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: 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: A method includes receiving, at a master agent, announcements from candidate consumer agents indicating the presence of the candidate consumer agents. Each announcement includes display parameters for a display of the corresponding candidate consumer agent. The method further includes receiving at the master agent content parameters from a producer agent, the content parameters defining characteristics of content to be provided by the consumer agent. A mosaic screen is configured based on the received announcements and the content parameters. This configuring of the mosaic screen includes selecting ones of the consumer agents for which an announcement was received and generating content distribution parameters based on the content parameters and the display parameters of the selected ones of the consumer agents. The generated content distribution parameters are provided to the consumer agent.

Abstract: A method includes receiving, at a master agent, announcements from candidate consumer agents indicating the presence of the candidate consumer agents. Each announcement includes display parameters for a display of the corresponding candidate consumer agent. The method further includes receiving at the master agent content parameters from a producer agent, the content parameters defining characteristics of content to be provided by the consumer agent. A mosaic screen is configured based on the received announcements and the content parameters. This configuring of the mosaic screen includes selecting ones of the consumer agents for which an announcement was received and generating content distribution parameters based on the content parameters and the display parameters of the selected ones of the consumer agents. The generated content distribution parameters are provided to the consumer agent.

Abstract: A distributed computing system for artificial intelligence in autonomously appreciating a circumstance context of a smart device. Raw context data is detected by sensors associated with the smart device. The raw context data is pre-processed by the smart device and then provided to a cloud based server for further processing. At the cloud based server, various sets of feature data are obtained from the pre-processed context data. The various sets of feature data are compared with corresponding classification parameters to determine a classification of a continuous event and/or a classification of transient event, if any, which occur in the context. The determined classification of the continuous event and the transient event will be used to autonomously configure the smart device or another related smart device to fit the context.

Abstract: A device for generating a control signal based on the linear movement of a linear member is provided. The device includes a linear member, a rotatable member, a first inertial measurement unit (IMU) coupled to the rotatable member and a second IMU having a fixed position. The device also includes a processing circuit which uses sensing signals from the IMUS to determine an attitude of the first IMU referenced to the second IMU and generate a control signal based on the attitude.

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: The disclosure describes methods and apparatus for quickly prototyping of a solution developed using one or more sensing devices (e.g., sensors), functional blocks, algorithm libraries, and customized logic. The methods produce firmware executable by a processor (e.g., a microcontroller) on an embedded device such as a development board, expansion board, or the like. By performing these methods on the apparatus described, a user is able to create a function prototype without having deep knowledge of the particular sensing device or any particular programming language. Prototypes developed as described herein enable the user to rapidly test ideas and develop sensing device proofs-of-concept. The solutions produced by the methods and apparatus improve the functioning of the sensor being prototyped and the operation of the embedded device where the sensor is integrated.

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 method and apparatus for classifying a spatial environment as open or enclosed are provided. In the method and apparatus, one or more microphones detect ambient sound in a spatial environment and output an audio signal representative of the ambient sound. A processor determines a spatial environment impulse response (SEIR) for the audio signal and extracts one or more features of the SEIR. The processor classifies the spatial environment as open or enclosed based on the one or more features of the SEIR.

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 method includes receiving, at a master agent, announcements from candidate consumer agents indicating the presence of the candidate consumer agents. Each announcement includes display parameters for a display of the corresponding candidate consumer agent. The method further includes receiving at the master agent content parameters from a producer agent, the content parameters defining characteristics of content to be provided by the consumer agent. A mosaic screen is configured based on the received announcements and the content parameters. This configuring of the mosaic screen includes selecting ones of the consumer agents for which an announcement was received and generating content distribution parameters based on the content parameters and the display parameters of the selected ones of the consumer agents. The generated content distribution parameters are provided to the consumer agent.

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: Motion activity data is collected from at least one sensor. An initial motion activity classifier function is applied to the motion activity data to produce an initial motion activity posteriorgram. Pre-processing and segmenting the motion activity data into windows produces segmented motion activity data from which sensor specific features are extracted. An updated motion activity classifier function is generated from the extracted sensor specific features. Subsequent motion activity data is also collected from the at least one sensor, and the updated motion activity classifier function is applied to the subsequent motion activity data to produce an updated motion activity posteriorgram.

Abstract: A method includes receiving, at a master agent, announcements from candidate consumer agents indicating the presence of the candidate consumer agents. Each announcement includes display parameters for a display of the corresponding candidate consumer agent. The method further includes receiving at the master agent content parameters from a producer agent, the content parameters defining characteristics of content to be provided by the consumer agent. A mosaic screen is configured based on the received announcements and the content parameters. This configuring of the mosaic screen includes selecting ones of the consumer agents for which an announcement was received and generating content distribution parameters based on the content parameters and the display parameters of the selected ones of the consumer agents. The generated content distribution parameters are provided to the consumer agent.

Abstract: A distributed computing system for artificial intelligence in autonomously appreciating a circumstance context of a smart device. Raw context data is detected by sensors associated with the smart device. The raw context data is pre-processed by the smart device and then provided to a cloud based server for further processing. At the cloud based server, various sets of feature data are obtained from the pre-processed context data. The various sets of feature data are compared with corresponding classification parameters to determine a classification of a continuous event and/or a classification of transient event, if any, which occur in the context. The determined classification of the continuous event and the transient event will be used to autonomously configure the smart device or another related smart device to fit the context.