Venkatakrishnan S.V.,Purdue University |
Drummy L.F.,Air Force Research Lab |
Jackson M.A.,BLUE Software |
De Graef M.,Carnegie Mellon University |
And 2 more authors.
IEEE Transactions on Image Processing | Year: 2013
High angle annular dark field (HAADF)-scanning transmission electron microscope (STEM) data is increasingly being used in the physical sciences to research materials in 3D because it reduces the effects of Bragg diffraction seen in bright field TEM data. Typically, tomographic reconstructions are performed by directly applying either filtered back projection (FBP) or the simultaneous iterative reconstruction technique (SIRT) to the data. Since HAADF-STEM tomography is a limited angle tomography modality with low signal to noise ratio, these methods can result in significant artifacts in the reconstructed volume. In this paper, we develop a model based iterative reconstruction algorithm for HAADF-STEM tomography. We combine a model for image formation in HAADF-STEM tomography along with a prior model to formulate the tomographic reconstruction as a maximum a posteriori probability (MAP) estimation problem. Our formulation also accounts for certain missing measurements by treating them as nuisance parameters in the MAP estimation framework. We adapt the iterative coordinate descent algorithm to develop an efficient method to minimize the corresponding MAP cost function. Reconstructions of simulated as well as experimental data sets show results that are superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast. © 2013 IEEE.
Brun Y.,University of Washington |
Edwards G.,BLUE Software |
Bang J.Y.,University of Southern California |
Medvidovic N.,University of Southern California
Proceedings - International Conference on Distributed Computing Systems | Year: 2011
Many distributed software systems allow participation by large numbers of untrusted, potentially faulty components on an open network. As faults are inevitable in this setting, these systems utilize redundancy and replication to achieve fault tolerance. In this paper, we present a novel "smart" redundancy technique called iterative redundancy, which ensures efficient replication of computation and data given finite processing and storage resources, even when facing Byzantine faults. Iterative redundancy is more efficient and more adaptive than comparable state-ofthe- art techniques that operate in environments with unknown system resource reliability. We show how systems that solve computational problems using a network of independent nodes can benefit from iterative redundancy. We present a formal analytical analysis and an empirical analysis, demonstrate iterative redundancy on a real-world volunteer-computing system, and compare it to existing methods. © 2011 IEEE.
Medvidovic N.,University of Southern California |
Tajalli H.,University of Southern California |
Garcia J.,University of Southern California |
Krka I.,University of Southern California |
And 2 more authors.
Computer | Year: 2011
RoboPrism, a framework that supports software-architecture-based development of robotic systems, is accessible to nonexperts in robotics, deals effectively with heterogeneity in distributed and mobile robotics systems, and facilitates adaptation in complex, dynamic environments. © 2011 IEEE.
BLUE Software | Date: 2014-06-11
computer software for pet care facility management, namely, scheduling systems for boarding, daycare, grooming, training and pet sitting.
BLUE Software | Date: 2014-09-02
Computer aided design (CAD) software for Electronic Design.