A method for analyzing signal irregularity of radiation in a lightning leader is provided, which relates to a field of lightning signal processing and includes: S1: acquiring electric field signals; S2: dividing the electric field signals into multiple segments according to time, and calculating an average sample entropy value of each segment of the electric field signals; S3: dividing the multiple segments of electric field signals into three regions according to the time, quantifying irregularity of radiation of the three regions in the leader according to sample entropies and performing classification based on numerical variation characteristics of the irregularity; S4: comparing the classified data with lightning positioning data to form an analysis database; and S5: acquiring real-time electric field signals, calculating average sample entropy values of the electric field signals, and predicting development characteristics of irregular radiation in the lightning leader.

The present invention relates to methods for analyzing gait of a subject. In particular, the present invention relates to a method for analyzing gait of a subject, said method comprising: providing data representing the 3D-movement of a foot of said subject over time; identifying within said data first data segments that each represent of at least one stride; determining one or more stride features for each of said first data segments; and defining one or more clusters on the basis of at least one stride feature of said one or more stride features. Each of the defined clusters represents a class of strides, e.g. a class may represent the typical stride of a subject. The present invention also provides for corresponding systems that are configured to perform the methods of the present invention and the use of these systems for analyzing in assessing gait of a subject, preferably a subject suffering from a movement-impairment.

Embodiments of the present disclosure provide improved user identity validation. Embodiments of the present disclosure provide accurate and secure user identity validation in contexts where existing user validation algorithm(s), for example facial recognition algorithms, fail due to obfuscation of user physical characteristic(s) by clothing, equipment such as PPE masks, and the like. Some example embodiments receive captured data associated with a user, the captured data comprising at least imaging data associated with the user, detect, from the imaging data, machine decodable data associated with the user, determine an asserted user identity associated with the user by decoding the machine decodable data, and validate the asserted user identity associated with the user utilizing at least a remaining portion the captured data.

Methods and systems for predicting failure in a cyber-physical system include determining a prediction index based on a comparison of input time series, from respective sensors in a cyber-physical system, to failure precursors. A failure precursor is detected in the input time series, responsive to a comparison of the prediction index to a threshold. A subset of the sensors associated with the failure precursor is determined, based on a gradient of the prediction index. A corrective action is performed responsive to the determined subset of sensors.

Disclosed are an electronic apparatus for recognizing a multimedia signal and an operating method of the electronic apparatus, including segmenting a detection signal into a plurality of frames; segmenting each of the frames into a plurality of blocks; and representing each of the blocks as a hash word based on a time feature and a frequency feature for each of the blocks.

Abnormality detection device includes: processing circuitry performing a process that: extracts a first feature amount using a sliding window of a first time length and a second feature amount using a sliding window of a second time length longer than the first time length; calculates a unit incremental value by dividing a specific value difference subtracting a first specific value in the first feature amount from a second specific value in the second feature amount by a time length difference subtracting the first time length from the second time length; sequentially calculates, for each abnormality detection time length different from each other, a threshold based on the unit incremental value; and sequentially generates, for each abnormality detection time length, a plurality of partial time series having the abnormality detection time lengths from the time series data, and detects an abnormality in the time series data based on those and the threshold.

There are provided methods and systems that enable the use of the backscattering pattern produced by an optical fiber in an OTDR trace as a signature (also referred to herein as the “RBS fingerprint”) to recognize an optical fiber. It was found that it may be difficult to obtain repeatable signatures as those are sensitive to the wavelength of the OTDR laser source and the temperature of the fiber. OTDR methods and systems that are adapted to compare the backscattering pattern in a more repeatable manner are therefore provided. Once the repeatability issue is overcome, such signature can be used for identification purposes and enable new applications.

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.

To be capable of efficiently transmitting appropriate information on the motion improvement to a person in motion. A motion evaluation system includes a sensor unit, an information processing device, and an information presentation device. The information processing device includes a communication device, a storage device, and an arithmetic device. The arithmetic device acquires motion data acquired by observing a user through the use of a sensor via the communication device, checks the motion data against information about the correctness of motions in the reference information, determines a state of motion of the user, specifies a motion in a state to be improved as an improvement, check the motion data after the motion corresponding to the improvement against information about busy levels of the user to specify a busy level of the user, and outputs, as improvement suggestion information about the improvement, information with different contents at each of multiple times to an information presentation device based on the improvement and a rule predetermined according to each situation of the busy level.

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.