Piezoelectric actuators are considered as standard structural elements in Microsystems Technology. A piezoelectric transducer conventionally works as a pure linear motor ͑piezotranslator͒. If a rotation is required from the piezoelectric transducer, a transmission mechanism must be designed. This article presents a study on the development of a novel two-degree-of-freedom piezoelectric rotary-linear actuator system, including ͑1͒ conceptual design, ͑2͒ dynamic modeling, ͑3͒ control, and ͑4͒ prototype. The experimental validation of these concepts was performed. The preliminary result of the experiment has shown that the proposed concept works very well; in particular, the system has achieved ͑1͒ the linear displacement resolutions: 26 nm, ͑2͒ the angular displacement resolution: 0.019°, ͑3͒ the maximum driving force: 2.09 N, and ͑4͒ the maximum driving torque: 12.20 N mm.

For audio-frequency magnetotelluric surveys where the signals are lightning-stroke transients, the conventional Fourier transform method often fails to produce a high quality impedance tensor. An alternative approach is to use the wavelet transform method which is capable of localizing target information simultaneously in both the temporal and frequency domains. Unlike Fourier analysis that yields an average amplitude and phase, the wavelet transform produces an instantaneous estimate of the amplitude and phase of a signal. In this paper a complex well-localized wavelet, the Morlet wavelet, has been used to transform and analyze audio-frequency magnetotelluric data. With the Morlet wavelet, the magnetotelluric impedance tensor can be computed directly in the wavelet transform domain. The lightning-stroke transients are easily identified on the dilation-translation plane. Choosing those wavelet transform values where the signals are located, a higher signal-to-noise ratio estimation of the impedance tensor can be obtained.In a test using real data, the wavelet transform showed a significant improvement in the signal-tonoise ratio over the conventional Fourier transform.

No abstract

No abstract