Dr. Randy C. Paffenroth Profile

Dr. Randy C. Paffenroth

Associate Professor at Worcester Polytechnic Institute

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Publications (16)

Proceedings Article | 27 April 2020 Paper

A robust principal component analysis approach to DoS-related network anomaly detection

Cody Doucette, Regan Broderick-Sander, Benjamin Toll, Aaron Helsinger, Nathaniel Soule, Partha Pal, Chong Zhou, Randy Paffenroth

Proceedings Volume 11417, 114170B (2020) https://doi.org/10.1117/12.2562774

KEYWORDS: Detection and tracking algorithms, Principal component analysis, Floods, Network security, Inspection, Prototyping, Machine learning, Internet, Mining, Network architectures

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Proceedings Article | 27 November 2019 Paper

CNNs in the frequency domain for image super-resolution

Yingnan Liu, Randy Clinton Paffenroth

Proceedings Volume 11321, 113210D (2019) https://doi.org/10.1117/12.2539288

KEYWORDS: Neural networks, Super resolution, Compressed sensing, Image restoration, Reconstruction algorithms, Image compression, Image quality, Algorithm development, Evolutionary algorithms, Image processing

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Proceedings Article | 30 September 2013 Paper

On covariance structure in noisy, big data

Randy Paffenroth, Ryan Nong, Philip Du Toit

Proceedings Volume 8857, 88570E (2013) https://doi.org/10.1117/12.2037882

KEYWORDS: Sensors, Matrices, Computer networks, Algorithms, Principal component analysis, Sensor fusion, Computer intrusion detection, Optimization (mathematics), Data fusion, Veins

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Proceedings Article | 15 May 2012 Paper

Information-based data prioritization in distributed tracking systems

Nick Coult, J. Knight, Woody Leed, Scott Danford, Randy Paffenroth, Aubrey Poore

Proceedings Volume 8393, 83930L (2012) https://doi.org/10.1117/12.919157

KEYWORDS: Sensors, Electronic filtering, Filtering (signal processing), Distributed computing, Composites, Systems modeling, Kinematics, Motion models, Target detection, Computing systems

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Proceedings Article | 15 May 2012 Paper

Space-time signal processing for distributed pattern detection in sensor networks

Randy Paffenroth, Philip Du Toit, Louis Scharf, Anura Jayasumana, Vidarshana Banadara, Ryan Nong

Proceedings Volume 8393, 839309 (2012) https://doi.org/10.1117/12.919711

KEYWORDS: Matrices, Sensor networks, Algorithm development, Signal processing, Computer networks, Sensors, Algorithms, Time metrology, Social networks, Principal component analysis

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Proceedings Article | 15 May 2012 Paper

Ambiguous data association and entangled attribute estimation

David Trawick, Philip Du Toit, Randy Paffenroth, Gregory Norgard

Proceedings Volume 8393, 83930H (2012) https://doi.org/10.1117/12.920457

KEYWORDS: Kinematics, Sensors, Data analysis, Data modeling, Databases, Computing systems, Space sensors, Forensic science, Remote sensing, Electronic filtering

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Proceedings Article | 8 May 2012 Paper

Detecting clustered chem/bio signals in noisy sensor feeds using adaptive fusion

Scott Lundberg, Chris Calderon, Randy Paffenroth

Proceedings Volume 8393, 839303 (2012) https://doi.org/10.1117/12.921072

KEYWORDS: Sensors, Signal detection, Data modeling, Target detection, Data fusion, Detection and tracking algorithms, Sensor networks, Statistical analysis, Environmental sensing, Control systems

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Proceedings Article | 5 May 2012 Paper

Distributed pattern detection in cyber networks

Randy Paffenroth, Philip Du Toit, Louis Scharf, Anura Jayasumana, Vidarshana Banadara, Ryan Nong

Proceedings Volume 8408, 84080J (2012) https://doi.org/10.1117/12.919587

KEYWORDS: Algorithm development, Sensors, Detection and tracking algorithms, Sensor networks, Computer networks, Matrices, Databases, Signal detection, Computer intrusion detection, Compressed sensing

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Proceedings Article | 17 September 2011 Paper

A data-driven approach for processing heterogeneous categorical sensor signals

Christopher Calderon, Austin Jones, Scott Lundberg, Randy Paffenroth

Proceedings Volume 8137, 813704 (2011) https://doi.org/10.1117/12.894671

KEYWORDS: Sensors, Statistical analysis, Environmental sensing, Error analysis, Signal detection, Monte Carlo methods, Surveillance, Binary data, Sensor performance, Chemical analysis

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False alarms generated by sensors pose a substantial problem to a variety of fusion applications. We focus on situations where the frequency of a genuine alarm is "rare" but the false alarm rate is high. The goal is to mitigate the false alarms while retaining power to detect true events. We propose to utilize data streams contaminated by false alarms (generated in the field) to compute statistics on a single sensor's misclassification rate. The nominal misclassification rate of a deployed sensor is often suspect because it is unlikely that these rates were tuned to the specific environmental conditions in which the sensor was deployed. Recent categorical measurement error methods will be applied to the collection of data streams to "train" the sensors and provide point estimates along with confidence intervals for the parameters characterizing sensor performance. By pooling a relatively small collection of random variables arising from a single sensor and using data-driven misclassification rate estimates along with estimated confidence bands, we show how one can transform the stream of categorical random variables into a test statistic with a limiting standard normal distribution. The procedure shows promise for normalizing sequences of misclassified random variables coming from different sensors (with a priori unknown population parameters) to comparable test statistics; this facilitates fusion through various downstream processing mechanisms. We have explored some possible downstream processing mechanisms that rely on false discovery rate (FDR) methods. The FDR methods exploit the test statistics we have computed in a chemical sensor fusion context where reducing false alarms and maintaining substantial power is important. FDR methods also provide a framework to fuse signals coming from non-chem/bio sensors in order to improve performance. Simulation results illustrating these ideas are presented. Extensions, future work and open problems are also briefly discussed.

Proceedings Article | 16 April 2010 Paper

A tracker adjunct processing system for reconsideration of firm tracker decisions

David Trawick, Benjamin Slocumb, Randy Paffenroth

Proceedings Volume 7698, 76980N (2010) https://doi.org/10.1117/12.852461

KEYWORDS: Kinematics, Sensors, Data modeling, Telecommunications, Motion models, Computing systems, Filtering (signal processing), Electronic filtering, Detection and tracking algorithms, Data processing

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Most modern maximum likelihood multiple target tracking systems (e.g., Multiple Hypothesis Tracking (MHT) and Numerica's Multiple Frame Assignment (MFA)) need to determine how to separate their input measurements into subsets corresponding to the observations of individual targets. These observation sets form the tracks of the system, and the process of determining these sets is known as data association. Real-time constraints frequently force the use of only the maximum likelihood choice for data association (over some time window), although alternative data association choices may have been considered in the process of choosing the most likely. This paper presents a Tracker Adjunct Processing (TAP) system that captures and manages the uncertainty encountered in making data association decisions. The TAP combines input observation data and the data association alternatives considered by the tracker into a dynamic Bayesian network (DBN). The network efficiently represents the combined alternative tracking hypotheses. Bayesian network evidence propagation methods are used to update the network in light of new evidence, which may consist of new observations, new alternative data associations, newly received late observations, hypothetical connections, or other flexible queries. The maximum likelihood tracking hypothesis can then be redetermined, which may result in changes to the best tracking hypothesis. The recommended changes can then be communicated back to the associated tracking system, which can then update its tracks. In this manner, the TAP's interpretation makes the firm, fixed (formerly maximum likelihood) decisions of the tracker "softer," i.e., less absolute. The TAP can also assess (and reassess) track purity regions by ambiguity level. We illustrate the working of the TAP with several examples, one in particular showing the incorporation of critical, late or infrequent data. These data are critical in the sense that they are very valuable in resolving ambiguities in tracking and combat identification; thus, the motivation to use these data is high even though there are complexities in applying it. Some data may be late because of significant network delays, while other data may be infrequently reported because they come from "specialized" sensors that provide updates only every once in a while.

Proceedings Article | 16 April 2010 Paper

Algorithms for distributed chemical sensor fusion

Scott Lundberg, Randy Paffenroth, Jason Yosinski

Proceedings Volume 7698, 769806 (2010) https://doi.org/10.1117/12.849588

KEYWORDS: Sensors, Clouds, Chemical analysis, Diffusion, Data modeling, Infrared sensors, Chemical fiber sensors, Sensor fusion, Electro optical modeling, Visualization

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Proceedings Article | 16 April 2010 Paper

Nonlinear estimation for arrays of chemical sensors

Jason Yosinski, Randy Paffenroth

Proceedings Volume 7698, 769809 (2010) https://doi.org/10.1117/12.849589

KEYWORDS: Sensors, Chemical analysis, Environmental sensing, Chemical detection, Detection and tracking algorithms, Signal processing, Chemical fiber sensors, Spectrometers, Pollution control, Biological research

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Proceedings Article | 4 September 2009 Paper

Network-centric angle only tracking

Jason Yosinski, Nick Coult, Randy Paffenroth

Proceedings Volume 7445, 74450O (2009) https://doi.org/10.1117/12.826565

KEYWORDS: Sensors, Network architectures, Monte Carlo methods, Detection and tracking algorithms, Sensor networks, Composites, Error analysis, Telecommunications, Kinematics, Warfare

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Proceedings Article | 16 April 2008 Paper

Out-of-sequence measurement updates for multi-hypothesis tracking algorithms

Stephanie Chan, Randy Paffenroth

Proceedings Volume 6969, 69691H (2008) https://doi.org/10.1117/12.778022

KEYWORDS: Detection and tracking algorithms, Filtering (signal processing), Time metrology, Electronic filtering, Algorithm development, Fourier transforms, Sensors, Optimal filtering, Network architectures, Data processing

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Proceedings Article | 16 April 2008 Paper

A distributed database view of network tracking systems

Jason Yosinski, Randy Paffenroth

Proceedings Volume 6969, 696915 (2008) https://doi.org/10.1117/12.778039

KEYWORDS: Databases, Sensors, Detection and tracking algorithms, Monte Carlo methods, Network architectures, Data modeling, Distributed computing, Telecommunications, Optical tracking, Visualization

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Proceedings Article | 21 September 2007 Paper

Mitigation of biases using the Schmidt-Kalman filter

Randy Paffenroth, Roman Novoselov, Scott Danford, Marcio Teixeira, Stephanie Chan, Aubrey Poore

Proceedings Volume 6699, 66990Q (2007) https://doi.org/10.1117/12.734130

KEYWORDS: Sensors, Error analysis, Electronic filtering, Filtering (signal processing), Monte Carlo methods, Detection and tracking algorithms, Performance modeling, Stochastic processes, Computer simulations, Data fusion

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Showing 5 of 16 publications

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