Samsung Electronics Co., Ltd.

University of Southern California

Guanbo Chen

Wenxun Qiu

Abhishek Sehgal

Hao Chen

Mahta Moghaddam

John Stang

Khuong N Nguyen

Mark S Haynes

Boon Loong Ng

Jianhua Mo

Peshal Nayak

Hoang Viet Nguyen

Rebal Al Jurdi

Jin Cao

Hao-Hsuan Chang

Pratik Shah

Dong Li

A method includes receiving CSI data for at least one communication link between a first electronic device (ED1) and a Wi-Fi device in a 3D space. The method includes preprocessing the CSI data; and determining whether a motion of an object is detected within a threshold distance to the ED1 based on the preprocessed CSI data. The detection of the motion of the object corresponds to a determination that the object is present in the space. The detection of no motion of the object corresponds to a determination that the object is not present in the space. The method includes outputting at least one of: a first indicator indicating that the space is empty, in response to the determination that the object is not present in the space; or a second indicator indicating that the space is occupied, in response to the determination that the object is present in the space.

Presence detection and recognition with Wi-Fi

A method and apparatus for detecting network service types by grouping together applications that have similar latency requirement and data characteristics to form a service type. Machine learning algorithms may be used to detect the traffic pattern in the traffic stream by using features extracted from packet information and optionally additional sensor information. Subsequently, the output of the machine learning module may go through a post-processing process that may employ different techniques to use current prediction and past predictions to make a final decision about the network service type.

Methods and apparatus for detecting network services

A method for operating an electronic device includes transmitting signals to an external electronic device. The method also includes receiving signals from the external electronic device. The method further includes generating location information of the external electronic device relative to the electronic device based on a differences between the transmitted signals and the received signals. Additionally, the method includes determining an action for changing a state on at least one of the electronic device or the external electronic device based on the location information. In response to determining that the action is associated with the electronic device, the method includes performing the action. Additionally or alternatively, in response to determining that the action is associated with the external electronic device, the method includes sending an instruction to the external electronic device to perform the action.

Interactive control with ranging and gesturing between devices

A method includes obtaining statistical information of incoming network traffic. The method also includes using a state machine to classify the incoming network traffic as a traffic pattern based on the statistical information. The method further includes using the traffic pattern to adapt one or more Target Wakeup Time (TWT) parameters to optimize power consumption of a Wi-Fi station. The traffic pattern includes at least one of bursty traffic, random traffic, and stable traffic.

System and method for traffic type detection and Wi-Fi target wake time parameter design

A method includes obtaining network traffic information based on network traffic received during a time window. The method also includes determining a network service type using a machine learning classification system operating on the network traffic information. The method also includes determining a latency requirement based on the network service type. The method also includes adjusting one or more Target Wakeup Time (TWT) intervals and a wake duration based on the latency requirement to optimize power consumption of a Wi-Fi station.

System and method for determining Wi-Fi target wake time wake interval based on network traffic service

A method includes obtaining input features based on network traffic received during a time window. The method also includes generating multiple network service type predictions about the network traffic during the time window using a machine learning (ML) classification system operating on the input features. The method also includes storing the multiple network service type predictions in different time steps in a first-in first-out (FIFO) buffer and generating decisions about a presence of each of multiple service types in the network traffic using a voting algorithm. The method also includes reducing fluctuations in the generated decisions using a logic-based stabilizer module to generate a final network service type decision.

System and method for detecting network services based on network traffic using machine learning

A method includes obtaining wireless traffic statistics indicating current conditions and future trends for at least one of throughput, latency, and device power consumption. The method also includes generating penalty functions that reflect the wireless traffic statistics. The method further includes determining a target wakeup time (TWT) interval based on the penalty functions. The method also includes adapting the TWT interval based on a change in the wireless traffic statistics.

System and method for throughput-based optimization for target wake time interval adjustment

A wireless communication device comprising a processor configured to obtain first information on network conditions and second information on packets delivered to another communication device during a current target wake time (TWT) session, and to update a TWT service period (SP) duration for a future TWT session based on the first information and the second information.

Adaptive adjustment for target wake time duration configuration

Embodiments of the present disclosure provide methods and apparatuses for updating a target wake time (TWT) service period and interval. The apparatuses include a communication device comprising a transceiver and a processor. The transceiver is configured to transmit and receive higher layer data packets in a TWT operation during a time period. The processor is configured to determine, based on PHY data rates during the time period, effective higher layer data rates, estimate an initial data transceiving time based on the effective higher layer data rates and total lengths of the data packets, adjust the initial data transceiving time to obtain a higher layer data transceiving time based on an estimated network congestion level, an estimated re-transmission rate, and a total amount of TWT overhead during the time period, and determine a new TWT service period and interval based on the higher layer data transceiving time.

Apparatus and method for target wake time parameter design

Methods and systems of monitoring and controlling thermal therapy treatments. One method includes determining, with an electronic processor, a propagation constant for a plurality of waveports. The method also includes modeling an object within an imaging cavity using a sparse mesh model. The method also includes determining a simulated impedance of the object based on the model of the object. The method also includes calibrating the simulated impedance with a theoretical impedance numerically calculated for the object. The method also includes determining a distribution of an electric field and a distribution of a magnetic field for a mode in a conformal modeled waveport based on the calibrated impedance and the model of the object. The method also includes exciting the plurality of waveports to generate the determined distribution of the electric field and the distribution of the magnetic field.

Real-time imaging system for monitoring and control of thermal therapy treatments

A method for determining a change of temperature of an object. The method may include heating an object and measuring scattering parameters (S-parameters) of scattered microwave electric fields from the object. A distorted Born iterative method may be used to determine a change of a dielectric property of the object based on the measured S-parameters. A change of temperature of the object may be determined based on the change of the dielectric property of the object.

Real-time 3D microwave monitoring of thermal therapy

A method includes receiving from a transceiver, at a processor of an electronic device, a radar signal, the radar signal including a set of signal strength values across a range of Doppler frequency shift values and a range of time values. The method further includes extracting a time series of frequency features from the radar signal, wherein frequency features of the time series of frequency features include a determined Doppler frequency shift value for each time value of the range of time values, identifying segments within the time series of frequency features, analyzing the segments to determine a start time for a classification engine, at the start time, analyzing, by the classification engine, at least one of a subset of the time series of frequency features or a subset of the radar signal to identify a control gesture, and triggering a control input associated with the control gesture.

Method and apparatus of radar-based activity detection

A Convolutional Neural Network (CNN) assisted dielectric imaging method is provided. The method used CNN to incorporate the abundant image information from Magnetic Resonance (MR) images into the inverse scattering model-based microwave imaging process and generate high-fidelity dielectric images. A CNN is designed and trained to learn the complex mapping function from MR T1 images to dielectric images. Once trained, the new patients' MR T1 images are fed into the CNN to generate predicted dielectric images, which are used as the starting image for the microwave inverse scattering imaging. The CNN-predicted dielectric image significantly reduces the non-linearity and ill-posedness of the inverse scattering problem. The application of the proposed method to recover human brain dielectric images at 4 mm and 2 mm resolution with single-frequency and multi-frequency microwave measurements is provided.

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Allen, TX, United States of America

Guanbo Chen