Almost sure fault tolerance in random graphs
SIAM Journal on Computing
Built-In Testing of Integrated Circuit Wafers
IEEE Transactions on Computers
Introduction to algorithms
Distributed Diagnosis Algorithms for Regular Interconnected Structures
IEEE Transactions on Computers
Efficient Diagnosis of Multiprocessor Systems Under Probabilistic Models
IEEE Transactions on Computers
A Diagnosis Algorithm for Constant Degree Structures and Its Application to VLSI Circuit Testing
IEEE Transactions on Parallel and Distributed Systems
A Graph Partitioning Approach to Sequential Diagnosis
IEEE Transactions on Computers
Correct and Almost Complete Diagnosis of Processor Grids
IEEE Transactions on Computers
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Almost Sure Diagnosis of Almost Every Good Element
IEEE Transactions on Computers
Fault-diagnosis of grid structures
Theoretical Computer Science - Dependable computing
Diagnosis of Regular Structures
DSN '00 Proceedings of the 2000 International Conference on Dependable Systems and Networks (formerly FTCS-30 and DCCA-8)
Self diagnosis of processor arrays using a comparison model
SRDS '95 Proceedings of the 14TH Symposium on Reliable Distributed Systems
Number of mutual connections in neighborhoods and its application to self-diagnosable systems
Information Processing Letters
Diagnosabilities of Regular Networks
IEEE Transactions on Parallel and Distributed Systems
Worst-Case Diagnosis Completeness in Regular Graphs under the PMC Model
IEEE Transactions on Computers
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The problem of identifying the faulty units in regularly interconnected systems is addressed. The diagnosis is based on mutual tests of units, which are adjacent in the "system graph" describing the interconnection structure. This paper evaluates an algorithm named EDARS (Efficient Diagnosis Algorithm for Regular Structures). The diagnosis provided by this algorithm is provably correct and almost complete with high probability. Diagnosis correctness is guaranteed if the cardinality of the actual fault set is below a "syndrome-dependent bound," asserted by the algorithm itself along with the diagnosis. Evaluation of EDARS relies upon extensive simulation which covered grids, hypercubes, and cube-connected cycles (CCC). Simulation experiments showed that the degree of the system graph has a strong impact over diagnosis completeness and affects the "syndrome-dependent bound," ensuring correctness. Furthermore, a comparative analysis of the performance of EDARS, with hypercubes and CCCs on one side and grids of the same size and degree on the other side, showed that diameter and bisection width of the system graph also influence the diagnosis correctness and completeness.