A survey of comparison-based system-level diagnosis
ACM Computing Surveys (CSUR)
Theoretical Computer Science
Determining the conditional diagnosability of k-ary n-cubes under the MM* model
SIROCCO'11 Proceedings of the 18th international conference on Structural information and communication complexity
Diagnosability of star graphs with missing edges
Information Sciences: an International Journal
Theoretical Computer Science
Conditional Diagnosability of k-Ary n-Cubes under the PMC Model
ACM Transactions on Design Automation of Electronic Systems (TODAES)
t/t-Diagnosability of regular graphs under the PMC model
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Online Distributed Fault Diagnosis in Wireless Sensor Networks
Wireless Personal Communications: An International Journal
Wide-diameter of Product Graphs
Fundamenta Informaticae
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The notion of diagnosability has long played an important role in measuring the reliability of multiprocessor systems. Such a system is $t$-diagnosable if all faulty nodes can be identified without replacement when the number of faults does not exceed $t$, where $t$ is some positive integer. Furthermore, a system is strongly $t$-diagnosable if it can achieve $(t+1)$-diagnosability, except for the case where a node's neighbors are all faulty. In this paper, we investigate the strong diagnosability of a class of product networks, under the comparison diagnosis model. Based on our results, we can determine the strong diagnosability of several widely used multiprocessor systems, such as hypercubes, mesh-connected $k$-ary $n$-cubes, torus-connected $k$-ary $n$-cubes, and hyper Petersen networks.