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In this work, we present the development of a polymer-based capacitive sensing array. The proposed device is capable of measuring normal and shear forces, and can be easily realized by using micromachining techniques and flexible printed circuit board (FPCB) technologies. The sensing array consists of a polydimethlysiloxane (PDMS) structure and a FPCB. Each shear sensing element comprises four capacitive sensing cells arranged in a 2 × 2 array, and each capacitive sensing cell has two sensing electrodes and a common floating electrode. The sensing electrodes as well as the metal interconnect for signal scanning are implemented on the FPCB, while the floating electrodes are patterned on the PDMS structure. This design can effectively reduce the complexity of the capacitive structures, and thus makes the device highly manufacturable. The characteristics of the devices with different dimensions were measured and discussed. A scanning circuit was also designed and implemented. The measured maximum sensitivity is 1.67%/mN. The minimum resolvable force is 26 mN measured by the scanning circuit. The capacitance distributions induced by normal and shear forces were also successfully captured by the sensing array.

The major radiation of the Sun can be roughly divided into three regions: ultraviolet, visible, and infrared light. Detection in these three regions is important to human beings. The metal-insulator-semiconductor photodetector, with a simpler process than the pn-junction photodetector and a lower dark current than the MSM photodetector, has been developed for light detection in these three regions. Ideal UV photodetectors with high UV-to-visible rejection ratio could be demonstrated with III–V metal-insulator-semiconductor UV photodetectors. The visible-light detection and near-infrared optical communications have been implemented with Si and Ge metal-insulator-semiconductor photodetectors. For mid- and long-wavelength infrared detection, metal-insulator-semiconductor SiGe/Si quantum dot infrared photodetectors have been developed, and the detection spectrum covers atmospheric transmission windows.

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The hybrid recurrent fuzzy neural network (HRFNN) control permanent magnet synchronous motor (PMSM) drive system using rotor flux estimator is developed to control electric motorcycle in this paper. First, the dynamic models of a PMSM drive system and electric motorcycle are builted though experimental tests and parameters measurements. Then, a HRFNN control control system using rotor flux estimator is developed to control PMSM drive system in order to drive electric motorcycle. The rotor flux estimator consists of the estimation algorithm of rotor flux position and speed based on the back electromagnetic force (EMF). Moreover, the HRFNN controller consists of the supervisor control, RFNN and variable structure control (VSC) is applied to PMSM drive system using rotor flux estimator. The parameters of RFNN are trained to control command current in order to achieve different output torque of various speeds. The electric motorcycle is operated to provide constant disturbance torque. Finally, the effectiveness of the proposed control schemes is demonstrated by experimental results.