Library
Export 17 results:
Filters: Author is Cory Henson [Clear All Filters]
. SPARQL Query Re-writing for Spatial Datasets Using Partonomy Based Transformation Rules. In SPARQL Query Re-writing for Spatial Datasets Using Partonomy Based Transformation Rules. 2009. 
(0 bytes)
(0 bytes). An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology. 2011 ;:345-376. 
(1.42 MB)
(1.42 MB). Semantic Sensor Web. 2008 ;. 
(8.17 MB)
(8.17 MB). Semantic Provenance for eScience: Managing the Deluge of Scientific Data. 2008 ;. (0 bytes)
(0 bytes). Representation of Parsimonious Covering Theory in OWL-DL. In 8th International Workshop on OWL: Experiences and Directions (OWLED 2011). San Francisco, California; 2011.
. A Survey of the Semantic Specification of Sensors. In 2nd International Workshop on Semantic Sensor Networks (SSN09); 2009. (0 bytes)
(0 bytes). Trusted Semantic Sensor Network. In AFRL Strategic Expansion Workshop on Cognitive Modeling. Dayton, OH: AFRL; 2011.
. Semantic Sensor Web. In AGU Fall Meeting; 2008.
. Semantic Sensor Web. In Semantic Interoperability Community of Practice (SICoP) Conference: Building Semantic Interoperability Solutions for Information Sharing and Integration; 2008.
. Active Perception Over Machine and Citizen Sensing. In Semantic Technology Conference; 2011. (0 bytes)
(0 bytes). Sensor Networks Survey. In daytaOhio; 2007. (8.53 MB)
(8.53 MB). Sensor Data and Perception: Can Sensors Play 20 Questions?. Dagstuhl Seminar 10042: Semantic Challenges in Sensor Networks. Dagstuhl, Germany: Schloss Dagstuhl; 2010. (250.48 KB)
(250.48 KB). Sensor Data Management. In 2007. (4.89 MB)
(4.89 MB). Semantic Sensor Web. In Semantic Interoperability Community of Practice (SICoP): Sensor Standards Harmonization WG; 2008.
. A Semantics-based Approach to Machine Perception. Department of Engineering and Computer Science. [Dayton]: Wright State University; 2013. (4.33 MB)
(4.33 MB). Semantic Perception: Converting Sensory Observations to Abstractions. IEEE Internet Computing; 2012 ;16(2):26-34. (3.85 MB)
(3.85 MB). An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices. In 11th International Semantic Web Conference. Boston, MA: Springer; 2012. p. 149-164. (1.11 MB) (5.88 MB)
(1.11 MB) (5.88 MB)