Methods, apparatus and systems for radio-based voice activity detection are described. In one example, a described system comprises: a transmitter configured to transmit a radio signal through a wireless channel of a venue; a receiver configured to receive the radio signal through the wireless channel, wherein the wireless channel is impacted by a voice activity of a target voice source in the venue; and a processor. The processor is configured for: computing a time series of channel information (CI) of the wireless channel based on the radio signal, and detecting the voice activity of the target voice source based on the time series of CI (TSCI) of the wireless channel, without using any media signal.

A computer-implemented method for determining the state of a system, which includes steps of: collecting data relating to a system, the data being noisy data comprising data of interest and noise; generating a signal to be analyzed from the collected data, the signal being a noisy signal comprising a signal of interest and noise; analyzing the regularity of the signal of interest by compensating the influence of the noise in the computation of the power of the difference between the integrated noisy signal and its trend; and determining the state of the system depending on the result of the analysis of the regularity of the signal of interest.

A system, method and apparatus for detecting facial expressions according to EMG signals.

Methods, apparatus and systems for wireless vital sign monitoring are described. In one example, a described system comprises: a transmitter configured to transmit a wireless signal through a wireless channel of a venue; a receiver configured to receive the wireless signal through the wireless channel that is being impacted by an object motion of an object in the venue; and a processor. At least one of the transmitter or the receiver comprises an array of antennas used to transmit or receive the wireless signal. The object motion comprises at least one non-periodic body motion of the object and at least one periodic vital-sign motion of the object. The processor is configured for: segmenting space around the venue into a plurality of sectors based on a beamforming and the received wireless signal, wherein each sector of the plurality of sectors is associated with a spatial direction relative to the array of antennas, obtaining a plurality of time series of channel information (CI) of the wireless channel based on the beamforming, wherein each time series of CI (TSCI) of the plurality of TSCI is associated with a respective sector of the plurality of sectors, isolating the object motion of the object in the plurality of TSCI to generate a plurality of isolated TSCI, compensating for the at least one non-periodic body motion of the object in the plurality of isolated TSCI to generate a plurality of compensated TSCI, and monitoring the at least one periodic vital-sign motion of the object based on the plurality of compensated TSCI.

Systems and methods for processing measurement data in an electric power system include acquiring the measurement data by a phasor measurement unit (PMU) coupled to a line of the electric power system, and inputting a plurality of the measurement data within a predetermined time window into a K-nearest neighbor (KNN) for identifying bad data among the plurality of the measurement data, wherein when one of the plurality of measurement data contains a bad datum, the machine learning module sends the bad datum to a denoising autoencoder module for correcting the bad datum, wherein the denoising autoencoder module outputs a corrected part corresponding to the bad datum, and when one of the plurality of measurement data contains no bad datum, the machine learning module bypasses the denoising autoencoder module and outputs the one of the plurality of measurement data as an untouched part.

Disclosed is a method for causal inference based on collective movement of active group, comprising obtaining leader time series and follower time series of the collective movement of the active group; obtaining fixed time lag, obtaining optimal fixed time lag based on the fixed time lag, obtaining aligned time lag series based on the leader time series and follower time series; relaxing the fixed time lag, updating the optimal fixed time lag based on the relaxed results; obtaining optimal aligned time lag series based on the aligned time lag series and the updated optimal fixed time lag; distorting the leader time series and the follower time series based on the optimal aligned time lag series; performing Grange Causality inference on the distorted leader time series and follower time series to obtain results of Granger Causality inference of the collective movement of the active group.

A method for diagnosing and predicting operation conditions of large-scale equipment based on feature fusion and conversion, including: collecting a vibration signal of each operating condition of the equipment, and establishing an original vibration acceleration data set of the vibration signal; performing noise reduction on the original vibration acceleration data set, and calculating a time domain parameter; performing EMD on a de-noised vibration acceleration and calculating a frequency domain parameter; constructing a training sample data set through the time domain parameter and the frequency domain parameter; establishing a GBDT model, and inputting the training sample data set into the GBDT model; extracting a leaf node number set from a trained GBDT model; performing one-hot encoding on the leaf node number set to obtain a sparse matrix; and inputting the sparse matrix into a factorization machine to obtain a prediction result.

A method for calibrating the position and orientation of a camera, in particular a vehicle-mounted camera, relative to a calibration pattern includes the steps of: A] acquiring an image of the calibration pattern by the camera; B] determining a parameter of the image or of the calibration pattern; C] transforming the image based on the parameter; D] identifying characteristic points or possible characteristic points of the calibration pattern within the transformed image; E] deriving the position or orientation of the camera relative to the calibration pattern from the identified characteristic points or possible characteristic points; F] in dependence of a confidence value of the derived position or orientation of the camera or in dependence of the number of iterations of steps B to F so far, repeating steps B to F; and G] outputting the position or orientation of the camera derived in the last iteration of step E.

Disclosed are an electronic apparatus for recognizing a multimedia signal and an operating method of the electronic apparatus, including segmenting a detection signal into a plurality of frames; segmenting each of the frames into a plurality of blocks; and representing each of the blocks as a hash word based on a time feature and a frequency feature for each of the blocks.

The present disclosure provides a method for recognizing a human motion, a method for recognizing a user action and a smart terminal. The method for human motion recognition comprises: collecting human motion data to train to obtain a feature extraction parameter and a template data sequence; in one human motion recognition, collecting data for performing human motion recognition to obtain an original data sequence; using the feature extraction parameter to perform feature extraction on the original data sequence, reducing the number of data dimensions of the original data sequence, and obtaining a test data sequence after the dimension reduction; matching the test data sequence with the template data sequence, and confirming that a human motion corresponding to the template data sequence associated with the test data sequence occurs when a successfully-matched test data sequence exists. By performing dimension reduction on the test data sequence, the present disclosure lowers requirements for human motion postures and cancels noise, then matches the data after the dimension reduction with the template, realizes accurate recognition of human motions while reducing the computing complexity, and improves the user experience.