中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

9 records (took 0.021 seconds)

    DIFFRACTIVE OPTICAL NETWORK FOR SEEING THROUGH DIFFUSIVE OR SCATTERING MEDIA
    5.
    发明公开
    DIFFRACTIVE OPTICAL NETWORK FOR SEEING THROUGH DIFFUSIVE OR SCATTERING MEDIA 审中-公开

    公开(公告)号：US20240288701A1

    公开(公告)日：2024-08-29

    申请号：US18571653

    申请日：2022-06-29

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yi Luo ,  Ege Cetintas ,  Yair Rivenson

    IPC: G02B27/09 ,  G02B5/18 ,  G02B6/42

    CPC classification number: G02B27/0944 ,  G02B5/1866 ,  G02B6/4206

    Abstract: A computer-free system and method is disclosed that uses an all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning, a set of transmissive layers are trained to all-optically reconstruct images of arbitrary objects that are distorted by random phase diffusers. After the training stage, the resulting diffractive layers are fabricated and form a diffractive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown, new phase diffuser. Unlike digital methods, all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusive and/or scattering media can be extended to other wavelengths, and can fuel various applications in biomedical imaging, astronomy, atmospheric sciences, oceanography, security, robotics, autonomous vehicles, among many others.

    DEVICES AND METHODS EMPLOYING OPTICAL-BASED MACHINE LEARNING USING DIFFRACTIVE DEEP NEURAL NETWORKS
    6.
    发明申请
    DEVICES AND METHODS EMPLOYING OPTICAL-BASED MACHINE LEARNING USING DIFFRACTIVE DEEP NEURAL NETWORKS 有权

    公开(公告)号：US20220366253A1

    公开(公告)日：2022-11-17

    申请号：US17843720

    申请日：2022-06-17

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Xing Lin ,  Deniz Mengu ,  Yi Luo

    IPC: G06N3/08 ,  G02B5/18 ,  G02B27/42 ,  G06K9/62 ,  G06N3/04 ,  G06V10/94 ,  G06V10/147

    Abstract: An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

    OPTICAL SYSTEMS AND METHODS USING BROADBAND DIFFRACTIVE NEURAL NETWORKS
    7.
    发明申请
    OPTICAL SYSTEMS AND METHODS USING BROADBAND DIFFRACTIVE NEURAL NETWORKS 有权

    公开(公告)号：US20220253685A1

    公开(公告)日：2022-08-11

    申请号：US17629346

    申请日：2020-09-09

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yi Luo ,  Deniz Mengu ,  Yair Rivenson

    IPC: G06N3/067 ,  G06N3/08

    Abstract: A broadband diffractive optical neural network simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using network learning. The optical neural network design was verified by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tunable, single passband as well as dual passband spectral filters, and (2) spatially-controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep learning-based design, broadband diffractive optical neural networks help engineer light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning. The optical neural network may be implemented as a reflective optical neural network.

    DIFFRACTIVE DEEP NEURAL NETWORKS WITH DIFFERENTIAL AND CLASS-SPECIFIC DETECTION
    8.
    发明申请
    DIFFRACTIVE DEEP NEURAL NETWORKS WITH DIFFERENTIAL AND CLASS-SPECIFIC DETECTION 有权

    公开(公告)号：US20220327371A1

    公开(公告)日：2022-10-13

    申请号：US17616983

    申请日：2020-06-05

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Jingxi Li ,  Deniz Mengu ,  Yi Luo

    IPC: G06N3/067 ,  G06N3/08 ,  G06N20/00 ,  G02B27/42

    Abstract: A diffractive optical neural network device includes a plurality of diffractive substrate layers arranged in an optical path. The substrate layers are formed with physical features across surfaces thereof that collectively define a trained mapping function between an optical input and an optical output. A plurality of groups of optical sensors are configured to sense and detect the optical output, wherein each group of optical sensors has at least one optical sensor configured to capture a positive signal from the optical output and at least one optical sensor configured to capture a negative signal from the optical output. Circuitry and/or computer software receives signals or data from the optical sensors and identifies a group of optical sensors in which a normalized differential signal calculated from the positive and negative optical sensors within each group is the largest or the smallest of among all the groups.

    DEVICES AND METHODS EMPLOYING OPTICAL-BASED MACHINE LEARNING USING DIFFRACTIVE DEEP NEURAL NETWORKS
    9.
    发明申请
    DEVICES AND METHODS EMPLOYING OPTICAL-BASED MACHINE LEARNING USING DIFFRACTIVE DEEP NEURAL NETWORKS 有权

    公开(公告)号：US20210142170A1

    公开(公告)日：2021-05-13

    申请号：US17046293

    申请日：2019-04-12

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Xing Ling ,  Deniz Mengu ,  Yi Luo

    IPC: G06N3/08 ,  G02B5/18 ,  G02B27/42 ,  G06N3/04 ,  G06K9/62 ,  G06K9/00 ,  G06K9/20

    Abstract: An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

Patent Agency Ranking