A method for mapping underground sensors onto a network map may include obtaining a plurality of magnetic measurements from a plurality of sensors. The method may include using the plurality of magnetic measurements for determining a plurality of sensor locations in an initial network map. The method may include generating updated network maps from the perspective of each localized sensor. The method may include merging the updated network maps into a final network map, the final network map comprising a most accurate location for each sensor. The method may include determining inner localized sensors out of the plurality of sensors in the final network map. The method may include identifying the inner localized sensors as new base station anchors. The method may include mapping the inner localized sensors onto the final network map as new base station anchors.

The present disclosure describes methods and apparatuses for secure fine timing measurement (FTM) with encoded long training fields (LTFs). In some aspects, a device transmits an announcement frame for an FTM exchange that includes an indication of encoding. Based on the indication of encoding, an LTF of a first null data packet (NDP) of the FTM exchange is encoded. The device transmits the first NDP having the encoded LTF to another device, and receives, from the other device, a second NDP having an encoded LTF. A round-trip time for the first and second NDPs with encoded LTFs is determined. Based on this round-trip time, a distance between the device and the other device can be determined. By encoding the LTFs of the NDPs of the FTM exchange, third parties can be prevented from spoofing an FTM frame to compromise the FTM exchange and related proximity-based security.

The present disclosure describes methods and apparatuses for secure fine timing measurement (FTM) with encoded long training fields (LTFs). In some aspects, a device transmits an announcement frame for an FTM exchange that includes an indication of encoding. Based on the indication of encoding, an LTF of a first null data packet (NDP) of the FTM exchange is encoded. The device transmits the first NDP having the encoded LTF to another device, and receives, from the other device, a second NDP having an encoded LTF. A round-trip time for the first and second NDPs with encoded LTFs is determined. Based on this round-trip time, a distance between the device and the other device can be determined. By encoding the LTFs of the NDPs of the FTM exchange, third parties can be prevented from spoofing an FTM frame to compromise the FTM exchange and related proximity-based security.

A speed determination system for an aircraft including one or more interfaces arranged to receive first speed data from a first speed measurement system and second speed data from a second speed measurement system. The first speed measurement system provides the first speed data using global positioning system data. The second speed measurement system provides the second speed data based on a second speed measurement. The speed determination system includes a processor arranged to determine whether the data received from the first speed measurement system is reliable. If global positioning data is determined to be reliable, the speed determination system determines a speed from the first speed data and determines correction values for the second speed measurement system. If global positioning data is determined to be unreliable, the speed determination system determines a speed from the second speed data and the correction values.

In one embodiment, a system of detecting seat belt operation in a vehicle includes at least one light source configured to emit a predetermined wavelength of light onto structures within the vehicle, wherein at least one of the structures is a passenger seat belt assembly having a pattern that reflects the predetermined wavelength at a preferred luminance. At least one 3-D time of flight camera is positioned in the vehicle to receive reflected light from the structures in the vehicle and provide images of the structures that distinguish the preferred luminance of the pattern from other structures in the vehicle. A computer processor connected to computer memory and the camera includes computer readable instructions causing the processor to reconstruct 3-D information in regard to respective images of the structures and calculate a depth measurement of the distance of the reflective pattern on the passenger seat belt assembly from the camera.

In one embodiment, a system of detecting seat belt operation in a vehicle includes at least one light source configured to emit a predetermined wavelength of light onto structures within the vehicle, wherein at least one of the structures is a passenger seat belt assembly having a pattern that reflects the predetermined wavelength at a preferred luminance. At least one 3-D time of flight camera is positioned in the vehicle to receive reflected light from the structures in the vehicle and provide images of the structures that distinguish the preferred luminance of the pattern from other structures in the vehicle. A computer processor connected to computer memory and the camera includes computer readable instructions causing the processor to reconstruct 3-D information in regard to respective images of the structures and calculate a depth measurement of the distance of the reflective pattern on the passenger seat belt assembly from the camera.

Embodiments described herein relate to a photonic apparatus for processing optical signals within an integrated optical pathway. The photonic apparatus a light perception device that perceives light from a surrounding environment of the apparatus. The photonic apparatus also includes an optical neural network (ONN) connected with the light perception device via an optical relay. The optical neural network configured to perform optical processing on the light according to a deep learning algorithm and using optical components.

Embodiments described herein relate to a photonic apparatus for processing optical signals within an integrated optical pathway. The photonic apparatus a light perception device that perceives light from a surrounding environment of the apparatus. The photonic apparatus also includes an optical neural network (ONN) connected with the light perception device via an optical relay. The optical neural network configured to perform optical processing on the light according to a deep learning algorithm and using optical components.

In one embodiment, a system of detecting seat belt operation in a vehicle includes at least one light source configured to emit a predetermined wavelength of light onto structures within the vehicle, wherein at least one of the structures is a passenger seat belt assembly having a pattern that reflects the predetermined wavelength at a preferred luminance. At least one 3-D time of flight camera is positioned in the vehicle to receive reflected light from the structures in the vehicle and provide images of the structures that distinguish the preferred luminance of the pattern from other structures in the vehicle. A computer processor connected to computer memory and the camera includes computer readable instructions causing the processor to reconstruct 3-D information in regard to respective images of the structures and calculate a depth measurement of the distance of the reflective pattern on the passenger seat belt assembly from the camera.

In one embodiment, a system of detecting seat belt operation in a vehicle includes at least one light source configured to emit a predetermined wavelength of light onto structures within the vehicle, wherein at least one of the structures is a passenger seat belt assembly having a pattern that reflects the predetermined wavelength at a preferred luminance. At least one 3-D time of flight camera is positioned in the vehicle to receive reflected light from the structures in the vehicle and provide images of the structures that distinguish the preferred luminance of the pattern from other structures in the vehicle. A computer processor connected to computer memory and the camera includes computer readable instructions causing the processor to reconstruct 3-D information in regard to respective images of the structures and calculate a depth measurement of the distance of the reflective pattern on the passenger seat belt assembly from the camera.