Embodiments of the disclosure are directed towards electricity fraud detection systems that involve a behavioral detection ecosystem to improve the detection rate of electricity fraud while reducing the rate of false-positives. More specifically, machine learning algorithms are eschewed in favor of two separate models that are applied sequentially. The first model is directed to improving the detection rate of electricity fraud through the use of detectors to identify customers engaging in suspicious behavior based on the demand profiles of those customers. The second model is directed to reducing the rate of false-positives by identifying potential legitimate explanations for any suspicious behavior. Subtracting away the suspicious behavior with legitimate explanations leaves only the identified, unexplained suspicious behavior that is highly likely to be associated with fraudulent activity.

A system and/or method configured to determine highlight segments. Content files that define content in a content segment set may be obtained. A highlight segment set may be determined from the content segment set. Determining the highlight segment set may include iterating (a)-(c) for multiple iterations. At (a), individual content segments included in the content segment set may be selected as a selected content segment for inclusion in the highlight segment set. At, (b) diversity scores for content segments that are (i) included in the content segment set and (ii) not yet selected for inclusion in the highlight segment set may be determined. At (c), one or more of the content segments may be disqualified for inclusion in the highlight segment set for future iterations based on the diversity scores.

Systems and methods are described for identifying a behavior of a machine. A computer system receives a signal indicative of operation of a field machine and applies a deep learning algorithm to identify a pattern in a collection of signals stored on a computer-readable memory. The collection of signals includes the received signal indicative of operation of the field machine and other signals. A series of targeted tests are performed using a test machine while monitoring a signal indicative of operation of the test machine. A behavior is identified during the series of targeted tests that produces a signal that matches the pattern identified by the deep learning algorithm. An occurrence of the behavior is then automatically identified in the field machine in response to detecting the pattern in the received signal indicative of operation of the field machine.

A signal processing apparatus includes a unit configured to generate noise cut data by deducting a predetermined noise value from values of respective signals constituting input data and a stochastic resonance processing unit configured to subject the noise cut data to a predetermined stochastic resonance processing. The predetermined stochastic resonance processing is processing to output, in a method of synthesizing a result of parallelly performing steps of adding new noise to the noise cut data to subject the resultant data to a binary processing, a value obtained in a case where the parallel number is infinite.

A human identifier system capable of distinguishing between multiple known people includes a first antenna and a second antenna. Particularly, the second antenna is operably selects the distance between to allow multiple people to walk, run or move between the antenna pairs. Additionally, the system also includes a radio frequency transmitter for generating multiple radio signals and a radio frequency receiver for receiving the transmitted radio signals. The system also includes a data processor operably connected to the radio frequency receiver with a processing means for processing the received signals from radio frequency receiver to provide output signals to identify a person walking, running or moving between the first antenna and the second antenna.

Method and system including displaying a first representation of a medication treatment parameter profile, displaying a first representation of a physiological profile associated with the medication treatment parameter profile, detecting a modification to a segment of the medication treatment parameter profile, displaying a modified representation of the medication treatment parameter profile and the physiological profile based on the detected modification to the segment of the medication treatment parameter profile, modifying an attribute of the first representation of the medication treatment parameter profile, and modifying an attribute of the first representation of the physiological profile are provided.

There is provided a method of extracting a physiological information which comprises, for a defined time-period, extracting a plurality of signal segments in a time-domain form, selecting a subset of the plurality of signal segments according their maximum signal level, converting at least said subset to produce a plurality of transformed signal segments, and combining the at least said subset of the plurality of transformed signal segments to produce a combined signal segment. This has the advantage of reducing the influence of motion and or illumination-induced changes on the final result by removing parts of the raw signal segments which are worst affected by these.

An image processing apparatus is described comprising a processor configured to access a template of image elements. The processor is configured to search an image which is larger than the template to find a region which is similar to the template, where similarity is measured using a similarity metric. The similarity metric comprises a normalized cross correlation function which is modified to include at least one factor related to a statistic of both the template and the region.

The present invention relates to systems and methods for self-learning of locomotion characteristic and speed during bipedal locomotion using sensor and GPS technology.

Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.