Texas Instruments Corporation

Samsung Electronics Co., Ltd.

Yuming Zhu

Manish Goel

Boon Loong Ng

Neha Dawar

Hoang Viet Nguyen

Songwei Li

Clive D Bittlestone

Sai Zhang

Srinath Mathur Ramaswamy

Yi Yang

Hyunchul Kim

Moonseok Kang

Jianzhong Zhang

Aditya V Padaki

Jiho Shin

Sujeet Milind Patole

Moongee Cho

Leonardo William Estevez

Khuong N Nguyen

Mandar Narsinh Kulkarni

Hao Chen

Seok-Jun Lee

Chenchi Luo

Yang Sun

Jing-Fei Ren

Dale E Hocevar

Abhishek Sehgal

John Seokjun Lee

Yanni Chen

Yunchen Qiu

Indranil Sinharoy

Domingo G Garcia

Rebal Al Jurdi

Vitali Loseu

Siddharth K Advani

Hyejung Kim

Raghunandan M Rao

Qiang Xie

Chiyu Zhang

A method for contactless vital sign monitoring includes transmitting, via a transceiver, radar signals for object detection. The method also includes generating a clutter removed channel impulse response from received reflections of the radar signals a portion of which are reflected off of a living object. The method further includes identifying a set of range bins corresponding to a position of the living object. Additionally, the method includes identifying a first set of signal components representing a respiration rate of the living object and a second set of signal components representing a heart rate of the living object.

Joint estimation of respiratory and heart rates using ultra-wideband radar

A method includes, when a handheld device is in motion within a service area to be traversed by a robot: obtaining ultra-wideband (UWB) ranging measurements between the handheld device and multiple anchors located in the service area; and obtaining image data and inertial measurement unit (IMU) data. The method also includes determining a trajectory of motion of the handheld device based on the UWB ranging measurements. The method also includes adjusting the trajectory based on image features obtained from the image data and motion estimates obtained from the IMU data. The method also includes identifying the adjusted trajectory as a boundary of the service area.

System and method for RF based robot localization

A method includes obtaining a channel impulse response (CIR) based on multiple ultrawide band (UWB) measurements between a mobile device and a target object. The method also includes estimating a first distance between the mobile device and the target object based on an energy of the CIR. The method also includes estimating a second distance between the mobile device and the target object based on a phase of the CIR. The method also includes obtaining an estimated distance between the mobile device and the target object based on at least one of the first distance and the second distance.

System and method for distance measurement using UWB radar

A method for localizing a target device (TD) by a user device (UD) includes determining whether the TD is within a range and within a field-of-view (FoV) of the UD based on a UWB channel between the TD and the UD. In response to a determination that the TD is outside the range and not within the FoV, the method includes identifying a first electronic device. The method also includes transmitting, to the first electronic device, a request for localizing the TD using an out-of-band channel. The method further includes receiving, from the first electronic device, location information of the TD on the out-of-band channel. The location information represents a candidate location of the TD in a coordinate system of the first or a second electronic device. Additionally, the method includes identifying a TD location with respect to a coordinate system of the UD based on the candidate location.

Coverage extension for device localization through collaborative ranging

A method includes obtaining signal information based on wireless signals communicated between an electronic device and a target device. The method also includes obtaining motion information based on movement of the electronic device. The method further includes identifying first location information based on the motion information and the channel information. Additionally, the method includes identifying second location information based on the orientation of the electronic device and the AoA information. The method also includes determining whether the electronic device is in motion. The method further includes determining whether the target device is within the FoV or outside the FoV of the electronic device based in part on the first location information in response to determining that the electronic device is not in motion or the second location information in response to determining that the electronic device is in motion.

Angle of arrival capability in electronic devices with motion sensor fusion

An electronic device includes a Lidar sensor, a radar sensor, and a processor. The processor is configured to identify one or more objects from Lidar scans. The processor is configured to transmit, via the radar sensor, radar signals for object detection based on reflections of the radar signals received by the radar sensor. While the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with the missed object.

Apparatus and methods for multi-sensor SLAM systems

A method includes determining one or more active slots and inactive slots in an ultra-wide band (UWB) ranging round. The method also includes determining multiple silent periods in the ranging round based on an arrangement of the inactive slots. The method also includes determining a target silent period in the ranging round, among the multiple silent periods, to be used for WiFi communication. The method also includes determining multiple target wake time (TWT) parameters such that a TWT session period defined by the TWT parameters overlaps with the target silent period. The method also includes determining one or more second silent periods preceding the target silent period in which the TWT parameters can be negotiated with an access point (AP) for the WiFi communication. The method also includes negotiating the TWT parameters with the AP during the one or more second silent periods.

Apparatus and method for promoting in-device coexistence between Wi-Fi and ultra-wide band communication

A method includes obtaining signal information based on wireless signals communicated between an electronic device and a target device. The method also includes obtaining motion information based on movement of the electronic device. The method further includes identifying first location information based on the signal information, the first location information indicating whether the target device is within a field of view (FoV) of the electronic device. Additionally, the method includes identifying second location information based on the motion information and the signal information, the second location information indicating whether the target device is within the FoV of the electronic device. The method also includes determining that the target device is within the FoV or outside the FoV of the electronic device based on at least one of the first location information or the second location information.

Angle of arrival determination in electronic devices with fused decision from motion

An electronic device includes a first set of sensors configured to generate motion information. The electronic device also includes a second set of sensors configured to receive information from multiple anchors. The electronic device further includes a processor configured to generate a path to drive the electronic device within an area. The processor is configured to receive the motion information. The processor is configured to generate ranging information based on the information that is received. While the electronic device is driven along the path, the processor is configured to identify a location and heading direction within the area of the electronic device based on the motion information. The processor is configured to modify the estimate of location and the heading direction of the electronic device based on the ranging information. The processor is configured to drive within the area according to the path, based on the location and heading direction.

Sensor fusion for localization and path planning

A method includes obtaining signal information based on wireless signals received from an external electronic device via a first antenna pair and a second antenna pair. The first and second antenna pairs are aligned along different axes. The signal information includes channel information, range information, a first angle of arrival (AoA) based on the first antenna pair, and a second AoA based on the second antenna pair. The method also includes generating an initial prediction of a presence of the external electronic device relative to a field of view (FoV) of the electronic device. The method further includes performing a smoothing operation on the range information and the first and second AoA, the smoothing operation generating a predicted location of the external electronic device relative to the FoV of the electronic device. Additionally, the method includes generating, based on the smoothing operation, confidence values associated with the predicted location.

Three-dimensional angle of arrival capability in electronic devices

A method includes obtaining signal information based on wireless signals received from a target electronic device via a first antenna pair and a second antenna pair. The first and second antenna pairs are aligned along different axes. The signal information includes channel information, range information, a first angle of arrival (AoA) based on the first antenna pair, and a second AoA based on the second antenna pair. The method also includes obtaining tagging information that identifies an environment in which the electronic device is located. The method also includes generating encoded information from a memory module based on the tagging information. The method further includes initializing a field of view (FoV) classifier based on the encoded information. Additionally, the method includes determining whether the target electronic device is in a FoV of the electronic device based on the FoV classifier operating on the signal information.

3D angle of arrival capability in electronic devices with adaptability via memory augmentation

A method includes obtaining channel information, range information, and angle of arrival (AoA) information based on wireless signals communicated between an electronic device and an external electronic device. The method also includes generating an initial prediction of a presence of the external electronic device relative to a field of view (FoV) of the electronic device based on the channel information and at least one of the range information or the AoA information. The initial prediction includes an indication of whether the external electronic device is within the FoV or outside the FoV of the electronic device. The method further includes performing, using a tracking filter, a smoothing operation on the range information and the AoA information. Additionally, the method includes determining that the external electronic device is within the FoV of the electronic device based on the AoA information, the smoothed AoA information, and the initial prediction.

Angle of arrival capability in electronic devices

Methods, apparatus, systems and articles of manufacture to determine and apply polarity-based error correction code are disclosed. In some examples, the methods and apparatus create an array by setting a first set of bit locations of a code word to have a first value and setting a second set of bit locations of the code word to have a second value different from the first value. In some examples, when the array satisfies a parity check, the methods and apparatus determine that bit locations having the first value from the array form a polarity-based error correction code.

Methods and apparatus to determine and apply polarity-based error correction code

A method, an electronic device, and computer readable medium are provided. The method includes receiving an input into a neural network that includes a kernel. The method also includes generating, during a convolution operation of the neural network, multiple panel matrices based on different portions of the input. The method additionally includes successively combining each of the multiple panel matrices with the kernel to generate an output. Generating the multiple panel matrices can include mapping elements within a moving window of the input onto columns of an indexing matrix, where a size of the window corresponds to the size of the kernel.

Convolution streaming engine for deep neural networks

Methods and apparatus to measure and analyze vibration signatures are disclosed. In some examples, a meter is provided comprising a waveform generator to generate a waveform based on first distance measurements of an object. In some examples, the meter includes a waveform generator to determine a first vibration characteristic of the object based on the waveform. In some examples, the meter includes a comparator to compare the first vibration characteristic to a signature vibration characteristic of the object, the signature vibration characteristic of the object indicative of normal characteristics of the object. In some examples, the meter includes a reporter to, in response to determining the first vibration characteristic does not match the signature vibration characteristic, generate an alert.

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Plano, TX, United States of America

Yuming Zhu