Abstract: Described herein are techniques to enable a mobile device to perform multi-source estimation of an altitude for a location. A baseline altitude may be determined at ground level for a location and used to calibrate a barometric pressure sensor on the mobile device. The calibrated barometric pressure sensor can then estimate changes in altitude relative to ground level based on detected pressure differentials, allowing a relative altitude to ground to be determined. Baseline calibration for the barometric sensor calibration can be performed to determine an ambient ground-level barometric pressure.

Abstract: Various embodiments of the present disclosure are directed towards a semiconductor structure including a first substrate comprising a first semiconductor material. A first light sensor is disposed within the first substrate. The first light sensor is configured to absorb electromagnetic radiation within a first wavelength range. A second light sensor is disposed within an absorption structure underlying the first substrate. The second light sensor is configured to absorb electromagnetic radiation within a second wavelength range different from the first wavelength range. The absorption structure underlies the first light sensor and comprises a second semiconductor material different from the first semiconductor material.

Abstract: A feed forward echo cancellation device includes a first impedance circuit, a second impedance circuit, and an echo cancellation current generator circuit. The first impedance circuit is configured to output a first current to a node in response to a transmission current. The second impedance circuit is configured to output a second current to a node in response to the transmission current. The echo cancellation current generator circuit is configured to drain an echo cancellation current from the node. The node is connected to an input terminal of a programmable gain amplifier circuit via a gain control circuit, and the gain control circuit is configured to set a gain of the programmable gain amplifier circuit.

Abstract: Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone. In some embodiments, a method comprises: detecting a crash event on a crash device; extracting multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features, wherein at least one multimodal feature is a rotation rate about a mean axis of rotation; and determining that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.

Abstract: Various embodiments of the present disclosure are directed towards an integrated chip including a first conductive structure and a second conductive structure. A dielectric structure is arranged between the first conductive structure and the second conductive structure. The dielectric structure comprises an upper region over a lower region. The lower region comprises a first lateral surface and a second lateral surface on opposing sides of the upper region. A passivation layer overlies the second conductive structure and the dielectric structure. The passivation layer comprises a lateral segment contacting the first lateral surface. A height of the lateral segment is greater than a height of the upper region. A top surface of the lateral segment is below a top surface of the passivation layer.

Abstract: Compositions including photoreactive and cleavable probes and methods of using the probes. The probes may include a tag conjugatable to a label, a cleavable linker linkable to a bait molecule, and a light activated warhead, which may be configured to covalently bond an anchoring strand to a probing strand upon application of light energy. The compositions and methods may be useful for analyzing biomolecules, such as identifying proximal molecules in cell or tissue samples.

Abstract: Described herein are techniques to enable a mobile device to perform multi-source estimation of an altitude for a location. A baseline altitude may be determined at ground level for a location and used to calibrate a barometric pressure sensor on the mobile device. The calibrated barometric pressure sensor can then estimate changes in altitude relative to ground level based on detected pressure differentials, allowing a relative altitude to ground to be determined. Baseline calibration for the barometric sensor calibration can be performed to determine an ambient ground-level barometric pressure.

Abstract: Embodiments are disclosed for submersion detection and underwater depth and low-latency temperature estimation. In an embodiment, a method comprises: determining a first set of vertical accelerations obtained from an inertial sensor of a wearable device; determining a second set of vertical accelerations obtained from pressure data; determining a first feature associated with a correlation between the first and second sets of vertical accelerations; and determining that the wearable device is submerged or not submerged in water based on a machine learning model applied to the first feature. In another embodiment, a method comprises: determining a submersion state of a wearable device; and responsive to the submersion state being submerged, computing a forward estimate of water temperature based on measured ambient water temperature at the water surface, a temperature error lookup table, and a rate of change of the ambient water temperature.

Abstract: Embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone). In some embodiments, a method comprises: detecting, with at least one processor, a crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.

Abstract: A water filter includes a filtering head, a filter bottle assembly, a switch member, and a quick release device. The filtering head includes a flow channel module. The filter bottle assembly includes a filter bottle. The quick release device includes a press lever, two links, and two push members. When the press lever is pivoted, the two links and the two push members are driven by the press lever to move the filter bottle simultaneously so that the filter bottle is mounted on or detached from the filtering head quickly. The a switch member functions as a waterway switch to control a water supply of the filtering head and functions as a locking mechanism for locking or unlocking the quick release device.

Abstract: Disclosed herein is a water-in-oil emulsion composition that includes, based on the total weight of the water-in-oil emulsion composition, 2 wt % to 10 wt % of Avena sativa kernel oil, 15 wt % to 38 wt % of octyldodecanol, and water. The water-in-oil emulsion composition is free from a surfactant and a gelling agent.

Abstract: Described herein are techniques to enable a mobile device to perform multi-source estimation of an altitude for a location. A baseline altitude may be determined at ground level for a location and used to calibrate a barometric pressure sensor on the mobile device. The calibrated barometric pressure sensor can then estimate changes in altitude relative to ground level based on detected pressure differentials, allowing a relative altitude to ground to be determined. Baseline calibration for the barometric sensor calibration can be performed to determine an ambient ground-level barometric pressure.

Abstract: A surfactant-free emulsion composition includes water, 18 wt % to 25 wt % of an oil component, and 0.35 wt % to 0.8 wt % of a gelling agent, based on. 100 wt % of the emulsion composition.

Abstract: A limb rehabilitation and training system includes a horizontal position adjuster movably mounted at a bottom side of a base, an expansion rotary member mounted at the horizontal position adjuster, a shoulder joint traction mechanism linked to the expansion rotary member through a first arm segment robotic arm and a height adjuster, and an upper-limb rehabilitation device linked to the shoulder joint traction mechanism. The upper-limb rehabilitation device is able to rapidly be adjusted to fit the left arm or right arm through the horizontal position adjuster, the expansion rotary member, the first arm segment robotic arm and a shoulder positioning-lifting rotary member and. Further, by means of the shoulder joint traction mechanism, the user's stiffened shoulder joint can be timely moved for a separation distance, achieving the function of loosening the joint and facilitating performance of successive rehabilitation treatment or training.

Abstract: A limb rehabilitation and training system includes a horizontal position adjuster movably mounted at a bottom side of a base, an expansion rotary member mounted at the horizontal position adjuster, a shoulder joint traction mechanism linked to the expansion rotary member through a first arm segment robotic arm and a height adjuster, and an upper-limb rehabilitation device linked to the shoulder joint traction mechanism. The upper-limb rehabilitation device is able to rapidly be adjusted to fit the left arm or right arm through the horizontal position adjuster, the expansion rotary member, the first arm segment robotic arm and a shoulder positioning-lifting rotary member and. Further, by means of the shoulder joint traction mechanism, the user's stiffened shoulder joint can be timely moved for a separation distance, achieving the function of loosening the joint and facilitating performance of successive rehabilitation treatment or training.