Abstract: The present disclosure is directed to an air filter sensor system that can monitor a status of a filter and provide information to a remote system regarding the filter's status. The system can receive, by a computing server via one or more computer networks and from each of a plurality of sensor assemblies coupled to a corresponding plurality of air filters, information indicative of filter contamination levels respectively associated with each corresponding air filter of the plurality of air filters. Each of the respective filter contamination levels being provided by one sensor assembly of the plurality of sensor assemblies based at least in part on a difference in detected air pressure between first and second sides of the corresponding air filter.

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: Systems, methods and devices are provided to improve management and accuracy of timestamps associated with sensor-based data. An indication is received of a sensor event associated with data samples provided from a sensor having an output data rate. A respective timestamp is assigned to each of the data samples. Assigning the respective timestamp may include, responsive to a determination that the indicated event is an interrupt event, calculating an actual data sampling interval based at least in part on time intervals between previous sensor events and a on a quantity of the one or more data samples.

Abstract: Systems, methods and devices are provided to improve management and accuracy of timestamps associated with sensor-based data. An indication is received of a sensor event associated with data samples provided from a sensor having an output data rate. A respective timestamp is assigned to each of the data samples. Assigning the respective timestamp may include, responsive to a determination that the indicated event is an interrupt event, calculating an actual data sampling interval based at least in part on time intervals between previous sensor events and a on a quantity of the one or more data samples.

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: 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 method and apparatus for auto-directive adaptive beamforming for a microphone array using microelectromechanical systems (MEMS) sensor orientation information are provided. The microphone array captures audio and the MEMS sensor detects an orientation of the microphone array. A direction of arrival of a source signal is estimated based on the data representative of the audio. A change in an orientation of the microphone array is detected based on the orientation and the direction of arrival is compensates based on the change in the orientation of the microphone array. The apparatus pre-steers a beam of a beam pattern of the microphone array based on the compensated direction of arrival to retain the source signal in a broadside of the microphone array and performs adaptive wideband beamforming to null one or more interfering sources in the beam pattern while retaining the source signal in the broadside of the microphone array.

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 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: 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: A portable device includes one or more memories and travel mode control circuitry coupled to the one or more memories. The travel mode control circuitry, in operation, monitors motion data and detects a first travel state of the device based on the monitored motion data and an acceleration profile. When the first travel state of the device is detected, motion data and pressure data are monitored to detect a transition from the first travel state to a second travel state of the device. When the transition to the second travel state of the device is detected, one or more control signals are generated to cause the device to enter a first travel mode of operation.

Abstract: A portable device includes one or more memories and travel mode control circuitry coupled to the one or more memories. The travel mode control circuitry, in operation, monitors motion data and detects a first travel state of the device based on the monitored motion data and an acceleration profile. When the first travel state of the device is detected, motion data and pressure data are monitored to detect a transition from the first travel state to a second travel state of the device. When the transition to the second travel state of the device is detected, one or more control signals are generated to cause the device to enter a first travel mode of operation.

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 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 system may include a motion sensor configured to generate a motion signal in response to a movement of an electronic device, and at least one feature detection circuit configured to determine at least one metric based on the motion signal. The system may further include a classifying circuit configured to determine whether the electronic device is in contact with a human body based on the at least one metric.

Abstract: Disclosed herein is a method of operating an electronic device. The method includes activating a first sensing device, and determining a first probabilistic context of the electronic device relative to its surroundings. The method includes outputting the first probabilistic context, and determining a confidence measure of the first probabilistic context. Where the confidence measure of the first probabilistic context is below a threshold, the method includes activating a second sensing device, determining a second probabilistic context of the electronic device relative to its surroundings. outputting the second probabilistic context, and determining a confidence measure of the second probabilistic context. Where the confidence measure of the second probabilistic context is above the threshold, the second sensing device is deactivated and the method returns to determining the first probabilistic context.

Abstract: A sensor chip includes registers storing and outputting configuration data, an extraction circuit receiving digital data and extracting features of the digital data in accordance with the configuration data, and a classification circuit applying a decision tree to the extracted features to generate a context of an electronic device into which the sensor chip is incorporated relative to its surroundings, the decision tree operating according to the configuration data. The classification unit outputs the context to the registers for storage. The configuration data includes which features for the extraction circuit to extract from the digital data, and a structure for the decision tree. The structure for the decision tree includes conditions that the decision tree is to apply to the at least one extracted feature, and outcomes to be effectuated based upon whether the extracted features meet or do not meet the conditions.

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: 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 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.