Engineering Awareness

Abstract

Generalized Process Tracking. Defined a rigorous concept of "trackability" of processes in a distributed sensing system. Established fundamental properties of processes and sensing infrastructure that are necessary and sufficient for certain types of trackability to be feasible. Problem addressed and solved: determine the "complexity" of estimating state trajectories of a target process based on a discrete-time sequence of noisy "observations". Conducted a comparative analysis of design methodologies for Agent-Based Systems. Machine Learning complex processes from data: discovery of a new algorithm to learn Hidden Markov Models (HMMs) from typical realizations of the associated stochastic process. The new method is based on the non-negative matrix factorization (NMF) of higher order Markovian statistics and is structurally different from the classical Baum-Welsh and associated approaches. Cognitive Complexification: development of new methods to shape network communications for preventing covert transmissions from hiding behind the statistics of ordinary traffic.

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Document Details

Document Type
Technical Report
Publication Date
Feb 07, 2010
Accession Number
ADA573423

Entities

People

  • George Cybenko
  • Valentino Crespi

Organizations

  • California State University, Los Angeles

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Automata
  • Computer Network Security
  • Computer Networks
  • Engineering
  • Hidden Markov Models
  • Information Science
  • Learning
  • Machine Learning
  • Markov Models
  • Models
  • Observation
  • Probabilistic Models
  • Probability
  • Sensor Networks
  • Statistics
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Linear Algebra
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms