Abstract: A system includes inertial sensors and a GPS. The system generates a first estimated vehicle velocity based on motion data and positioning data, generates a second estimated vehicle velocity based on the processed motion data and the first estimated vehicle velocity, and generates fused datasets indicative of position, velocity and attitude of a vehicle based on the processed motion data, the positioning data and the second estimated vehicle velocity. The generating the second estimated vehicle velocity includes: filtering the motion data, transforming the filtered motion data in a frequency domain based on the first estimated vehicle velocity, generating spectral power density signals, generating an estimated wheel angular frequency and an estimated wheel size based on the spectral power density signals, and generating the second estimated vehicle velocity as a function of the estimated wheel angular frequency and the estimated wheel size.

Abstract: The present technology is directed to providing fault management with dynamic restricted access in a tenant network. The tenant network can be a private 5G cellular network or other wireless communication network. The present technology can identify a fault event within the tenant network based on received telemetry data, associate the fault event with a vendor component included in the tenant network, and generate a vendor fault context. The vendor fault context can be generated to include only the portion of telemetry data that is determined to be related to the fault event or the vendor component. The present technology can further use the vendor fault context to create a time-bound user account for remotely accessing the tenant network for fault triage and management. The time-bound user account can be associated to a static role-based access control (RBAC) scheme configured with access restrictions determined based on the vendor fault context.

Abstract: An electronic device includes a magnetometer that outputs magnetometer sensor signals and a gyroscope that outputs gyroscope sensor signals. The electronic device includes a magnetometer calibration module that calibrates the magnetometer utilizing the gyroscope sensor signals. The electronic device generates a first magnetometer calibration parameter based on a Kalman filter process. The electronic device generates a second magnetometer calibration parameter based on a least squares estimation process.

Abstract: A system includes inertial sensors and a GPS. The system generates a first estimated vehicle velocity based on motion data and positioning data, generates a second estimated vehicle velocity based on the processed motion data and the first estimated vehicle velocity, and generates fused datasets indicative of position, velocity and attitude of a vehicle based on the processed motion data, the positioning data and the second estimated vehicle velocity. The generating the second estimated vehicle velocity includes: filtering the motion data, transforming the filtered motion data in a frequency domain based on the first estimated vehicle velocity, generating spectral power density signals, generating an estimated wheel angular frequency and an estimated wheel size based on the spectral power density signals, and generating the second estimated vehicle velocity as a function of the estimated wheel angular frequency and the estimated wheel size.

Abstract: An electronic device includes a magnetometer that outputs magnetometer sensor signals and a gyroscope that outputs gyroscope sensor signals. The electronic device includes a magnetometer calibration module that calibrates the magnetometer utilizing the gyroscope sensor signals. The electronic device generates a first magnetometer calibration parameter based on a Kalman filter process. The electronic device generates a second magnetometer calibration parameter based on a least squares estimation process.

Abstract: A device including microelectromechanical systems (MEMS) sensors are used in dead reckoning in conditions where Global Positioning System (GPS) signals or Global Navigation Satellite System (GNSS) signals are lost. The device is capable of tracking the location of the device after the GPS/GNSS signals are lost by using MEMS sensors such as accelerometers and gyroscopes. By calculating a misalignment angle between a forward axis of a sensor frame of the device and a forward axis of a vehicle frame using the data received from the MEMS sensors, the device can accurately calculate the location of a user or the vehicle of the device even without the GPS/GNSS signals. Accordingly, a device capable of tracking the location of the user riding in the vehicle in GPS/GNSS signals absent environment can be provided.

Abstract: The present disclosure is directed to a device and method for detection of motion events including towing of the vehicle, jacking of the vehicle, and the vehicle being hit by another object. Processing is split between an MCU and a sensor unit. After the vehicle is turned off and before the MCU enters a sleep mode, the MCU calculates a gravity vector of the vehicle using accelerometer data, calculates threshold values based on the gravity vector, and saves the threshold values. After the MCU enters the sleep mode, the sensor unit subsequently monitors and detects motion events with the saved threshold values.

Abstract: A device including microelectromechanical systems (MEMS) sensors are used in dead reckoning in conditions where Global Positioning System (GPS) signals or Global Navigation Satellite System (GNSS) signals are lost. The device is capable of tracking the location of the device after the GPS/GNSS signals are lost by using MEMS sensors such as accelerometers and gyroscopes. By calculating a misalignment angle between a forward axis of a sensor frame of the device and a forward axis of a vehicle frame using the data received from the MEMS sensors, the device can accurately calculate the location of a user or the vehicle of the device even without the GPS/GNSS signals. Accordingly, a device capable of tracking the location of the user riding in the vehicle in GPS/GNSS signals absent environment can be provided.

Abstract: The present technology is directed to providing fault management with dynamic restricted access in a tenant network. The tenant network can be a private 5G cellular network or other wireless communication network. The present technology can identify a fault event within the tenant network based on received telemetry data, associate the fault event with a vendor component included in the tenant network, and generate a vendor fault context. The vendor fault context can be generated to include only the portion of telemetry data that is determined to be related to the fault event or the vendor component. The present technology can further use the vendor fault context to create a time-bound user account for remotely accessing the tenant network for fault triage and management. The time-bound user account can be associated to a static role-based access control (RBAC) scheme configured with access restrictions determined based on the vendor fault context.

Abstract: Digital signal processing circuitry, in operation, determines, based on accelerometer data, a carry-position of a device. Double-tap detection parameters are set using the determined carry-position. Double-taps are detected using the set double-tap detection parameters. In response to detection of a double-tap, control signals, such as a flag or an interrupt signal, are generated and used to control operation of the device. For example, a device may enter a wake mode of operation in response to detection of a double-tap.

Abstract: An approach to configure enterprise wireless mobile network slices. A method includes receiving slice definition information representative of a network slice, the slice definition information including an expected slice efficiency index of the network slice, provisioning the network slice, consistent with the slice definition information, in a wireless network, receiving telemetry corresponding to operational metrics of an instance of the network slice that is used by one or more devices in the wireless network, calculating an actual slice efficiency index for the instance of the network slice based on the telemetry corresponding to the operation metrics of the instance of the network slice, determining whether the expected slice efficiency index differs from the actual slice efficiency index by a predetermined threshold, and indicating a course of action to cause the actual slice efficiency index to more closely align with the expected slice efficiency index.

Abstract: An approach to configure enterprise wireless mobile network slices. A method includes receiving slice definition information representative of a network slice, the slice definition information including an expected slice efficiency index of the network slice, provisioning the network slice, consistent with the slice definition information, in a wireless network, receiving telemetry corresponding to operational metrics of an instance of the network slice that is used by one or more devices in the wireless network, calculating an actual slice efficiency index for the instance of the network slice based on the telemetry corresponding to the operation metrics of the instance of the network slice, determining whether the expected slice efficiency index differs from the actual slice efficiency index by a predetermined threshold, and indicating a course of action to cause the actual slice efficiency index to more closely align with the expected slice efficiency index.

Abstract: An electronic device includes a magnetometer that outputs magnetometer sensor signals and a gyroscope that outputs gyroscope sensor signals. The electronic device includes a magnetometer calibration module that calibrates the magnetometer utilizing the gyroscope sensor signals. The electronic device generates a first magnetometer calibration parameter based on a Kalman filter process. The electronic device generates a second magnetometer calibration parameter based on a least squares estimation process.

Abstract: Technological advancements are disclosed that utilize inertial sensor data for multiple classes to select a combination of filters to extract information though features to train a machine learning core decision tree. A determination is made whether the data for a class includes a frequency peak or dominating frequency that contains significant information about the class. In response to the data for the class including a frequency peak, a peak-based frequency range is determined. An entropy value is calculated for multiple frequency ranges in the data for the class. An entropy-based frequency range is selected from the multiple frequency ranges having a minimum entropy value. A frequency of interest is selected from the peak-based frequency range and the entropy-based frequency range for the class. A combination of filters is selected for each frequency of interest for each class and a decision tree is trained based on selected filter combination.

Abstract: A system includes inertial sensors and a GPS. The system generates a first estimated vehicle velocity based on motion data and positioning data, generates a second estimated vehicle velocity based on the processed motion data and the first estimated vehicle velocity, and generates fused datasets indicative of position, velocity and attitude of a vehicle based on the processed motion data, the positioning data and the second estimated vehicle velocity. The generating the second estimated vehicle velocity includes: filtering the motion data, transforming the filtered motion data in a frequency domain based on the first estimated vehicle velocity, generating spectral power density signals, generating an estimated wheel angular frequency and an estimated wheel size based on the spectral power density signals, and generating the second estimated vehicle velocity as a function of the estimated wheel angular frequency and the estimated wheel size.

Abstract: A device including microelectromechanical systems (MEMS) sensors is used in dead reckoning in conditions where Global Positioning System (GPS) signals or Global Navigation Satellite System (GNSS) signals are lost. The device is capable of tracking the location of the device after the GPS/GNSS signals are lost by using MEMS sensors such as accelerometers and gyroscopes. By calculating a misalignment angle between a sensor frame of the device with either the movement direction of the vehicle or the walking direction of a pedestrian using the MEMS sensors, the device can accurately calculate the location of a user of the device even without the GPS/GNSS signals. Accordingly, a device capable of tracking the location of a pedestrian and a user riding in a vehicle without utilizing GPS/GNSS signals can be provided.

Abstract: A device including microelectromechanical systems (MEMS) sensors is used in dead reckoning in conditions where Global Positioning System (GPS) signals or Global Navigation Satellite System (GNSS) signals are lost. The device is capable of tracking the location of the device after the GPS/GNSS signals are lost by using MEMS sensors such as accelerometers and gyroscopes. By calculating a misalignment angle between a sensor frame of the device with either the movement direction of the vehicle or the walking direction of a pedestrian using the MEMS sensors, the device can accurately calculate the location of a user of the device even without the GPS/GNSS signals. Accordingly, a device capable of tracking the location of a pedestrian and a user riding in a vehicle without utilizing GPS/GNSS signals can be provided.

Abstract: A sensor management system includes a cloud-based sensor configuration system and an electronic device. The electronic device includes a sensor unit. The sensor unit includes configuration data that controls operation of the sensor unit. The configuration data includes a classifier that classifies feature sets generated from sensor signals of the sensor unit. The electronic device sends sensor data to the cloud-based sensor configuration system. The cloud-based sensor configuration system analyzes the sensor data and generates a new classifier customized for the sensor unit based on the sensor data. The cloud-based sensor configuration system sends the new classifier to the electronic device. The electronic device replaces the classifier in the sensor unit with the new classifier.

Abstract: A sensor management system includes a cloud-based sensor configuration system and an electronic device. The electronic device includes a sensor unit. The sensor unit includes configuration data that controls operation of the sensor unit. The configuration data includes a classifier that classifies feature sets generated from sensor signals of the sensor unit. The electronic device sends sensor data to the cloud-based sensor configuration system. The cloud-based sensor configuration system analyzes the sensor data and generates a new classifier customized for the sensor unit based on the sensor data. The cloud-based sensor configuration system sends the new classifier to the electronic device. The electronic device replaces the classifier in the sensor unit with the new classifier.

Abstract: Digital signal processing circuitry, in operation, determines, based on accelerometer data, a carry-position of a device. Double-tap detection parameters are set using the determined carry-position. Double-taps are detected using the set double-tap detection parameters. In response to detection of a double-tap, control signals, such as a flag or an interrupt signal, are generated and used to control operation of the device. For example, a device may enter a wake mode of operation in response to detection of a double-tap.