We report a resonant torsional micro-mirror with all-piezoelectric driving and tunable spring stiffness. Stiffness modulation finds two practical applications. First is tuning of the resonance frequency, achieved by applying DC bias voltage to the stiffness modulating structures. A tuning rate was found to be 0.95 Hz V−1 with up to 20 Hz of usable frequency range. Second, when direct excitation of the torsional mode is combined with the harmonic modulation of the spring stiffness, an optical scan angle is shown to be increased by more than 4° through 2:1 degenerate parametric amplification. By varying the phase of the parametric pump with respect to the direct excitation, the Q-factor is tuned between 617 and 898, corresponding to the minimum and maximum parametric gain factors of 0.84× and 1.21×, respectively, achieved at a nominal unpumped optical scan angle of 16.3°. Increasing the pump amplitude shows a moderate increase in the amplifier's gain with clear saturation at 1.43× in the superthreshold pumping regime, indicating a presence of the third order stiffness nonlinearity. The results show potential to apply parametric amplification to future piezo-micro-electro-mechanical-system actuators for large frequency and large-stroke mechanical response achieved at ambient pressure.
This study shows how unimorph deformable mirrors can be effectively fabricated out of commercially available piezo-buzzers without any high-tech equipment. Our design involves a quad-channel mirror with monolithically integrated flexures. The mirror is controlled by a custom quad-channel high-voltage driver unit, capable of 93 Vp-p and 75 kpps performance. The system exhibits 16-bit resolution over the angular working range of ±1.85 mrad, with 0.04 mrad V −1 angular sensitivity and 14% hysteresis. We demonstrate the mirror performance for focus control in microscopy and high-precision laser beam steering, opening a wide variety of applications in computational imaging, confocal microscopy, optical tweezing and laser lithography.
The paper presents a method that enables automated segmentation of the low-contrast shadowgraph images, e.g., acquired in the studies of laser induced shockwave phenomena. The method is especially suitable for the analysis of large image data sets, such as obtained at studying the evolution of laser-induced shockwaves with high spatial and temporal resolution. The method comprises two active contours algorithms. First, the approximate shape of the shockwave is detected by a traditional snake algorithm using external energies that base on texture cues. The outcome of the coarse detection serves as an initialization to the second refining stage detection introducing a Greedy snake algorithm. Local optimum is searched with respect to responses of steerable filtering and edge orientation similarity by exploiting the Bayesian formalism. The paper presents validation of the method on sample of 12 low contrast shadowgraphs by comparison to the manual segmentation technique. The obtained results demonstrate overall good performance, robustness and high accuracy of the method.
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