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- Alan Edelman, Tomás A. Arias, Steven T. Smith
- SIAM J. Matrix Analysis Applications
- 1998

In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal processing. In addition to the new algorithms, we show how the… (More)

- Timothy A. Barton, Steven T. Smith
- ICASSP
- 1997

Adaptive algorithms require a good estimate of the interference covariance matrix. In situations with limited sample support such an estimate is not available unless there is structure to be exploited. In applications such as space-time adaptive processing (STAP) the underlying covariance matrix is structured (e.g., block Toeplitz), and it is possible to… (More)

- Steven T. Smith, Edward K. Kao, Kenneth D. Senne, Garrett Bernstein, Scott Philips
- IEEE Transactions on Signal Processing
- 2014

A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a… (More)

- Steven T. Smith, Andrew Silberfarb, Scott Philips, Edward K. Kao, Christian Anderson
- 14th International Conference on Information…
- 2011

Network discovery of clandestine groups and their organization is a primary objective of wide-area surveillance systems. An overall approach and workflow to discover a foreground network embedded within a much larger background, using vehicle tracks observed in wide-area video surveillance data is presented and analyzed in this paper. The approach consists… (More)

Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relatively small subgraph in an enormous, potentially uninteresting background. This aspect characterizes network detection as a “big data” problem. Graph… (More)

- Steven T. Smith, Scott Philips, Edward K. Kao
- 2012 IEEE International Conference on Acoustics…
- 2012

This paper addresses threat propagation on space-time graphs, defined to be a time-sampled graph. The application considered is geographical sites connected by tracks, though such graphs arise in many fields. Several new concepts and efficient algorithms are introduced, specifically, the space-time adjacency matrix and harmonic threat propagation. The cued… (More)

- A. Rubaai, J. Jerry, S. T. Smith
- IEEE Transactions on Industry Applications
- 2011

This paper presents the development and implementation of a new and practical fuzzy switching position controller for process industry control. Compatible with the structure of a bang-bang controller, a two-input fuzzy switching position controller with five rules is proposed. The idea is based on constructing a fuzzy switching control law which is… (More)

- Michael J. Yee, Scott Philips, +5 authors Frederick R. Waugh
- 2013

was seeking a courier believed to be connected to Osama bin Laden. The courier was known as Abu Ahmed al-Kuwaiti, and his identity had been confirmed by more than one captured high-level al-Qaeda operative. In 2010, a telephone wiretap of a suspect led intelligence analysts to al-Kuwaiti. On the basis of this connection, al-Kuwaiti was eventually located in… (More)

- John B. Collins, Steven T. Smith
- 17th International Conference on Information…
- 2014

Network discovery involves analyzing the edge set of a graph to determine subsets of vertices that belong to a subgraph of interest. Applications include clandestine network detection and detection of botnet activity on a computer network. Network discovery performance can be degraded by uncertainty about edge existence, connection ambiguity, and confused… (More)

Network analysis has been a major research area over the last ten years, driven by interest in biological networks, cyber attacks, social networks, and criminal or terrorist organizations. This range of applications is illustrated in Figure 1. Detection of a covert community is most likely to be effective if the community exhibits high levels of connection… (More)