Abstract: In some examples, a device can include a magnetic element coupled to a cap, a compressible energizing element surrounding the magnetic element, an electrical device coupled to the compressible energizing element to provide a current, and a processor resource to adjust a current applied to the compressible energizing element based on a selected resistance level.

Abstract: In some examples, a device can include a magnetic element coupled to a cap, a compressible energizing element surrounding the magnetic element, an electrical device coupled to the compressible energizing element to provide a current, and a processor resource to adjust a current applied to the compressible energizing element based on a selected resistance level.

Abstract: An electronic device employing optical communications including a light cell array, each light cell individually operable to output a controllable collimated light beam, the light beams of the light cell array to form a data encoded optical output signal to transmit to a destination electronic device. A photocell array receives a data encoded optical input signal from the destination electronic device, each photocell to provide an output signal representative of an amount of energy received from the optical input signal. A controller measures an overlap of the optical input signal with the photocell array based on the output signals of the array of photocells, and adjusts a position of the photocell array based on the measured overlap to align the photocell array with the with the optical input signal.

Abstract: One example provides a human-machine interface (HMI) to receive interaction inputs including user interaction content representing different user interaction modalities (UIMs). The HMI includes a plurality of machine learning (ML) models corresponding to different UIMs to process user interaction content representing the corresponding UIM to provide model outputs. An interface manager dynamically adjusts a selected set of UIMs and a selected set of ML models to enable the selected set of UIMs based on monitoring of at least the user interaction content of the plurality of interaction inputs, and implements a user interface including ML models of a currently selected set of ML models interconnected with one another with an interconnect structure to convert user interaction content corresponding to the currently selected set of UIMs to operational inputs based on the model outputs of the currently selected set of ML models.

Abstract: In one example in accordance with the present disclosure, a computing device is described. The computing device includes a configurable logic element. The configurable logic element 1) connects to a number of peripheral electronic devices, with at least one peripheral electronic device having a different native protocol relative to another peripheral electronic device and 2) prepares and packages a number of signals to be transmitted across a uniform transmission protocol. The computing device also includes a communication pathway to transmit packaged signals to a driver using the uniform transmission protocol. The computing device also includes the driver to 1) unpack the number of signals from the aggregated data transmission and 2) represents the number of peripheral electronic devices to an operating system of the computing device.

Abstract: One example provides a reconfigurable computing fabric to manage machine learning (ML) processing including a configurable interconnect structure and a number programmable logic blocks each having a configurable set of operations. For each of a number of fabric configurations of the computing fabric, each programmable logic block has a corresponding set of operations and the interconnect structure has a corresponding data path structure to interconnect the programmable logic blocks with one another and with inputs and outputs of the computing fabric. The programmable logic blocks include an input/output block having a set of operations including to provide virtual interfaces to receive external session requests for ML processing from request sources, and an elastic AI/ML processing block having a set of operations including to configure a number of AI/ML engines with a session implementation for each external session request and each of a number of event-driven internal session requests.

Abstract: In an example implementation according to aspects of the present disclosure, a wireless anchor node comprises a wireless communication interface and a processor communicatively coupled to the wireless communication interface. The processor receives a wireless signal from a source access node. The location of the source access node is verified. The processor determines a distance measurement between the wireless anchor node and the source access node based on the wireless signal and transfers the distance measurement between the wireless anchor node and the source access node to a target access node over an intermediate anchor node. In response, the processor receives a location error from the target access node over the intermediate anchor node. The location of the target access node is verified. The processor calibrates the distance measurement between the wireless anchor node and the source access node based on the location error.

Abstract: In some examples an electronic device includes a sensor, and a controller to detect a proximity of a second electronic device via the sensor. In response to determining that the proximity is within a threshold distance, the controller causes transmission of context information to the second electronic device. The context information is generated based on a location, a time, an application executed by the second electronic device, or a combination thereof. In response to the transmission of the context information, the controller receives a first signal from the second electronic device, and, in response to the first signal, the controller causes transmission of a second signal, the second signal to enable coupling to the second electronic device.

Abstract: An example module includes: a radio to communicate with a sensor; a communication interface to communicate with a second module; a chassis to mate with the second module; and a processor connected to the radio, the communication interface, and a memory, the processor to execute instructions stored in the memory. The instructions are to: receive, via the radio, sensor data from the sensor; process the sensor data to generate processed sensor data; and transmit the processed sensor data to a remote device.

Abstract: An example module includes: a radio to communicate with a sensor; a communication interface to communicate with a second module; a chassis to mate with the second module; and a processor connected to the radio, the communication interface, and a memory, the processor to execute instructions stored in the memory. The instructions are to: receive, via the radio, sensor data from the sensor; process the sensor data to generate processed sensor data; and transmit the processed sensor data to a remote device.

Abstract: A customizable work station is described with a computing device that has a local processor, a local display, a connector communicably coupled to the local processor, and a plurality of sensors that collect proximal information at the station to provide at least one personalized enhancement to a user of the station.