System, processes and devices for monitoring betting activities using bet recognition devices and a server. Each bet recognition device has an imaging component for capturing image data for a gaming table surface. The bet recognition device receives calibration data for calibrating the bet recognition device. A server processor coupled to a data store processes the image data received from the bet recognition devices over the network to detect, for each betting area, a number of chips and a final bet value for the chips.

Introduced here are computer programs and associated computer-implemented techniques for determining reflectance of an image on a per-pixel basis. More specifically, a characterization module can initially acquire a first data set generated by a multi-channel light source and a second data set generated by a multi-channel image sensor. The first data set may specify the illuminance of each color channel of the multi-channel light source (which is configured to produce a flash), while the second data set may specify the response of each sensor channel of the multi-channel image sensor (which is configured to capture an image in conjunction with the flash). Thus, the characterization module may determine reflectance based on illuminance and sensor response. The characterization module may also be configured to determine illuminance based on reflectance and sensor response, or determine sensor response based on illuminance and reflectance.

An apparatus for visualizing physical movements includes: a device for acquiring video image files; a data analysis system including processor and memory; a computer program operating in the processor to identify an area in the images where periodic motions associated with physical movement of an object may be detected and quantified, and compute a new image sequence in which the motions are visually amplified; and, a user interface that displays the motion-amplified video image of the mechanical component. An associated method for using the apparatus is also disclosed.

An electronic device is provided. The electronic device includes at least one sensor and at least one processor configured to identify a gesture identification request from a first application being executed by the at least one processor, in response to the identification of the gesture identification request, identify a gesture using at least one sensing data from at least one first sensor module activated, among the at least one sensor, based on a gesture application, provide the identified gesture to the first application, and perform at least one operation corresponding to the identified gesture based on the first application. Other various embodiments are possible as well.

Systems and methods for non-intrusive program tracing of a device are disclosed. The methods involve generating a program trace signal from at least one of power consumption and electromagnetic emission of the device; digitizing and decomposing the program trace signal into at least two digital program trace component signals including a low frequency program trace component and one or more high frequency program trace component signals; classifying fragments of the at least two digital program trace component signals of the program trace signal as at least one of a known portion of program code and an observed behavior of the device. Each of the at least two digital program trace component signals have different frequency bands and the bandwidth of each program trace component signal is smaller than the bandwidth of the program trace signal. Each of the high frequency program trace component signals include an envelope.

Systems and methods of the present disclosure enable movement recognition and tracking by receiving movement measurements associated with movements of a user. The movement measurements are converted into feature values. An action recognition machine learning model having trained action recognition parameters generates, based on the feature values, an action label representing an action performed during an action-related interval. An activity recognition machine learning model having trained activity recognition parameters generates, based on the action label, an activity label representing an activity performed during an activity-related interval, where the activity includes the action. A task recognition machine learning model having trained task recognition parameters generates, based on the action label and the activity label, a task label representing a task performed during a task-related interval, where the task includes the activity and action. An activity log is updated based on the action label, the activity label, and the task label.

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 computer system includes processor hardware configured to execute instructions that include joining at least a portion of multiple call transcription data entries with at least a portion of multiple agent call log data entries according to timestamps associated with the entries to generate a set of joined call data entries, and validating the joined call data entry by determining whether a transcribed entity name matches with entity identifier information associated with the agent call log data entry. The instructions include preprocessing the joined call data entry according to word confidence score data entries associated with the call transcription data entry to generate preprocessed text, performing natural language processing vectorization on the preprocessed text to generate an input vector, and supplying the input vector to an unsupervised machine learning model to assign an output topic classification of the model to the joined call data entry associated with the input vector.

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 fault arc signal detection method using a convolutional neural network, comprising: enabling a sampling signal subjected to analog-digital conversion to respectively pass through three different band-pass filters; respectively extracting a time-domain feature and a frequency-domain feature from a half wave output of each filter; constructing a two-dimensional feature matrix by means of extracted time-frequency feature vectors from the output of each filter, and stacking the feature matrices corresponding the outputs of the three filters to construct a three-dimensional matrix for each half wave; and processing a multi-channel feature matrix by using a multi-channel two-dimensional convolutional neural network, and determining, according to the output result of the neural network, whether the half wave is an arc. The detection method based on the convolutional neural network has higher accuracy and reliability in recognizing a fault arc half wave, can implement targeted training for different load conditions, and is self-adaptive.