Samsung Electronics Co., Ltd.

Khuong N Nguyen

Boon Loong Ng

Wenxun Qiu

Abhishek Sehgal

Hao Chen

Vutha Va

Guanbo Chen

Jianhua Mo

Yuming Zhu

Anum Ali

Neha Dawar

Rebal Al Jurdi

Han Wang

Jianzhong Zhang

Hyunchul Kim

Yi Yang

Moonseok Kang

Peshal Nayak

Namjoon Park

Jiho Shin

Eric Earl Johnson

Hoang Viet Nguyen

Matthew Tonnemacher

Songwei Li

Jihoon Sung

Junyeop Jung

In-sick Jung

John Wensowitch

An electronic device and a method for gesture recognition are disclosed. The electronic device includes a radar transceiver and a processor operably connected to the radar transceiver. The processor is configured to detect a triggering event. In response to detecting the triggering event, the processor is configured to transmit, via the radar transceiver, radar signals. The processor is also configured to identify a gesture from reflections of the radar signals received by the radar transceiver. The processor is further configured to determine whether the gesture is associated with the triggering event. Based on determining that the gesture is associated with the triggering event, the processor is configured to perform an action indicated by the gesture.

Methods for gesture recognition and control

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 includes determining one or more active slots and inactive slots in an ultra-wide band (UWB) ranging round. The method also includes determining multiple UWB silent periods in the ranging round based on an arrangement of the inactive slots. The method also includes determining a sleep-wake-up schedule that aligns with at least one selected silent period of the multiple UWB silent periods in the ranging round, the sleep-wake-up schedule to be used for Wi-Fi communication. The method also includes waking up a Wi-Fi station (STA) for the Wi-Fi communication during the at least one selected silent period, then going back to sleep according to the sleep-wake-up schedule.

Power saving for in-device coexistence between Wi-Fi and Ultra-Wide Band communication

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 for end-to-end dynamic gesture recognition using mmWave radar is provided. The method includes triggering an electronic device to activate a gesture recognition mode in response to detecting that a condition for activating the gesture recognition mode is satisfied. The method includes obtaining radar data while the gesture recognition mode is activated, wherein the radar data includes time-velocity data (TVD). The method includes detecting a start and an end of a gesture based on the TVD of the obtained radar data. To classify the gesture, the method includes determining a gesture, from among a set of gesture, that corresponds to a portion of the TVD between the start and the end of the gesture. The method includes outputting an event indicator indicating that a user of the electronic device performed the gesture classified.

Dynamic gesture recognition using mmWave radar

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

An electronic device for gesture recognition, includes a processor operably connected to a transceiver. The transceiver is configured to transmit and receive signals for measuring range and speed. The processor is configured to transmit the signals, via the transceiver. in response to a determination that a triggering event occurred, the processor is configured to track movement of an object relative to the electronic device within a region of interest based on reflections of the signals received by the transceiver to identify range measurements and speed measurements associated with the object. The processor is also configured to identify features from the reflected signals, based on at least one of the range measurements and the speed measurements. The processor is further configured to identify a gesture based in part on the features from the reflected signals. Additionally, the processor is configured to perform an action indicated by the gesture.

Adaptive thresholding and noise reduction for radar data

An electronic device, a method, and computer readable medium are disclosed. The method includes transmitting radar signals via a radar transceiver. The method also includes identifying signals of interest that represent biometric information of a user based on reflections of the radar signals received by the radar transceiver. The method further includes generating an input based on the signals of interest that include the biometric information. The method additionally includes extracting a feature vector based on the input. The method also includes authenticating the user based on comparison of the feature vector to a threshold of similarity with preregistered user data.

Method and apparatus for biometric authentication using face radar signal

A method for beam management by a user equipment (UE) in a wireless communication system. The method includes determining reference signal measurements from signals received from one or more base stations. The method also includes obtaining, from a motion sensor, motion information of the UE. The method further includes generating beam management information for the beam management based on the reference signal measurements and the motion information. The method additionally includes identifying a beam based on the generated beam management information. The method also includes performing wireless communication based on the identified beam.

Method and apparatus of fusing radio frequency and sensor measurements for beam management

A method of an electronic device for a connection management is provided. The method comprises: establishing a communication link with an access point (AP); receiving a triggering indication based on link information measured by the electronic device; determining a quality of the communication link between the electronic device and the AP based on the received triggering indication; comparing, based on the determined quality of the communication link, at least two Q values fetched from a Q table that is determined according to context in which in the electronic device is being used; setting a quantized aggressive index (QAI) value based on the compared at least two Q values; and generating a disconnection command based on the QAI value, wherein the disconnection command is a physical disconnection command or a virtual disconnection command of the communication link between the electronic device and the AP.

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Frisco, TX, United States of America

Khuong N Nguyen