This paper presents a practical approach to developing comprehensive sensor ontologies based upon deep knowledge models rather than capturing only superficial sensor attributes. It is proposed that the representation and utilization of deep sensor ontologies will enable a variety of sensor information system applications induding sensor parts compatibility determination, dynamic sensor selection and tasking, and reasoning about systems of sensors in which data must be fused and queried from a variety of sensor types within a myriad of environments.
This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including humans, animals, and vehicles using a sparse detector array comprised of only sixteen sensing elements deployed in a vertical configuration. The prototype sensor was built to collect silhouettes for a variety of objects and to evaluate several algorithms for classifying the data obtained from the sensor into two classes: human versus non-human. Initial tests show that the classification of individually sensed objects into two classes can be achieved with accuracy greater than ninety-nine percent (99%) with a subset of the sixteen detectors using a representative dataset consisting of 512 signatures. The prototype also includes a Webservice interface such that the sensor can be tasked in a network-centric environment. The sensor appears to be a low-cost alternative to traditional, high-resolution focal plane array imaging sensors for some applications. After a power optimization study, appropriate packaging, and testing with more extensive datasets, the sensor may be a good candidate for deployment in vast geographic regions for a myriad of intelligent electronic fence and persistent surveillance applications, including perimeter security scenarios.
This paper describes the development of an architecture for the discovery of sensor services leveraging ontology-based semantics in the search query. A prototype has been implemented based upon the architecture and can be used to support the development of expert system applications in which sensors of certain types, operational capabilities or physical properties are required to support applications within a network-centric environment. In the prototype, sensor services are listed in a registry that references a machineinterpretable ontology. The registry conforms to the Universal Discovery and Description Interface (UDDI) specification, but it is augmented with semantic matching via an ontology to increase the likelihood that relevant sensor services are discovered when needed by expert system applications.
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