"We are developing software that will monitor dangerous algae and various biochemical anomalies for public health," said Cai, who noted other applications for this software might range from studying cancer tumor growth to tracking serial killers. "The other very important part of this research is to adapt our findings to homeland security where we may use this method to track the bioterrorism activities pertaining to the nation's waterways."
Cai said his NASA/ESTO-funded research team is developing a spatiotemporal data mining system to track harmful ocean objects from NASA's SeaWiFS satellite images and NOAA's oceanographic data. These harmful ocean objects, known as red tide, include a naturally occurring microscopic algae Karenia brevis, which form only in the Gulf of Mexico and release toxins deadly to fish and marine mammals. The red tide algae are also potentially harmful to humans if ingested through unmanaged shellfish. On the worst days, the irritating vapors from red tide algae or the noxious smell from the rotting carcasses of poisoned fish linger along the coast, impacting tourism.
Nationwide, all forms of red tide algae cause commercial fisheries to lose $18 million a year. More than $20 million was spent on public healthcare to handle thousands of cases in which humans ingested shellfish poisoned by red tide between 1987 and 1992, according to NOAA's 2005 Economic Statistics Report.
At present, NOAA researchers must manually scan through thousands of images in order to test and evaluate models that determine where red tide is impacting both humans and sea life.
"We are pleased to work with Carnegie Mellon researchers to improve our monitoring of red tide," said Richard P. Stumpf, an oceanographer with the NOAA National Ocean Service and a co-investigator for this project. "In fact, a huge, huge problem for us is trying to validate any algorithm for the harmful algae. Evaluating the imagery is very time-consuming."
Stumpf said he is collaborating with Carnegie Mellon researchers to devise a way to simply convert harmful algae data into an image field that can be easily accessed to map out the deadly algae's location and where it may travel. The new software is designed to incorporate multiple computer vision algorithms and the domain knowledge from NOAA scientists.
"Spatiotemporal data mining extracts changing spatial patterns from continuous data flow. It is a rapidly growing field, which includes inverse physics, machine learning and human-computer interaction," Cai said.
About Carnegie Mellon CyLab:
Carnegie Mellon CyLab is a university-wide, multidisciplinary initiative that builds on more than two decades of Carnegie Mellon's leadership in information technology and involves more than 200 faculty, students and staff from six departments and three colleges within Carnegie Mellon. CyLab's comprehensive research program spans technology, management and policy issues. CyLab offers professional master's degree programs in networking and security through the Information Networking Institute, the education arm of CyLab, and a cache of executive education programs. CyLab also participates in the Federal Cyber Scholarship for Service (SFS) program for graduate information security education and offers an education and research capacity building program for faculty and members of minority serving institutions. Carnegie Mellon has been designated as a Center of Academic Excellence by the National Security Agency.