A method that includes operating a bus monitoring system having at least one interface configured to be coupled to at least one communication bus and receive bus traffic transmitted over the communication bus(es). The method also includes, using a device authentication system of the bus monitoring system, analyzing the bus traffic received via the at least one interface. Analyzing the bus traffic includes obtaining a message in the bus traffic (where the message identifies a source), identifying a support vector machine that corresponds to the source of the message, applying a wave transform to a waveform of the received message in order to generate a transformed waveform, inputting the transformed waveform to the identified support vector machine, and taking action in response to the identified support vector machine determining that the transformed waveform or the associated information does not correspond to the source.

A method for creating a mother wavelet function. The method includes preparing a plurality of vectors, extracting a kernel from the plurality of vectors, and extracting the mother wavelet function from the kernel. The kernel includes a mode value of a vector of the plurality of vectors.

A method for detecting and predicting an epileptic seizure. The method includes preparing a plurality of electrical signals, extracting a plurality of patterns from the plurality of electrical signals, extracting a plurality of features from the plurality of electrical signals by applying the plurality of patterns on the plurality of electrical signals, optimizing the plurality of patterns and the plurality of features, and classifying each of the plurality of electrical signals in a plurality of classes by applying a plurality of classifiers on the plurality of features. The plurality of electrical signals include a plurality of samples. The plurality of classes include a seizure class and a non-seizure class, and the plurality of classifiers include a plurality of cascaded AdaBoost classifiers.

A method and apparatus for resolving individual signals in detector output data are disclosed. One inventive aspect includes a processing circuit configured to receive detector output data wherein the detector output data may be stepped data or non-stepped data; transform the detector output data to produce stepped data wherein the detector output data is received as non-stepped data; detect at least one signal at least partially based on the stepped data; and estimate a parameter associated with the signal, wherein estimating the parameter may preferably comprise estimating a signal energy or signal time of arrival associated with the signal.

A device, method, and computer readable storage medium generate a biometric public key for an individual based on both the individual's biometric data and a secret, in a manner that verifiably characterizes both while tending to prevent recovery of either by anyone other than the individual. The biometric public key may be later used to authenticate a subject purporting to be the individual, using a computing facility that need not rely on a hardware root of trust. Such biometric public keys may be distributed without compromising the individual's biometric data. In operation, a confident subset of a set of biometric values of the subject is extracted, including by performing a transform of the set of biometric values. The transform may variously be a Gabor transform, a wavelet transform, processing by a machine learning system, etc.

The present disclosure relates to systems and methods for detecting and identifying anomalies within a discrete wavelet database. In one implementation, the system may include one or more memories storing instructions and one or more processors configured to execute the instructions. The instructions may include instructions to receive a new wavelet, convert the net transaction to a wavelet, convert the wavelet to a tensor using an exponential smoothing average, calculate a difference field between the tensor and a field having one or more previous transactions represented as tensors, perform a weighted summation of the difference field to produce a difference vector, apply one or more models to the difference vector to determine a likelihood of the new wavelet representing an anomaly, and add the new wavelet to the field when the likelihood is below a threshold.

An analog circuit fault feature extraction method based on a parameter random distribution neighbor embedding winner-take-all method, comprising the following steps: (1) collecting a time-domain response signal of an analog circuit under test, wherein the input of the analog circuit under test is excited by using a pulse signal, a voltage signal is sampled at an output end, and the collected time-domain response signal is an output voltage signal of the analog circuit; (2) applying a discrete wavelet packet transform for the collected time-domain response signal to acquire each wavelet node signal; (3) calculating energy values and kurtosis values of the acquired wavelet node signals to form an initial fault feature data set of the analog circuit; and (4) analyzing the initial fault feature data by the parameter random distribution neighbor embedding winner-take-all method, to acquire optimum low-dimensional feature data. The invention effectively reduces redundancy and interference elements in the fault features, and greatly improves degree of separation of different fault features and degree of polymerization of samples of same fault category.

A method for performing Multivariable Matrix Spectral Factorization has been developed, which allows factorization in real time high-dimensional matrices with multivariable high-order polynomial or non-rational entries. Systems implementing the method provide improved performance and capabilities in applications reducible to multivariable matrix spectral factorization.

Methods, systems and programs for denoising a signal using discrete wavelet transformation are provided. For example, a method for denoising a signal may include determining a number of resolution levels to denoise, determining variable threshold(s) for each resolution level, applying the determined variable threshold(s) to denoise at least a detail component of each of the determined resolution levels. Each variable threshold includes a separately determined lower threshold and upper threshold. The method for denoising a signal may further include transforming, using an inverse discrete wavelet transformation, at least the denoised detail component for each of the determined resolution levels into a denoised signal.

A control device, a machine (tool) having the control device, a method for providing a travel profile and a computer program for providing the travel profile, wherein a reference line is generated, e.g., from a CAD drawing, to provide a travel profile for a tool, where approximation curves are created from the reference line using wavelet base functions, transformation curves are formed from the approximation curves via difference generation, and these are each adapted to a desired accuracy of the processing mode via modification, where modification curves are created via the modification of the transformation curves, where the travel profile is the sum of the modification curves, and where drive elements of the machine tool are controlled based on the travel profile such that the travel profile can be optimized via the selection of wavelet base functions, based on a processing mode, e.g. rough milling, fine milling, laser cutting.