A distribution-based selective optimization method for eliminating periodic defects in harmonic signals.

Due to environmental interference and defects in measured objects, measurement signals are frequently affected by unpredictable noise and periodic defects. Moreover, there is a lack of effective methods for accurately distinguishing defect components from measurement signals. In this study, a distribution-based selective optimisation method (SOM) is proposed to mitigate the effects of noise and defect components.

Artificial intelligence X-ray measurement technology of anatomical parameters related to lumbosacral stability.

Purpose: To develop a deep learning-based model for measuring automatic lumbosacral anatomical parameters from lateral lumbar radiographs and compare its performance to that of attending-level radiologists.

Methods: A total of 1791 lateral lumbar radiographs were collected through the PACS system and used to develop the deep learning-based model. Landmarks for the four used parameters, including the lumbosacral lordosis angle (LSLA), lumbosacral angle (LSA), sacral horizontal angle (SHA), and sacral inclination angle (SIA), were identified and automatically labeled by the model.

Fluorescence decay route of optical transition calculation for trivalent rare earth ions and its application for Er-doped NaYF phosphor.

Usually, the optical transition properties of trivalent rare earth (RE) ions in transparent hosts can be quantitatively investigated in the framework of Judd-Ofelt theory. A standard and commonly accepted calculation procedure based on the absorption spectrum has already been established. However, it is hard to assess the optical transition properties of trivalent RE ions doped in powdered and film materials owing to the difficulty in the absolute absorption spectrum measurements.