Dr. Mirela Popa
at Chemring Detection Systems Inc
SPIE Involvement:
Conference Program Committee | Author | Instructor
Publications (2)

PROCEEDINGS ARTICLE | May 5, 2012
Proc. SPIE. 8358, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIII
KEYWORDS: Mirrors, Optical design, FT-IR spectroscopy, Digital signal processing, Standoff detection, Sensors, Scanners, Spectroscopy, Clouds, Scanning electron microscopy

PROCEEDINGS ARTICLE | May 12, 2005
Proc. SPIE. 5795, Chemical and Biological Sensing VI
KEYWORDS: Mirrors, Electronics, Digital signal processing, Standoff detection, Sensors, Scanners, Spectroscopy, Control systems, Telecommunications, Neural networks

Conference Committee Involvement (6)
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016
21 April 2016 | Baltimore, Maryland, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015
21 April 2015 | Baltimore, Maryland, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014
6 May 2014 | Baltimore, Maryland, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013
30 April 2013 | Baltimore, Maryland, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012
25 April 2012 | Baltimore, Maryland, United States
Showing 5 of 6 published special sections
Course Instructor
SC994: Multisensor Data Fusion for Object Detection, Classification and Identification
This course describes sensor and data fusion methods that improve the probability of correct target detection, classification, and identification. The methods allow the combining of information from collocated or dispersed sensors that utilize similar or different operating phenomenologies. Examples provide insight as to how different phenomenology-based sensors enhance a data fusion system. After introducing the JDL data fusion model, sensor and data fusion architectures are described in terms of sensor-level, central-level, and hybrid fusion, and pixel-, feature-, and decision-level fusion. The exploration of data fusion algorithm taxonomies provides an introduction to the algorithms and methods utilized for detection, classification, identification, and state estimation and tracking – the Level 1 fusion processes. These algorithms support the higher-level data fusion processes of situation and impact assessment. Subsequent sections of the course more fully develop the Bayesian, Dempster-Shafer, and voting logic data fusion algorithms. Examples abound throughout the material to illustrate the major techniques being presented. The illustrative problems demonstrate that many of the data fusion methods can be applied to combine information from almost any grouping of sensors as long as the input data are of the types required by the fusion algorithm.
SC719: Chemical & Biological Detection: Overview of Point and Standoff Sensing Technologies
This course introduces chemical and biological detection, discrimination & identification techniques which are commonly utilized for military and civil applications. Standoff, (remote) and point detection, discrimination, and identification techniques are introduced with design parameters, performance models, and detection algorithms and sensor/data fusion techniques. A sampling of specific technology applications for chemical point, chemical standoff, biological point, and biological standoff sensing will be described. These technologies include Mass Spectrometry, Ion Mobility Spectrometry, Surface Acoustic Waves, Fiber Optic, Fourier Transform Infrared Spectrometry, Differential Absorption Lidar, Laser-Induced Fluorescence, Surface Plasmon Resonance, Antigen/Antibody Immunoassay, Polymerase Chain Reaction, Laser-Induced Breakdown Spectroscopy, Raman Spectroscopy and Lidar Backscatter systems. The course will include a brief overview of chemical and biological agents of interest and features which may be interrogated by detection systems.
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