Dr. John C. Sciortino Profile

Dr. John C. Sciortino

Section Head of SAG at Naval Research Lab

SPIE Involvement:

Author

Proceedings Article | 30 April 2009 Paper

Distributed task allocation in dynamic environments

Sean Mondesire, Annie Wu, Misty Blowers, John Sciortino

Proceedings Volume 7347, 734708 (2009) https://doi.org/10.1117/12.821901

KEYWORDS: Distributed computing, Sensor networks, Analytical research, Sensors, Computer simulations, Lead, Control systems, Surveillance, Biomimetics, Current controlled current source

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Proceedings Article | 1 May 2008 Paper

Team-based resource allocation using a decentralized social decision-making paradigm

Joshua Hecker, Annie Wu, Jared Herweg, John Sciortino

Proceedings Volume 6964, 696409 (2008) https://doi.org/10.1117/12.785379

KEYWORDS: Antennas, Systems modeling, Receivers, Telecommunications, Complex adaptive systems, Computer simulations, Distributed computing, Control systems, Visualization, Data acquisition

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Proceedings Article | 1 May 2008 Paper

Using a multi-objective genetic algorithm for developing aerial sensor team search strategies

Jeffrey Ridder, Jared Herweg, John Sciortino

Proceedings Volume 6964, 696408 (2008) https://doi.org/10.1117/12.785362

KEYWORDS: Radar, Algorithm development, Genetic algorithms, Sensors, Evolutionary algorithms, Receivers, Detection and tracking algorithms, Missiles, Computer simulations, Pollution control

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Proceedings Article | 4 May 2007 Paper

Classifying and evolving multi-agent behaviors from animal packs in search and tracking problems

George Vilches, Annie Wu, John Sciortino, Daniel Pack, Jeffrey Ridder

Proceedings Volume 6563, 656303 (2007) https://doi.org/10.1117/12.719281

KEYWORDS: Control systems, Classification systems, Chemical elements, Defense and security, Sensors, Target detection, Telecommunications, Computing systems, Genetic algorithms, Solids

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Proceedings Article | 2 May 2007 Paper

Optimizing a search strategy for multiple mobile agents

Pedro DeLima, Daniel Pack, John Sciortino

Proceedings Volume 6563, 65630B (2007) https://doi.org/10.1117/12.724967

KEYWORDS: Unmanned aerial vehicles, Sensors, Target detection, Detection and tracking algorithms, Genetic algorithms, Intelligence systems, Cameras, Algorithm development, Optimization (mathematics), Computer simulations

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Proceedings Article | 17 May 2006 Paper

Measures of effectiveness for analysis of radar pulse train deinterleavers

Michael Thompson, Samuel Lin, John Sciortino

Proceedings Volume 6235, 62351I (2006) https://doi.org/10.1117/12.663904

KEYWORDS: Distance measurement, Radar, Analytical research, Mahalanobis distance, Neural networks, Fuzzy logic, Defense and security, Vegetation, Evolutionary algorithms, Statistical analysis

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This paper will compare the various methods of analyzing the results of radar pulse train deinterleavers. This paper is divided into three sections. The first section of this paper will describe the basic methods, such as the confusion matrix, and some measures that can be obtained from the matrix. The measures will include correct correlation, miscorrelation, ambiguity and track purity. Correct correlation is calculated by dividing the total number of correctly clustered pulses by the total number of pulses in the collect. Miscorrelation measures the fraction of received pulses that incorrectly deinterleaved. Ambiguity measures the fraction of received pulses that are rejected by the deinterleaver as having uncertain association with a ground truth track. Track purity measures the ability of the deinterleaver to create a constructed track comprised of pulses from a single ground truth track. These metrics will show the quality of the deinterleaving operation. The second section of this paper will describe some of the advanced similarity measures of effectiveness. This section will also describe how distance measures will be used to analyze deinterleaver results. The two main similarity measures to be discussed in this paper will be the Rand Adjust and Jaccard coefficient. These similarity measures are also known as criterion indices and are used for evaluating the capacity to recover true cluster structure. The reason for the selection of the Jaccard and Rand Adjust as measures is that they both allow a value to be obtained that is between 0 and 1 that will show how good the clusterer in question has performed. The Rand Adjust also allows for more variability in the range between 0 and 1 and appears to provide a more accurate evaluation of the cluster. The distance measures that will be described include Euclidean, Mahalanobis and Minkowski distances. These distance measures have different methods to evaluate each cluster for purity. These measures will provide an indication of the quality of the deinterleaver operation. The third section of this paper will provide the results from these measures on a deinterleavered dataset and will discuss the comparison of these metrics.

Proceedings Article | 17 May 2006 Paper

Transition matrices for the detection and removal of signal contamination in deinterleaved pulse trains

Dorwin Black, John Altoft, John Sciortino

Proceedings Volume 6235, 62351J (2006) https://doi.org/10.1117/12.664462

KEYWORDS: Contamination, Matrices, Signal detection, Analytical research, Pattern recognition, Electronic support measures, Modulation, Transition metals, Image classification, Signal to noise ratio

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Proceedings Article | 17 May 2006 Paper

Comparison of time of arrival vs. multiple parameter based radar pulse train deinterleavers

Samuel Lin, Michael Thompson, Stephen Davezac, John Sciortino

Proceedings Volume 6235, 62351K (2006) https://doi.org/10.1117/12.665044

KEYWORDS: Radar, Fuzzy logic, Neurons, Neural networks, Algorithms, Evolutionary algorithms, Algorithm development, Electronic support measures, Analytical research, Warfare

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Proceedings Article | 9 August 2004 Paper

High-accuracy, low-ambiguity emitter classification using an advanced Dempster-Shafer algorithm

Dorwin Black, John Sciortino, John Altoft

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.542593

KEYWORDS: Electronic support measures, Signal processing, Modulation, Library classification systems, Algorithm development, Matrices, Radar, Switches, Teeth, Computer simulations

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Proceedings Article | 9 August 2004 Paper

Analysis of complex radar data sets using fuzzy adaptive resonance theory map

Michael Thompson, John Sciortino

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.541302

KEYWORDS: Fuzzy logic, Radar, Evolutionary algorithms, Neural networks, Algorithms, Binary data, Analytical research, Algorithm development, Analog electronics, Warfare

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Proceedings Article | 12 April 2004 Paper

Designing teams of unattended ground sensors using genetic algorithms

Ayse Yilmaz, Brian McQuay, Annie Wu, John Sciortino

Proceedings Volume 5421, (2004) https://doi.org/10.1117/12.542612

KEYWORDS: Sensors, Radar, Unattended ground sensors, Genetic algorithms, Lead, Sensor networks, Radar sensor technology, Genetics, Gallium

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Proceedings Article | 29 August 2001 Paper

Implementation of battlespace agents for network-centric electronic warfare

John Sciortino, James Smith, Behzad Kamgar-Parsi, Randall Franciose

Proceedings Volume 4396, (2001) https://doi.org/10.1117/12.438305

KEYWORDS: Sensors, Warfare, Fuzzy logic, Unmanned aerial vehicles, Analytical research, Complex adaptive systems, Control systems, Neural networks, Aluminum, Complex systems

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Showing 5 of 12 publications

Conference Committee Involvement (4)

Evolutionary and Bio-Inspired Computation: Theory and Applications IV

7 April 2010 | Orlando, Florida, United States

Evolutionary and Bio-Inspired Computation: Theory and Applications III

14 April 2009 | Orlando, Florida, United States

Evolutionary and Bio-Inspired Computation: Theory and Applications II

17 March 2008 | Orlando, Florida, United States

Evolutionary and Bio-inspired Computation: Theory and Applications

12 April 2007 | Orlando, Florida, United States

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