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    MULTI-OBJECT POSITIONING USING MIXTURE DENSITY NETWORKS
    1.
    发明申请
    MULTI-OBJECT POSITIONING USING MIXTURE DENSITY NETWORKS 审中-公开

    公开(公告)号：WO2022178473A1

    公开(公告)日：2022-08-25

    申请号：PCT/US2022/070281

    申请日：2022-01-21

    Applicant: QUALCOMM INCORPORATED

    Inventor： GHAZVINIAN ZANJANI, Farhad ,  BEHBOODI, Arash ,  DIJKMAN, Daniel Hendricus Franciscus ,  KARMANOV, Ilia ,  MERLIN, Simone ,  WELLING, Max

    IPC: G06N3/04 ,  G06N3/08 ,  G06N7/00

    Abstract: Certain aspects of the present disclosure provide techniques for object positioning using mixture density networks, comprising: receiving radio frequency (RF) signal data collected in a physical space; generating a feature vector encoding the RF signal data by processing the RF signal data using a first neural network; processing the feature vector using a first mixture model to generate a first encoding tensor indicating a set of moving objects in the physical space, a first location tensor indicating a location of each of the moving objects in the physical space, and a first uncertainty tensor indicating uncertainty of the locations of each of the moving objects in the physical space; and outputting at least one location from the first location tensor.

    UNSUPERVISED LOCATION ESTIMATION AND MAPPING BASED ON MULTIPATH MEASUREMENTS
    7.
    发明申请
    UNSUPERVISED LOCATION ESTIMATION AND MAPPING BASED ON MULTIPATH MEASUREMENTS 审中-公开

    公开(公告)号：WO2023086912A1

    公开(公告)日：2023-05-19

    申请号：PCT/US2022/079680

    申请日：2022-11-11

    Applicant: QUALCOMM INCORPORATED

    Inventor： KADAMBI, Shreya ,  BEHBOODI, Arash ,  SORIAGA, Joseph, Binamira ,  WELLING, Max

    IPC: G01S5/02 ,  G06N3/02

    Abstract: Certain aspects of the present disclosure provide methods, apparatus, and systems for predicting a location of a device in a spatial environment using a machine learning model. An example method generally includes measuring a plurality of signals received from a network entity at a device. A channel state information (CSI) measurement is generated from the measured plurality of signals. Generally, the CSI measurement includes a multipath component. Positions of one or more anchors in a spatial environment are identified based on a machine learning model trained to identify the positions of the one or more anchors based on the CSI measurement. A location of the device is estimated based on the identified positions of the one or more anchors.

    MODEL COMPRESSION VIA QUANTIZED SPARSE PRINCIPAL COMPONENT ANALYSIS
    8.
    发明申请
    MODEL COMPRESSION VIA QUANTIZED SPARSE PRINCIPAL COMPONENT ANALYSIS 审中-公开

    公开(公告)号：WO2023059723A1

    公开(公告)日：2023-04-13

    申请号：PCT/US2022/045785

    申请日：2022-10-05

    Applicant: QUALCOMM INCORPORATED

    Inventor： KUZMIN, Andrey ,  VAN BAALEN, Marinus Willem ,  NAGEL, Markus ,  BEHBOODI, Arash

    IPC: G06N3/04 ,  G06N3/08 ,  G06N3/045 ,  G06N3/084

    Abstract: A processor-implemented method includes retrieving, for a layer of a set of layers of an artificial neural network (ANN), a dense quantized matrix representing a codebook and a sparse quantized matrix representing linear coefficients. The dense quantized matrix and the sparse quantized matrix may be associated with a weight tensor of the layer. The processor-implemented method also includes determining, for the layer of the set of layers, the weight tensor based on a product of the dense quantized matrix and the sparse quantized matrix. The processor-implemented method further includes processing, at the layer, an input based on the weight tensor.

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