Systems and methods relating to fault detection and diagnosis. Signals received from sensors are first filtered to remove noise and are then analyzed using wavelet packet transform (WPT) based PCA. The results of the PCA analysis are then automatically classified to thereby quickly and easily determine what issues there may be in a finished product or in a machine being monitored.

Devices, methods, and non-transitory program storage devices for spatiotemporal image noise reduction are disclosed, comprising: maintaining an accumulated image in memory; and obtaining a first plurality of multispectral images (e.g., RGB-IR images). For each image in the first plurality of multispectral images, the method may: calculate a multispectral guide image for the current image; calculate blending weights for the current image; apply the calculated blending weights to each channel of the current image to generate a denoised current image; and update the accumulated image based on pixel differences between the denoised current image and the accumulated image. In some embodiments, additional images (e.g., the accumulated image and/or other images captured prior to or after a given current image) may also be included in the denoising operations for a given current image. Finally, the method may generate a denoised output image for each input image, based on the updated accumulated image.

In some embodiments, a style transfer computing system generates a set of discriminator models corresponding to a set of styles based on a set of training datasets labeled for respective styles. The style transfer computing system further generates a style transfer language model for a target style combination that includes multiple target styles from the set of styles. The style transfer language model includes a cascaded language model and multiple discriminator models selected from the set of discriminator models. The style transfer computing system trains the style transfer language model to minimize a loss function containing a loss term for the cascaded language model and multiple loss terms for the multiple discriminator models. For a source sentence and a given target style combination, the style transfer computing system applies the style transfer language model on the source sentence to generate a target sentence in the given target style combination.

The present disclosure describes systems and methods of using iris data for authentication. A biometric encoder may translate an image of the iris into a rectangular representation of the iris. The rectangular representation may include a plurality of rows corresponding to a plurality of annular portions of the iris. The biometric encoder may extract an intensity profile from at least one of the plurality of rows, the intensity profile modeled as a stochastic process. The biometric encoder may obtain a stationary stochastic component of the intensity profile by removing a non-stationary stochastic component from the intensity profile. The biometric encoder may remove at least a noise component from the stationary component using auto-regressive based modeling, to produce at least a non-linear background signal, and may combine the non-stationary component and the at least the non-linear background signal, to produce a biometric template for authenticating the person.

This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least one gestures from a plurality of gestures is preprocessed to obtain plurality of spike frames. The plurality of spike frames is processed by a multi layered convolutional spiking neural network to learn plurality of spatial features from the at least one gesture. A filter block is deactivated from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt. A spatio-temporal features is obtained by allowing the spike activations from CSNN layer to flow through the reservoir. The spatial feature is classified by classifier from the CSNN layer and the spatio-temporal features from the reservoir to obtain set of prioritized gestures.

The display of correct and reliable operating image data which may change over the time is correctly and reliably displayed. A secured image is generated based on a source image by generating at least one secured image part. The secured image part is generated by combining a source image part of the source image with a corresponding secure pattern. The displaying of the secured image is monitored by detecting the corresponding secure pattern of the secured image part, updating the detected secure pattern, checking the currentness or correctness of the detected secure pattern

An ECG signal processing system which removes the CPR-induced artifact from measured ECG signals obtained during the administration of CPR.

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.

In some embodiments of the present invention, a method of reducing artifacts includes obtaining OCT/OCTA data from an OCT/OCTA imager; preprocessing OCTA/OCT volume data; extracting features from the preprocessed OCTA/OCT volume data; classifying the OCTA/OCT volume data to provide a probability determination data; determining a percentage data from the probability data determination; and reducing artifacts in response to the percentage data.

Techniques for improved text normalization are provided. Signatures are generated for a first word and a second word using a locality-sensitive hashing technique. A graph is constructed based on the first and second signatures, by creating a first node in the graph for the first word, creating a second node in the graph for the second word, and creating an edge in the graph connecting the first and second nodes upon determining that the first and second signatures match. A mapping from the first word to the second word is then generated based on the graph.