Patents by Inventor Suraj S. Jog

Suraj S. Jog has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Neural network-based millimeter-wave imaging system
    Patent number: 11836852
    Abstract: A method includes receiving data including a plurality of data items, each data item of the plurality of data items including a three-dimensional (3D) radar heat map of an object and a corresponding two-dimensional (2D) image of the object captured by a stereo camera, inputting the training dataset into a machine learning model including a neural network (NN) that generates, from the 3D radar heat map, a 2D depth map for the object and outputs a probability that the 2D depth map is the corresponding 2D image of the object, and training the machine learning model based on a training dataset to generate a trained machine learning model that iteratively learns to generate an updated 2D depth map that approximates the corresponding 2D image. The training dataset includes the plurality of data items, the 2D depth map and the probability.
    Type: Grant
    Filed: December 17, 2020
    Date of Patent: December 5, 2023
    Assignee: Board of Trustees of the University of Illinois
    Inventors: Junfeng Guan, Seyedsohrab Madani, Suraj S. Jog, Haitham Al Hassanieh, Saurabh Gupta
  • NEURAL NETWORK-BASED MILLIMETER-WAVE IMAGING SYSTEM
    Publication number: 20210192762
    Abstract: A method includes receiving data including a plurality of data items, each data item of the plurality of data items including a three-dimensional (3D) radar heat map of an object and a corresponding two-dimensional (2D) image of the object captured by a stereo camera, inputting the training dataset into a machine learning model including a neural network (NN) that generates, from the 3D radar heat map, a 2D depth map for the object and outputs a probability that the 2D depth map is the corresponding 2D image of the object, and training the machine learning model based on a training dataset to generate a trained machine learning model that iteratively learns to generate an updated 2D depth map that approximates the corresponding 2D image. The training dataset includes the plurality of data items, the 2D depth map and the probability.
    Type: Application
    Filed: December 17, 2020
    Publication date: June 24, 2021
    Inventors: Junfeng Guan, Seyedsohrab Madani, Suraj S. Jog, Haitham Al Hassanieh, Saurabh Gupta