Abstract: This disclosure describes techniques to aggregate sensor data. The techniques perform operations comprising: receiving, from a first sensor, a first profile representing a first set of movement attributes detected by the first sensor in an area at a given point in time; receiving, from a second sensor, a second profile representing a second set of movement attributes detected by the second sensor in the area at the given point in time; computing a similarity measure between the first and second sets of movement attributes of the first and second profiles; determining that the similarity measure exceeds a threshold value; and in response to determining that the similarity measure exceeds the threshold value, associating the first and second profiles with a same first object that is in the area at the given point in time.

Abstract: This disclosure describes a software licensing process that can be divided into two phases: 1) during production of electronic devices and 2) a renewal phase during operations after the electronic devices are deployed. In the first phase, a default software license goes out along with the firmware when the electronic devices are manufactured. The default software license does not need a hardware unique ID (HUID) and can be part of the firmware file, which makes it easy for manufacturing personnel. The second phase is the renewal phase. Before or after expiration of the software license, a customer can collect and send the HUIDs of the electronic devices to the licensor, e.g., manufacturer, and the licensor can generate a single software license for multiple devices.

Abstract: A method and system for detecting tampering of an energy meter can involve, in a first phase: gathering magnetic field sample data, an average magnetic field strength, a minimum magnetic field strength, and a maximum magnetic field strength from a magnetic field condition applied to an energy meter by a magnetic sensor that measures a magnetic field in one or more directions, and using learning coefficients calculated from the magnetic field sample data and the average magnetic field strength, the minimum magnetic field strength, and the maximum magnetic field strength to classify with a classifier, magnet tampering conditions with respect to the energy meter. In a second phase, a magnet tamper event can be identified with respect to the energy meter when the magnet tampering condition classified by the classifier is greater than a magnetic detection threshold.

Abstract: A method and system for detecting tampering of an energy meter can involve, in a first phase: gathering magnetic field sample data, an average magnetic field strength, a minimum magnetic field strength, and a maximum magnetic field strength from a magnetic field condition applied to an energy meter by a magnetic sensor that measures a magnetic field in one or more directions, and using learning coefficients calculated from the magnetic field sample data and the average magnetic field strength, the minimum magnetic field strength, and the maximum magnetic field strength to classify with a classifier, magnet tampering conditions with respect to the energy meter. In a second phase, a magnet tamper event can be identified with respect to the energy meter when the magnet tampering condition classified by the classifier is greater than a magnetic detection threshold.

Abstract: This disclosure describes techniques to count objects in an area. The techniques include operations comprising: receiving, from a first sensor, data that identifies a location of a first object in an area at a given point in time; receiving, from a second sensor, data that identifies a location of a second object in the area at the given point in time; and aggregating the data from the first sensor and the data from the second sensor to compute a count value indicating how many objects are present in the area at the given point in time.

Abstract: This disclosure relates to a method and apparatuses for detecting and reporting suspected tampering of an energy meter to a utility. The method and apparatus uses a plurality of sensors to obtain data relating to one or more input parameters based on the type of input signal. The input parameters are mapped with a tamper type to provide a classification of one or more output parameters. The output parameters are compared to one or more operating conditions of the energy meter to identify one or more suspected tampers. Any suspected tampers are reported to the utility.

Abstract: This disclosure relates to a method and apparatuses for detecting and reporting suspected tampering of an energy meter to a utility. The method and apparatus uses a plurality of sensors to obtain data relating to one or more input parameters based on the type of input signal. The input parameters are mapped with a tamper type to provide a classification of one or more output parameters. The output parameters are compared to one or more operating conditions of the energy meter to identify one or more suspected tampers. Any suspected tampers are reported to the utility.

Abstract: This disclosure describes techniques to aggregate sensor data. The techniques perform operations comprising: receiving, from a first sensor, a first profile representing a first set of movement attributes detected by the first sensor in an area at a given point in time; receiving, from a second sensor, a second profile representing a second set of movement attributes detected by the second sensor in the area at the given point in time; computing a similarity measure between the first and second sets of movement attributes of the first and second profiles; determining that the similarity measure exceeds a threshold value; and in response to determining that the similarity measure exceeds the threshold value, associating the first and second profiles with a same first object that is in the area at the given point in time.

Abstract: This disclosure describes techniques to count objects in an area. The techniques include operations comprising: receiving, from a first sensor, data that identifies a location of a first object in an area at a given point in time; receiving, from a second sensor, data that identifies a location of a second object in the area at the given point in time; and aggregating the data from the first sensor and the data from the second sensor to compute a count value indicating how many objects are present in the area at the given point in time.

Abstract: A system and method for removing noise from images are disclosed herein. An exemplary system includes an edge-detection-based adaptive filter that identifies edge pixels and non-edge pixels in an image and selects a filtering technique for at least one non-edge pixel based on a comparison of the at least one non-edge pixel to a neighboring pixel region, wherein such comparison indicates whether the at least one non-edge pixel is a result of low-light noise.

Abstract: A line-detection system computes, using a local memory, a result of a partial conversion of image-space pixel data from image space to Hough space. The result is analyzed for edges corresponding to a line present in the partial conversion. The line is compared against other lines detected in previously computed partial results to identify a longest line in the image.

Abstract: A system and method for removing noise from images are disclosed herein. An exemplary system includes an edge-detection-based adaptive filter that identifies edge pixels and non-edge pixels in an image and selects a filtering technique for at least one non-edge pixel based on a comparison of the at least one non-edge pixel to a neighboring pixel region, wherein such comparison indicates whether the at least one non-edge pixel is a result of low-light noise.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: A line-detection system computes, using a local memory, a result of a partial conversion of image-space pixel data from image space to Hough space. The result is analyzed for edges corresponding to a line present in the partial conversion. The line is compared against other lines detected in previously computed partial results to identify a longest line in the image.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.