Abstract: A medical device is utilized to monitor physiological parameters of a patient and capture segments of the monitored physiological parameters. The medical device includes circuitry configured to monitor one or more physiological parameters associated with the patient and an analysis module that includes a buffer and a processor. The buffer stores monitored physiological parameters and the processor analyzes the monitored physiological parameters and triggers capture of segments from the buffer in response to a triggering criteria being satisfied. The analysis module selects a pre-trigger duration based at least in part on the triggering criteria.

Abstract: In an exemplary technique, speech input including one or more instructions is received. After the speech input has stopped, if it is determined that one or more visual characteristics indicate that further speech input is not expected, a response to the one or more instructions is provided. If it is determined that one or more visual characteristics indicate that further speech input is expected, a response to the one or more instructions is not provided.

Abstract: Example techniques for processing service notes are described. In an example, labeled service notes, associated with fuser units of a plurality of image rendering devices, are processed to generate a vector corresponding to each of the labeled service notes, a labeled service note comprising natural language text describing an error event and a corresponding service activity associated with a fuser unit, wherein the labeled service note is assigned a label based on a category of failure of the fuser unit. Based on the processing, a relationship between vectors and labels corresponding to the labeled service notes is generated.

Abstract: Examples are disclosed that relate to determining a capacitance based on a charge accumulated on a sense capacitor electrode. One example provides a head-mounted device comprising a facial-tracking sensor, a controller, and a charge sensing circuit connected to the facial-tracking sensor. The facial-tracking sensor comprises a sense capacitor electrode configured to be positioned proximate to a surface of a face and form a capacitance based upon a distance between the sense capacitor electrode and the surface of the face, and the controller configured to apply a reference voltage to the sense capacitor electrode. The charge sensing circuit is configured to determine the capacitance of the sense capacitor electrode by determining an amount of charge accumulated on the sense capacitor electrode resulting from the reference voltage.

Abstract: Techniques and mechanisms for selectively increasing or decreasing an amount of cache resources which are to be available for use in the provisioning of decoded micro-operations in a processor. In an embodiment, a processor core comprises both a first cache which is dedicated to caching micro-operations, and a second cache which is coupled to receive data, or non-decoded instructions. The core further comprises circuitry to monitor one or more cache performance characteristics of the core. Based on the one or more cache performance characteristics, the circuitry performs an evaluation to determine whether to increase—or alternatively, to decrease—the size of a pool of one or more caches which are to be available to receive micro-operations. In another embodiment, the second cache is added to the pool based on an indication of an overutilization of the first cache.

Abstract: Method and apparatus for offloading upstream processing tasks to peer groups of downstream data storage devices. A peer control circuit forms a peer group of storage devices in response to a detected processing bottleneck associated with a network controller. One of the storage devices in the peer group is designated as a primary device, and is responsible for interface communications, for subdividing the processing task for execution by secondary devices in the peer group, and coordinating overall execution. The peer group and the processing task are selected to avoid or minimize the processing bottleneck at the network controller level while maintaining ongoing data transfer performance at the storage device level. A list of available device resources and capabilities may be maintained by the peer control circuit. Offloaded tasks can include data rebuilds, cryptographic functions, new device authentication operations, and the like. Multiple overlapping peer groups can be formed as needed.

Abstract: The disclosed computer-implemented method may include receiving, from a client device, a request for multimedia content, where the request includes both a manifest request that includes client identification data and a license request that includes a license challenge. The method may further include validating the received request for multimedia content using the client identification data in the manifest request and generating a manifest response that includes an identification of a specified multimedia content stream that is to be provided to the client device. The method may also include acquiring at least one license in response to the license request, where the license includes a response to the license challenge having various content keys, and then providing the specified multimedia content stream, including the generated manifest response and the acquired license, to the client device. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

Abstract: Disclosed is a high performance low cost multiband antenna configuration that has a low band dipole having dipole arms formed of stamped sheet metal with a plurality of inductor structures. The plurality of inductor structures are oriented along a longitudinal axis of the low band dipole arm, and others are oriented orthogonal to the longitudinal axis. The plurality of the inductor structures act as cloaking structures that make the low band dipole substantially transparent to high band RF energy without inhibiting the performance of the dipole in the low band.

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

Abstract: Systems and methods for automated headlamp adjustments for autonomous vehicles are disclosed. In one aspect, an autonomous vehicle includes at least one headlamp configured to generate and emit light to illuminate an environment of the autonomous vehicle, at least one actuator configured to adjust a direction in which the light is emitted from the at least one headlamp, and at least one perception sensor configured to generate perception data based at least in part on the light emitted from the at least one headlamp. The autonomous vehicle further includes a processor configured to receive one or more inputs including a navigational map, determine a headlamp angle based at least in part on the navigational map, and provide a command to the at least one actuator to adjust an orientation of the at least one headlamp based on the headlamp angle.

Abstract: Methods, computing systems, and computer-readable media for aggregating a plurality of individual asset models of which the method includes storing information representing the plurality of individual asset models, generating a plurality of grouped asset models by aggregating the plurality of individual asset models based on a common property shared by the plurality of individual asset models, constructing a tree comprising a hierarchy of a plurality of levels and sub-levels associated with the plurality of grouped asset models and the plurality of individual asset models, and displaying the tree in a user interface.

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

Abstract: Disclosed is an antenna having a plurality of radiator columns and an integrated phase shifter/calibration board. The radiator columns have radiator clusters that may be differentially phase to provide beam tilt. The input traces of each of the phase shifters is capacitively coupled to a Wilkinson power divider that sums the power of all the input signals, thereby providing a calibration function. The output traces of each of the phase shifters has a designated meander pattern that provides phase alignment for all the output signals to prevent phase mismatches between signals fed to the radiator clusters.

Abstract: Machine learning systems and methods that learn glare, and thus determine gaze direction in a manner more resilient to the effects of glare on input images. The machine learning systems have an isolated representation of glare, e.g., information on the locations of glare points in an image, as an explicit input, in addition to the image itself. In this manner, the machine learning systems explicitly consider glare while making a determination of gaze direction, thus producing more accurate results for images containing glare.

Abstract: In one embodiment, a first entity of a 5G network identifies an action that the first entity would like performed in the 5G network. The first entity of the 5G network sends a signed Holochain transaction indicative of the action to a second entity of the 5G network. The second entity of the 5G network generates a mutually-signed Holochain transaction by signing the signed Holochain transaction. The first entity of the 5G network receives the mutually-signed Holochain transaction from the second entity of the 5G network. The first entity of the 5G network publishes the mutually-signed Holochain transaction to a distributed hash table.

Abstract: This relates to an intelligent automated assistant in a video communication session environment. An example method includes, during a video communication session between at least two user devices, and at a first user device: receiving a first user voice input; in accordance with a determination that the first user voice input represents a communal digital assistant request, transmitting a request to provide context information associated with the first user voice input to the first user device; receiving context information associated with the first user voice input; obtaining a first digital assistant response based at least on a portion of the context information received from the second user device and at least a portion of context information associated with the first user voice input that is stored on the first user device; providing the first digital assistant response to the second user device; and outputting the first digital assistant response.

Abstract: A method of controlling an Internet of Things (IoT) device to perform an action, is performed by an electronic device and includes obtaining at least one input from a user, based on a predefined action corresponding to the obtained at least one input being not identifiable, transmitting the obtained at least one input to a server, receiving, from the server, information regarding at least one activity that is mapped to at least one IoT device that is identified based on the transmitted at least one input, and information regarding at least one object and/or at least one context, and identifying an action to be performed on an IoT device among the at least one IoT device, based on a correlation between the obtained at least one input and the at least one activity mapped to the at least one IoT device.

Abstract: Embodiments provide a context-aware digital assistant at multiple user devices participating in a video communication session by using context information from a first user device to determine a digital assistant response at a second user device. In this manner, users participating in the video communication session may interact with the digital assistant during the video communication session as if the digital assistant is another participant in the video communication session. Embodiments further describe automatically determining candidate digital assistant tasks based on a shared transcription of user voice inputs received at user devices participating in a video communication session. In this manner, a digital assistant of a user device participating in a video communication session may proactively determine one or more tasks that a user of the user device may want the digital assistant to perform based on conversations held during the video communication session.

Abstract: Provided herein is a one-component polyurethane adhesive composition showing excellent adhesive strength on glass surfaces.