Abstract: A light tower includes a base, an extendible mast coupled to the base, a battery pack supported on the base and including a plurality of lithium-ion battery cells, a light assembly including a plurality of light emitting diodes electrically coupled to the battery pack, a display configured to receive inputs and display information, and a controller in communication with the battery pack, the light assembly, and the display. The configured being to: receive a light intensity change input from the display, change a power provided by the battery pack to the light emitting diodes in response to the light intensity change input, and display on the display a change in runtime resulting from the change of the power provided by the battery pack to the light emitting diodes.

Abstract: Operating a gaseous fuel engine system includes conveying hydrogen fuel and hydrocarbon fuel into a cylinder in a gaseous fuel engine for combustion. Operating a gaseous fuel engine system further includes receiving an increased engine power output request, boosting a power output of the gaseous fuel engine by varying a ratio of the hydrogen fuel and the hydrocarbon fuel combusted in the cylinder, and varying an in-cylinder combustion parameter based on the varying a ratio. Perturbation to a performance profile of the gaseous fuel engine is thereby limited. Related apparatus and control logic is also disclosed.

Abstract: Vehicle interior object management uses analysis for detection of an object within a vehicle. The object can include a cell phone, a computing device, a briefcase, a wallet, a purse, or luggage. The object can include a child or a pet. A distance between an occupant and the object can be calculated. The object can be within a reachable distance of the occupant. Two or more images of a vehicle interior are collected using imaging devices within the vehicle. The images are analyzed to detect an object within the vehicle. The object is classified. A level of interaction is estimated between an occupant of the vehicle and the object within the vehicle. The object can be determined to have been left behind once the occupant leaves the vehicle. A control element of the vehicle is changed based on the classifying and the level of interaction.

Abstract: Various aspects include approaches for managing tinnitus in a user. In some particular aspects, a computer-implemented method of managing tinnitus of a user with at least one wearable device includes: actuating, according to a prescribed pattern and using the at least one wearable device: a) an electro-acoustic transducer to provide an audio output to the user, and b) a contact element to apply vibration to a body part of the user, where the prescribed pattern both stimulates the nervous system of the user and delivers a sound to the user, which together mitigate the tinnitus.

Abstract: Example implementations relate to provisioning a deduplication data store. An example includes receiving inputs indicating an expected data amount and at least one retention period for the deduplication data store; performing a comparison of the at least one retention period to a stored data structure; determining at least one storage multiplier based on the comparison of the at least one retention period to the stored data structure; and determining an amount of storage to provision for the deduplication data store based on the expected data amount and the at least one storage multiplier.

Abstract: Vehicular in-cabin sensing is performed using machine learning. In-cabin sensor data of a vehicle interior is collected. The in-cabin sensor data includes images of the vehicle interior. An occupant is detected within the vehicle interior. The detecting is based on identifying an upper torso of the occupant, using the in-cabin sensor data. The imaging is accomplished using a plurality of imaging devices within a vehicle interior. The occupant is located within the vehicle interior, based on the in-cabin sensor data. An additional occupant within the vehicle interior is detected. A human perception metric for the occupant is analyzed, based on the in-cabin sensor data. The detecting, the locating, and/or the analyzing are performed using machine learning. The human perception metric is promoted to a using application. The human perception metric includes a mood for the occupant and a mood for the vehicle. The promoting includes input to an autonomous vehicle.

Abstract: Various implementations include conversation assistance audio devices with settings that are adjustable based upon user feedback. In some cases, a computer-implemented method of personalizing a conversation assistance audio device includes: receiving a command from a user for assistance with audio settings in the conversation assistance audio device; applying a baseline set of audio settings to the conversation assistance audio device in response to receiving the user command for assistance; receiving a user command to change the baseline set of audio settings; and applying a modified set of audio settings to the conversation assistance audio device in response to receiving the user command to change the baseline set of audio settings, wherein the modified set of audio settings are applied based upon usage of the conversation assistance audio device and a population model of usage of similar conversation assistance audio devices.

Abstract: There are provided systems and methods for prognostic analytics of an asset. For example, there is provided a system for monitoring a reliability of a component of an asset. The system includes a processor and a memory comprising instructions that, when executed by the processor, cause it to perform certain operations. These operations may include receiving input data, which can include performance data relating to the component, configuration data relating to the component, and statistical data relating to a plurality of assets. The operations can further include providing a pre-emptive recommendation for the component based on the input data.

Abstract: Various implementations include systems for processing audio signals. In particular implementations, a process includes receiving an audio signal, wherein the audio signal includes a speech component of the user and a noise component; filtering the audio signal with a self-speech filter that utilizes an intrinsic user vector to filter out the speech component, wherein the intrinsic user vector is determined based on a voice input of the user; and outputting a filtered audio signal in which the speech component of the user has been substantially removed from the audio signal.

Abstract: In some examples, a first computing device may receive, from a second computing device, partition mapping information indicating partitions of a metadata database. The first computing device may be able to communicate with a plurality of metadata nodes, each metadata node maintaining a portion of the metadata database based on the partitioning of the metadata database to distribute the metadata database across the plurality of metadata nodes. The first computing device may determine to send a request to the metadata database based at least on key information. The first computing device may determine, based on the partition mapping information, a first metadata node of the plurality of metadata nodes indicated to maintain a partition of the metadata database corresponding to the key information. The first computing device, may send, to the first metadata node, based on the partition mapping information, a request to perform a database operation.

Abstract: Various embodiments of the present application set forth a computer-implemented method comprising aggregating positional signals that were received from device sensors, where each positional signal has an observed signal strength, identifying a subset of the positional signals, where each positional signal in the subset was received by a device sensor while the device sensor was located within the physical location, for each positional signal, generating, by an estimation device and based at least on a position of the device sensor, an approximate object height for an object located at the physical location, for each approximate object height, determining an expected signal strength for the positional signal corresponding to the approximate object height, generating a cost value that is based on a difference between the expected signal strength and the observed signal strength, and determining, by the estimation device and based on the cost values, an estimated object height.

Abstract: There are provided systems and methods for prognostic analytics of an asset. For example, there is provided a system for monitoring a reliability of a component of an asset. The system includes a processor and a memory comprising instructions that, when executed by the processor, cause it to perform certain operations. These operations may include receiving input data, which can include performance data relating to the component, configuration data relating to the component, and statistical data relating to a plurality of assets. The operations can further include providing a pre-emptive recommendation for the component based on the input data.

Abstract: Object-sensing systems including a light transmitter subsystem and a light receiver subsystem. The light transmitter subsystem main be configured to generate a collimated linear beam of light at a predetermined wavelength and having a length of at least 3 inches. The light receiver subsystem may include a linear sensor array having a length of at least 3 inches. The linear sensor array may be positioned to receive the collimated linear beam of light and to detect shadows caused by objects blocking at least a portion of the collimated linear beam of light. Various other systems and methods are also disclosed.

Abstract: Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

Abstract: Various implementations include systems for processing audio signals to remove artifacts introduced by a machine learning system in challenging environments. In particular implementations, a method includes generating a processed audio signal for a hearing assistance device in which the processed audio signal is intended to perceptually dominate a user auditory experience, including: processing an unprocessed audio signal received by the hearing assistance device, wherein the processing includes utilizing a machine learning (ML) system to generate an ML enhanced audio signal; determining a mixing coefficient from an environmental noise assessment; mixing the ML enhanced audio signal with the unprocessed audio signal using the mixing coefficient to generate the processed audio signal; and outputting the processed audio signal.

Abstract: Various implementations include systems for processing audio signals to remove artifacts introduced by a machine learning system in challenging environments. In particular implementations, a method includes generating a processed audio signal for a hearing assistance device in which the processed audio signal is intended to perceptually dominate a user auditory experience, including: processing an unprocessed audio signal received by the hearing assistance device, wherein the processing includes utilizing a machine learning (ML) system to generate an ML enhanced audio signal; determining a mixing coefficient from an environmental noise assessment; mixing the ML enhanced audio signal with the unprocessed audio signal using the mixing coefficient to generate the processed audio signal; and outputting the processed audio signal.

Abstract: Provided herein are implants and methods for producing implants. In at least one embodiment, the implants include sheet-based, triply periodic, minimal surface (TPMS) portions. According to one embodiment, the TPMS portions include a gyroid architecture that provides for improved osseointegration and mechanical performance over previous implants due to novel ratios of porosity to compressive strength, among other features. In one or more embodiments, the gyroid architecture is organized into unit cells that demonstrate anisotropic mechanical performance along an insertion direction. In various embodiments, the present methods include novel selective laser melting (SLM) techniques for forming the TPMS portions of implants in a manner that reduces defect formation, thereby improving compressive performance and other implant properties.