A method for acoustic source separation comprises inputting acoustic data from a plurality of acoustic sensors, combined from a plurality of acoustic sources, converting the acoustic data to time-frequency domain data comprising time-frequency data frames, and constructing a multichannel filter for the time-frequency data frames to separate signals from the acoustic sources. The constructing comprises determining a set of de-mixing matrices (W.sub.f) to apply to each time-frequency data frame to determine a vector of separated outputs (y.sub.ft) by modifying each of the de-mixing matrices by a respective gradient value (G;G′) for a frequency dependent upon a gradient of a cost function measuring a separation of the sources by the respective de-mixing matrix. The respective gradient values for each frequency are each calculated from a stochastic selection of the time-frequency data frames.

A method for estimating position of a mobile device which includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server. The OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA. A position estimate for the mobile device is determined based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells. The initial OTDOA assistance data may be generated by the micro-BSA based upon an initial seed estimate.

The embodiments of the disclosure provide a method for selecting a task network, a system and a method for determining actions based on sensing data. The method of the embodiments of the disclosure includes: mapping the sensing data into an input feature vector; feeding the input feature vector into a specific task network to generate an output feature vector via the specific task network, in which the specific task network is trained based on a plurality of first individuals and a plurality of second individuals, the first individuals belong to a first population, the second individuals belong to a second population, and the first individuals and the second individuals are evolved via a coevolution process; and determining an output action according to the output feature vector, and setting a second specific individual based on the output action, in which the second specific individual belongs to the second population.

A data processing device that makes effective use of a data group containing missing data is provided. A series of learning data containing missing data is acquired, and a representative value of data and a validity ratio representing a proportion of valid data being present are calculated from the series of learning data according to a predefined unit of aggregation. Then, learning of an estimation model is performed so as to minimize an error which is based on a difference between an output resulting from inputting the representative value and the validity ratio to the estimation model, and the representative value. Also, a series of estimation data containing missing data is acquired, and a representative value of data and a validity ratio representing a proportion of valid data being present are calculated from the series of estimation data according to a predefined unit of aggregation. Then, the representative value and the validity ratio are input to the learned estimation model and a feature value is acquired or data estimation is performed for the series of estimation data.

A method is proposed for processing a measurement signal, in particular for a steering system. The method comprises the following steps: A measured variable is acquired based on the measurement signal, wherein the measured variable comprises items of information about a physical variable, and wherein the measured variable is a superposition of the actual value of the physical variable and the measurement noise. Filter parameters of a filter are ascertained based on the measured variable and a mathematical model of the measurement noise. The measurement signal is filtered by means of the filter, whereby an estimated value of the physical variable is obtained, wherein the filter has the ascertained filter parameters. The filter parameters are ascertained in such a way that a deviation between the estimated value of the physical variable and the actual value of the physical variable is approximated and minimized. Furthermore, a control unit for a steering system, a steering system, a computer program, and a computer-readable data carrier are disclosed.

This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

Segments of a network having connectivity issues are detected in a network environment that may include one or more cloud computing platforms. A mutual information algorithm is used to determine relevance of network element factors, a subset of factors are selected based on relevance, and clustered according to values for the subset of factors, and quality of the clusters evaluated. Various thresholds for selecting the subset of factors may be used to determine which provides improved cluster quality. An approach for performing root cause analysis of events in a network environment selects bad events for logging alerts based on whether a factor is found to distinguish bad events according to a mutual information algorithm. Events for alerts maybe aggregated based on temporal proximity or similarity. Visualization may be performed using Sankey diagrams with each column representing a factor.

Disclosed herein are systems and methods of artificial intelligence learning systems. In some embodiments the artificial intelligence system presents options to users based on their life stage and personality profile. Family or group structures may be created within an application. Options may be created and presented based on the family structure such as chores may be assigned to children, money may be transferred between family members, and scores may be assigned to different users.

A method for estimating position of a mobile device which includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server. The OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA. A position estimate for the mobile device is determined based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells. The initial OTDOA assistance data may be generated by the micro-BSA based upon an initial seed estimate.

An image analysis system and method classify cells in a tissue image. The system and method may extract at least one image feature characterizing an object in the tissue image. Based on the extracted image feature, cells may be classified according to at least one predefined membrane pattern. For each classified cell, a class label that identifies a class to which the classified cell belongs may be outputted.