Dr. Jerry A. Burman Profile

Dr. Jerry A. Burman

CTO

SPIE Involvement:

Author | Editor

Proceedings Article | 30 August 2017 Paper

Fiber optic sensors for automobiles and intelligent highways

Jerry Burman

Proceedings Volume 10318, 1031806 (2017) https://doi.org/10.1117/12.2284061

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Proceedings Article | 10 June 2014 Paper

Architecture for persistent surveillance using mast and UAS-based autonomous sensing with bio-inspired technologies

Jerry Burman

Proceedings Volume 9079, 90790A (2014) https://doi.org/10.1117/12.2050421

KEYWORDS: Sensor networks, Sensors, Target detection, Surveillance, Imaging systems, Cameras, Unmanned aerial vehicles, Mid-IR, Biomimetics, Network architectures

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Proceedings Article | 22 May 2013 Paper

Acoustic network event classification using swarm optimization

Jerry Burman

Proceedings Volume 8742, 87420V (2013) https://doi.org/10.1117/12.2018250

KEYWORDS: Sensors, Acoustics, Sensor networks, Unmanned aerial vehicles, Particles, Algorithm development, Particle swarm optimization, Classification systems, Biomimetics, Signal detection

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Proceedings Article | 24 May 2012 Paper

Autonomous UAV persistent surveillance using bio-inspired strategies

Jerry Burman, Joao Hespanha, Upamanyu Madhow, Jason Isaacs, Sriram Venkateswaran, Tien Pham

Proceedings Volume 8389, 838917 (2012) https://doi.org/10.1117/12.920018

KEYWORDS: Unmanned aerial vehicles, Sensors, Sensor networks, Acoustics, Biomimetics, Data communications, Surveillance, Unattended ground sensors, Source localization, Environmental sensing

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Proceedings Article | 23 May 2011 Paper

Bio-inspired UAV routing, source localization, and acoustic signature classification for persistent surveillance

Jerry Burman, Joao Hespanha, Upamanyu Madhow, Tien Pham

Proceedings Volume 8047, 80470Y (2011) https://doi.org/10.1117/12.882802

KEYWORDS: Sensors, Unmanned aerial vehicles, Acoustics, Sensor networks, Biomimetics, Source localization, Digital filtering, Unattended ground sensors, Algorithm development, Signal to noise ratio

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A team consisting of Teledyne Scientific Company, the University of California at Santa Barbara and the Army Research Laboratory* is developing technologies in support of automated data exfiltration from heterogeneous battlefield sensor networks to enhance situational awareness for dismounts and command echelons. Unmanned aerial vehicles (UAV) provide an effective means to autonomously collect data from a sparse network of unattended ground sensors (UGSs) that cannot communicate with each other. UAVs are used to reduce the system reaction time by generating autonomous collection routes that are data-driven. Bio-inspired techniques for search provide a novel strategy to detect, capture and fuse data. A fast and accurate method has been developed to localize an event by fusing data from a sparse number of UGSs. This technique uses a bio-inspired algorithm based on chemotaxis or the motion of bacteria seeking nutrients in their environment. A unique acoustic event classification algorithm was also developed based on using swarm optimization. Additional studies addressed the problem of routing multiple UAVs, optimally placing sensors in the field and locating the source of gunfire at helicopters. A field test was conducted in November of 2009 at Camp Roberts, CA. The field test results showed that a system controlled by bio-inspired software algorithms can autonomously detect and locate the source of an acoustic event with very high accuracy and visually verify the event. In nine independent test runs of a UAV, the system autonomously located the position of an explosion nine times with an average accuracy of 3 meters. The time required to perform source localization using the UAV was on the order of a few minutes based on UAV flight times. In June 2011, additional field tests of the system will be performed and will include multiple acoustic events, optimal sensor placement based on acoustic phenomenology and the use of the International Technology Alliance (ITA) Sensor Network Fabric (IBM).

Proceedings Article | 8 May 2010 Paper

Heterogeneous sensor networks: a bio-inspired overlay architecture

Jerry Burman, Joao Hespanha, Upamanyu Madhow, Daniel Klein, Jason Isaacs, Sriram Venkateswaran, Tien Pham

Proceedings Volume 7694, 76940T (2010) https://doi.org/10.1117/12.851533

KEYWORDS: Sensors, Unmanned aerial vehicles, Acoustics, Sensor networks, Biomimetics, Source localization, Cameras, Unattended ground sensors, Telecommunications, Bacteria

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Proceedings Article | 5 May 2009 Paper

Heterogeneous sensor networks: a bio-inspired overlay architecture

Jerry Burman, Joao Hespanha, Upamanyu Madhow, Daniel Klein, Tien Pham, Ananthram Swami

Proceedings Volume 7333, 73330E (2009) https://doi.org/10.1117/12.819221

KEYWORDS: Sensors, Source localization, Biomimetics, Sensor networks, Unattended ground sensors, Unmanned aerial vehicles, Algorithm development, Acoustics, Databases, Signal to noise ratio

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Proceedings Article | 14 December 1999 Paper

Hybrid pattern recognition method using evolutionary computing techniques applied to the exploitation of hyperspectral imagery and medical spectral data

Jerry Burman

Proceedings Volume 3871, (1999) https://doi.org/10.1117/12.373240

KEYWORDS: Hyperspectral imaging, Evolutionary algorithms, Sensors, Infrared radiation, Pattern recognition, Image processing, Atmospheric modeling, Computing systems, Atmospheric sensing, Infrared signatures

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Proceedings Article | 31 October 1997 Paper

Nonliteral pattern recognition method for hyperspectral imagery exploitation using evolutionary computing methods

Jerry Burman

Proceedings Volume 3118, (1997) https://doi.org/10.1117/12.278935

KEYWORDS: Evolutionary algorithms, Image processing, Sensors, Linear filtering, Hyperspectral imaging, Atmospheric modeling, Pattern recognition, Neural networks, Image sensors, Neurons

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Proceedings Article | 1 August 1991 Paper

Random mapping network for tactical target reacquisition after loss of track

Susan Church, Jerry Burman

Proceedings Volume 1471, (1991) https://doi.org/10.1117/12.44878

KEYWORDS: Image segmentation, Detection and tracking algorithms, Object recognition, Associative arrays, Logic, Binary data, Target detection, Optical correlators, Target recognition, Automatic target recognition

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Proceedings Article | 1 November 1990 Paper

Image-restoration aggregation

Gregory Baraghimian, William Lincoln, Jerry Burman

Proceedings Volume 1349, (1990) https://doi.org/10.1117/12.23564

KEYWORDS: Image filtering, Sensors, Point spread functions, Image processing, Digital image processing, Signal to noise ratio, Signal detection, Image restoration, Image sensors, Infrared sensors

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Showing 5 of 11 publications

SPIE Conference Volume | 28 September 1989

Technologies for Intelligent Vehicle Highways

Conference Committee Involvement (1)

Technologies for Intelligent Vehicle Highways

1 January 1989 | Bellingham, WA, United States

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