Performance Modeling of Stochastic Diagnosis Engines

Critical systems are complex, consisting of thousands of components, which can fail at any time. Diagnosing these systems within a certain time is highly desirable. Traditional diagnosis algorithms are mostly deterministic, able to find single faults extremely fast and double faults reasonably quick as well. However, these algorithms fail to...

ANTARES: Automatic Diagnosis of Software/Hardware Systems

With the market becoming increasingly competitive, there is a pressure to deliver systems with more functionality and at the same cost, which thus leads to more complexity in terms of number of components. Moreover, the society is becoming increasingly dependent on these systems for its critical functions. This coupled with shrinking time-to...

BACINOL: Bayesian Circuit Analysis by Topology

Due to the high availability demands placed on modern hardware systems and the high cost associated with downtime, finding and fixing faults in these systems is an important problem. Unfortunately, the complexity of these systems is very large, making this problem very difficult. While recent innovations in the field of model-based diagnosis...

Evaluating model-based diagnosis for wireless sensor networks

Model-based diagnosis is a technique where a model of a system is combined with observations from that system, to generate diagnoses for failures of the system. This thesis looks at how model-based diagnosis can be applied to wireless sensor networks (WSNs). WSNs are ad-hoc wireless networks of small form-factor, embedded nodes with limited...

Analytical cost estimation for embedded systems

In the today's market of microcontrollers and FPGAs, there are so much different makes and models that making the right choice for a hardware platform for an electronics design is impossible. The spectrum is so wide that an electronics designer is unable to make a good choice for the best microcontroller or FPGA for his/her design based on the...