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公开(公告)号：US20240037908A1
公开(公告)日：2024-02-01
申请号：US17877575
申请日：2022-07-29
发明人： Souvik Hazra , Avik Santra
IPC分类号： G06V10/764 , G06V10/82 , G06V10/84 , G06V40/20 , G01S13/89
CPC分类号： G06V10/764 , G06V10/82 , G06V10/84 , G06V40/23 , G01S13/89
摘要： In an embodiment, a method includes: receiving raw data from a millimeter-wave radar sensor; generating a first radar-Doppler image based on the raw data; generating a first radar point cloud based on the first radar-Doppler image; using a graph encoder to generate a first graph representation vector indicative of one or more relationships between two or more parts of the target based on the first radar point cloud; generating a first cadence velocity diagram indicative of a periodicity of movement of one or more parts of the target based on the first radar-Doppler image; and classifying an activity of a target based on the first graph representation vector and the first cadence velocity diagram.
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公开(公告)号：US20240028962A1
公开(公告)日：2024-01-25
申请号：US18346532
申请日：2023-07-03
发明人： Lorenzo Servadei , Huawei Sun , Avik Santra
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： In accordance with an embodiment, a method of training of a machine-learning algorithm includes: obtaining a training dataset comprising multiple training feature vectors and associated ground-truth labels, the multiple training feature vectors representing respective radar measurement datasets; determining, for each one of the multiple training feature vectors, a respective weighting factor by employing an explainable artificial-intelligence analysis of the machine-learning algorithm in a current training state; and training the machine-learning algorithm based on loss values that are determined based on a difference between respective classification predictions made by the machine-learning algorithm in the current training state for each one of the multiple training feature vectors and the ground-truth labels, wherein the loss values are weighted using the respective weighting factors associated with each training feature vector.
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公开(公告)号：US20230393240A1
公开(公告)日：2023-12-07
申请号：US18317749
申请日：2023-05-15
发明人： Lorenzo Servadei , Souvik Hazra , Julius Ott , Avik Santra , Huawei Sun , Michael Stephan
CPC分类号： G01S7/417 , G01S13/584 , G01S7/415
摘要： In accordance with an embodiment, a method includes estimating a people count of one or more persons included in the scene based on a first range-Doppler measurement map and the second range-Doppler measurement map derived from a radar measurement dataset. Estimating the people count includes inputting the first range-Doppler measurement map into a first data processing pipeline of a neural network algorithm, and inputting the second range-Doppler measurement map into a second data processing pipeline of the neural network algorithm. The first data processing pipeline and the second data processing pipeline includes range-Doppler convolutional layers implementing two-dimensional convolutions along the range dimension and the Doppler dimension, and the neural network algorithm includes an output section for processing a combination of a first output of the first data processing pipeline and a second output of the second data processing pipeline in a regression block.
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公开(公告)号：US11719787B2
公开(公告)日：2023-08-08
申请号：US17085448
申请日：2020-10-30
发明人： Avik Santra , Laurent Remont , Michael Stephan
IPC分类号： G01S7/295 , G01S7/35 , G01S7/41 , G06N3/063 , G06F18/214
CPC分类号： G01S7/2955 , G01S7/352 , G01S7/417 , G06F18/214 , G06N3/063 , G01S7/356
摘要： In an embodiment, a method for generating a target set using a radar includes: generating, using the radar, a plurality of radar images; receiving the plurality of radar images with a convolutional encoder; and generating the target set using a plurality of fully-connected layers based on an output of the convolutional encoder, where each target of the target set has associated first and second coordinates.
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公开(公告)号：US11640208B2
公开(公告)日：2023-05-02
申请号：US16691021
申请日：2019-11-21
发明人： Souvik Hazra , Ashutosh Baheti , Avik Santra
摘要： A method for operating a distributed neural network having a plurality of intelligent devices and a server includes: generating, by a first intelligent device of the plurality of intelligent devices, a first output using a first neural network model running on the first intelligent device and using a first input vector to the first neural network model; outputting, by the first intelligent device, the first output; receiving, by the first intelligent device, a gesture feedback on the first output from a user; determining, by the first intelligent device, a user rating of the first output from the gesture feedback; labeling, by the first intelligent device, the first input vector with a first label in accordance with the user rating; and training, by the first intelligent device, the first neural network model using the first input vector and the first label.
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公开(公告)号：US11585891B2
公开(公告)日：2023-02-21
申请号：US16853011
申请日：2020-04-20
发明人： Avik Santra , Muhammad Arsalan , Christoph Will
IPC分类号： G01S7/35 , G01S13/88 , G01S7/41 , G01S13/34 , G01S13/56 , A61B5/113 , G01S7/288 , A61B5/024 , A61B5/08 , G01S7/295
摘要： In an embodiment, a method includes: receiving radar signals with a millimeter-wave radar; generating range data based on the received radar signals; detecting a target based on the range data; performing ellipse fitting on in-phase (I) and quadrature (Q) signals associated with the detected target to generate compensated I and Q signals associated with the detected target; classifying the compensated I and Q signals; when the classification of the compensated I and Q signals correspond to a first class, determining a displacement signal based on the compensated I and Q signals, and determining a vital sign based on the displacement signal; and when the classification of the compensated I and Q signals correspond to a second class, discarding the compensated I and Q signals.
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公开(公告)号：US20220382381A1
公开(公告)日：2022-12-01
申请号：US17818513
申请日：2022-08-09
IPC分类号： G06F3/01
摘要： An earphone device includes a housing comprising a top region and a bottom region, an acoustic transducer disposed in the bottom region of the housing, and a radar system disposed in the top region of the housing. The radar system includes a first side and an opposite second side. The radar system is configured to detect a first object located on the first side of the radar system, and detect biometric data from a second object located on the second side of the radar system.
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公开(公告)号：US11448721B2
公开(公告)日：2022-09-20
申请号：US16452028
申请日：2019-06-25
发明人： Avik Santra , Jagjit Singh Bal
摘要： In an embodiment, a method of interference mitigation in a device that includes a millimeter-wave radar, includes transmitting radar signals with the millimeter-wave radar; receiving reflected radar signals with the millimeter-wave radar, the reflected radar signals corresponding to the transmitted radar signals; generating a first spectrogram based on the reflected radar signals; generating a second spectrogram indicative of movement of a non-target object; generating a compensated radar spectrogram based on the first and second spectrograms to compensate for an influence of the movement of the non-target object in the first spectrogram; and detecting a target or a property of the target based on the compensated radar spectrogram.
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公开(公告)号：US20220137181A1
公开(公告)日：2022-05-05
申请号：US17085448
申请日：2020-10-30
发明人： Avik Santra , Laurent Remont , Michael Stephan
摘要： In an embodiment, a method for generating a target set using a radar includes: generating, using the radar, a plurality of radar images; receiving the plurality of radar images with a convolutional encoder; and generating the target set using a plurality of fully-connected layers based on an output of the convolutional encoder, where each target of the target set has associated first and second coordinates.
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公开(公告)号：US20210396843A1
公开(公告)日：2021-12-23
申请号：US16905335
申请日：2020-06-18
摘要： In an embodiment, a method includes: transmitting a plurality of radar signals using a millimeter-wave radar sensor towards a target; receiving a plurality of reflected radar signals that correspond to the plurality of transmitted radar signals using the millimeter-wave radar; mixing a replica of the plurality of transmitted radar signals with the plurality of received reflected radar signals to generate an intermediate frequency signal; generating raw digital data based on the intermediate frequency signal using an analog-to-digital converter; processing the raw digital data using a constrained L dimensional convolutional layer of a neural network to generate intermediate digital data, where L is a positive integer greater than or equal to 2, and where the neural network includes a plurality of additional layers; and processing the intermediate digital data using the plurality of additional layers to generate information about the target.
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