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The University of New Hampshire
Institute for Study of Earth, Oceans, and Space
2006 Research and Discover Program
Using Active and Passive Microwave
Records for Dectecting Firn Characteristics in Greenland:
A New Indication of Melt
Mentor: Dr. Fahnestock
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View Research Documentation |
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| Abstract: Satellite-based active microwave imaging instruments
(Synthetic Aperture Radars and Scatterometers) and passive microwave
radiometers are recording the modification of the structure
of the firn, the compacted snow layer that has remained at or
near the surface of an ice sheet for one season but has not
yet compressed into glacial ice, on the Greenland ice sheet
caused by new melting at high elevation. Even a few days of
melt at a site on the ice sheet that has not melted in decades
can produce a large sustained change in the microwave scattering
properties of the snowpack; this change is clearly reflected
for years, diminishing only slowly as the layer generated by
the melt event is buried. In the last 5 years, x% of the area
of the dry snow zone has been modified in this manner by surface
melt, compared with y% (little modification) in the previous
9 (to 13 or 20+ years, depending on the data used); these effects
decay slowly, and if the present trend toward increased melting
continues, the dry snow zone (melt-free snowpack) of the interior
of Greenland could disappear completely in the near future.
A melt detection technique calculated data for the passive microwave
record, and it produced an approximate amount of melt days compared
to the active microwave records done by previous investigators;
this allowed for a new indication of melt as well as greater
time series in the passive microwave record. |
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The University of Kanas
Depeartment of Computer Science and Electrical Engineering
2005 Research Experience for Undergraduates (REU)
Using Ensemble Learning for Detecting
Data Abnormaties in Databases
Mentors: Drs. Gogineni, Tsatsoulis, and Miss. Lee |
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| Abstract: Software engineers at the University of Kansas have
developed SmartXAutofill, an intelligent data entry assistant
for predicting and automating inputs for eXtensible Markup Language
(XML) and other text forms based on the contents of historical
documents in the same domain. SmartXAutofill utilizes an ensemble
classifier, which is a collection of a number of classification
algorithms where each individual internal classifier predicts
the optimum value for a particular data field. As the system
operates, the ensemble classifier learns which individual internal
classifier works better for a particular domain and adapts to
the domain without the need to develop special classifiers.
The ensemble classifier has proven that it performs at least
as well as the best individual internal classifier. The ensemble
classifier contains a voting and weighting system for inputting
values into a particular data field.
Because the existing technology can predict, suggest and
automate data fields, the investigator tested whether the
same technology can be used to identify incorrect data. Given
existing data transmitted by sensors and other instruments,
the investigator studied whether the ensemble classifier technology
can identify data abnormalities and correctness in future
sensor data transmission. The solution would be applied in
a project funded by the National Science Foundation, Polar
Radar for Ice Sheet Measurements (PRISM), using innovative
sensors to measure the thickness and characteristics of the
ice sheets in Greenland and Antarctica, with the goal of understanding
how the ice sheets are being affected by global climate change.
PRISM sensors continuously send information that is collected
and catalogued. The ensemble classifier will check the data
for correctness by predicting which values should be there,
and if the actual values are different, it will flag the data
as possibly corrupted, and allow an operator to later study
it and determine if it is correct or not. This technology
will allow the PRISM intelligent systems to automatically
determine the correctness of sensor and other data, and contributes
to the PRISM project by adding a level of intelligence and
prediction to the sensor suite. |
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The University of Kanas
Department of Computer Science and Electrical Engineering
2004 Research Experience for Undergraduates (REU)
UML Class Diagrams of PRISM Multi-Agent Subsystem
Using UML Class Diagrams
Mentors: Drs. Gogineni and Tsatsoulis
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| Abstract: Hurricanes, tornados, thunderstorms, and other
natural disasters can have many devastating outcomes. Global
warming, the prime investigated natural disaster of the PRISM
(Polar Radar for Ice Sheet Measurements) project, has a tremendous
effect on the sea level rise. Scientists and researchers have
theorized that the excess water is being allocated from the
polar ice sheets of Greenland and Antarctica due to the long-term
results of global warming; however, there are few resources
to confirm the gain or loss ice.
Scientists and researchers of the PRISM project have applied
their expertise on teams based on the areas of robotics, communications,
intelligent systems, and radar. These areas were essential
in measuring the ice thickness an determining the bedrock
below the ice sheets in Greenland and Antarctica. In this
research, the investigator worked with the Intelligent Systems
team by learning the messaging patterns between the data producing
agents and the requesting agents. The investigator also created
Unified Modeling Language (UML) class diagrams of a messaging
subsystem to represent the collaboration and communication
between these two types of agents using eXtensible Markup
Language (XML) with the Foundations for Intelligent Physical
Agents (FIPA) standard codes. The class diagrams assisted
scientists and researchers in planning new features for the
multi-agent system.
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