Pulse repetition rate multiplier (PRRM) is an essential component of microwave photonics systems, designed not only to alleviate photodiode saturation but also to provide more frequent pulses. However, the presence of interleaving time errors is known to compromise the advantages of PRRM. In this study, we present a high-sensitivity detection method for identifying these time errors using an electro-optic sampling-based timing detector (EOS-TD). We utilize two EOS-TDs: one for generating precise timing ruler signals and the other as a high-precision timing detector. In comparison to the conventional power ratio comparison method, our approach demonstrates sensitivity improvement by two orders of magnitude. This enhancement facilitates the measurement of femtosecond-level time errors. By enabling higher pulse rates while maintaining the ultralow jitter, this method can be useful for building higher-speed photonic systems.
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http://dx.doi.org/10.1364/OL.504910 | DOI Listing |
Sci Rep
January 2025
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images.
View Article and Find Full Text PDFIntroduction: There is a general impression that patient-based quality control (PBQC) requires a high volume of laboratory results to detect errors effectively. However, internal quality control (IQC) performed infrequently may be associated with increased risk of missed error (i.e.
View Article and Find Full Text PDFCogn Process
January 2025
Institute of Cognitive Sciences and Technologies (ISTC-CNR), Via Nomentana 56, 00161, Rome, Italy.
Face masks can impact processing a narrative in sign language, affecting several metacognitive dimensions of understanding (i.e., perceived effort, confidence and feeling of understanding).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
As a multivariate time series, the prediction of curling trajectories is crucial for athletes to devise game strategies. However, the wide prediction range and complex data correlations present significant challenges to this task. This paper puts forward an innovative deep learning approach, CasLSTM, by introducing integrated inter-layer memory, and establishes an encoder-predictor curling trajectory forecasting model accordingly.
View Article and Find Full Text PDFISA Trans
January 2025
State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; Beijing Key Laboratory of Transformative High-end Manufacturing Equipment and Technology, Tsinghua University, Beijing, 100084, China. Electronic address:
Multi-axis contouring control is crucial for ultraprecision manufacturing industries, contributing to meeting the ever-increasingly stringent performance requirements. In this article, a novel contouring adaptive real-time iterative compensation (CARIC) method is proposed to achieve extreme multi-axis contouring accuracy, remarkable trajectory generalization, disturbance rejection, and parametric adaptation simultaneously. Specifically, control actions generated by CARIC consist of robust feedback, adaptive feedforward, and online trajectory compensation components.
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