Self-stabilizing Algorithm for Maximal Graph Partitioning into Triangles

Neggazi, Brahim; Haddad, Mohammed; Kheddouci, Hamamache

doi:10.1007/978-3-642-33536-5_3

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…In order to apply the matrix vectorization approach to arbitrary patterns, an automated method of partitioning the pattern into smaller repeating blocks is required. Many partitioning algorithms have been developed for geometry partitioning with rectangles [35,36] and triangles [37]. This method has already been used for very large-scale integrated circuit layout Graphic Data System (GDS) files for data compression.…”

Section: Matrix Vectorizationmentioning

confidence: 99%

Machine vision methodology for inkjet printing drop sequence generation and validation

Brishty¹,

Grau²

2021

Flex. Print. Electron.

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Inkjet printing technology for printed microelectronics suffers from a number of non-idealities due to unwanted ink flow on the substrate. This can be mitigated and pattern fidelity can be improved by using an optimized drop placement sequence in contrast to the standard raster-scanning approach. However, it is challenging to auto-generate such printing sequences for complex printed patterns. Here, the generation and evaluation of the printing sequence are turned into a computer-vision problem. The desired printed pattern is taken as an input image and converted into a printing sequence using contour, symmetric, and matrix sequencing and corner compensation. After printing, pattern defects are detected by automated image processing to evaluate the printed pattern against the designed ground truth image and to determine the best possible algorithm for printing sequence generation for different pattern types. The machine vision-based experimental approach identifies the best solutions for solving the printing and defect optimization problem in terms of precision, recall, and accuracy. This methodology will enable the automated design of electronic circuits for applications such as wearable sensors, low-cost radio-frequency identification tags, or flexible displays.

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Section: Matrix Vectorizationmentioning

confidence: 99%

Machine vision methodology for inkjet printing drop sequence generation and validation

Brishty¹,

Grau²

2021

Flex. Print. Electron.

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“…In [18], authors considered a particular graph partitioning problem that consists of decomposing the graph into partitions of order k. [18] considered a particular graph decomposition problem that consisted in partitioning a graph with k 2 nodes into k partitions of order k. The proposed algorithm relies on self-stabilizing spanning tree construction and converges within 3(h + 1) steps where h is the height of the spanning tree. Furthermore, [16], [17] and [19] focused on decomposing the graph into clusters while [20] considered decomposition of graphs into triangles. Other self-stabilizing algorithms were proposed for graph colorings [21,22] that can be considered as decompositions into independent sets.…”

Section: Related Workmentioning

confidence: 99%

Polynomial Silent Self-Stabilizing p-Star Decomposition†

Haddad

Johnen

Köhler

2019

The Computer Journal

Self Cite

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We present a silent self-stabilizing distributed algorithm computing a maximal $\ p$-star decomposition of the underlying communication network. Under the unfair distributed scheduler, the most general scheduler model, the algorithm converges in at most $12\Delta m + \mathcal{O}(m+n)$ moves, where $m$ is the number of edges, $n$ is the number of nodes and $\Delta $ is the maximum node degree. Regarding the time complexity, we obtain the following results: our algorithm outperforms the previously known best algorithm by a factor of $\Delta $ with respect to the move complexity. While the round complexity for the previous algorithm was unknown, we show a $5\big \lfloor \frac{n}{p+1} \big \rfloor +5$ bound for our algorithm.

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“…For this reason, most publications assume that all faults are transient, i.e. no further faults occur during the stabilization of the system [12]. Fig.…”

Section: Self-stabilizing Systemmentioning

confidence: 99%

Self-Stabilizing Fault Tolerance Distributed Cyber Physical Systems

Ali¹,

Krayem²,

Chramcov³

et al. 2018

Proceedings of the 29th International DAAAM Symposium 2018

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Distributed Cyber-physical systems (DCPS) represent a new field in automatic control and recently they are increasingly used in life critical system, where the probability of tragic failure has to be kept below very low levels. In other side, fault tolerance has been used to face failures and achieve best performance of systems. Thus, it becomes critical to develop models that are tolerant toward the failure of system. This will enable guarantee of DCPS that will continue to run even through failure status. In this paper we take self-stabilizing as an example of fault tolerance in distributed cyber physical systems, where self-stabilization provides non-masking approach to fault tolerance. Then the Event-B approach is presented as formal method for system-level modelling and analysis. It is proposed using the Rodin modelling tool for Event-B that integrates modelling and proving.

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Self-stabilizing Algorithm for Maximal Graph Partitioning into Triangles

Cited by 7 publications

References 21 publications

Machine vision methodology for inkjet printing drop sequence generation and validation

Machine vision methodology for inkjet printing drop sequence generation and validation

Polynomial Silent Self-Stabilizing p-Star Decomposition†

Self-Stabilizing Fault Tolerance Distributed Cyber Physical Systems

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