Origin Wireless, Inc.

Origin Research Wireless, Inc.

Oscar Chi-Lim Au

Beibei Wang

K J Ray Liu

Chenshu Wu

Hung-Quoc Duc Lai

Feng Zhang

Chen Chen

Yi Han

Dan Bugos

Min Wu

Sai Deepika Regani

Chao-Lun Mai

Qinyi Xu

Yuqian Hu

Hangfang Zhang

David N Claffey

Chau-Wai Wong

Zhung-Han Wu

Muhammed Zahid Ozturk

Xiaolu Zeng

Chun-I Chen

Guozhen Zhu

Fengyu Wang

Jeng-Feng Lee

Zoltan Safar

Peng Zan

Masato Marumo

Weihang Gao

Chun-Chia Jack Shih

Wang Yin Chen

Yan Chen

Kj Ray Liu

Wei-Hsiang Wang

Thomas Nelson Buttermore, Jr

Sakila Sandeepani Jayaweera Samaranayake Arachchige Dona

Spencer Maid

Sakila S Jayaweera

Yusen Fan

Yexin Cao

KJ Ray Liu

Zhang-Han Wu

Wireless sensing using classifier probing and refinement is described. In one example, a described method comprises: computing output analytics by a classifier based on input data constructed based on raw measurement data; mapping each output analytics to a respective mapped outcome; identifying at least one reference input data each associated with a reference output analytics and a reference mapped outcome; each reference input data being one of the input data for the classifier for which a respective reference outcome is available and is different from the reference mapped outcome; and for each reference input data for the classifier: computing a respective plurality of perturbed output analytics by the classifier, generating selected perturbed input data based on the plurality of perturbed output analytics; and re-training the classifier based on the selected perturbed input data and the associated reference outcome.

Wireless sensing using classifier probing and refinement

Methods, apparatus and systems for wireless sensing are described. In one example, a described system in a wireless data communication network comprises: a transmitter configured to transmit a time series of at least one wireless sounding signal (WSS) based on a standard wireless network protocol associated with the wireless data communication network, and a receiver. The wireless data communication network comprises a physical (PHY) layer, a medium access control (MAC) layer, and at least one higher layer. The receiver is configured to: receive the time series of at least one WSS (TSWSS) based on the standard wireless network protocol through a wireless channel of a venue, and perform a plurality of wireless sensing measurements based on the received TSWSS to obtain sensing measurement results based on the standard wireless network protocol. The PHY layer or the MAC layer of the receiver provides the sensing measurement results locally to the at least one higher layer of the receiver based on the standard wireless network protocol. The sensing measurement results are available to the at least one higher layer of the receiver for performing a sensing-based task based on the sensing measurement results.

Method, apparatus, and system for wireless sensing measurement and reporting

Methods, apparatus and systems for radio-assisted signal estimation are described. In one example, a described system comprises: a sensor configured to obtain a baseband mixture signal in a venue; a transmitter configured to transmit a first radio signal through a wireless channel of the venue; a receiver configured to receive a second radio signal through the wireless channel; and a processor. The baseband mixture signal comprises a mixture of a first source signal and an additional signal. The first source signal is generated by a first motion of a first object in the venue. The second radio signal differs from the first radio signal due to the wireless channel and at least the first motion of the first object in the venue. The processor is configured for: obtaining a radio feature of the second radio signal, constructing a first adaptive filter for the baseband mixture signal based on the radio feature, filtering the baseband mixture signal using the first adaptive filter to obtain a first output signal, and generating an estimation of the first source signal based on the first output signal.

Method, apparatus, and system for speech enhancement and separation based on audio and radio signals

Methods, apparatus and systems for wireless sensing based on deep learning are described. For example, a described method comprises: transmitting a wireless signal through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue; receiving the wireless signal through the wireless multipath channel of the venue, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the motion of the object; obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal; computing a plurality of autocorrelation functions based on the TSCI, each autocorrelation function (ACF) computed based on CI of the TSCI in a respective sliding time window; constructing at least one ACF vector, wherein each respective ACF vector is a vector associated with a respective ACF comprising multiple vector elements each associated with a respective time lag, each vector element being a value of the respective ACF evaluated at the respective time lag; rearranging the at least one ACF vector into rearranged ACF data, wherein each ACF vector is a one-dimensional (1D) ACF-block; and performing a wireless sensing task based on a task engine to do a processing using the rearranged ACF data as an input.

Method, apparatus, and system for wireless sensing based on deep learning

Wireless sensing for in-vehicle child presence detection is described. In one example, a described method comprises: obtaining a time series of channel information (TSCI) of a wireless channel in a vehicle; computing a motion statistics (MS) based on channel information (CI) of the TSCI in a time period; detecting a motion of a user in the time period by comparing the motion statistics with a first threshold, wherein the motion of the user is detected when the motion statistics exceed the first threshold; constructing a 2-dimensional (2D) transform matrix based on 1-dimensional (D) transforms of CI of TSCI in sliding time windows in the time period; detecting a number of horizontal streaks of the 2D transform matrix; detecting a breathing motion of the user in the time period based on the number of detected horizontal streaks of the 2D transform matrix and an associated criterion.

Wireless sensing for in-vehicle child presence detection

Methods, apparatus and systems for enhanced wireless monitoring of vital signs are described. In one example, a described system comprises: a transmitter configured to transmit a wireless signal through a wireless channel of a venue; a receiver configured to receive the wireless signal through the wireless channel; and a processor. The received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue. The processor is configured for: obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, computing a two dimensional (2D) decomposition of the time series of CI (TSCI), enhancing the 2D decomposition, and monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

Method, apparatus, and system for enhanced wireless monitoring of vital signs

Methods, apparatus and systems for wireless monitoring and tracking are described. In one example, a described method of a wireless monitoring system comprises: transmitting a wireless signal that is impacted by a wireless multipath channel in a venue and a modulation of an object undergoing a motion in the venue, to obtain a set of channel information (CI) of the wireless multipath channel; performing a monitoring task by monitoring the object and the motion; determining a plurality of admissible system states of the wireless monitoring system, wherein each admissible system state is associated with a respective setting of at least one of: the wireless signal, a series of sounding signals in the wireless signal, or the monitoring task; choosing one of the admissible system states to be a system state of the wireless monitoring system based on the monitoring task; and applying a setting associated with the chosen admissible system state to the wireless monitoring system.

Method, apparatus, and system for automatic and adaptive wireless monitoring and tracking

Methods, apparatus and systems for radio-based voice activity detection are described. In one example, a described system comprises: a transmitter configured to transmit a radio signal through a wireless channel of a venue; a receiver configured to receive the radio signal through the wireless channel, wherein the wireless channel is impacted by a voice activity of a target voice source in the venue; and a processor. The processor is configured for: computing a time series of channel information (CI) of the wireless channel based on the radio signal, and detecting the voice activity of the target voice source based on the time series of CI (TSCI) of the wireless channel, without using any media signal.

Method, apparatus, and system for voice activity detection based on radio signals

Methods, apparatus and systems for wireless sensing and proximity detection are described. For example, a described method comprises: transmitting, by a first wireless device, a wireless signal through a wireless multipath channel in a venue; receiving, by a second wireless device, the wireless signal through the wireless multipath channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and a movement of an object in the venue; obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal; computing a first motion statistics based on a time-domain correlation of channel information (CI) in the TSCI; computing a second motion statistics based on a frequency-domain correlation of the CI in the TSCI; and detecting a proximity of the object to a target in the venue based on the first motion statistics and the second motion statistics.

Method, apparatus, and system for wireless proximity detection

Methods, apparatus and systems for wireless sensing based on linkwise motion statistics (link-MS) are described. In one example, a described method comprises: determining a plurality of heterogeneous wireless devices of a wireless sensing system in a venue; determining a plurality of device-links each of comprises two of the heterogeneous wireless devices, one functioning as a transmitter and the other functioning as a receiver; for each respective device-link of the plurality of device-links, transmitting a respective wireless signal from a respective transmitter of the respective device-link through a wireless multipath channel of the venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue, receiving the respective wireless signal by a respective receiver of the respective device-link through the wireless multipath channel, wherein the respective received wireless signal differs from the respective transmitted wireless signal due to the wireless multipath channel of the venue and the motion of the object, obtaining a respective time series of channel information (TSCI) of the wireless multipath channel based on the respective received wireless signal of the respective device-link, computing a respective link-MS for the respective device-link based on the respective TSCI; performing a sensing task associated with the motion of the object based on at least one of: all the TSCI or all the link-MS; and computing a location in the venue associated with the sensing task based on all the link-MS.

Method, apparatus, and system for wireless sensing based on linkwise motion statistics

Methods, apparatus and systems for wireless monitoring with improved accuracy are described. In one example, a described method comprises: transmitting a wireless signal through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue; receiving the wireless signal through the wireless multipath channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the motion; obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal; performing a classification of a sliding time window by analyzing channel information (CI) of the TSCI in the sliding time window; computing a motion information (MI) for the sliding time window based on the TSCI and the classification of the sliding time window; and monitoring the motion of the object based on the MI.

Method, apparatus, and system for wireless monitoring with improved accuracy

Methods, apparatus and systems for map reconstruction based on wireless tracking are described. In one example, a described system comprises: a sensor configured to collect sensing data in a venue and obtain a plurality of trajectories, and a processor. Each trajectory is a time series of spatial coordinates (TSSC) representing a path traversed by a respective object in the venue. Each TSSC is accompanied by at least one respective time series of sensing data (TSSD) collected while the respective object traverses the path in the venue. The processor is configured for: segmenting each TSSC and its accompanying at least one TSSD into segments, bundling the plurality of trajectories based on similarity measures between pairs of the segments, fusing the bundled trajectories to generate fused trajectories, computing a shape of the fused trajectories, and generating a map of the venue based on the computed shape.

Method, apparatus, and system for map reconstruction based on wireless tracking

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San Jose, CA, United States of America

Oscar Chi-Lim Au