In this paper we present a model-based approach for designing quality of service adaptive applications. We have developed a prototype distributed QoS modeling environment (DQME) that captures important elements of dynamic QoS adaptation at the model level. This modeling environment is designed independent of and can be integrated with, specific application domains to capture their QoS features and adaptation strategies. It combines the domain-specific modeling capability of the generic modeling environment with the QoS adaptation mechanisms of the quality objects middleware framework. DQME captures both the QoS and the functional concerns of distributed real-time embedded systems, and provides clear separation of these two. Integrated code-synthesis tools facilitate code generation and model refinement. We present a signal analyzer case study to demonstrate the use of the DQME modeling tool in real world applications.

We consider the problem of estimating a parametric model that describes radar backscattering from synthetic aperture radar imagery. We adopt a scattering center model that incorporates both frequency and aspect dependence of scattering. We develop an approximate maximum likelihood algorithm for parameter estimation directly on regions of the SAR image. The algorithm autonomously selects model order and structure. Results are presented for both synthetic and measured SAR imagery, and algorithm accuracy is compared with the Cramer-Rao bound.