The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m.sub.1 . . . m.sub.n . . . m.sub.N} and corresponding center frequencies {ω.sub.1 . . . ω.sub.n . . . ω.sub.N}; (4) searching a fault related modal m.sub.I, guiding parameter optimization by using a center frequency ω.sub.I of the fault related modal m.sub.I, and retrieving an optimal target component m.sub.I including fault information; and (5) performing envelopment analysis on the optimal target component m.sub.I, and diagnosing the rotary machine equipment according to an envelope spectrum.

A network authentication system can be configured for sampling a plurality of signal samples from a device on a network, providing the plurality of signal samples to a first machine-learned model that is configured to determine a device fingerprint based at least in part on the plurality of signal samples, and providing the device fingerprint to a second machine-learned model that is configured to classify the device based at least in part on the device fingerprint.

The present invention relates a novel system and method to diagnose and predict systemic disorders including brain disorders and mental states in early stage and more accurately. More particularly, this invention relates to a novel method of EEG recording and processing through which multiple output data streams are taken together from a system like brain and the structure of their correlation matrix is studied through its eigenvector, eigendirection and eigenspaces and other signal processing techniques including compression sensing, wavelet transform, fast fourier transform etc.

Methods, structures and computer program products for digital sample rate conversion are presented. An input digital sample with a first frequency is converted to an output sample with a second frequency. A sample rate conversion circuit is provided which provides an enhanced transposed farrow structure that enables an optimised trade-off between noise levels and computational complexity. Each output sample is derived by convolution of a continuous time interpolation kernel with a continuous time step function representing the input sample stream. In a sample rate conversion structure, there is a trade-off between the quality and the computational complexity. The quality is defined as a ratio between the (wanted) signal power and the (unwanted) noise power. The computational complexity may be defined as the average number of arithmetic operations that are required to generate one output sample. A higher computational complexity will generally lead to a higher power consumption and larger footprint.

In an evaluation information provision system, subject's motion data is stored in association with attributes. When an attribute is assigned, the evaluation information provision system selects feature data from a plurality of the subject's motion data associated with the assigned attribute. The evaluation information provision system calculates a score for the assigned attribute, for the user's motion, using a statistical distance between the selected feature data and the user's motion data. The calculated score is displayed, for example, as a screen.

Systems and methods that yield highly-accurate classification of acoustic and other non-image events, involving pre-processing data from one or more transducers and generating a visual representation of the source as well as associated features and processing, are disclosed. According to certain exemplary implementations herein, such pre-processing steps may be utilized in situations where 1) all impulsive acoustic events have many features in common due to their point source origin and impulsive nature, and/or 2) the error rates that are considered acceptable in general purpose image classification are much higher than the acceptable levels in automatic impulsive incident classification. Further, according to some aspects, the data may be pre-processed in various ways, such as to remove extraneous or irrelevant details and/or perform any required rotation, alignment, scaling, etc. tasks, such that these tasks do not need to be “learned” in a less direct and more expensive manner in the neural network.

A method and system for detecting and characterizing weak signals of risk exposure in an industrial system based on industrial system data collected over a given time period. The system is configured for implementing: a module (36) for computing a risk predictive signature, from collected data relating to the industrial system, using a first term obtained by summing elementary signatures associated with elementary initiating events, dependent on parameters comprising a severity value, a characteristic function and a weighting function of the elementary initiating event, at least a part of said parameters being determined by using a neural network, a module (38) for detecting the presence of a weak signal of risk exposure by comparing the computed risk predictive signature with predetermined reference risk signatures.

Systems and techniques for facilitating digital data prognostics are presented. A system can processes a corpus of stored data, generate respective digital signatures representing respective subsets of the corpus of the stored data, and tag the respective digital signatures with tags corresponding to extrinsic events. The digital signatures can be stored and indexed in a digital signature library. The system can also compare a new digital signature to learned digital signatures in order to identify one or more matches, and prognose an upcoming event associated with the new digital signature based on the matches and generated inferences for the learned digital signatures.

A method is provided for use in optimizing ethanol yield from agricultural products. The method includes imaging agricultural products to determine predicted ethanol yields for the agricultural products and assigning characterizations to the imaged agricultural products based on their predicted ethanol yields. An apparatus is provided for collecting, retaining, and/or transporting bulk quantities of agricultural products. The apparatus includes an analyzer configured to image the agricultural products for use in determining the predicted ethanol yields. And, a system is provided for tracking and/or monitoring agricultural products. The system includes an analyzer configured to image the agricultural products for use in determining the predicted ethanol yields, a central processor configured to communicate with the analyzer to thereby link the imaged agricultural products with their predicted ethanol yields, and a telecommunications link coupling the analyzer to the central processor for allowing the communication between the analyzer and the central processor.

An input unit (20) obtains a sequence of image frames over time. A segmentation unit (22) segments image frames of the sequence of image frames. A tracking unit (24) tracks segments of the segmented image frame over time in the sequence of image frames. A clustering unit (26) clusters the tracked segments to obtain clusters representing skin of a subject by use of one or more image features of the tracked segments.