Force Estimation and Prediction from Time-Varying Density Images

• Main Authors: Ratilal, Purnima (Author), Jagannathan, Srinivasan (Contributor), Horn, Berthold Klaus Paul (Contributor), Makris, Nicholas (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Horn, Berthold K. P. (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2012-02-23T16:58:32Z.
• Subjects: Article
• Online Access: Get fulltext

We present methods for estimating forces which drive motion observed in density image sequences. Using these forces, we also present methods for predicting velocity and density evolution. To do this, we formulate and apply a Minimum Energy Flow (MEF) method which is capable of estimating both incompressible and compressible flows from time-varying density images. Both the MEF and force-estimation techniques are applied to experimentally obtained density images, spanning spatial scales from micrometers to several kilometers. Using density image sequences describing cell splitting, for example, we show that cell division is driven by gradients in apparent pressure within a cell. Using density image sequences of fish shoals, we also quantify 1) intershoal dynamics such as coalescence of fish groups over tens of kilometers, 2) fish mass flow between different parts of a large shoal, and 3) the stresses acting on large fish shoals.
United States. Office of Naval Research
Alfred P. Sloan Foundation
National Oceanographic Partnership Program (U.S.)