Alcatel Lucent

Schlumberger Technology Corporation

At&t Intellectual Property I, L.p.

New York University

Piotr Mirowski

Tin Kam Ho

Srinivas Bangalore

Philip Alfred Whiting

T V Lakshman

Sumit Chopra

Hyunseok Chang

Suhrid Balakrishnan

Byron H Chen

William Michael MacDonald

Ruben I Kuzniecky

Claude Signer

David McCormick

Daniel M Tetzlaff

Doohee Yun

Harald Steck

Ravishankar Palaniappan

Nneka Williams

Yann Lecun

Amy Ortega

Deepak Madhavan

A system and method for position tagging within an environment includes at least one mobile electronic device adapted to be moved within the environment and a pedestrian dead reckoning module. The at least one mobile electronic device includes an inertial measurement unit and at least one of a near field communication chip, barcode scanner, global positioning system, Bluetooth receiver or WiFi receiver. The pedestrian dead reckoning module is adapted to query and record time stamped readings from the inertial measurement unit and the at least one of the near field communication chip, barcode scanner, global positioning system, Bluetooth receiver or WiFi receiver. The system and method may generate one or more spatial maps of the environment through the collection of radio frequency signal data along with the time stamped readings.

Method and apparatus for indoor position tagging

An exemplary methodology, procedure, system, method and computer-accessible medium can be provided for receiving physiological data for the subject, extracting one or more patterns of features from the physiological data, and classifying the at least one state of the subject using a spatial structure and a temporal structure of the one or more patterns of features, wherein at least one of the at least one state is an ictal state.

Method, system, and computer-accessible medium for classification of at least one ICTAL state

A system and method for providing localization of a mobile electronic device within an environment includes at least one server in communication with the mobile electronic device. The at least one server is adapted to receive at least one picture and at least one accelerometer measurement from the mobile electronic device. The at least one server includes a localization module that provides localization to the mobile electronic device based on the at least one picture, the at least one accelerometer measurement and on at least one 3-dimensional map of the environment.

Localization systems and methods

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for predicting probabilities of words for a language model. An exemplary system configured to practice the method receives a sequence of words and external data associated with the sequence of words and maps the sequence of words to an X-dimensional vector, corresponding to a vocabulary size. Then the system processes each X-dimensional vector, based on the external data, to generate respective Y-dimensional vectors, wherein each Y-dimensional vector represents a dense continuous space, and outputs at least one next word predicted to follow the sequence of words based on the respective Y-dimensional vectors. The X-dimensional vector, which is a binary sparse representation, can be higher dimensional than the Y-dimensional vector, which is a dense continuous space. The external data can include part-of-speech tags, topic information, word similarity, word relationships, a particular topic, and succeeding parts of speech in a given history.

System and method for feature-rich continuous space language models

Embodiments are directed to mobile localization, and more specifically, but not exclusively, to tracking mobile devices. Embodiments include methods that consider probability kernels with distance-like metrics between distributions. Also described are probabilistic kernels that can be used for a regression of location, which can achieve up to about inn accuracy in an office environment.

KL-divergence kernel regression for non-gaussian fingerprint based localization

A computer-implemented method is provided that automatically characterizes and verifies stationarity of a training image for use in multipoint geostatistical analysis. The stationarity is preferably characterized by statistical measures of orientation stationarity, scale stationarity, and category distribution stationarity.

Computer-based generation and validation of training images for multipoint geostatistical analysis

A system for inferring geological classes from oilfield well input data is described using a neural network for inferring class probabilities and class sequencing knowledge and optimising the class probabilities according to the sequencing knowledge.

System and method for inferring geological classes

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New York, NY, United States of America

Piotr Mirowski