Methods, techniques, apparatus, and algorithms are described for robustly measuring real-world distances using any mobile device equipped with an accelerometer and monocular camera. A general software implementation processes 2D video, precisely tracking points of interest across frames to estimate the unsealed trajectory of the device, which is used to correct the device's inertially derived trajectory. The visual and inertial trajectories are then aligned in scale space to estimate the physical distance travelled by the device and the true distance between the visually tracked points.

Graphical interactive model selection is provided. A response variable vector for each value of a group variable and an explanatory variable vector are defined. A wavelet function is fit to the explanatory variable vector paired with the response variable vector defined for each value of the group variable. Each fit wavelet function defines coefficients for each value of the group variable. A curve is presented for each value of the group variable and is defined by the plurality of coefficients of an associated fit wavelet function. An indicator is received of a request to perform functional analysis using the coefficients for each value of the of the group variable based on a predefined factor variable. A model is trained using the coefficients for each value of the group variable and a factor variable value associated with each observation vector of each plurality of observation vectors as a model effect.

The disclosed invention provides a system and method for adaptive interference suppression for a signal in general and an ECG signal in particular. The method includes acquisition of a signal through an acquisition process. The signal is further passed through an FFT module configured to perform time windowing and FFT operation on the signal. The method further includes setting one or more fixed or adaptive thresholds for detecting tonal interferers in the signal. The method further includes choosing an appropriate filter for filtering the detected tonal interferers in the signal. Further, the detected tonal interferers are removed using the chosen appropriate filter. Finally, the method includes removing padding from the signal to eliminate residual edge effects.

Systems and methods for realizing practical applications of high speed digital image correlation (DIC) for dynamic quantities of interest are provided. In particular, a series of images are captured for a component of interest in which a non-filtered sensor and an analog low-pass filtered sensor are included within the region of interest for the series of images. Displacement signals are obtained for the component of interest, the non-filtered sensor, and the analog low-pass filtered sensor by applying digital image correlation processing to the series of images, which may also be wavelet filtered. Dynamic quantities of interest may be generated and derived from the displacement signals after having been wavelet filtered. Such dynamic quantities of interest based on the wavelet filtered DIC-derived displacement signal may be compared to sensor-derived dynamic quantities of interest to determine if aliasing is or is likely to be present.

Techniques, including systems and methods for monitoring a rotating equipment, are provided. A sensor that is in proximity of the rotating equipment senses vibrations of the rotating equipment. The sensor generates a digital signal corresponding to the vibrations of the rotating equipment and transmits the digital signal over a communication network. A server receives the digital signal and pre-processes the digital signal using ensemble empirical mean decomposition (EEMD) technique. The server processes the digital signal using wavelet neural network (WNN) to detect faults in the rotating equipment. Further, the server processes the digital signal using the wavelet neural network to predict remaining useful life (RUL) of the rotating equipment.

Graphical interactive model selection is provided. A response variable vector for each value of a group variable and an explanatory variable vector are defined. A wavelet function is fit to the explanatory variable vector paired with the response variable vector defined for each value of the group variable. Each fit wavelet function defines coefficients for each value of the group variable. A curve is presented for each value of the group variable and is defined by the plurality of coefficients of an associated fit wavelet function. An indicator is received of a request to perform functional analysis using the coefficients for each value of the of the group variable based on a predefined factor variable. A model is trained using the coefficients for each value of the group variable and a factor variable value associated with each observation vector of each plurality of observation vectors as a model effect.

Methods and systems to correct low-resolution measurements corresponding to unobservable high-resolution measurements by introducing variation in the plurality of low-resolution measurements to obtain perturbed values for the low-resolution measurements. The perturbed values have a higher resolution than another resolution of the low-resolution measurements. A distribution test is performed on the perturbed values.

An information processing device includes processing circuitry configured to classify a plurality of partial waveform patterns that characterize a plurality of time series data into a plurality of classes based on the plurality of time series data classified into the plurality of classes, update shapes of the partial waveform patterns by fitting the partial waveform patterns to the time series data of the corresponding class, and reclassify the plurality of time series data into the plurality of classes based on the updated partial waveform patterns and difficulty levels that represent degrees of difficulty of classification and interpretation of the time series data.

Disclosed is a technique of determining a measure of proximity between two devices (4, 6). A method implementation of the technique comprises obtaining a first device signature comprising an indication of a first point in time and a first parameter characteristic of a first measurement performed by a first sensor (10) comprised in the first device (4); obtaining a second device signature comprising an indication of a second point in time and a second parameter characteristic of a second measurement performed by a second sensor (12) comprised in the second device (6); and determining, based on the first device signature and the second device signature, the measure of proximity between the first device (4) and the second device (6).

Systems, methods, and other embodiments associated with off-duty-cycle-robust machine learning for anomaly detection in assets with random downtimes are described. In one embodiment, a method includes inferring ranges of asset downtime from spikes in a numerical derivative of a time series signal for an asset; extracting an asset downtime signal from the time series signal based on the inferred ranges of asset downtime; determining that the asset downtime signal carries telemetry based on the variance of the asset downtime signal; training a first machine learning model for the asset downtime signal; detecting a first spike in the numerical derivative of the time signal that indicates a transition to asset downtime; and in response to detection of the first spike, monitoring the time series signal for anomalous activity with the trained first machine learning model.