Systems and method are claimed for forming an artifact attenuated seismic image. The method includes obtaining an input seismic image, selecting a seismic partition from the input seismic image and determining a seismic dip for the seismic partition. The method further includes determining flattened seismic partition from the seismic partition based, at least in part, on the seismic dip, determining a filtered seismic partition from the flattened seismic partition, and determining an unflattened seismic segment based on the filtered seismic partition. The method still further includes determining the artifact attenuated seismic image based on the unflattened seismic segment. The system includes a seismic source, a plurality of seismic receivers for detecting and recording an observed seismic dataset generated by the radiated seismic wave; and a seismic processor configured form the artifact attenuated seismic image.

A method for performing bearing defect auto-detection provides an algorithm for processing condition monitoring data including vibration harmonics of at least one bearing coupled to a rotatable shaft, the bearing having an inner and an outer ring. The algorithm is used to confirm with high degree of confidence that a bearing defect is present or not.

Disclosed is a method for determining the relationship between a track-circuit current signal and a railway vehicle location, including: sending, by a track circuit, current signal across a railway track block; measuring the current signal for different railway vehicles running successively on the railway track block; aligning the measured current signals and calculating a reference curve as the average value of all the aligned curves by using a Dynamic Time Warping algorithm, this reference curve representing the relationship between the track-circuit current signal and the railway vehicle location on the railway track block.

A mass spectrometer support apparatus includes a deconvolution logic and a centroider logic. The deconvolution logic is configured to deconvolve a mass spectrum measured by a mass spectrometer using an approximate peak shape. The centroider logic is configured to integrate the deconvolved spectrum and populate a sparse vector of peak locations.

The present invention relates to a device and a method for detecting a peak of an intracranial pressure (ICP) waveform using a morphological feature of an arterial blood pressure waveform. A peak extracting method of an ICP waveform using a morphological feature of an arterial blood pressure waveform according to an aspect of the present invention includes: extracting a pulse onset from a continuous ICP waveform based on systolic peak from arterial blood pressure waveform; dividing individual ICP waveforms in the continuous ICP waveform based on the pulse onset; deriving a derivative value from each of the ICP waveforms to extract a peak, a trough, and a flat; calculating latencies from the pulse onset extracted in each of the ICP waveforms to the extracted peaks to cluster peaks with a similar time interval and generate a peak cluster; searching a notch from each of the ICP waveforms based on the latency of a dicrotic notch of the arterial blood pressure waveform; and extracting P1, P2, and P3 peaks from each of the ICP waveforms by referring to the searched notch of the ICP.

The computer implemented method makes it possible to discern, for a variety of sensations, whether a sensation is a pleasant (comfortable) sensation or a sensation of discomfort. A classifier is generated for discerning the stress or comfort/discomfort of a subject. The method comprising: a) imparting, to a subject, different stimuli under the same environment, and obtaining brain wave data or analysis data thereof for the environment; b) correlating a reaction of the subject relating to the stimulation and the difference of the brain wave data or analysis data thereof obtained under the environment; c) generating a classifier for discerning the stress or comfort/discomfort of the subject, on the basis of the correlation; and d) performing comfort/discomfort discernment using a basic step for amplifying a sample from a small stimulation.

A signal translating method may include, according to one aspect of the present application, receiving a source signal of a first domain; identifying erroneous features and effective features from the source signal; translating the source signal of the first domain into a first virtual signal of a second domain, the first virtual signal is that in which erroneous features included in the source signal has been removed; and outputting the first virtual signal. Therefore, the virtual signal of the second domain in which the erroneous features removed may be output.

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.

When evaluating the quality of photoplethysmography (PPG) signal (52) measured from a patient monitor (e.g., a finger sensor or the like), multiple features of the PPG signal are extracted and analyzed to facilitate assigning a score to the PPG signal or portions (e.g., heartbeats) thereof. Heartbeats in the PPG signal are segmented out using concurrently captured electrocardiograph (ECG) signal (50), and for each heartbeat, a plurality of extracted features are analyzed. If all extracted features satisfy one or more predetermined criteria for each feature, then the heartbeat waveform is compared to a predefined heartbeat template. If the waveform matches the template (e.g., within a predetermined match percentage or the like), then the heartbeat is classified as “clean.” If the heartbeat does not patch the template, or if one or more of the extracted features fails to satisfy its one or more pre-determined criteria, the heartbeat is classified as “noisy.”

A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.