A computer implemented method and system for confirming a device documented arrhythmia in cardiac activity are provided. The method is under control of one or more processors configured with executable instructions. The method obtains a cardiac activity (CA) data set that includes CA signals for a series of cardiac events and includes device documented (DD) markers within the series of cardiac events. The device documented markers are indicative of atrial fibrillation (AF) detected by the ICM utilizing an on-board R-R interval irregularity (ORI) process to analyze the CA signals. The method applies a feature enhancement function to the CA signals to form modified CA signals with enhanced sinus features and analyzes the enhanced sinus features in the modified CA signals. The method utilized a confirmatory feature detection process to identify false AF detection by the ORI process. The method records a result of the analysis identifying false AF detection by the ORI process.

The present disclosure provides an exercise assistive device. The present disclosure also provides a method for estimating stamina level and a method for adjusting stamina category. The present disclosure further provides the several types and applications of the exercise assistive device.

Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

A method for controlling operations of an electronic device through ambient light detection and associated apparatus are provided, where the method includes: utilizing an ambient light sensor of the electronic device to detect ambient light for the electronic device, to generate an ambient light detection signal, sampling the ambient light detection signal to convert the ambient light detection signal into a converted signal, and performing pattern detection on the converted signal to detect at least one pattern of the converted signal; and according to a pattern and event database, determining whether the detected pattern of the converted signal matches a predetermined pattern within a plurality of predetermined patterns, to selectively trigger a predetermined operation associated with the predetermined pattern, wherein the pattern and event database stores the plurality of predetermined patterns.

Auto-completion of an input partial line pattern. Upon detecting that the user has input the partial line pattern, the scope of the input partial line pattern is matched against corresponding line patterns from a collection of line pattern representations to form a scoped match set of line pattern representations. For one or more of the line pattern representations in the scoped match set, a visualization of completion options is then provided. For example, the corresponding line pattern representation might be displayed in a distinct portion of the display as compared to the input partial line pattern, or perhaps in the same portion in which case, in which case the remaining portion of the line pattern representation might extend off of the input partial line pattern representation.

A method of processing an acoustic image includes the steps of acquiring acoustic signals generated by acoustic sources in a predetermined region of space, generating a multispectral 3D acoustic image that includes a collection of 2D acoustic images, performing a frequency integration of the multispectral acoustic image for generating a 2D acoustic map locating at least one target acoustic source of interest and modeling the signal spectrum associated with the target acoustic source, generating a classification map obtained by comparing the signal spectrum of each signal associated with each pixel of the multispectral acoustic image and the model of the signal spectrum associated with the target acoustic source to distinguish the spectrum of the signal associated with the target acoustic source from the signal spectra associated with the remaining acoustic sources, and merging the classification map and the acoustic map to obtain a merged map.

A quality evaluation method includes an acquisition step of acquiring spectral data related to transmitted light or diffusely reflected light from a cell mass by irradiating the cell mass with measurement light including near-infrared light, and an evaluation step of evaluating quality of the cell mass, based on the spectral data of the cell mass acquired in the acquisition step.

An anomaly detecting device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; calculating a series probability distribution being a probability distribution which the series feature amount follows; storing a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to the reference probability distribution; and detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data.

The present invention relates to the technical field of image processing, in particular to a method for determining a cardiac cycle and ultrasonic equipment. The method comprises: acquiring a cardiac ultrasound video; classifying the cardiac ultrasound video by using a section type recognition model to determine a section type of the cardiac ultrasound video; and processing the cardiac ultrasound video by using a systole and diastole recognition model corresponding to the section type to obtain the cardiac cycle corresponding to the cardiac ultrasound video. The model is used to process the cardiac ultrasound video to detect the corresponding cardiac cycle. Model detection can avoid the use of an electrocardiograph and simplify the detection of the cardiac cycle. Furthermore, real-time detection of the cardiac cycle can be realized during echocardiography.

There are provided methods and systems that enable the use of the backscattering pattern produced by an optical fiber in an OTDR trace as a signature (also referred to herein as the “RBS fingerprint”) to recognize an optical fiber. It was found that it may be difficult to obtain repeatable signatures as those are sensitive to the wavelength of the OTDR laser source and the temperature of the fiber. OTDR methods and systems that are adapted to compare the backscattering pattern in a more repeatable manner are therefore provided. Once the repeatability issue is overcome, such signature can be used for identification purposes and enable new applications.