Abstract-The application of model-based diagnosis schemes to real systems introduces many significant challenges, such as building accurate system models for heterogeneous systems with complex behaviors, dealing with noisy measurements and disturbances, and producing valuable results in a timely manner with limited information and computational resources. The Advanced Diagnostics and Prognostics Testbed (ADAPT), which was deployed at the NASA Ames Research Center, is a representative spacecraft electrical power distribution system that embodies a number of these challenges. ADAPT contains a large number of interconnected components, and a set of circuit breakers and relays that enable a number of distinct power distribution configurations. The system includes electrical dc and ac loads, mechanical subsystems (such as motors), and fluid systems (such as pumps). The system components are susceptible to different types of faults, i.e., unexpected changes in parameter values, discrete faults in switching elements, and sensor faults. This paper presents Hybrid TRANSCEND, which is a comprehensive model-based diagnosis scheme to address these challenges. The scheme uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. The computational methods are implemented as a suite of software tools that enable diagnostic analysis and testing through simulation, diagnosability studies, and deployment on the experimental testbed. Simulation and experimental results demonstrate the effectiveness of the methodology.Index Terms-Distributed diagnosis, electrical power distribution systems, hybrid bond graphs (HBGs), hybrid systems, model-based diagnosis.

In this article, we describe a distributed, peer-to-peer gesture recognition system along with a software architecture modeling technique and authority control protocol for ubiquitous cameras. This system performs gesture recognition in real time by combining imagery from multiple cameras without using a central server. We propose a system architecture that uses a network of inexpensive cameras to perform in-network video processing. A methodology for transforming well-designed single-node algorithm to distributed system is also proposed. Applications for ubiquitous cameras can be modeled as the composition of a finite-state machine of the system, functional services, and middleware. A service-oriented software architecture is proposed to dynamically reconfigure services when system state changes. By exchanging data and control messages between neighboring sensors, each node can maintain broader view of the environment with integrated video-processing results. Our prototype system is built on Windows machines, and uses standard video cameras as sensors and local network as a communication channel.