No abstract

In this paper, a nonlinear feedback control algorithm is proposed to improve the performance of an electromagnetic actuated polymer MEMS hard-magnetic micromirror. The enhanced performance and effectiveness of the proposed algorithm are verified experimentally through National Instrument Field-Programmable Gate Array hardware. Considering the critical requirements in the micromirror-based optical switching applications, the set-point regulation tests are performed to investigate the transient and positioning performance of the system. The proposed scheme provides enhanced transient response when compared to traditional proportional-integral-derivative control. Compared with open loop control, the experimental results of set-point regulation have demonstrated that the 95% setting time is shortened from 50 to 10 ms while the 30% overshoot is eliminated with high positioning performance by using the proposed scheme. A magnetic micromirror-based laser scanning system is developed to verify the tracking and imaging performance of the closed-loop system with the proposed scheme. The results confirm that the closed-loop controlled magnetic micromirror follows the given sinusoidal and triangle trajectories precisely with the proposed scheme and an image of the scanned target is obtained.

Noninvasive neurostimulation plays a pivotal role in direct control of neural circuit and modulating neuronal function. However, it is difficult to balance both spatial focality and depth of penetration in stimulating deep neurons. Here, we designed time-division, frequency & polarity modulation synthesis (TMFPMS) for stimulating deep neurons noninvasively with low-frequency envelope. We demonstrated its spatial resolution: mm-level via computational simulation including finite element analysis and Hodgkin-Huxley action potential model. Taken together, the results of this study indicate that TMFPMS neurostimulation with high spatial resolution is steerable and may replace traditional implanted electrode.

SUMMARYA sensor selection scheme for source extraction in heterogeneous wireless sensor networks is proposed. The proposed scheme selects the sensors corresponding to the reconstructed mixtures with the largest signal-to-noise-ratios to transmit their data to a fusion center. It exploits the heterogeneity of the sensor observations and reduces the amount of transmissions. The results demonstrate that the proposed scheme outperforms the full utilization scheme and the random selection scheme.