Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

Abstract: Touch sensor panels/screens can include a first region having a plurality of touch electrodes and a second region without touch electrodes. In some examples, to improve touch sensing performance, a first algorithm or a second algorithm is applied to determine whether an object corresponding to the touch patch is in contact with the touch screen. Whether to apply the first algorithm or the second algorithm is optionally dependent on the location of the touch patch.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface. In some examples, the electronic device can use machine learning techniques to estimate the (x, y) position and/or tilt and/or azimuth angles of the stylus tip at the touch sensitive surface based on a subset of stylus data.

Abstract: In some examples, touch data can include noise. The noise can be generated by a component of an electronic device that includes a touch screen. For example, one or more signals transmitted to the display circuitry of an electronic device can become capacitively coupled to the touch circuitry of the device and cause noise in the touch data. Machine learning techniques, such as gated recurrent units and/or convolutional neural networks can estimate and reduce or remove noise from touch data when provided data or information about the displayed image as input. In some examples, the algorithm includes one or more of a gated recurrent unit stage and a convolutional neural network stage. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network.

Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

Abstract: Finger detection and separation techniques on a multi-touch touch sensor panel can be improved using machine learning models (particularly for touch sensor panels with relatively low signal-to-noise ratio). In some examples, a machine learning model can be used to process an input patch to disambiguate whether the input patch corresponds to one contact or two contacts. In some examples, the machine learning model can be implemented using a neural network. The neural network can receive a sub-image including an input patch as an input, and can output a number of contacts. In some examples, the neural network can output one or more sub-image masks representing the one or more contacts.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface. In some examples, the electronic device can use machine learning techniques to estimate the (x, y) position and/or tilt and/or azimuth angles of the stylus tip at the touch sensitive surface based on a subset of stylus data.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface.

Abstract: In some examples, touch data can include noise. The noise can be generated by a component of an electronic device that includes a touch screen. For example, one or more signals transmitted to the display circuitry of an electronic device can become capacitively coupled to the touch circuitry of the device and cause noise in the touch data. Machine learning techniques, such as gated recurrent units and/or convolutional neural networks can estimate and reduce or remove noise from touch data when provided data or information about the displayed image as input. In some examples, the algorithm includes one or more of a gated recurrent unit stage and a convolutional neural network stage. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network.

Abstract: A wireless power transmission system has a wireless power receiving device with a wireless power receiving coil that is located on a charging surface of a wireless power transmitting device with a wireless power transmitting coil array. Control circuitry in the wireless power transmitting device may use inverter circuitry to supply alternating-current signals to coils in the coil array, thereby transmitting wireless power signals. The control circuitry may also be used to detect foreign objects on the coil array such as metallic objects without wireless power receiving coils. For example, control circuitry may use inductance measurements from the coils in the coil array to determine a probability value indicative of whether a foreign object is present on the charging surface. The control circuitry may compare the probability value to a threshold and take suitable action in response to the comparison.

Abstract: Disclosed herein is an electronic device having a proximity sensor for determining whether an object, such as a user's finger, is in proximity to or in contact with an input mechanism of the electronic device.

Abstract: Disclosed are electronic devices and methods of their operation that use force sensors to detect user applied forces on an input surface and determine their locations on the input surface, using only force sensors. The locations may be determined using weighted averages of the positions of the force sensors and their values. The methods may compare dynamically updated baseline force values to received force sensor values to distinguish user applied forces from changes in the force sensor values caused other sources. After detection of a user applied force, the baseline force values are frozen, and the force sensor values used to find the location on the input surface where the user applied the force. The electronic device can operate according to a state space model, with a first state in which there is no user applied force, and a second state in which there is user applied force.

Abstract: A wireless power transmission system has a wireless power receiving device that is located on a charging surface of a wireless power transmitting device. The wireless power receiving device has a wireless power receiving coil and the wireless power transmitting device has a wireless power transmitting coil array. Signal measurement circuitry coupled to the coil array may make measurements while the control circuitry uses the inverter circuitry to apply excitation signals to each of the coils. Foreign objects on the coil array such as metallic objects without wireless power receiving coils can be detected using foreign object detection. When multiple wireless power receiving devices are present on the wireless power transmitting device, steps may be taken to isolate measurements from the coils associated with each wireless power receiving device. Foreign object detection may then be performed on these modified measurements.

Abstract: Disclosed herein is an electronic device having a proximity sensor for determining whether an object, such as a user's finger, is in proximity to or in contact with an input mechanism of the electronic device.

Abstract: Disclosed herein is an electronic device having a proximity sensor for determining whether an object, such as a user's finger, is in proximity to or in contact with an input mechanism of the electronic device.

Abstract: A light source in a rotary optical encoder can illuminate a pattern on a rotatable shaft and an optical sensor can detect either the light that is reflected or transmitted based on the pattern. A sampling rate of the optical sensor is dynamically adjusted based on a rotational speed of the rotatable shaft. A pulse rate of the light source may also be dynamically adjusted based on the sampling rate of the optical sensor.

Abstract: Disclosed are electronic devices and methods of their operation that use force sensors to detect user applied forces on an input surface and determine their locations on the input surface, using only force sensors. The locations may be determined using weighted averages of the positions of the force sensors and their values. The methods may compare dynamically updated baseline force values to received force sensor values to distinguish user applied forces from changes in the force sensor values caused other sources. After detection of a user applied force, the baseline force values are frozen, and the force sensor values used to find the location on the input surface where the user applied the force. The electronic device can operate according to a state space model, with a first state in which there is no user applied force, and a second state in which there is user applied force.

Abstract: A wireless power transmission system has a wireless power receiving device with a wireless power receiving coil that is located on a charging surface of a wireless power transmitting device with a wireless power transmitting coil array. Control circuitry in the wireless power transmitting device may use inverter circuitry to supply alternating-current signals to coils in the coil array, thereby transmitting wireless power signals. The control circuitry may also be used to detect foreign objects on the coil array such as metallic objects without wireless power receiving coils. For example, control circuitry may use inductance measurements from the coils in the coil array to identify segments of the coil array that correspond to potential wireless power receiving devices. The control circuitry may control wireless power transmission based on a comparison between the number of identified segments corresponding to potential wireless power receiving devices and a number of received device-identifiers.

Abstract: A wireless power transmission system has a wireless power receiving device that is located on a charging surface of a wireless power transmitting device. The wireless power receiving device has a wireless power receiving coil and the wireless power transmitting device has a wireless power transmitting coil array. Signal measurement circuitry coupled to the coil array may make measurements while the control circuitry uses the inverter circuitry to apply excitation signals to each of the coils. Foreign objects on the coil array such as metallic objects without wireless power receiving coils can be detected using foreign object detection. When multiple wireless power receiving devices are present on the wireless power transmitting device, steps may be taken to isolate measurements from the coils associated with each wireless power receiving device. Foreign object detection may then be performed on these modified measurements.