Abstract: 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.

Abstract: A method includes obtaining radar pulse configuration information at an electronic device. The method also includes generating a data unit of a Wi-Fi communications protocol based on the radar pulse configuration information, the data unit comprising a preamble and a data field. The method also includes transmitting at least one radar pulse within a duration of the data field. The method also includes receiving at least one reflection of the at least one radar pulse within the duration of the data field. The method also includes processing the at least one reflection to determine a distance between an object and the electronic device.

Abstract: 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.

Abstract: 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.

Abstract: Methods and apparatuses for detecting conditions associated with a target wake time (TWT) parameter adjustment. A communication device comprises a transceiver and a processor operably coupled to the transceiver. The transceiver is configured to receive information including packets associated with a TWT. The processor is configured to determine a network service type based on the packets, and when the determined network service type is a real-time service type: determine whether a condition associated with the TWT is satisfied, and modify the TWT based on the condition.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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. In response to determining that the action is associated with the external electronic device, method includes sending an instruction to the external electronic device to perform the action.

Abstract: 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.

Abstract: 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.

Abstract: 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.