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    METHOD AND APPARATUS FOR PROCESSING AND RECONSTRUCTING DATA
    14.
    发明申请
    METHOD AND APPARATUS FOR PROCESSING AND RECONSTRUCTING DATA 有权
    用于处理和重构数据的方法和装置

    公开(公告)号：US20150006598A1

    公开(公告)日：2015-01-01

    申请号：US14487898

    申请日：2014-09-16

    Applicant: Qualcomm Incorporated

    Inventor： Harinath Garudadri ,  Pawan Kumar Baheti ,  Somdeb Majumdar

    IPC: G06F7/02

    CPC classification number: G06F7/02 ,  G06F2207/02 ,  H03M7/30 ,  H03M7/3059 ,  H03M7/3062

    Abstract: Certain aspects of the present disclosure relate to a method for quantizing signals and reconstructing signals, and/or encoding or decoding data for storage or transmission. Points of a signal may be determined as local extrema or points where an absolute rise of the signal is greater than a threshold. The tread and value of the points may be quantized, and certain of the quantizations may be discarded before the quantizations are transmitted. After being received, the signal may be reconstructed from the quantizations using an iterative process.

    Abstract translation: 本公开的某些方面涉及用于量化信号和重构信号的方法，和/或用于存储或传输的数据的编码或解码的方法。 可以将信号的点确定为局部极值或信号的绝对上升大于阈值的点。 可以对点的胎面和值进行量化，并且在量化被发送之前某些量化可能被丢弃。 在接收之后，可以使用迭代过程从量化重构信号。

    Generating a sparse feature vector for classification
    15.
    发明授权
    Generating a sparse feature vector for classification 有权

    公开(公告)号：US11423323B2

    公开(公告)日：2022-08-23

    申请号：US15077873

    申请日：2016-03-22

    Applicant: QUALCOMM Incorporated

    Inventor： Somdeb Majumdar ,  Regan Blythe Towal

    IPC: G06N20/00 ,  G06N3/08 ,  G06K9/62 ,  G06K9/46 ,  G06V10/44

    Abstract: An apparatus for classifying an input includes a classifier and a feature extractor. The feature extractor is configured to generate a feature vector based on the input. The feature vector is also configured to set a number of elements of the feature vector to zero to produce a sparse feature vector. The sparse feature vector has the same dimensions as the feature vector generated by the feature extractor. However, the sparse feature vector includes fewer non-zero elements than the feature vector generated by the feature extractor. The feature vector is further configured to forward the sparse feature vector to the classifier to classify the input.

    Approximation of non-linear functions in fixed point using look-up tables
    17.
    发明授权
    Approximation of non-linear functions in fixed point using look-up tables 有权

    公开(公告)号：US10037306B2

    公开(公告)日：2018-07-31

    申请号：US15255015

    申请日：2016-09-01

    Applicant: QUALCOMM Incorporated

    Inventor： Dexu Lin ,  Edward Liao ,  Somdeb Majumdar ,  Aaron Lamb ,  Karamvir Chatha

    IPC: G06F1/02 ,  G06F17/17

    CPC classification number: G06F17/17 ,  G06F7/544 ,  G06F2207/5354 ,  G06N3/0481 ,  G06N3/063

    Abstract: Computing a non-linear function ƒ(x) in hardware or embedded systems can be complex and resource intensive. In one or more aspects of the disclosure, a method, a computer-readable medium, and an apparatus are provided for computing a non-linear function ƒ(x) accurately and efficiently in hardware using look-up tables (LUTs) and interpolation or extrapolation. The apparatus may be a processor. The processor computes a non-linear function ƒ(x) for an input variable x, where ƒ(x)=g(y(x),z(x)). The processor determines an integer n by determining a position of a most significant bit (MSB) of an input variable x. In addition, the processor determines a value for y(x) based on a first look-up table and the determined integer n. Also, the processor determines a value for z(x) based on n and the input variable x, and based on a second look-up table. Further, the processor computes ƒ(x) based on the determined values for y(x) and z(x).

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