Patents by Inventor Kai-Jiun YANG

Kai-Jiun YANG 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).

  • Patent number: 11569873
    Abstract: A MIMO symbol detection and search method, a decoding circuit and a receiving antenna system are provided. The signal detection and search method includes the following steps. A symbol search tree is obtained, and a plurality of candidate symbols at each layer of the symbol search tree are sorted. The candidate symbols are traversed in sequence at each layer of the symbol search tree. At each layer of the symbol search tree, if a cumulative partial Euclidean distance is greater than or equal to a threshold, un-scanned candidate symbols are excluded. If the cumulative partial Euclidean distance is less than the threshold, the threshold is updated by the cumulative partial Euclidean distance. When all of the candidate symbols have been calculated, an estimated symbol combination is outputted, and the scan of the symbol search tree is terminated.
    Type: Grant
    Filed: December 23, 2021
    Date of Patent: January 31, 2023
    Assignee: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
    Inventors: Kai-Jiun Yang, Chi-Tien Sun, Shang-Ho Tsai
  • Publication number: 20220207342
    Abstract: A data compression method, a data compression system and an operation method of a deep learning acceleration chip are provided. The data compression method includes the following steps. A filter coefficient tensor matrix of a deep learning model is obtained. A matrix decomposition procedure is performed according to the filter coefficient tensor matrix to obtain a sparse tensor matrix and a transformation matrix, which is an orthonormal matrix. The product of the transformation matrix and the filter coefficient tensor matrix is the sparse tensor matrix. The sparse tensor matrix is compressed. The sparse tensor matrix and the transformation matrix, or the sparse tensor matrix and a restoration matrix, are stored in a memory. A convolution operation result is obtained by the deep learning acceleration chip using the sparse tensor matrix. The convolution operation result is restored by the deep learning acceleration chip using the restoration matrix.
    Type: Application
    Filed: December 30, 2020
    Publication date: June 30, 2022
    Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
    Inventors: Kai-Jiun YANG, Gwo-Giun LEE, Chi-Tien SUN
  • Publication number: 20140181166
    Abstract: An apparatus for low complexity sub-Nyquist sampling of sparse wideband signals is provided, including a mixer, a periodic random sequence generator and a filter bank. The periodic random sequence generator generates a periodic pseudo-random sequence. The mixer is connected to the periodic random sequence generator for receiving the periodic pseudo-random sequence and mixing with an input signal to obtain a modulated signal. The filter bank further includes a plurality of filters and is connected to the mixer for filtering the modulated signal. The sub-Nyquist sampling apparatus may further includes a plurality of analog-to-digital convertors (ADCs), with each ADC connected to each filter of the filter bank to sample the signal from the filter bank and output a sampling signal.
    Type: Application
    Filed: December 26, 2012
    Publication date: June 26, 2014
    Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
    Inventors: Chun-Hsiung CHUANG, Chia-Hua LIN, Shang-Ho TSAI, Kai-Jiun YANG