The rapid evolution of distributed sensor systems involving multiple heterogeneous sensors, the combination of mobile and stationary sensors, and dynamic information requirements presents a challenge for system resource utilization. On one hand, evolving standards and service oriented architecture concepts enables creation and use of multiple sensors and resources (including human reports). These technologies allow sensors and sources to be treated as Web services that can join the network at any time. On the other hand, traditional methods for optimizing these dynamic resources (e.g., classic optimization methods) are not feasible when the information requirements are rapidly changing, when there are multiple consumers of the requested information, and when the resources may dynamically appear (or disappear) from availability. This paper describes the concept of using a market-based approach for resource allocation. In this approach, sensors and network communications resources are treated as suppliers and human users (and data fusion algorithms) are treated as consumers. Dynamic auction methods are used to seek to match the dynamic information needs of the consumers with the capabilities of the suppliers. The "value" of the information to consumers is based on quantities such as accuracy, level of specificity, timeliness, etc. The paper describes the overall approach and presents results of numerical simulations.

Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion.

Feature extraction is one of the more difficult steps in image pattern recognition. Some sources of difficulty are the presence of irrelevant information and the relativity of a feature set to a particular application. Several preprocessing techniques for enhancing selected features and removing irrelevant data are described and compared. The techniques include gray level distribution linearization, digital spatial filtering, contrast enhancement, and image subtraction. Also, several feature extraction techniques are illustrated. The techniques are divided into spatial and Fourier domain operations. The spatial domain operations of directional signatures and contour tracing are first described. Then, the Fourier domain techniques of frequency signatures and template matching are illustrated. Finally, a practical image pattern recognition problem is solved using some of the described techniques.