This paper reports an acceleration sensing method based on two weakly coupled resonators (WCRs) using the phenomenon of mode localization. When acceleration acts on the proof masses, differential electrostatic stiffness perturbations will be applied to the WCRs, leading to mode localization, and thus, mode shape changes. Therefore, acceleration can be sensed by measuring the amplitude ratio shift. The proposed mode localization with the differential perturbation method leads to a sensitivity enhancement of a factor of 2 than the common single perturbation method. The theoretical model of the sensitivity, bandwidth, and linearity of the accelerometer is established and verified. The measured relative shift in amplitude ratio (~312162 ppm/g) is 302 times higher than the shift in resonance frequency (~1035 ppm/g) within the measurement range of ±1 g. The measured resolution based on the amplitude ratio is 0.619 mg and the nonlinearity is ~3.5% in the open-loop measurement operation.

This paper reports a high-sensitivity resonant electrometer based on the mode localization of two degree-of-freedom weakly coupled resonators (WCRs). When charges are applied to the input electrodes, the effective stiffness of a specific resonator of the WCRs will be perturbed, leading to a drastic change in mode shape owing to the mode localization phenomenon. By measuring the shift of the amplitude ratio, the small charge fluctuation can be accurately sensed. The theoretical mode of the electrometer is established based on the transfer functions of the WCRs. In particular, we establish the design rules of the coupling factor according to the -3-dB bandwidth, amplitude ratio measurement errors, and frequency misalignment between the resonators. The experimental results show that the amplitude ratio-based sensitivity is ~2151 times higher than the frequency-based sensitivity. The amplitude ratio-based resolution of the electrometer is approximately 1.269 fC.

In this paper, a handheld inertial pedestrian navigation system (IPNS) based on low-cost microelectromechanical system sensors is presented. Using the machine learning method of support vector machine, a multiple classifier is developed to recognize human step modes and device poses. The accuracy of the selected classifier is >85%. A novel step detection model is created based on the results of the classifier to eliminate the over-counting and under-counting errors. The accuracy of the presented step detector is >98%. Based on the improvements of the step modes recognition and step detection, the IPNS realized precise tracking using the pedestrian dead reckoning algorithm. The largest location error of the IPNS prototype is ~40 m in an urban area with a 2100-m-long distance.

Triboelectric nanogenerators have attracted wide attention due to their promising capabilities of scavenging the ambient environmental mechanical energy. However, efficient energy management of the generated high-voltage for practical low-voltage applications is still under investigation. Autonomous switches are key elements for improving the harvested energy per mechanical cycle, but they are complicated to implement at such voltages higher than several hundreds of volts. This paper proposes a self-sustained and automatic hysteresis plasma switch made from silicon micromachining, and implemented in a two-stage efficient conditioning circuit for powering low-voltage devices using triboelectric nanogenerators. The hysteresis of this microelectromechanical switch is controllable by topological design and the actuation of the switch combines the principles of micro-discharge and electrostatic pulling, without the need of any power-consuming control electronic circuits. The experimental results indicate that the energy harvesting efficiency is improved by two orders of magnitude compared to the conventional full-wave rectifying circuit.