An apparatus and method for detecting a bio-signal feature are provided. The apparatus according to one aspect may include: a bio-signal acquirer configured to acquire a bio-signal; and a processor configured to generate an envelope signal of the bio-signal, and detect at least one feature of the bio-signal based on a difference between the envelope signal and the bio-signal.

A method for predicting variables of interest related to a system includes collecting one or more sensor streams over a time period from sensors in the system and generating one or more anomaly streams for the time period based on the sensor streams. Values for variables of interest for the time period are determined based on the sensor streams and the anomaly streams. Next, a time-series predictive algorithm is applied to the (i) the sensor streams, (ii) the anomaly streams, and (iii) the values for the variables of interest to generate a model for predicting new values for the variables of interest. The model may then be used to predict values for the variables of interest at a time within a new time period based on one or more new sensor streams.

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.

A method for suppressing sensor noise in a spatially oversampled sensor array includes receiving spatially oversampled multi-channel sensor data from a region of interest and building a spatial model from the data for essential spatial degrees of freedom. The method further includes decomposing the data into the underlying spatial model to obtain associated amplitude components containing a mixture of original temporal waveforms of the data and, for each channel of the multi-channel sensor, estimating time-domain amplitude components using cross-validation. Next, for each channel, based on the estimated time-domain amplitude components, sensor noise and/or artifacts for that channel are identified. Finally, for each channel, the identified sensor noise and/or artifacts can be suppressed from the data.

A numerical processing method for a nucleic acid mass spectrum, including: step S1, recalibrating a single mass spectrum, for each detection point of a sample, obtaining a plurality of mass spectra corresponding to different positions of the detection point, each mass spectrum being recalibrated by using anchor peaks with an expected mass-to-charge ratio; step S2, synthesizing the mass spectra, where the mass spectra corresponding to the different positions of the detection point are synthesized into a unitary mass spectrum of the detection point; step S3: performing wavelet filtering on the unitary mass spectrum to eliminate high-frequency noise and a baseline through a wavelet-based digital filter; and step S4: extracting a peak feature value, performing peak fitting to obtain a fitted curve of the unitary mass spectrum, and obtaining a peak height, a peak width, a peak area, a mass offset, and a signal-noise ratio based on the fitted curve.

The present invention provides a method and a system for quickly eliminating signal spikes of structural health monitoring in civil engineering, including the following steps: (1) quickly identifying, by using a threshold method, a spike position in a time domain; (2) extracting spike features in a time-frequency domain through wavelet transform for a signal within a set range near the spike position; and (3) eliminating spike feature components in wavelet coefficients, and effectively eliminating a spike through inverse wavelet transform. The method and system combine the advantages of a high calculation speed of a time domain method and high resolution of a time-frequency domain method, which can make an algorithm fast and accurate. In addition to eliminating a spike, the method and system also retain wanted signal components, and have good applicability to structural health monitoring signals in civil engineering with complex time-frequency characteristics and a large amount of data.

The disclosed embodiments relate to a system that optimizes a prognostic-surveillance system to achieve a user-selectable functional objective. During operation, the system allows a user to select a functional objective to be optimized from a set of functional objectives for the prognostic-surveillance system. Next, the system optimizes the selected functional objective by performing Monte Carlo simulations, which vary operational parameters for the prognostic-surveillance system while the prognostic-surveillance system operates on synthesized signals, to determine optimal values for the operational parameters that optimize the selected functional objective.

An apparatus and method for detecting a bio-signal feature are provided. The apparatus according to one aspect may include: a bio-signal acquirer configured to acquire a bio-signal; and a processor configured to generate an envelope signal of the bio-signal, and detect at least one feature of the bio-signal based on a difference between the envelope signal and the bio-signal.

A computer-implemented method for context aware anomaly detection includes receiving a signal including real-time data, dividing the received signal into a set of training wavelets bounded by a respective training context, generating a prediction model for the training context by processing the set of training wavelets through a recurrent neural network to identify patterns in a sequence of data of a respective training wavelet. The method includes, in response to execution of a current context that is different from the training context, extracting current wavelets from a signal of the current context, identifying a pairwise match between same dimensions of the current wavelets and a wavelet pattern of the prediction model by aligning sequences of the current wavelet and sequences of the wavelet pattern of the prediction model. Upon determining that a total cumulative distance between the two sequences exceeds a predetermined threshold, an anomaly is identified.

A contactless biometrics monitoring system includes: a first device to transmit a modulated signal into an environment, the modulated signal being modulated to amplify one or more biometric patterns of a user located within the environment; and a second device to receive a reflection of the modulated signal off the user located within the environment. The reflection includes a vibration component, and the vibration component indicates biometric information of the user corresponding to the one or more biometric patterns amplified by the modulated signal.