A target speech signal extraction method for robust speech recognition includes: initializing a steering vector for a target speech source and an adaptive vector, setting a real output channel of the target speech source as an output by the adaptive vector, initializing adaptive vectors for a noise and setting a dummy channel as an output by the adaptive vectors for the noise; setting a cost function for minimizing dependency between a real output for the target speech source and a dummy output for the noise; setting an auxiliary function to the cost function, and updating the adaptive vector for the target speech source and the adaptive vectors for the noise by using the auxiliary function and the steering vector; estimating the target speech signal by using the adaptive vector thereby extracting the target speech signal from the input signals; and updating the steering vector for the target speech source.

An adaptive processing method for new scenes in autonomous driving, comprising: obtaining scene data corresponding to new scene of vehicle driving, wherein the scene data describes vehicles state and driving operations in the new scene; obtaining a test set of the new scene based on processing the scene data by a preset distribution; updating parameters of a pre-training model by inputting the test set, and obtaining a scene model adapted to the new scene based on gradient iteration of general model parameters of the pre-training model, wherein the scene model is configured to output an autonomous driving strategy for the vehicle in the new scene. Therefore, the autonomous driving vehicle transforms a new scene to a known scene, and no longer be troubled by unpredictable new scenes, and greatly enhance the reliability and stability of autonomous driving.

A method may comprise receiving, via a processor, a first indication that an object is in a first zone of interest of a first sensor in the plurality of sensors; receiving, via the processor, a second indication that the object is in a second zone of interest of a second sensor in the plurality of sensors; and determining, via the processor, whether the first sensor or the second sensor is falsely detecting the object within the respective zone of interest.

A method checks a plug connection, in which a first plug part is connected to a second plug part. The method determines a force-time curve of a force applied by an assembler during an assembly process of a plug connection. In addition, the method determines characteristic values of a plurality of characteristics of the force-time curve. The method also classifies the plug connection by use of a machine-learned classifier on the basis of the characteristic values of the plurality of characteristics.

A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow. The system includes a scorecard interface operable to select a target and an indication of the metric control to apply for the target, and to apply the metric control to generate and display a historical performance visualization and a performance feed of the metric for the target.

An adaptive processing method for new scenes in autonomous driving, comprising: obtaining scene data corresponding to new scene of vehicle driving, wherein the scene data describes vehicles state and driving operations in the new scene; obtaining a test set of the new scene based on processing the scene data by a preset distribution; updating parameters of a pre-training model by inputting the test set, and obtaining a scene model adapted to the new scene based on gradient iteration of general model parameters of the pre-training model, wherein the scene model is configured to output an autonomous driving strategy for the vehicle in the new scene. Therefore, the autonomous driving vehicle transforms a new scene to a known scene, and no longer be troubled by unpredictable new scenes, and greatly enhance the reliability and stability of autonomous driving.

A hybrid method of estimating position of a mobile device which utilizes both received signal power and timing measurements. Received signal power of signals received by the mobile device from a plurality of cells are measured and corresponding received signal power measurements are stored. The method further includes measuring, at the mobile device, times of arrival of signals received from the plurality of cells. A plurality of time difference of arrival (TDOA) measurements are determined from the times of arrival. A power-time hybrid Gaussian maximum likelihood estimator and positioning assistance data for the plurality of cells are used to generate a maximum likelihood estimate of the position of the mobile device by evaluating a joint conditional probability of the received signal power measurements and the plurality of TDOA measurements. Gaussian random variables may be used to represent the received signal power measurements and the TDOA measurements.

A biometric authentication system, including an image input unit configured to obtain an image by imaging a living body, a storage unit configured to store registration information relating to a plurality of biological features obtained from a biological region of an image of each person, and an authentication processing unit configured to process the biological region of the image obtained by the image input unit to execute biometric authentication based on the registration information, wherein the plurality of biological features obtained from the biological region of the each person are a plurality of biological features having a low pattern correlation with one another, and wherein the authentication processing unit is configured to combine the plurality of biological features having a low pattern correlation with one another, which are obtained by processing the image, to execute the biometric authentication.

A method for assessing the quality of a coach includes collecting information; determining a set of metrics based on the information; and determining a coach quality based on the set of metrics. Additionally or alternatively, the method can include any or all of: determining a set of one or more outcomes key drivers (OKDs) associated with success of a participant and/or the health program; determining a set of models associated with the set of one or more OKDs; for each of the set of coaches, determining a baseline quality associated with the coach; producing an output and/or triggering an action based on the coach quality; and/or any other suitable processes.

Aspects of an embodiment of the present invention disclose a method, computer program product, and computing system for selecting representative scores. A processor receives first scores generated by a plurality of object part classifiers classifying a first part of an object. A processor also receives second scores generated by the plurality of object part classifiers classifying a second part of the object. A processor also determines a first aggregation of the first scores. A processor also determines a second aggregation of the second scores. A processor also selects the representative scores from the first scores and the second scores based on a comparison between the first aggregation and the second aggregation.