Abstract: An apparatus, and method, for a garment embedded secretion analysis. The system includes a liner that includes at least a sensor from a plurality of sensors. The system also includes a computing device embedded in the liner and communicatively connected to the at least a sensor, where the computing device includes a detection module configured to extract at least a biological sample from the user, authenticate the user as a function of the biological sample and a biological data of the user, detect a condition datum as a function of the biological sample and biological data of the user and determine an event datum as a function of the condition datum. Computing device also includes a safety module configured to receive the event datum and generate an alert datum as a function of the event datum.

Abstract: An apparatus, and method, for a garment embedded secretion analysis. The system includes a liner that includes at least a sensor from a plurality of sensors. The system also includes a computing device embedded in the liner and communicatively connected to the at least a sensor, where the computing device includes a detection module configured to extract at least a biological sample from the user, authenticate the user as a function of the biological sample and a biological data of the user, detect a condition datum as a function of the biological sample and biological data of the user and determine an event datum as a function of the condition datum. Computing device also includes a safety module configured to receive the event datum and generate an alert datum as a function of the event datum.

Abstract: An apparatus, and method, for a garment embedded secretion analysis. The system includes a liner that includes at least a sensor from a plurality of sensors. The system also includes a computing device embedded in the liner and communicatively connected to the at least a sensor, where the computing device includes a detection module configured to extract at least a biological sample from the user, authenticate the user as a function of the biological sample and a biological data of the user, detect a condition datum as a function of the biological sample and biological data of the user and determine an event datum as a function of the condition datum. Computing device also includes a safety module configured to receive the event datum and generate an alert datum as a function of the event datum.

Abstract: Commercial personal mobile vehicles (PMVs) managed by a fleet management system are sometimes equipped with a radar sensor to detect objects in an environment external to the PMVs. Specifically, the PMV may be equipped with a variety of sensors, such as a radar, a sonar sensor, a (optional) camera, an inertia measurement unit (IMU), and/or the like. The combination of a radar reflection signal and a sonar signal may provide measurements of characteristics such as a Doppler velocity and height information of a nearby object, which may be input to a machine learning classifier to determine the probability that the nearby object is a VRU. For another example, the reflection pattern from radar and ultrasonic may be used to input to a machine learning classifier to determine a type of the road surface, e.g., an asphalt road surface, a concrete sidewalk surface, a wet-grass lawn surface, and/or the like.

Abstract: Embodiments provide a hardware-based mechanism to continuously calibrate a set of sensors on a vehicle while the vehicle is in motion or in use. Specifically, each sensor is mounted on a respective rigid mounting device placed on the vehicle. A secondary sensing device for calibration is also rigidly mounted on the mounting device such that the relative position between the secondary device and the camera sensor can remain unchanged. The secondary sensing devices can then be used to detect a relative distance and/or position of the same calibration target. The sensed data is then used to determine, via stereo triangulation, the relative positions of the two secondary sensing devices, which in turn indicates the relative positions of the sensors.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.

Abstract: Conventional methods for imaging transient targets are constrained by a trade-off between resolution and frame rate, and transient targets moving faster than the detector frame typically result in image blurring. Imagers using digital-pixel focal plane arrays (“DFPAs”) have on-chip global pixel operation capability for extracting a single transient-feature (i.e., single-frequency discrimination) in a snapshot that depends on the number of counters implemented per pixel. However, these DFPA systems are not capable of multi-target and multi-frequency discrimination. Imagers described herein achieve multi-target transient signature discrimination orders of magnitude faster than the readout frame rate using in-pixel electronic shuttering with a known time-encoded modulation. Three-dimensional (x,y,t) data cube reconstruction is performed using compressive sensing algorithms.