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A femtosecond laser metrology with nanometer precision and dynamic range of centimeter incorporating time-stretch interferometry and phase delay retrieval method is proposed and experimentally demonstrated. Displacement encoded phase-sensitive temporal interferogram is generated when phase stabilized femtosecond laser pulses transmitting through a time-stretch interferometer. To avoid the chirp of temporal interferogram due to high-order dispersion, time-to-frequency mapping is established to transform the temporal interferogram to the spectral interferogram for high-speed detection. The phase delay of spectral interferogram corresponding to specific displacements is retrieved and accumulated for linear fitting. The fitted slope is the time delay between two arms in the interferometer therefore displacement can be calculated. For precision verification, the preset displacements are measured. Mean error and standard deviation are presented after sliding average of measured results in 4 μs. [ABSTRACT FROM AUTHOR]

A real-time lidar with 0.1 mega-hertz update rate and few-micrometer resolution incorporating dispersive Fourier transformation and instantaneous microwave frequency measurement is proposed and demonstrated. As a femtosecond laser pulse passing through an all-fiber Mach–Zehnder interferometer, where the detection light beam is inserted into the optical path of one arm, the displacement is encoded to the frequency variation of the temporal interferogram. To deal with the challenges in storage and real-time processing of the microwave pulse generated on a photodetector, we turn to microwave photonic signal processing. A carrier wave is modulated by the time-domain interferogram using an intensity modulator. After that, the frequency variation of the microwave pulse is uploaded to the first order sidebands. Finally, the frequency shift of the sidebands is turned into transmission change through a symmetric-locked frequency discriminator. In experiment, a real-time ranging system with adjustable dynamic range and detection sensitivity is realized by incorporating a programmable optical filter. Standard deviation of 7.64 μm, overall mean error of 19.10 μm over 15 mm detection range and standard deviation of 37.73 μm, overall mean error of 36.63 μm over 45 mm detection range are realized, respectively. [ABSTRACT FROM AUTHOR]

Due to complex dynamic characteristics of the ball mill system, it is difficult to measure load parameters inside the ball mill. It has been noticed that the traditional single-model and ensemble-model based soft sensor approaches demonstrate weak generalization power. Also, mill motor current, feature subsets of the shell vibration and acoustical frequency spectra contain different useful information. To achieve better solutions and overcome these problems mentioned above, a selective ensemble multisource information approach is proposed in this paper. Only the useful feature subsets of vibration and acoustical frequency spectra are portioned and selected. Some modeling techniques, such as fast Fourier transform (FFT), mutual information (MI), kernel partial least square (KPLS), brand and band (BB), and adaptive weighting fusion (AWF), are combined effectively to model the mill load parameters. The simulation is conducted using real data from a laboratory-scale ball mill. The results show that our proposed approach can effectively fusion the shell vibration, acoustical and mill motor current signals with improved model generalization. [ABSTRACT FROM AUTHOR]