Hrl Laboratories, LLC

Raytheon Company

Gm Global Technolgoy Operations, Inc.

The Aerospace Corporation

Hlr Laboratories

Shubha Kadambe

Narayan Srinivasa

Peter Petre

Qin Jiang

Kalin Spariosu

Willie W Ng

Robert R Hayes

George C Valley

Pulak Bandyopadhyay

Leandro G Barajas

Todd S Kaplan

Youngkwan Cho

Krzysztof Wojtek Przytula

Payam Saisan

Jason Anthony Slepicka

Alexander Niechayev

Juan Carlos Sotelo

Robert Belvin

Markus Iseli

Benjamin T Wright

Ron Burns

Kim A Phan

Ted Y Lumanlan

Jason Slepicka

An optical system for capturing an image using compressive sensing includes: a digital micromirror device (DMD) array; an optical lens system; a first optical detector array; a first optical channel for projecting spatial information onto the first detector array; a second optical detector array; a second optical channel; a spectral filter and a polarization filter for projecting spectral and polarization information onto the second detector array; and an image processor to control the DMD array to generate a first and a second set of samples of the image using a sampling rate lower than required by the Shannon-Nyquist sampling theorem, and to reconstruct the image from the samples collected and digitized by the first and second optical detector arrays.

Multi-channel compressive sensing-based object recognition

A system creates a dynamic stochastic network using data relating to events. The dynamic stochastic network includes super nodes, local nodes, and agents. Connections among the super nodes and local nodes include events that are related to the super nodes and the local nodes. Strengths of the connections between the super nodes and local nodes are a function of a number of events that are common to the super nodes and local nodes. The connections are made and broken as the agents interact over time. The strengths of the connections increase and decrease as a function of a change in the number of events that the super nodes and local nodes have in common. An instability metric is calculated for the dynamic stochastic network, and an emerging group threat behavior is detected based on a deviation from the instability metric.

Proactive emerging threat detection

Methods and apparatus to receive radar pulses, process the received pulses using weighted finite state machine to learn a model of an unknown emitter generating the received radar pulses, and estimate a state/function of the unknown emitter based on the received radar pulses using the learned model, and predict the next state/function of the unknown emitter based on the received radar pulses and applying maximum likelihood estimation.

Processing unknown radar emitters

In one aspect, a method to identify a candidate object includes receiving an image of the candidate object and projecting the received image onto an image subspace. The image subspace is formed from images of known objects of a class. The method also includes determining whether the candidate object is in the object class based on the received image and the image subspace using a likelihood ratio. The likelihood ratio includes a first probability density indicating a probability an object is in the object class and a second probability density indicating a probability an object is not in the class. The first probability density and the second probability are each a function of a distance of the received image to the image subspace.

Identifying whether a candidate object is from an object class

An active sonar system and associated method combine signals from a plurality of receive beams in order to reduce a noise in a reference one of the signals from the plurality of receive beams.

Active sonar system and active sonar method using noise reduction techniques and advanced signal processing techniques

Disclosed herein are a system and method for trend prediction of signals in a time series using a Markov model. The method includes receiving a plurality of data series and input parameters, where the input parameters include a time step parameter, preprocessing the plurality of data series according to the input parameters, to form binned and classified data series, and processing the binned and classified data series. The processing includes initializing a Markov model for trend prediction, and training the Markov model for trend prediction of the binned and classified data series to form a trained Markov model. The method further includes deploying the trained Markov model for trend prediction, including outputting trend predictions. The method develops an architecture for the Markov model from the data series and the input parameters, and disposes the Markov model, having the architecture, for trend prediction.

System and method for signal prediction

Transversal equalization is used to obtain broadband linearization of photonic modulation. A photonic link comprises a signal path and a feed-forward path. The feed-forward path includes an optical linearizer and a transversal equalizer connected with the optical linearizer. In this way, amplitude and phase matching of the error in the signal path is obtained over a wide bandwidth. This, in turn, enables a broadband enhancement of the link's spur free dynamic range (SFDR).

Broadband linearization of photonic modulation using transversal equalization

The present invention comprises a method, an apparatus, and a computer program product for simulating a mixed-signal system. The invention comprises a first operation of generating a matrix-based wavelet operator representation of equations characterizing a system, with the matrix-based wavelet operator representation including wavelet connection coefficients. A second operation is performed by selecting a number of wavelets and a set of wavelet basis functions with which to represent a performance of the system, whereby the wavelet operator, the number of wavelets and the set of wavelet basis functions represent a wavelet model of the system. A third operation is performed by iteratively applying the wavelet model over a series of clock cycles to develop a behavioral model of the system. The invention has particular use in the area of computer-aided design and may be applied to any suitable system, whether electrical, mechanical, or other.

Method, apparatus, and computer program product for simulation of mixed-signal systems

Described is a system for diagnosis and prognosis of a component. The system is configured to receive a signal from a component. The signal is representative of a current health observation of the component. The system also computes a present likelihood of the component failure based on the signal. Additionally, the system computes a future likelihood of failure of the component for a given future mission. Through diagnosis, a user can determine the present health of the component, and based on the present health and future mission, determine whether or not the component will fail in the future mission.

Integrated framework for diagnosis and prognosis of components

A method, apparatus, and computer program product for determining and assessing information for collection from information sources for a desired level of decision accuracy are presented. Operations include: receiving a partial set of information; performing a minimax entropy-based test to determine a source with useful information; performing a mutual information or a conditional entropy-based test check minimax test validity. With an invalid result, the information source is excluded from further consideration and the minimax test is repeated; with a valid result, a cost/benefit analysis is determines whether to gather the information. If the cost/benefit analysis succeeds, the information is gathered. Otherwise, the information source is excluded and the minimax test is performed again. A consistency check ensures validity of the information prior to restarting the process. Thus, the set of information is iteratively augmented until there is no information to add or until adding information would be cost-prohibitive.

Method and apparatus for determining and assessing information to be collected based on information-theoretic measures

A fast on-line automatic speaker/environment adaptation suitable for speech/speaker recognition system, method and computer program product are presented. The system comprises a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving acoustic signals, and an output coupled with the processor for outputting recognized words or sounds. The system includes a model-adaptation system and a recognition system, configured to accurately and efficiently recognize on-line distorted sounds or words spoken with different accents, in the presence of randomly changing environmental conditions. The model-adaptation system quickly adapts standard acoustic training models, available on audio recognition systems, by incorporating distortion parameters representative of the changing environmental conditions or the speaker's accent. By adapting models already available to the new environment, the system does not need separate adaptation training data.

Method and apparatus for fast on-line automatic speaker/environment adaptation for speech/speaker recognition in the presence of changing environments

The present invention relates to techniques for contextual filtering for improving an output of a speech recognizer. The techniques comprise receiving a representation of a speech utterance into a shallow parser component in the form of a first word lattice including a set of potential language matches for the utterance, where the language matches include at least one word; the parser component operative for receiving and analyzing the first word lattice to produce a second word lattice, and including a filter that assigns a probability match for a potential word match in relation to another potential word match in the utterance based on a particular filter-specific criteria; and outputting the probability matches from the shallow parser component as a portion of a second word lattice for further processing to determine most likely combinations of words present in the speech utterance.

Contextual filtering

A data processing system blind source separation of an overcomplete set of signals generally includes means for storing input from sensors in a mixed signal matrix X, noise in a noise matrix V, an estimate of the individual signals from the mixture of signals from the signal sources in a source signal estimate matrix &#x15c;, and an estimate of environmental effects in a mixing matrix &#xc2;, the matrices related by X=&#xc2;&#x15c;+V; generating an initial estimate of &#xc2;; determining the number of, and associated lines of correlation of, each source from &#xc2;, and representing the sources in the source signal estimate matrix &#x15c;; jointly optimizing &#x15c; and &#xc2; in an iterative manner to generate an optimized source signal estimate matrix &#x15c;and a final estimated mixing matrix &#xc2;; and restoring the separated source signals from the optimized source signal estimate matrix &#x15c;

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Thousand Oaks, CA, United States of America

Shubha Kadambe