Dr. Robert S. Lynch Profile

Dr. Robert S. Lynch

Adjunct Professor at Univ of Connecticut

SPIE Involvement:

Author

Area of Expertise:

Classification , Information Fusion , Tracking , Signal Processing , Decision Support

Profile Summary

Dr. Robert Lynch is currently a signal & information processing systems consultant. He was formerly a senior research scientist with the Naval Undersea Warfare Center in Newport, RI. He holds BS and MS degrees, both in Electrical Engineering, from Union College, Schenectady, NY, and a PhD in Electrical Engineering from the University of Connecticut, Storrs, CT. He has presented more than fifty papers related to his research interests of pattern recognition and classification, detection, data fusion, tracking, and signal processing. Dr. Lynch is Vice President Communications of the International Society of Information Fusion, and is Managing Editor of the Journal of Advances in Information Fusion. Dr. Lynch is a Senior Member of the IEEE, and is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics Part B, Cybernetics. He is a former recipient of the NAVSEA Excellence in Science Award, and the Federal Laboratory Consortium’s Excellence in Technology Transfer Award. Dr. Lynch is an Adjunct Lecturer in the Electrical and Computer Engineering Department at the University of Connecticut.

Publications (26)

Proceedings Article | 22 May 2015 Paper

Paper

Effects of using a 3D model on the performance of vision algorithms

D. Paul Benjamin, Damian Lyons, Robert Lynch

Proceedings Volume 9498, 94980B (2015) https://doi.org/10.1117/12.2181225

KEYWORDS: 3D modeling, Visual process modeling, Data modeling, Cameras, 3D vision, Cognitive modeling, Remote sensing, Detection and tracking algorithms, Visualization, Motion models

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Proceedings Article | 22 May 2015 Paper

Paper

A theoretical performance analysis of discrete data classification when fusing two features

Robert Lynch, Peter Willett

Proceedings Volume 9498, 949806 (2015) https://doi.org/10.1117/12.2180063

KEYWORDS: Data modeling, Quantization, Performance modeling, Algorithms, Error analysis, Algorithm development, Scientific classification systems, Information fusion, Electrochemical etching, Systems modeling

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Proceedings Article | 22 May 2014 Paper

Paper

New results in semi-supervised learning using adaptive classifier fusion

Robert Lynch, Peter Willett

Proceedings Volume 9121, 912104 (2014) https://doi.org/10.1117/12.2050196

KEYWORDS: Binary data, Data modeling, Data fusion, Quantization, Machine learning, Iris, Algorithm development, Statistical modeling, Information fusion, Mathematical modeling

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Proceedings Article | 3 June 2013 Paper

Paper

Multiuser sonar watermarking and detection in an underwater acoustic channel

Bijan Mobasseri, Robert Lynch, David Andiario

Proceedings Volume 8724, 872408 (2013) https://doi.org/10.1117/12.2015636

KEYWORDS: Digital watermarking, Sensors, Signal to noise ratio, Acoustics, Signal detection, Bismuth, Receivers, Interference (communication), Holmium, Signal attenuation

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Proceedings Article | 29 May 2013 Paper

Paper

Using classifier fusion to improve the performance of multiclass classification problems

Robert Lynch, Peter Willett

Proceedings Volume 8756, 875608 (2013) https://doi.org/10.1117/12.2016359

KEYWORDS: Binary data, Data modeling, Quantization, Data fusion, Error analysis, Performance modeling, Databases, Feature selection, Classification systems, Systems modeling

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The problem of multiclass classification is often modeled by breaking it down into a collection of binary classifiers, as opposed to jointly modeling all classes with a single primary classifier. Various methods can be found in the literature for decomposing the multiclass problem into a collection of binary classifiers. Typical algorithms that are studied here include each versus all remaining (EVAR), each versus all individually (EVAI), and output correction coding (OCC). With each of these methods a classifier fusion based decision rule is formulated utilizing the various binary classifiers to determine the correct classification of an unknown data point. For example, with EVAR the binary classifier with maximum output is chosen. For EVAI, the correct class is chosen using a majority voting rule, and with OCC a comparison algorithm based minimum Hamming distance metric is used. In this paper, it is demonstrated how these various methods perform utilizing the Bayesian Reduction Algorithm (BDRA) as the primary classifier. BDRA is a discrete data classification method that quantizes and reduces the dimensionality of feature data for best classification performance. In this case, BDRA is used to not only train the appropriate binary classifier pairs, but it is also used to train on the discrete classifier outputs to formulate the correct classification decision of unknown data points. In this way, it is demonstrated how to predict which binary classification based algorithm method (i.e., EVAR, EVAI, or OCC) performs best with BDRA. Experimental results are shown with real data sets taken from the Knowledge Extraction based on Evolutionary Learning (KEEL) and University of California at Irvine (UCI) Repositories of classifier Databases. In general, and for the data sets considered, it is shown that the best classification method, based on performance with unlabeled test observations, can be predicted form performance on labeled training data. Specifically, the best method is shown to have the least overall probability of error, and the binary classifiers have the least overall average quantization complexity.

Proceedings Article | 12 June 2012 Paper

Paper

Sonar watermark embedding and detection: a sea trial report

Bijan Mobasseri, Nagarjun Chakilam, Robert Lynch

Proceedings Volume 8372, 837215 (2012) https://doi.org/10.1117/12.920842

KEYWORDS: Digital watermarking, Signal detection, Acoustics, Receivers, Time-frequency analysis, Doppler effect, Signal attenuation, Transmitters, Sensors, Warfare

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Proceedings Article | 25 May 2012 Paper

Paper

Acquiring neural signals for developing a perception and cognition model

Wei Li, Yunyi Li, Genshe Chen, Dan Shen, Erik Blasch, Khanh Pham, Robert Lynch

Proceedings Volume 8385, 83850I (2012) https://doi.org/10.1117/12.918767

KEYWORDS: Cognitive modeling, Brain, Brain-machine interfaces, Cognition, Human subjects, Signal processing, Electroencephalography, CCD cameras, Control systems, Electrodes

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Proceedings Article | 24 May 2011 Paper

Paper

Location-dependent RF geotags for positioning and security

Di Qiu, Robert Lynch, Chun Yang

Proceedings Volume 8061, 80610C (2011) https://doi.org/10.1117/12.882900

KEYWORDS: Information security, Quantization, Signal to noise ratio, Transmitters, Receivers, Data modeling, Binary data, Distance measurement, Sensors, Signal processing

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Proceedings Article | 13 May 2010 Paper

Paper

Jamming emitter localization with multiple UAVs equipped with smart antennas

Zhonghai Wang, Genshe Chen, Erik Blasch, Khanh Pham, Robert Lynch

Proceedings Volume 7696, 769607 (2010) https://doi.org/10.1117/12.849787

KEYWORDS: Unmanned aerial vehicles, Antennas, Error analysis, Receivers, Global Positioning System, Systems modeling, Filtering (signal processing), Analytical research, Sensors, Navigation systems

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Proceedings Article | 8 May 2010 Paper

Paper

Cooperative controls with intermittent communication

Dan Shen, Genshe Chen, Jose Cruz, Khanh Pham, Erik Blasch, Robert Lynch

Proceedings Volume 7691, 76910D (2010) https://doi.org/10.1117/12.849786

KEYWORDS: Unmanned aerial vehicles, Control systems, Feedback control, Radon, Algorithm development, Analytical research, Device simulation, Fuzzy logic, Ions, Stochastic processes

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Proceedings Article | 20 April 2010 Paper

Paper

Sonar authentication performance evaluation under realistically simulated undersea channels

Bijan Mobasseri, Robert Lynch

Proceedings Volume 7678, 76780R (2010) https://doi.org/10.1117/12.852577

KEYWORDS: Digital watermarking, Sensors, Signal detection, Acoustics, Receivers, Acoustic emission, Signal to noise ratio, Data hiding, Control systems, Computer simulations

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Proceedings Article | 13 April 2009 Paper

Paper

Estimating the threshold for maximizing expected gain in supervised discrete Bayesian classification

Robert Lynch, Peter Willett

Proceedings Volume 7344, 734408 (2009) https://doi.org/10.1117/12.819067

KEYWORDS: Data modeling, Quantization, Computer simulations, Data mining, Error analysis, Mining, Monte Carlo methods, Algorithm development, Warfare, Coastal modeling

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Proceedings Article | 22 April 2008 Paper

Paper

On modeling sea clutter by diffusive models

Jing Hu, Wen-wen Tung, Jianbo Gao, Robert Lynch, Genshe Chen

Proceedings Volume 6968, 69681F (2008) https://doi.org/10.1117/12.777202

KEYWORDS: Target detection, Data modeling, Radar, Speckle, Coastal modeling, Modulation, Backscatter, Antennas, Atmospheric modeling, Stochastic processes

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Proceedings Article | 17 April 2008 Paper

Paper

On modeling sea clutter by noisy chaotic dynamics

Wen-wen Tung, Jing Hu, Jianbo Gao, Robert Lynch, Genshe Chen

Proceedings Volume 6968, 69681G (2008) https://doi.org/10.1117/12.777204

KEYWORDS: Chaos, Stochastic processes, Target detection, Chaos theory, Radar, Coastal modeling, Motion models, Analytical research, Antennas, Atmospheric modeling

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Proceedings Article | 17 March 2008 Paper

Paper

A new approach to chemical agent detection, classification, and estimation

Tao Qian, Genshe Chen, Erik Blasch, Robert Lynch, Yongwei Qi

Proceedings Volume 6973, 69730K (2008) https://doi.org/10.1117/12.778501

KEYWORDS: Sensors, Error analysis, Statistical analysis, Chemical analysis, Humidity, Nose, Resistance, Neural networks, Biological research, Quantization

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Proceedings Article | 17 March 2008 Paper

Paper

Performance comparison of the automatic data reduction system (ADRS)

Dan Patterson, David Turner, Arturo Concepcion, Robert Lynch

Proceedings Volume 6973, 69730H (2008) https://doi.org/10.1117/12.777903

KEYWORDS: Error analysis, Principal component analysis, Statistical analysis, Algorithm development, Artificial neural networks, Quantization, Feature extraction, Data centers, Mining, Data modeling

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Proceedings Article | 9 April 2007 Paper

Paper

Mining unknown patterns in data when the features are correlated

Robert Lynch, Peter Willett

Proceedings Volume 6570, 657008 (2007) https://doi.org/10.1117/12.719423

KEYWORDS: Mining, Algorithm development, Detection and tracking algorithms, Data mining, Data modeling, Expectation maximization algorithms, Quantization, Error analysis, Binary data, Target recognition

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Proceedings Article | 18 April 2006 Paper

Paper

An algorithmic approach to mining unknown clusters in training data

Robert Lynch, Peter Willett

Proceedings Volume 6241, 624101 (2006) https://doi.org/10.1117/12.664731

KEYWORDS: Mining, Algorithm development, Data modeling, Detection and tracking algorithms, Expectation maximization algorithms, Data mining, Quantization, Binary data, Feature extraction, Error analysis

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Proceedings Article | 15 September 2005 Paper

Paper

UUV-platform cooperation in covert tracking

Wayne Blanding, Peter Willett, Yaakov Bar-Shalom, Robert Lynch

Proceedings Volume 5913, 59131U (2005) https://doi.org/10.1117/12.618930

KEYWORDS: Active sonar, Passive sonar, Detection and tracking algorithms, Target detection, Sensors, Error analysis, Algorithm development, Signal to noise ratio, Signal attenuation, Signal processing

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Proceedings Article | 28 March 2005 Paper

Paper

Utilizing unsupervised learning to cluster data in the Bayesian data reduction algorithm

Robert Lynch, Peter Willett

Proceedings Volume 5812, (2005) https://doi.org/10.1117/12.603522

KEYWORDS: Mining, Data modeling, Detection and tracking algorithms, Expectation maximization algorithms, Algorithm development, Binary data, Machine learning, Data centers, Quantization, Data mining

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Proceedings Article | 9 August 2004 Paper

Paper

Modeling feature uncertainty in the Bayesian data reduction algorithm

Robert Lynch, Peter Willett

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.543071

KEYWORDS: Data modeling, Error analysis, Quantization, Binary data, Feature selection, Computer simulations, Statistical analysis, Coastal modeling, Signal processing, Warfare

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Proceedings Article | 8 August 2003 Paper

Paper

Adaptive classification by maximizing class separability with respect to the unlabeled data

Robert Lynch, Peter Willett

Proceedings Volume 5107, (2003) https://doi.org/10.1117/12.487565

KEYWORDS: Data modeling, Binary data, Expectation maximization algorithms, Data acquisition, Quantization, Performance modeling, Target detection, Feature extraction, Feature selection, Data centers

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Proceedings Article | 16 July 2002 Paper

Paper

Classification performance results of various medical diagnostic data sets

Robert Lynch, Peter Willett

Proceedings Volume 4733, (2002) https://doi.org/10.1117/12.475497

KEYWORDS: Quantization, Medical diagnostics, Binary data, Feature selection, Error analysis, Breast cancer, Data modeling, Data centers, Machine learning, Statistical analysis

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Proceedings Article | 6 March 2002 Paper

Paper

Adaptive sequential Bayesian classification using Page's test

Robert Lynch, Peter Willett

Proceedings Volume 4731, (2002) https://doi.org/10.1117/12.458379

KEYWORDS: Binary data, Feature selection, Quantization, Detection and tracking algorithms, Algorithm development, Data modeling, Statistical analysis, Data fusion, Computer simulations, Target detection

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Proceedings Article | 16 August 2001 Paper

Paper

Utilizing a class labeling feature in an adaptive Bayesian classifier

Robert Lynch, Peter Willett

Proceedings Volume 4380, (2001) https://doi.org/10.1117/12.436977

KEYWORDS: Binary data, Data modeling, Algorithm development, Quantization, Feature selection, Error analysis, Warfare, Data centers, Feature extraction, Computer engineering

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Proceedings Article | 4 August 2000 Paper

Paper

Class-specific feature selection based on uniform dirichlet priors

Robert Lynch, Peter Willett

Proceedings Volume 4052, (2000) https://doi.org/10.1117/12.395060

KEYWORDS: Binary data, Feature selection, Quantization, Feature extraction, Error analysis, Warfare, Data centers, Algorithm development, Target detection, Analytical research

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Showing 5 of 26 publications

Conference Committee Involvement (4)

Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009

15 April 2009 | Orlando, Florida, United States

Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2007

10 April 2007 | Orlando, Florida, United States

Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006

17 April 2006 | Orlando (Kissimmee), Florida, United States

Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005

28 March 2005 | Orlando, Florida, United States

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