Intel Corporation

Hrl Laboratories, LLC

Denver H Dash

Vallabhajosyula S Somayazulu

Debabani Choudhury

Xingang Guo

Maynard C Falconer

Reza Arefi

Krzysztof Wojtek Przytula

Daniel J Dahle

John Mark Agosta

Simon Crosby

Systems and methods to generate maps or models of structures are disclosed. Features of the structure to be mapped may be determined for the purposes of generating the map or model based at least in part on images associated with the structure, sensor measurements associated with the structure, and phase data of communications signals that interact with the structure. The mapping or modeling processes may be performed at a mapping server that receives images, sensor data, and/or communications signal phase information from one or more user devices, such as mobile devices. The mapping servers may perform a simultaneous localization and mapping (SLAM) process and may enhance the generated maps using sensor and/or communications phase data to map one or more hidden features of the structure.

Systems and methods for mapping

A method for determining a probability of a hidden variable from an observed variable in a Dynamic Bayesian Network is presented. The method includes identifying the network based on predetermined criteria, determining a number of hidden variables in a time slice of the network, determining a number of the time slices of the network, and determining the probability of the hidden variable from the observed variable in less than exponential time with respect to the number of hidden variables.

Detection of epidemic outbreaks with Persistent Causal-Chain Dynamic Bayesian Networks

A method, apparatus, and computer program product are presented for automatically evaluating Bayesian network models. Operations performed comprise receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes that are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states. The states of conclusion nodes are set to desired conclusion states and corresponding probabilities of occurrence of evidence states are determined by propagating these states down the causal dependency links. Thus, samples of most likely states of the evidence nodes are generated. Then, states of the evidence nodes are set corresponding to the samples of the evidence states. These states are propagated back up the causal dependency links to obtain probabilities of the resulting states of the conclusion nodes. Finally, a representation is outputted for the probabilities of the states of the conclusion nodes.

Evaluation of Bayesian network models for decision support

Methods, apparatus, and systems are provided for distributed hypothesis testing in autonomic processing machines. Evidence about a network resource is gathered or contributed to and associated with a hypothesis about the network resource. The evidence is processed to determine whether a decision can be made with respect to activities or problems associated with the network resource. The hypothesis, the evidence, and the contributed evidence are propagated over a network to other network nodes or participants.

Methods, apparatus, and systems for distributed hypothesis testing in autonomic processing machines

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Pittsburgh, PA, United States of America

Denver H Dash