您选择的条件: Yuhang Li
  • Buildup dynamics of broadband Q-switched noise-like pulse

    分类: 光学 >> 量子光学 提交时间: 2023-02-19

    摘要: We investigate the buildup dynamics of broadband Q-switched noise-like pulse (QS-NLP) driven by slow gain dynamics in a microfiber-based passively mode-locked fiber laser. Based on shot-to-shot tracing of the transient optical spectra and qualitatively reproduced numerial simulation, we demonstrate that slow gain dynamics is deeply involved in the onset of such complex temporal and spectral instabilities of QS-NLP. The proposed gain dynamics model could contribute to deeper insight into such nonlinear phenomenon and transient dynamics simulation in ultrafast fiber laser.

  • To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects

    分类: 光学 >> 量子光学 提交时间: 2023-02-19

    摘要: Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view. After their fabrication, the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes, while instantaneously erasing objects of the other data classes at the output field-of-view. Using the same framework, we also demonstrate the design of class-specific permutation cameras, where the objects of a target data class are pixel-wise permuted for all-optical class-specific encryption, while the other objects are irreversibly erased from the output image. The success of class-specific diffractive cameras was experimentally demonstrated using terahertz (THz) waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset, all-optically erasing the other handwritten digits. This diffractive camera design can be scaled to different parts of the electromagnetic spectrum, including, e.g., the visible and infrared wavelengths, to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.