According to a method for object recognition by an active optical sensor system (2), a detector unit (2b) detects light (3b) reflected off an object (4) and generates a sensor signal (5a, 5b, 5c, 5d, 5e, 5f) on the basis thereof. A computing unit (2c) ascertains a first pulse width (D1) defined by a predetermined first limit value (G1) for an amplitude of the sensor signal (5a, 5b, 5c, 5d, 5e, 5f) as well as a second pulse width (D2) defined by a predetermined second limit value (G2). The signal pulse is assigned to one of at least two categories according to at least one predefined signal pulse parameter, and a scatter plot for object recognition is generated, said scatter plot containing exactly one entry for the signal pulse, said entry corresponding to the first pulse width (D1) or to the second pulse width (D2) according to the category to which the signal pulse belongs.

A method for engagement based contextual feedback, the method determines a required level of attention for a user interacting with content in a user interface of an electronic device and determines a level of attention for the user interacting with the content in the user interface of the electronic device. In responsive to determining the required level of attention is greater than the level of attention for the user, the method identifies available corrective actions performable by one or more electronic components on the electronic device. In responsive to identifying one or more user and electronic device interactions, the method selects one or more corrective actions from the available actions based on the one or more user and electronic device interactions. The method performs, via the one or more electronic components, the selected one or more corrections actions on the electronic device.

One or more processors to add identification information of a device to an initialized augmented device retrieval assistance (ADRA) data structure. Received data from ADRA clients monitoring the device within a location includes a network connection status and a time-based position of the device. The ADRA clients dynamically update the ADRA data structure with the location, a current position of the device, and a timeframe of the data from the monitoring by the ADRA clients. A request for ADR assistance is received that includes an identification of a target device, the timeframe, and a location. Location images are generated that include a location layout or floor plan. A time-spatial axis is rendered and sent to an ADRA client and includes location images and target device position within the respective images associated with a specific timeframe on the time-spatial axis.

A method for automatically identifying, analyzing and reducing extraneous waveform captures (WFCs) includes capturing at least one energy-related waveform in an electrical system using at least one waveform capture device. The at least one captured energy-related waveform is analyzed to determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a partially extraneous WFC, a redundant WFC, or a provisional WFC. In accordance with some embodiments of this disclosure, one or more actions are performed in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a partially extraneous WFC, a redundant WFC, or a provisional WFC.

Technologies and techniques for vehicle perception. A first contour of a current image a dna second contour of a next image are determined relative to an optical center. The current image is scaled to the next image relative to the optical center, wherein the scaling includes applying a scale vector to the first contour. A frame offset vector is determined, and the second contour is translated, based on the frame offset vector and the scale vector, to align the translated second contour to a focus of expansion. An image velocity is determined, based on the first contour and the translated second contour, wherein the image velocity is used to determine object movement from the image data.

Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

An automated point of sale system uses one or more cameras to capture images of food products selected by a user for purchase at a food establishment. The point of sale system then compares the images captured to previously captured reference images of such food products being offered at the establishment to find a match for each product, determines the price of each food product the user has selected based on the comparison and match, identifies an account of the user based on a biometric identification of the user and/or reading information from a machine readable token of the user, and automatically causes the account of the user to be charged or debited for the food products identified, thus reducing the check-out time and increasing efficiency of food establishment point of sale systems.

A system for classifying the usage of a handheld consumer device having a sensor and an analyzer. The sensor determines usage data at successive time instants and provides a temporally successive sequence of usage data during a usage session. The analyzer classifies the usage of the device with respect to at least one set usage classes and assembles a temporally successive sequence of input tuples of usage data relating to a predetermined time period of the usage session, each of the input tuples having at least one element representing the usage data at the respective time instant and for inputting the sequence of input tuples into at least one artificial neural network arranged to output at least one output tuple comprising a number of elements in accordance with the number of usage classes, each element of the output tuple representing a prediction value that the usage of the consumer device at the given time instant relates to a respective usage class.

Aspects of the present disclosure provide techniques for document classification through signal processing. Embodiments include receiving a document for classification. Embodiments include generating an image of the document. Embodiments include producing a signal representation of the document based on numbers of non-white pixels in each horizontal scan line or vertical scan line of the image of the document. Embodiments include comparing the signal representation of the document to signal representations of previously-classified documents. Embodiments include determining, based on the comparing, a classification for the document. Embodiments include performing additional processing with respect to the document based on the classification for the document.

Disclosed is a technique of determining a measure of proximity between two devices (4, 6). A method implementation of the technique comprises obtaining a first device signature comprising an indication of a first point in time and a first parameter characteristic of a first measurement performed by a first sensor (10) comprised in the first device (4); obtaining a second device signature comprising an indication of a second point in time and a second parameter characteristic of a second measurement performed by a second sensor (12) comprised in the second device (6); and determining, based on the first device signature and the second device signature, the measure of proximity between the first device (4) and the second device (6).