Tiling Under Tomographic Constraints: A Note on Reconstruction: Difference between revisions

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Title: Tiling Under Tomographic Constraints: A Note on Reconstruction
Title: Tiling Under Tomographic Constraints: A Note on Reconstruction


Research Question: Can we reconstruct a tiling from its projections, given a set of tiles with different shapes?
Research Question: How can we reconstruct a tiling from its projections, given a set of tiles with specific shapes?


Methodology: The researchers used a combination of combinatorial analysis and computer simulations to study the problem of reconstructing a tiling from its projections. They considered tiles that are hole-less polyominoes and investigated the complexity of reconstructing a tiling for different sets of tiles.
Methodology: The researchers used a combination of combinatorial analysis and computer simulations to study the problem of reconstructing a tiling from its projections. They focused on tiles that are hole-less polyominoes, which are shapes that can be formed by placing tiles together without any gaps.


Results: The researchers proved that for some sets of tiles, the problem of reconstructing a tiling from its projections is NP-complete, meaning that it is computationally difficult and may require exponential time to solve. They also provided a method to determine whether a given set of projections can be realized by a tiling.
Results: The researchers proved that the problem of reconstructing a tiling from its projections is NP-complete for certain sets of tiles. This means that the problem is computationally difficult and it is unlikely that an efficient algorithm can be found to solve it for all possible sets of tiles.


Implications: The results of this study have implications for the field of discrete tomography, which involves reconstructing discrete objects from their projections. The study provides insights into the complexity of this problem and may lead to more efficient algorithms for reconstructing tilings from their projections. Additionally, the results may have applications in other areas of combinatorial mathematics and computer science.
Implications: The results of this study have implications for the field of discrete tomography, which involves reconstructing discrete objects from their projections. The study provides a better understanding of the complexity of this problem and may lead to the development of more efficient algorithms for specific cases. Additionally, the study contributes to the ongoing research in combinatorial analysis and computer science.


Conclusion: In this study, the researchers made progress towards a comprehensive classification of tiling reconstruction problems by proving NP-completeness results for several sets of tiles. They also provided a method to determine whether a given set of projections can be realized by a tiling.
Link to Article: https://arxiv.org/abs/0108010v3
 
Link to Article: https://arxiv.org/abs/0108010v2
Authors:  
Authors:  
arXiv ID: 0108010v2
arXiv ID: 0108010v3


[[Category:Computer Science]]
[[Category:Computer Science]]
[[Category:Tiles]]
[[Category:Tiling]]
[[Category:Tiling]]
[[Category:From]]
[[Category:Projections]]
[[Category:Projections]]
[[Category:From]]
[[Category:Study]]
[[Category:Tiles]]
[[Category:Reconstructing]]

Latest revision as of 02:44, 24 December 2023

Title: Tiling Under Tomographic Constraints: A Note on Reconstruction

Research Question: How can we reconstruct a tiling from its projections, given a set of tiles with specific shapes?

Methodology: The researchers used a combination of combinatorial analysis and computer simulations to study the problem of reconstructing a tiling from its projections. They focused on tiles that are hole-less polyominoes, which are shapes that can be formed by placing tiles together without any gaps.

Results: The researchers proved that the problem of reconstructing a tiling from its projections is NP-complete for certain sets of tiles. This means that the problem is computationally difficult and it is unlikely that an efficient algorithm can be found to solve it for all possible sets of tiles.

Implications: The results of this study have implications for the field of discrete tomography, which involves reconstructing discrete objects from their projections. The study provides a better understanding of the complexity of this problem and may lead to the development of more efficient algorithms for specific cases. Additionally, the study contributes to the ongoing research in combinatorial analysis and computer science.

Link to Article: https://arxiv.org/abs/0108010v3 Authors: arXiv ID: 0108010v3