Accession Number:

ADA633838

Title:

Artificial Neural Networks to Extract Optical Properties of Marine Microorganisms from their Mueller Scattering Matrix

Descriptive Note:

Corporate Author:

TENNESSEE STATE UNIV NASHVILLE DEPT OF PHYSICS MATHEMATICS AND COMPUTER SCIENCE

Personal Author(s):

Report Date:

1999-09-30

Pagination or Media Count:

7.0

Abstract:

LONG-TERM GOAL. The long term goals of this project are to understand and quantify light scattering from ensembles of both spherical and non-spherical objects in ocean water, to characterize the effect of ensembles of micro-organisms and inorganic particulates on the propagation of polarized light through sea water, and to assess the feasibility of computer simulated artificial neural networks to extract optical properties of marine particulates from polarized light scattering measurements. OBJECTIVES. The scientific objectives of this project are to develop numerical or analytical models that predict angle-dependent scattering of polarized light from ensembles of non-spherical marine organisms, detritus, and inorganic particulates, and to verify and examine the validity and range of applications of the models by comparison with exact calculations andor experimental results. Specific tasks toward these objectives are 1 to develop an artificial neural network to recognize features in the Mueller matrix elements associated with the optical properties, size distribution, and irregular shape of ocean scatterers, 2 to make experimental measurements in the laboratory of light scattering from samples of micro-organisms and inorganic particles in ocean water, and 3 to continue to refine and enhance analytical models such as the coupled-dipole method for predicting polarized light scattering from non-spherical particles.

Subject Categories:

  • Microbiology
  • Physical and Dynamic Oceanography
  • Optics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE