Object recognition is an important problem that has many applications that are of interest to the Air Force. Object recognition is a key enabler to autonomous exploitation of intelligence, surveillance and reconnaissance (ISR) data, which can make the automatic searching of millions of hours of video practical.
Many biological molecules and common surfaces carry an electrical charge. For example, DNA has a strong negative charge, and so does an amorphous form of silicon dioxide known as silica, the material most people recognize as “glass.” A charged molecule or surface, along with the electrically compensating layer of ions in the adjacent solution, is known as the electrical double layer (EDL).
“When two genes interact to cause a clinically important phenotype, we can leverage genotypic information at one of the loci in order to improve our ability to detect the other,” said Veronica Vieland, Ph.D., vice president for computational research and director, Battelle Center for Mathematical Medicine.
A recently developed, evolutionary computation approach offers researchers an alternative approach to search for models that can best explain experimental data derived from applications such as economics. Esmail Bonakdarian, Ph.D., a Franklin University assistant professor of computing sciences and mathematics, leveraged Ohio Supercomputer Center resources to test the underlying algorithm.