR)-based zooming and extended counting (EC) schemes to achieve programmable trade-off capability of resolution and power consumption in various smart sensor applications. It mainly consists of an incremental delta-sigma modulator and the proposed SAR-EC sub-ADC for alternate operation of the coarse SAR conversion and EC. They can be reconfigured to operate separately depending on the application requirements. The SAR-based zooming structure allows the IADC to have better linearity and resolution, and additional activation of the EC function gives the further resolution. During this reconfigurable conversion process, pipelined reusing operation of sub-blocks reduces the silicon area and the number of cycles for target resolutions. A prototype ADC is fabricated in a 180-nm CMOS process, and its triple-mode operation of high-resolution, medium-resolution, and low-power is experimentally verified to achieve 116.1-, 109.4-, and 73.3-dB dynamic ranges, consuming 1.60, 1.26, and 0.39 mW, respectively.

This paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18-μm complementary metal-oxide-semiconductor (CMOS) process. Based on this ROIC, a wearable device module and its wireless system prototype are implemented to recognize five kinds of real-time handgesture motions, where the power consumption is further reduced by adopting low-power components. Real-time measurements of sEMG signals and APWP data through this wearable interface are wirelessly transferred to a laptop or a sensor hub, and then they are further processed to implement the pseudo-wavelet transform under the MATLAB environment. The resulting APWP-augmented pattern-recognition algorithm was experimentally verified to improve the accuracy by 7 % with a real-time frequency analysis.