VASODILATOR FOR NEONATAL PULMONARY HYPERTENSION

Not Available Unexpected breakdowns in turbine and other machinery costs government and industry billions of dollars, results in loss of life, and compromises military preparedness. Preventive maintenance and part replacement are routinely done in an attempt to reduce breakdown probability. This is very expensive and often exacerbates the problem by contributing to failures. High-value machines such as jet engines and power turbines are routinely monitored by an assortment of sensors to produce a larage quantity of diagnostic data. Processing tecniques currently applied to these data are typically far below the state-of-the-art processing techniques. Machine signatures tend to be unique and non-stationary. They typically exhibit a structure similar to that encountered in model-based target/patters recognition and automatic speech recognition. Much expertise in processing signaturs of similar structure to machine monitoring readouts exists in and around the WPAFAB and researc at the Air Force Research Laboratory, Frontier Technology, Inc. and Ohio State University to apply new algorithmus/paradigms from image processing and ATR to the probem of machine monitoring, with the goal of discovering new failure prognostics. Signal processing techniques developed for this effort will be of immediate utility to jet engine monitoring activities in the other military services and in the private sector.