The present disclosure relates to a method for learning and embodying a human facial expression by a robot, in which human emotional indicators are allowed to match with servo motor parameter values of a robot and the robot automatically and mechanically learns a human expression to imitate the human expression.

A processor-implemented method of generating feature data includes: receiving an input image; generating, based on a pixel value of the input image, at least one low-bit image having a number of bits per pixel lower than a number of bits per pixel of the input image; and generating, using at least one neural network, feature data corresponding to the input image from the at least one low-bit image.

A platform, device and process for capturing images of the surface of a gaming table and determining the quantity, identity, and arrangement of chips bet at a gaming table. Image data is captured corresponding to the one or more chips positioned in at least one betting area on a gaming surface of the respective gaming table and the data is processed to filter out the background, establish a two dimensional grid of points of interests and corresponding histograms for classifying the one or more chips through identifying a dominant classification of each row in the grid of points of interests.

A calibration unit 60 acquires detection signals each generated by one of a plurality of sensors in a sensor unit 40 and indicating detection results of a calibration target. A state detection unit 61 detects a state of the calibration target by using the detection signals. A time difference correction amount setting unit 65 calculates a time difference between the detection signals each generated by one of the sensors of the sensor unit 40 by using state detection results of the calibration target obtained by the state detection unit 61, and sets a time difference correction amount on the basis of a calculation result. Temporal misalignment between pieces of information acquired by the plurality of sensors of the sensor unit 40 can be corrected on the basis of the time difference correction amount set by the time difference correction amount setting unit 65.

Training accessories and methods for improving athletic techniques, such as, for example, tackling form for football. A display can be coupled to a piece of athletic equipment, such as a tackling dummy, and the display depicts a symbol at a time at which a player is or is about to interact with the equipment or perform a movement or action. An input is provided of the player's identification of the depicted symbol and compared to the symbol that was actually depicted. The player's correct identification of the depicted symbol can indicate that the player performed the movement or action, or the interaction with the equipment, with appropriate form. A player's failure to correctly identify the depicted symbol can indicate to a coach that the player may need further instruction on proper technique or form with which the movement or action, or the interaction with the equipment, should be performed.

Multi-level hierarchical routing matrices for pattern-recognition processors are provided. One such routing matrix may include one or more programmable and/or non-programmable connections in and between levels of the matrix. The connections may couple routing lines to feature cells, groups, rows, blocks, or any other arrangement of components of the pattern-recognition processor.

A method, system, and computer program storage product determine determining a trajectory information type of a receipt submitted by an employee. Trajectory information associated with the receipt submitted by the employee is retrieved based on the trajectory information type. Trajectory information corresponding to a device associated with the employee is also retrieved. The receipt is determined as a valid receipt in response to the trajectory information associated with the receipt submitted by the employee matching the trajectory information associated with the device associated with the employee.

Abnormal motions are detected in sensor data collected with respect to performance of repetitive human activities. An autoencoder network model is trained based on a set of standard activity. Repetitive activity is extracted from sensor data. A first score is generated indicative of distance of a repetition of the repetitive activity from the standard activity. The repetitive activity is used to retrain the autoencoder network model, using weights of the autoencoder network model as initial values, the weights being based on the training of the autoencoder network model using the set of standard activity. A second score is generated indicative of whether the repetition is an outlier as compared to other repetitions of the repetitive activity. A final score is generated based on a weighting of the first score and the second score.

A method for detecting an epileptic spike includes: obtaining, by a first module of a network model, a local feature of data to be detected, and obtaining, by a second module of the network model, a global feature of the data to be detected; and determining, by a third module of the network model, a detection result of whether there is the epileptic spike in the data to be detected according to the local feature and the global feature. The data to be detected contains a temporal domain and a spatial domain represented by multiple channels, the local feature is a single channel feature, and the global feature is a multichannel feature.

The present invention provides to a processing of data samples by an arrangement comprising a signal processing chip for executing predetermined operations and a processor for executing any kind of software code. In particular, it is not only possible that the general purpose processor, which executes the software code can process results from the signal processing chip, but also the signal processing chip can receive the results of the general purpose processor for applying further operations. In this way, the flexibility and efficiency of the processing can be further improved.