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.

A waveform generation identification method includes: comparing individual waveform data obtained by a plurality of sensors, with at least one piece of characteristic waveform information; determining appearance probability of characteristic waveform information in at least a certain section of the waveform data, based on a degree of correlation between a peak section of the waveform data and the characteristic waveform information; and identifying a time when a section matching with the characteristic waveform information appears and a concerned sensor, based on the appearance probability.

The present application discloses a lip-language recognition AAC system based on surface electromyography, which includes: a training subsystem configured to collect the facial and neck EMG signals during lip-language movements through the high-density electrode array, improve the signal quality through the signal preprocessing algorithm, classify the lip-language movements through the classification algorithm, select the optimal number of electrodes and optimal positions through the channel selection algorithm, and establish the optimal matching template between the EMG signals and the lip-language information, and upload it to the network terminal for storage; and a detection subsystem configured to collect the EMG signals at the optimal positions during the lip-language movements based on the optimal number and positions of electrodes selected by the training subsystem, call the optimal matching template, classify and decode the EMG signals, recognize the lip-language information, and convert it into corresponding voice and picture information for display in real time.

Disclosed in the present invention is a method for suppressing airborne transient electromagnetic in-band vibration noise, comprising: dividing the data after current turn-off into two segments according to whether the useful signal is attenuated to the system noise level: the segment A is the useful signal segment, and the segment B is the pure noise segment; limiting the bandwidth of the data of the segment B according to the frequency range of the in-band noise, and labeling the result as BL; training a neural network using the BL, utilizing the well trained neural network to predict the in-band vibration noise contained in the data of the segment A, and labeling the prediction result as PNA; and subtracting the PNA from the data of the segment A to suppress the in-band vibration noise contained in the data of the segment A.

Disclosed in the present invention is a method for suppressing airborne transient electromagnetic in-band vibration noise, comprising: dividing the data after current turn-off into two segments according to whether the useful signal is attenuated to the system noise level: the segment A is the useful signal segment, and the segment B is the pure noise segment; limiting the bandwidth of the data of the segment B according to the frequency range of the in-band noise, and labeling the result as BL; training a neural network using the BL, utilizing the well trained neural network to predict the in-band vibration noise contained in the data of the segment A, and labeling the prediction result as PNA; and subtracting the PNA from the data of the segment A to suppress the in-band vibration noise contained in the data of the segment A.

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.

The present invention belongs to the technical field of fault diagnosis of aero-engines, and provides a method for fault diagnosis of an aero-engine rolling bearing based on random forest of power spectrum entropy. Aiming at the above-mentioned defects existing in the prior art, a method for fault diagnosis of an aero-engine rolling bearing based on random forest is provided, wherein test measured data for an aero-engine rolling bearing provided by a research institute are used for establishing a training dataset and a test dataset first; and based on an idea of fault feature extraction, time domain statistical analysis and frequency domain analysis are conducted on original collection data by adopting wavelet analysis; thereby realizing effective fault diagnosis from the perspective of engineering application.

Concepts for classifying data are presented. Data to be classified is processed in accordance with a data decomposition algorithm so as to generate a plurality of data components, wherein each data component is associated with a respective different value or range of data transience. A subset of the data to be classified based on the plurality of data components. The selected subset of the obtained data is provided to a data classification process for classifying the data.

A method for improving the signal to noise ratio of an EEG signal in which a wavelet packet decomposition having a plurality of levels is first applied to a time slice of the EEG signal. A default signal is set to the first wavelet packet and a default peak response is then calculated for the first wavelet node. An update signal is set to the default signal combined with another of the wavelet nodes and an update peak response signal is then calculated of the update signal. If the update peak response signal exceeds the default peak response, the default peak response is set equal to the update peak response and the default signal is set equal to the update signal. Otherwise, the value of the current node is set to zero which effectively eliminates the signal data of the current wavelet node. These steps are reiterated for all of the wavelet nodes and, thereafter, a composite waveform of the EEG signal is reconstructed from the non-zero wavelet nodes.

Aspects of the present disclosure describe systems, methods and structures employing time-space de-noising for distributed sensor.