We introduce a new method for reducing phase noise in oscillators, thereby improving their frequency precision. The noise reduction is realized by a passive device consisting of a pair of coupled nonlinear resonating elements that are driven parametrically by the output of a conventional oscillator at a frequency close to the sum of the linear mode frequencies. Above the threshold for parametric instability, the coupled resonators exhibit self-oscillations which arise as a response to the parametric driving, rather than by application of active feedback. We find operating points of the device for which this periodic signal is immune to frequency noise in the driving oscillator, providing a way to clean its phase noise. We present results for the effect of thermal noise to advance a broader understanding of the overall noise sensitivity and the fundamental operating limits.
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http://dx.doi.org/10.1103/PhysRevLett.108.264102 | DOI Listing |
Sensors (Basel)
December 2024
Department of Electronic & Computer Engineer, University of Limerick, V94 T9PX Limerick, Ireland.
Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor scenes. The method trains on static datasets to learn unwrapped depth map prediction and then adapts to dynamic datasets using continuous learning methods.
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December 2024
School of Geology Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China.
To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal.
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December 2024
Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0G4, Canada.
This article reports a 110.2 MHz ultra-low-power phase-locked loop (PLL) for MEMS timing/frequency reference oscillator applications. It utilizes a 6.
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December 2024
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning.
View Article and Find Full Text PDFMicromachines (Basel)
November 2024
Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Hefei University of Technology, Hefei 230009, China.
This paper proposes an improved algorithm based on the phase extraction of the Moiré fringe for wafer-mask alignment in nanoimprint lithography. The algorithm combines the strengths of the two-dimensional fast Fourier transform (2D-FFT) and two-dimensional window Fourier filtering (2D-WFF) to quickly and accurately extract the fundamental frequencies of interest, eliminate noise in the fundamental frequency band by using the threshold of the local spectrum, and effectively suppress spectral leakage by using a Gaussian window with outstanding sidelobe characteristics while overcoming their limitations, such as avoiding the time-consuming parameter adjustment. The phase extraction accuracy determines the misalignment measurement accuracy, and the alignment accuracy is enhanced to the nanometer level, which is 15.
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