Volume 10, Number 2 • March/April 2002 • Moving Forward

Technology Opportunity Showcasehighlights some unique technologies that NASA has developed and which we believe have strong potential for commercial application. While the descriptions provided here are brief, they should provide enough information to communicate the potential applications of the technology.or more detailed information, contact the person listed. Please mention that you read about it in Innovation

Accelerating Electronic Thermometer Readings

NASA Stennis Space Center is seeking qualified companies to license this advanced adaptive predictive algorithm for use in commercial applications. The algorithm reduces the time required to determine final steady-state temperature. This technology accelerates temperature determination time by using sample readings and computing the final temperature with the predictive algorithm. It is quick, accurate and robust, and can be used in a variety of commercial electronic thermometer applications.

Benefits of the technology include: faster response time—can improve the response time of existing sensors without additional sensor development or creating a new sensor; accuracy—does not sacrifice accuracy for speed; robustness—can be used with various types of temperature sensors and potentially other types of sensors; and simple integration—can be implemented with erasable, programmable read-only memory (EPROM) in existing systems.

Potential commercial applications include uses for industrial control with nuclear power, chemical processing and industrial processing; medical applications in medical clinics, for home use and in veterinary clinics; and for hydrogen or gas-detection systems.

The adaptive predictive algorithm for electronic thermometers uses sample readings during the initial rise in temperature and applies an algorithm that accurately and rapidly predicts the steady-state temperature. The final steady-state temperature of an object can be calculated based on the analysis of the temperature signals acquired by the sensor and predetermined variables from the sensor characteristics. This advanced algorithm can be implemented in existing software or hardware with an EPROM. The capability for easy integration eliminates the expense of developing a whole new system that offers the benefits provided by this technology. f

For more information, contact the National Technology Transfer Center, Marketing Department, 316 Washington Ave., Wheeling, WV 26003, 800/678-6882, hottechnologies@nttc.edu. Please mention you read about it in Innovation..

Perilog Contextual Search and Retrieval Software Tools

NASA Ames Research Center seeks companies to commercialize Perilog, a suite of data-mining tools that retrieves and organizes contextually relevant data from any sequence of terms (text, musical notes, genetic data, etc.). It is an integrated set of methods that can be used to intelligently mine information from databases.

Perilog unearths data that is contextually relevant to the subject being investigated. The software measures the degree of contextual association for large numbers of term pairs in text to produce models that capture the structure of the text. Perilog statistically compares these models to measure their degree of similarity to a query model, develops a ranking and presents the search results to the user. Perilog was originally designed to support the FAA’s Aviation Safety Reporting System (ASRS). The ASRS testbed demonstrated Perilog’s power on a topical database of thousands of documents. The algorithm was powerful enough to produce the first quantitative evidence of situational relationships between reported commercial aviation incidents and a specific type of aviation accident.

Perilog relies on four methods for mining contextually relevant data: keyword-in-context search—retrieves narratives that contain one or more user-specified keywords in typical or selected contexts, and ranks the narratives on their relevance to the keywords in context; flexible, model-based phrase search—retrieves narratives that contain one or more user-specified phrases, and ranks the narratives on their relevance to the phrases; model-based phrase generation—produces a list of phrases from documents that contain a user-specified word or phrase; and narrative-based phrase discovery—finds phrases that are related to topics of interest by generating a list of narratives similar in meaning to the keyword or phrase query.

Perilog offers the following features: analysis by measuring contextual associations within sequences; relevance ranking by ranking collections of contexts on similarity to other collections of contexts; search by providing more effective key term and phrase search; phrase mining by helping users to know what phrases occur in a database and discovering phrases that are contextually related to key terms or phrases; and modeling by representing contexts within sequences as pairwise inter-term contextual associations having quantified degrees of association.

Perilog has applications in a multitude of fields and is available for licensing now. For additional technical descriptions, please see the following Web sites: http://ettc.usc.edu/ames/perilog/homepage.html and http://human-factors.arc.nasa.gov/IHpublications/ mcgreevy/ Q

For further information, contact the National Technology Transfer Center, Marketing Department, 316 Washington Ave., Wheeling, WV 26003, 800/678-6882.