Abstract: Systems and methods for transferring learning in sensor devices. Historical time-series measurement samples of one or more parameters associated with a biological function being monitored by the sensor device are received and assigned to clusters. Feature data extracted from the historical time-series measurement samples are used to generate cluster-specific source-domain classifiers for each cluster. Unlabeled time-series measurement samples of the one or more parameters associated with the biological function are received. A cluster-identifier is assigned to each unlabeled target-domain sample, the cluster-identifier including information identifying a cluster-specific source-domain classifier associated with the unlabeled target-domain sample.

Abstract: The present disclosure includes in one instance an optical article comprising a data rich plurality of retroreflective elements that are configured in a spatially defined arrangement, where the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties, and where data rich means information that is readily machine interpretable. The present disclosure also includes a system comprising the previously mentioned optical article, an optical system, and an inference engine for interpreting and classifying the plurality of retroreflective elements wherein the optical system feeds data to the inference engine.

Abstract: The present disclosure includes in one instance an optical article comprising a data rich plurality of retroreflective meats that are configured in a spatially defined arrangement, where the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties, and where data rich means information that is readily machine interpretable. The present disclosure also includes a system comprising the previously mentioned optical article, an optical system, and an inference engine for interpreting and classifying the plurality of retroreflective elements wherein the optical system feeds data to the inference engine.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: In some examples, a method includes determining, by a computing device, a plurality of regions of interest of an image for independently processing relative to a plane of interest; determining, by the computing device and for respective regions of interest of the plurality of regions of interest, a distance from objects within the respective regions of interest to the plane of interest; and processing, by the computing device and independently for the respective regions of interest, the respective regions of interest of the image based on the determined distance from the objects to the plane of interest.

Abstract: Optical articles including a spatially defined arrangement of a plurality of data rich retroreflective elements, wherein the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties and at least two different optical contrasts with respect to a background substrate when observed within an ultraviolet spectrum, a visible spectrum, a near-infrared spectrum, or a combination thereof.

Abstract: Systems and methods for monitoring the condition of an air filter installed in an HVAC system and for monitoring the condition of the HVAC system. The monitoring system includes a processing unit configured to receive data representative of at least a first temporal parameter of the HVAC system. The processing unit can process the data to obtain an indication of the condition of the air filter and can also process the data to obtain an indication of the condition of the HVAC system.

Abstract: Optical articles including a spatially defined arrangement of a plurality of data rich retroreflective elements, wherein the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties and at least two different optical contrasts with respect to a background substrate when observed within an ultraviolet spectrum, a visible spectrum, a near-infrared spectrum, or a combination thereof.

Abstract: Optical articles including a spatially defined arrangement of a plurality of data rich retroreflective elements, wherein the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties and at least two different optical contrasts with respect to a background substrate when observed within an ultraviolet spectrum, a visible spectrum, a near-infrared spectrum, or a combination thereof.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.

Abstract: Aspects of the present disclosure related to a computer system and method for analyzing whether a package has been opened. The system includes a memory and one or more processors communicatively coupled to the memory and can be configured to receive an image corresponding to a wrapped package. The one or more processors can also be configured to generate a set of feature values based on a visual representation of the wrapped package in the image. The one or more processors can also be configured to apply the set of feature values to at least one model that is trained based at least in part on a set of images that include the wrapped package having the status, to determine the status of the wrapped package.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: Systems and methods for detecting the quality of signals captured by a sensor device monitoring a biological function. Sensor data associated with the sensor device is received, the sensor data representing time-series measurement samples of one or more parameters associated with the biological function, the sensor data including usable and unusable samples of the time-series measurements. Data representing two or more features of samples of the time-series measurements is extracted and filtered to reduce outliers in the extracted data based on an expected outlier ratio. A machine learning algorithm is then trained to identify events based on the filtered extracted data.

Abstract: In an example, a system comprises at least one body-worn tracking device (BWTD) configured to transmit location data that indicates a location of the BWTD and a computing system configured to communicate with the at least one BWTD. The computing system is configured to receive the location data from the at least one BWTD, determine, based on a set of weighted filters associated with at least one monitoring attribute, at least one gradient value corresponding to the location data, where the at least one gradient value represents a level of the at least one monitoring attribute, and perform at least one operation based on the at least one gradient value.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: In an example, a method includes capturing one or more friction ridge images of a finger at an instance in time, the one or more friction ridge images including a plurality of perspectives of the finger. The method also includes determining, from the one or more friction ridge images, a rolled fingerprint representation of the finger, the rolled fingerprint representation comprising data from the plurality of perspectives, and outputting the rolled fingerprint representation.

Abstract: In some examples, a method includes determining, by a computing device, a plurality of regions of interest of an image for independently processing relative to a plane of interest; determining, by the computing device and for respective regions of interest of the plurality of regions of interest, a distance from objects within the respective regions of interest to the plane of interest; and processing, by the computing device and independently for the respective regions of interest, the respective regions of interest of the image based on the determined distance from the objects to the plane of interest.

Abstract: In one example, a system includes one or more personal protective equipment (PPE) devices each configured to be worn by a worker, the PPE devices each including one or more sensors that generate activity data indicative of activities of workers operating within one or more work environments. The system also includes a computing device, the computing device configured to: identify, based at least on the activity data, a plurality of clusters of one or more entities, wherein each entity of the entities is associated with one or more of the workers; and output an indication of a difference between performance by a target entity with respect to safety events and performance by the cluster that includes the target entity with respect to safety events.

Abstract: In some examples, a method includes determining, by a computing device, a plurality of regions of interest of an image for independently processing relative to a plane of interest; determining, by the computing device and for respective regions of interest of the plurality of regions of interest, a distance from objects within the respective regions of interest to the plane of interest; and processing, by the computing device and independently for the respective regions of interest, the respective regions of interest of the image based on the determined distance from the objects to the plane of interest.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.