Detection of microorganismic flows by linear and nonlinear optical methods and automatic correction of erroneous images artefacts and moving boundaries in image generating methods by a neuronumerical hybrid implementing the Taylor’s hypothesis as a priori knowledge

Detection of microorganismic flows by linear and nonlinear optical methods and automatic... In biological fluid mechanics powerful imaging methods for flow analysis are required for making progress towards a better understanding of natural phenomena being optimised in the course of evolution. At the same time it is of crucial importance that the measuring and flow visualisation techniques employed guarantee biocompatibility, i.e. they do not distort the behaviour of biosystems. Unfortunately, this restricts seriously the measures for optimising the image generation in comparison to other flow fields in which no biological systems are present. As a consequence, images of lower quality leading to erroneous artefacts are obtained. Thus, either novel detection techniques that are able to overcome these disadvantages or advanced evaluation methods enabling the sophisticated analysis and description of flow fields are essential. In the present contribution, both areas are covered. A novel so-called neuronumerical hybrid allows to detect artefacts in conventional experimental particle image velocimetry (PIV) data of microorganismic flow fields generated by ciliates. The handling of artefacts is performed by the hybrid using a priori knowledge of the flow physics formulated in numerical expressions and the enormous potential of artificial neural networks in predicting artefacts and correcting them. In fact, the neuronumerical hybrid based on the physical knowledge provided by the Taylor’s hypothesis can detect not only spurious velocity vectors but also additional phenomena like a moving boundary, in the present case caused by the contraction of the zooid of a microorganism. Apart from the detection of the artefacts, a correction of the spurious velocity vectors is possible. Furthermore, a method to detect microscopic velocity fields based on nonlinear optical filtering, optical novelty filter (ONF) is presented. On the one hand, it can be employed to expose phase changes in flow fields directly from the nonlinear response and without additional tracers. On the other hand, it can be used to preprocess low quality images of flow fields loaded with particles and extract the motion of particles with an enhanced contrast. The flow fields obtained by the correlation based PIV method of the ONF filtered and unfiltered image sequences are compared and discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Detection of microorganismic flows by linear and nonlinear optical methods and automatic correction of erroneous images artefacts and moving boundaries in image generating methods by a neuronumerical hybrid implementing the Taylor’s hypothesis as a priori knowledge

Loading next page...
 
/lp/springer_journal/detection-of-microorganismic-flows-by-linear-and-nonlinear-optical-40vjDMs2Lh
Publisher
Springer-Verlag
Copyright
Copyright © 2007 by Springer-Verlag
Subject
Engineering; Engineering Fluid Dynamics; Fluid- and Aerodynamics; Engineering Thermodynamics, Heat and Mass Transfer
ISSN
0723-4864
eISSN
1432-1114
D.O.I.
10.1007/s00348-007-0269-3
Publisher site
See Article on Publisher Site

Abstract

In biological fluid mechanics powerful imaging methods for flow analysis are required for making progress towards a better understanding of natural phenomena being optimised in the course of evolution. At the same time it is of crucial importance that the measuring and flow visualisation techniques employed guarantee biocompatibility, i.e. they do not distort the behaviour of biosystems. Unfortunately, this restricts seriously the measures for optimising the image generation in comparison to other flow fields in which no biological systems are present. As a consequence, images of lower quality leading to erroneous artefacts are obtained. Thus, either novel detection techniques that are able to overcome these disadvantages or advanced evaluation methods enabling the sophisticated analysis and description of flow fields are essential. In the present contribution, both areas are covered. A novel so-called neuronumerical hybrid allows to detect artefacts in conventional experimental particle image velocimetry (PIV) data of microorganismic flow fields generated by ciliates. The handling of artefacts is performed by the hybrid using a priori knowledge of the flow physics formulated in numerical expressions and the enormous potential of artificial neural networks in predicting artefacts and correcting them. In fact, the neuronumerical hybrid based on the physical knowledge provided by the Taylor’s hypothesis can detect not only spurious velocity vectors but also additional phenomena like a moving boundary, in the present case caused by the contraction of the zooid of a microorganism. Apart from the detection of the artefacts, a correction of the spurious velocity vectors is possible. Furthermore, a method to detect microscopic velocity fields based on nonlinear optical filtering, optical novelty filter (ONF) is presented. On the one hand, it can be employed to expose phase changes in flow fields directly from the nonlinear response and without additional tracers. On the other hand, it can be used to preprocess low quality images of flow fields loaded with particles and extract the motion of particles with an enhanced contrast. The flow fields obtained by the correlation based PIV method of the ONF filtered and unfiltered image sequences are compared and discussed.

Journal

Experiments in FluidsSpringer Journals

Published: Feb 21, 2007

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off