Bae Systems Information and Electronic Systems Integration Inc.

Icosystem Corporation

Denis Garagic

Eric Bonabeau

Pablo Funes

Paolo Gaudiano

Daphna Buchsbaum

Elena Popovici

M Ihsan Ecemis

Chris Bingham

John A Tranquilli, Jr

Scott A Kuzdeba

David J Couto

Amit Bhatia

Bradley J Rhodes

The discriminability of an RF fingerprint is increased by 'abstracting,' 'enhancing,' and &#x201c;reconstructing&#x201d; a digital signal before it is transmitted, where the abstraction is a reversible nonlinear compression, the enhancement is a modification of the abstracted data, and the reconstruction is a mapping-back of the abstraction. During a training phase, for each individual RF transmitter, RF fingerprints are analyzed and candidate enhancements are modified until a successful enhancement is identified that provides satisfactory discriminability improvement with minimal signal degradation. The successful enhancement is implemented in the RF transmitter, and the RF fingerprint is communicated to receivers for subsequent detection and verification. Reinforcement learning can direct modifications to the candidate enhancements. The abstraction can implement a deep generative model such as an auto-encoder. A covert data enhancement can encode covert data onto the RF fingerprint, whereby the covert data is transmitted covertly to a receiver.

RF fingerprint enhancement by manipulation of an abstracted digital signal

A generic online, probabilistic, approximate computational inference model for learning-based data processing is presented. The model includes detection, feature production and classification steps. It employs Bayesian Probabilistic Models (BPMs) to characterize complex real-world behaviors under uncertainty. The BPM learning is incremental. Online learning enables BPM adaptation to new data. The available data drives BPM complexity (e.g., number of states) accommodating spatial and temporal ambiguities, occlusions, environmental clutter, and large inter-domain data variability. Generic Sequential Bayesian Inference (GSBI) efficiently operates over BPMs to process streaming or forensic data. Deep Belief Networks (DBNs) learn feature representations from data. Examples include model applications for streaming imagery (e.g., video) and automatic target recognition (ATR).

Generic probabilistic approximate computational inference model for streaming data processing

Methods and systems for text data analysis and visualization enable a user to specify a set of text data sources and visualize the content of the text data sources in an overview of salient features in the form of a network of words. A user may focus on one or more words to provide a visualization of connections specific to the focused word(s). The visualization may include clustering of relevant concepts within the network of words. Upon selection of a word, the context thereof, e.g., links to articles where the word appears, may be provided to the user. Analyzing may include textual statistical correlation models for assigning weights to words and links between words. Displaying the network of words may include a force-based network layout algorithm. Extracting clusters for display may include identifying 'communities of words' as if the network of words was a social network.

Method and system for fast, generic, online and offline, multi-source text analysis and visualization

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Wayland, MA, United States of America

Denis Garagic