A method and apparatus are disclosed which enable the analysis of a break in a vehicle glazing panel without the attendance of a technician, the method and apparatus utilize capturing an image of the break and processing the image of the break to enable the suitability for repair or replacement of the glazing panel to be determined.

A method for compressing a digital representation of a stimulus includes encoding the digital representation as a feature vector within a feature space. The method also includes multiplying the feature vector with a Jacobian that maps the feature space to a non-Euclidean perceptual space according to a perceptual system that is capable of perceiving the stimulus. This multiplication generates a perceptual vector within the non-Euclidean perceptual space. The method also includes applying an update operator to the perceptual vector to move the perceptual vector in the perceptual space to an updated vector such that the updated vector has a lower entropy than the perceptual vector. The method also includes rounding the updated vector into a compressed vector that is smaller than the feature vector.

A computer-implemented method and system for Condition Monitoring (CM) for rotating machines. The method and system include continuously receiving samples of the envelope of physical quantity data such as speed, vibration, or current, updating in real-time accumulator variables, computing in real-time spectral features based on the accumulator variables and supplemental variables, and determining a condition based on the real-time spectral features. The spectral features, exemplary as amplitudes at specific frequencies, are computed in real-time by a Goertzel Algorithm. The totality of the accumulator variables is sufficient to determine the condition of the rotating machine and the supplemental variables are temporarily needed for computing the spectral features. The one or more supplemental variables, such as memory addresses, are not based on the received samples of the input data.

Systems and methods for configuring a reduced instruction set computer processor architecture to execute fully homomorphic encryption (FHE) logic gates as a streaming topology. The method includes parsing sequential FHE logic gate code, transforming the FHE logic gate code into a set of code modules that each have in input and an output that is a function of the input and which do not pass control to other functions, creating a node wrapper around each code module, configuring at least one of the primary processing cores to implement the logic element equivalents of each element in a manner which operates in a streaming mode wherein data streams out of corresponding arithmetic logic units into the main memory and other ones of the plurality arithmetic logic units.

A learning automaton can be trained to merge data from input data streams, optionally with different data rates, into a single output data stream. The learning automaton can learn over time from the input data streams. The input data streams can be low-pass filtered to suppress data having frequencies greater than a time-varying cutoff frequency. Initially, the cutoff frequency can be relatively low, so that the effective data rates of the input data streams are all equal. This can ensure that initially, high data-rate data does not overwhelm low data-rate data. As the learning automaton learns, an entropy of the learning automaton changes more slowly, and the cutoff frequency is increased over time. When the entropy of the learning automaton has stabilized, the training is completed, and the cutoff frequency can be large enough to pass all the input data streams, unfiltered, to the learning automaton.

A hardware accelerator for accelerating the zero knowledge succinct non-interactive argument of knowledge (zk-SNARK) protocol by reducing the computation time of the cryptographic verification is disclosed. The accelerator includes a zk-SNARK engine having one or more processing units running in parallel. The processing unit can include one or more multiply-accumulate operation (MAC) units, one or more fast Fourier transform (FFT) units; and one or more elliptic curve processor (ECP) units. The one or more ECP units are configured to reduce a bit-length of a scalar d.sub.i in an ECP algorithm used for generating a proof, thereby the cryptographic verification requires less computation power.

A source stochastic signal is deconstructed into its intrinsic components using a decomposition process. The intrinsic components are transformed, and a set of machine learning models are defined and trained to operate with individual ones of the transformed components. The source stochastic signal is thus empirically broken down into underlying components which are then used as learning datasets for the set of machine learning models to predict target components. The target components are then individually predicted and combined to reconstruct a predicted target stochastic signal. The source stochastic signal and the target stochastic signals can be biological signals having a related or common origin, such as photoplethysmogram signals and arterial blood pressure waveforms.

A source stochastic signal is deconstructed into its intrinsic components using a decomposition process. The intrinsic components are transformed, and a set of machine learning models are defined and trained to operate with individual ones of the transformed components. The source stochastic signal is thus empirically broken down into underlying components which are then used as learning datasets for the set of machine learning models to predict target components. The target components are then individually predicted and combined to reconstruct a predicted target stochastic signal. The source stochastic signal and the target stochastic signals can be biological signals having a related or common origin, such as photoplethysmogram signals and arterial blood pressure waveforms.

A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data. The multi-party computations can include a secret share reduction that transforms an instance of computed secret shared data stored in floating-point representation into an equivalent, equivalently precise, and equivalently secure instance of computed secret shared data having a reduced memory storage requirement.

Implementations described herein disclose an artificial intelligence (AI) based method for generating an oxygen saturation level output signal using the trained neural network. In one implementation, the method includes receiving a photoplethysmographic (PPG) signal, the PPG signal including a red PPG signal and an infrared PPG signal, generating an input feature matrix by performing time-frequency transform of the PPG signal, training a neural network using the input feature matrix and an oxygen saturation level input signal, and generating an oxygen saturation level output signal using the trained neural network.