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公开(公告)号:US12022358B2
公开(公告)日:2024-06-25
申请号:US17229825
申请日:2021-04-13
发明人: Ilia Karmanov , Daniel Hendricus Franciscus Dijkman , Farhad Ghazvinian Zanjani , Ishaque Ashar Kadampot , Simone Merlin , Brian Michael Buesker , Vamsi Vegunta , Harshit Joshi , Fatih Murat Porikli , Joseph Binamira Soriaga , Bibhu Mohanty
CPC分类号: H04W4/029 , G01S5/013 , G01S5/0278 , G06N20/00
摘要: Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform object location estimation.
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公开(公告)号:US11575452B2
公开(公告)日:2023-02-07
申请号:US17229798
申请日:2021-04-13
发明人: Simone Merlin , Bibhu Mohanty , Daniel Hendricus Franciscus Dijkman , Farhad Ghazvinian Zanjani , Ilia Karmanov , Brian Michael Buesker , Harshit Joshi , Vamsi Vegunta , Ishaque Ashar Kadampot
IPC分类号: H04B17/318 , H04W4/02 , H04W16/20 , H04W24/02 , H04W64/00
摘要: Disclosed are systems, methods, and non-transitory media for sensing radio frequency signals. For instance, radio frequency data can be received by an apparatus and from at least one wireless device in an environment. Based on the radio frequency data received from the at least one wireless device, the apparatus can determine sensing coverage of the at least one wireless device. The apparatus can further provide the determined sensing coverage and a position of at least one device to a user device.
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公开(公告)号:US20220383114A1
公开(公告)日:2022-12-01
申请号:US17804842
申请日:2022-05-31
发明人: Farhad Ghazvinian Zanjani , Ilia Karmanov , Daniel Hendricus Franciscus Dijkman , Hanno Ackermann , Simone Merlin , Brian Michael Buesker , Ishaque Ashar Kadampot , Fatih Murat Porikli , Max Welling
IPC分类号: G06N3/08
摘要: Certain aspects of the present disclosure provide techniques for training and inferencing with machine learning localization models. In one aspect, a method, includes training a machine learning model based on input data for performing localization of an object in a target space, including: determining parameters of a neural network configured to map samples in an input space based on the input data to samples in an intrinsic space; and determining parameters of a coupling matrix configured to transport the samples in the intrinsic space to the target space.
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