Belief Propagation Reconstruction for Discrete Tomography

International audienceWe consider the reconstruction of a two-dimensional discrete image from a set of tomographic measurements corresponding to the Radon projection. Assuming that the image has a structure where neighbouring pixels have a larger probability to take the same value, we follow a Bayesian approach and introduce a fast message-passing reconstruction algorithm based on belief propagation. For numerical results, we specialize to the case of binary tomography. We test the algorithm on binary synthetic images with different length scales and compare our results against a more usual convex optimization approach. We investigate the reconstruction error as a function of the number of tomographic measurements, corresponding to the number of projection angles. The belief propagation algorithm turns out to be more efficient than the convex-optimization algorithm, both in terms of recovery bounds for noise-free projections, and in terms of reconstruction quality when moderate Gaussian noise is added to the projections

Rapid Recovery for Systems with Scarce Faults

Our goal is to achieve a high degree of fault tolerance through the control of a safety critical systems. This reduces to solving a game between a malicious environment that injects failures and a controller who tries to establish a correct behavior. We suggest a new control objective for such systems that offers a better balance between complexity and precision: we seek systems that are k-resilient. In order to be k-resilient, a system needs to be able to rapidly recover from a small number, up to k, of local faults infinitely many times, provided that blocks of up to k faults are separated by short recovery periods in which no fault occurs. k-resilience is a simple but powerful abstraction from the precise distribution of local faults, but much more refined than the traditional objective to maximize the number of local faults. We argue why we believe this to be the right level of abstraction for safety critical systems when local faults are few and far between. We show that the computational complexity of constructing optimal control with respect to resilience is low and demonstrate the feasibility through an implementation and experimental results.Comment: In Proceedings GandALF 2012, arXiv:1210.202

CSP methods for identifying atomic actions in the design of fault tolerant concurrent systems

Limiting the extent of error propagation when faults occur and localizing the subsequent error recovery are common concerns in the design of fault tolerant parallel processing systems, Both activities are made easier if the designer associates fault tolerance mechanisms with the underlying atomic actions of the system, With this in mind, this paper has investigated two methods for the identification of atomic actions in parallel processing systems described using CSP, Explicit trace evaluation forms the basis of the first algorithm, which enables a designer to analyze interprocess communications and thereby locate atomic action boundaries in a hierarchical fashion, The second method takes CSP descriptions of the parallel processes and uses structural arguments to infer the atomic action boundaries. This method avoids the difficulties involved with producing full trace sets, but does incur the penalty of a more complex algorithm

Optimal recovery of integral operators and its applications

In this paper we present the solution to the problem of recovering rather arbitrary integral operator based on incomplete information with error. We apply the main result to obtain optimal methods of recovery and compute the optimal error for the solutions to certain integral equations as well as boundary and initial value problems for various PDE's