Abstract: A method for defect classification includes storing, in a computer system, a definition of a region in a feature space. The definition is associated with a class of defects and comprises a kernel function comprising a parameter. The parameter determines a shape of the region. A confidence threshold for automatic classification of at least one defect associated with the class is received. A value of the parameter associated with the confidence threshold is selected. Inspection data for a plurality of defects detected in one or more samples under inspection is received. The plurality of defects for the class are automatically classified using the kernel function and the selected value of the parameter.

Abstract: Embodiments herein relate to generating a personalized model using a machine learning process, identifying a learning engagement state of a learner based at least in part on the personalized model, and tailoring computerized provision of an educational program to the learner based on the learning engagement state. An apparatus to provide a computer-aided educational program may include one or more processors operating modules that may receive indications of interactions of a learner and indications of physical responses of the learner, generate a personalized model using a machine learning process based at least in part on the interactions of the learner and the indications of physical responses of the learner during a calibration time period, and identify a current learning state of the learner based at least in part on the personalized model during a usage time period. Other embodiments may be described and/or claimed.

Abstract: A method for classification includes receiving inspection data associated with a plurality of defects found in one or more samples and receiving one or more benchmark classification comprising a class for each of the plurality of defects. A readiness criterion for one or more of the classes is evaluated based on the one or more benchmark classification results, wherein the readiness criterion comprises for each class, a suitability of the inspection data for training an automatic defect classifier for the class. A portion of the inspection data is selected corresponding to one or more defects associated with one or more classes that satisfy the readiness criterion. One or more automatic classifiers are trained for the one or more classes that satisfy the readiness criterion using the selected portion of the inspection data.

Abstract: Embodiments herein relate to identifying a learning engagement state of a learner. A computing platform with one or more processors running modules may receive indications of interactions of a learner with an educational program as well as indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program. A current learning engagement state of the learner may be identified based at least in part on the received indications by using an artificial neural network associated that is calibrated to the learner. The artificial neural network may be trained and updated in part by human observation and learner self-reporting of the learner's current learning engagement state.

Abstract: A method for defect classification includes storing, in a computer system, a definition of a region in a feature space. The definition is associated with a class of defects and comprises a kernel function comprising a parameter. The parameter determines a shape of the region. A confidence threshold for automatic classification of at least one defect associated with the class is received. A value of the parameter associated with the confidence threshold is selected. Inspection data for a plurality of defects detected in one or more samples under inspection is received. The plurality of defects for the class are automatically classified using the kernel function and the selected value of the parameter.

Abstract: A method for classification includes receiving inspection data associated with a plurality of defects found in one or more samples and receiving one or more benchmark classification comprising a class for each of the plurality of defects. a readiness criterion for one or more of the classes is evaluated based on the one or more benchmark classification results, wherein the readiness criterion comprises for each class, a suitability of the inspection data for training an automatic defect classifier for the class. A portion of the inspection data is selected corresponding to one or more defects associated with one or more classes that satisfy the readiness criterion. One or more automatic classifiers are trained for the one or more classes that satisfy the readiness criterion using the selected portion of the inspection data.

Abstract: A method for defect analysis includes identifying single-class classifiers for a plurality of defect classes, the plurality of defect classes characterized by respective ranges of inspection parameter values. Each single-class classifier is configured for a respective class to identify defects belonging to the respective class based on the inspection parameter values, while identifying the defects not in the respective class as unknown defects. A multi-class classifier is identified that is configured to assign each defect to one of the plurality of the defect classes based on the inspection parameter values. Inspection data is received, and both the single-class and multi-class classifiers are applied to the inspection data to assign the defect to one of the defect classes.

Abstract: A method for defect analysis includes identifying single-class classifiers for a plurality of defect classes, the plurality of defect classes characterized by respective ranges of inspection parameter values. Each single-class classifier is configured for a respective class to identify defects belonging to the respective class based on the inspection parameter values, while identifying the defects not in the respective class as unknown defects. A multi-class classifier is identified that is configured to assign each defect to one of the plurality of the defect classes based on the inspection parameter values. Inspection data is received, and both the single-class and multi-class classifiers are applied to the inspection data to assign the defect to one of the defect classes.

Abstract: A method of automatic recognition of different materials in image segments including correlating sample image segment features to classes of materials, and identifying viewed image segments as material segments in accordance with the correlating step.

Abstract: A system for automatic object classification including means for applying a plurality of binary rules to an object, where any of the binary rules is operative to classify the object to one of a pair of classes, and means for determining to which of the classes the object is classified the greatest number of times subsequent to the application of the binary rules.

Abstract: A method for object class definition for a plurality of objects, the method including evaluating each of a plurality of features for each of the objects, thereby resulting in a feature value for each object-feature combination, performing cluster analysis on the objects to identify clusters of the objects having common features, calculating an average feature value for each feature in each of the clusters, and expressing a predefined statement associated with any of the cluster features in any of a positive, negative, and intermediate form corresponding to the cluster feature's average feature value.

Abstract: A stepper motor is accelerated and decelerated with a parabolic velocity profile to efficiently utilize the available torque of the motor. The times between pulses to obtain a parabolic velocity profile are determined by a microprocessor-based stepper motor controller from desired values of start/stop velocity, maximum velocity and time to reach maximum velocity. The required times are stored in a random access memory and are used to supply to the motor a pulse train which follows the parabolic velocity profile during acceleration and deceleration. The controller is easily programmable to accommodate different motor characteristics, different applications and different operating parameters.