Software architecture: perspectives on an emerging discipline
Software architecture: perspectives on an emerging discipline
Self-adaptive software for signal processing
Communications of the ACM
MILAN: A Model Based Integrated Simulation Framework for Design of Embedded Systems
OM '01 Proceedings of the 2001 ACM SIGPLAN workshop on Optimization of middleware and distributed systems
Adaptive Image Analysis for Aerial Surveillance
IEEE Intelligent Systems
Control Theory-Based Foundations of Self-Controlling Software
IEEE Intelligent Systems
The Vision of Autonomic Computing
Computer
Model-Integrated Program Synthesis Environment
ECBS '96 Proceedings of the IEEE Symposium and Workshop on Engineering of Computer Based Systems
On the Role of Software Architectures in Runtime System Reconfiguration
CDS '98 Proceedings of the International Conference on Configurable Distributed Systems
A Model-Based Self-Adaptive Approach to Image Processing
ECBS '04 Proceedings of the 11th IEEE International Conference and Workshop on Engineering of Computer-Based Systems
Online Control for Self-Management in Computing Systems
RTAS '04 Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium
An Architectural Approach to Autonomic Computing
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Metamodeling-rapid design and evolution of domain-specific modeling environments
ECBS'99 Proceedings of the 1999 IEEE conference on Engineering of computer-based systems
| Hi-index | 0.00 |
Implementing image-processing systems can require significant effort and resources due to information volume and algorithm complexity. Autonomic behavior is required for many image processing systems to perform consistently under real-world conditions. Model Integrated Computing (MIC) based image processing systems show promise in supporting solutions of these complex problems. While MIC has contributed to the advancement of performing complex image processing tasks on parallel-embedded systems, it has not addressed a challenging class of algorithms that adapt the image-processing algorithm based on the information or state of the image processing system. This paper addresses creating an autonomically grounded image-processing structure and environment based on MIC that allows solutions of complex image processing problems to be built and executed rapidly. The framework involves creating a new modeling representation for image processing adaptation mechanisms within a structure that allows growth in complexity and integration of autonomic constraints. The proposed MIC-based autonomically grounded image-processing environment will generate a solution given the modeling constraints and execute it on a number of hardware architectures.