A Geographic Information System Framework for the Management of Sensor Deployments

A prototype Geographic Information System (GIS) framework has been developed to map, manage, and monitor sensors with respect to other geographic features, including land base and in-plant features. The GIS framework supports geographic placement and subsequent discovery, query, and tasking of sensors in a network-centric environment using Web services. The framework couples the GIS feature placement logic of sensors with an extensible ontology which captures the capabilities, properties, protocols, integrity constraints, and other parameters of interest for a large variety of sensor types. The approach is significant in that custom, GIS-based interfaces can be rapidly developed via the integration of sensors and sensor networks into applications without having detailed knowledge of the sensors’ underlying device drivers by leveraging service-oriented computing infrastructure within the GIS framework

Real-Time Object Classification Using a Custom Sparse Array Profile Sensor on an Embedded Microcontroller

This thesis presents implementation of data acquisition and object classification algorithms on a low-resource microcontroller for real-time, broad-scale object classification using a low-cost sparse detector imaging sensor. The sensor is designed to detect and classify objects into the broad categories of human, vehicle, or animal, making note of objects of high interest. This paper encompasses software for implementation onboard a low-resource microcontroller platform to acquire, process, and classify crude images of subjects for classification purposes. This paper also encompasses improvements made to a prototype hardware system to form a custom sensor array from commercially available, off-the-shelf hardware components. The sensor is designed for deployment scenearios to monitor vast geographic areas where broad monitoring is required with low false-alarm rates generated by objects of less interest