Automated 3-D Feature Extraction from 3-D Data Sets
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'George K. Lea', 'PO_EMAI': None, 'PO_PHON': None}
The objective of the research is to decompose two-dimensional and               three-dimensional steady  state and two-dimensional time dependent              fluid flow data sets into elementary structures for purposes of                 description, analysis and comparison.  Results obtained from numerical          calculations and modern (optical) diagnostics are often too                     complicated for manual inspection, manipulation and display.  A                 simpler but still accurate description of these results is needed to            facilitate data understanding.                                                                                                                                  New methods will be developed for automatic extraction of features              from vector data.  Vector fields such as velocity, vorticity or                 pressure gradient are to be decomposed into elementary structures such          as critical points and dividing streamlines.  Topological features are          recognized and the connections between them determined.  The set of             features and connections are represented in the form of a graph, from           which displays of topologically significant features can be selected            and displayed.  This simplified feature description also aids data              interpretation and allows comparison between data bases using                   syntactic pattern recognition.   Such data can be both due to large-            scale computations or multi-channel experimental measurements.                                                                                                  The need for this research is becoming increasingly urgent with the             development of new optical three-dimensional flow diagnostic                    techniques and improved speed and storage capability of                         supercomputers.  Currently developed digital image processing and               pattern recognition algorithms are mainly aimed at two-dimensional              imagery.  Extension of these techniques to higher dimensional data              bases needs to be carefully considered and is not a simple task.                Research that appears to hold promise for developing automatic methods          for the manipulation, understanding and display of multi-dimensional            vector fields will be carried out.
{'FirstName': 'Lambertus', 'LastName': 'Hesselink', 'PI_MID_INIT': None, 'PI_SUFX_NAME': 'Jr', 'PI_FULL_NAME': 'Lambertus Hesselink', 'EmailAddress': 'bert@kaos.stanford.edu', 'NSF_ID': '000449360', 'StartDate': '05/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Stanford University', 'CityName': 'STANFORD', 'ZipCode': '943052004', 'PhoneNumber': '6507232300', 'StreetAddress': '450 JANE STANFORD WAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '16', 'CONGRESS_DISTRICT_ORG': 'CA16', 'ORG_UEI_NUM': 'HJD6G4D6TJY5', 'ORG_LGL_BUS_NAME': 'THE LELAND STANFORD JUNIOR UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
[{'Code': '140600', 'Text': 'TTP-Thermal Transport Process'}, {'Code': '144300', 'Text': 'FD-Fluid Dynamics'}, {'Code': '151800', 'Text': 'CONTROL, NETWORKS, & COMP INTE'}, {'Code': '917000', 'Text': 'Other PRA-Othr Prgrm Rltd Admn'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8815815.xml'}