TY - GEN
T1 - Collaborative computation in self-organizing particle systems
AU - Porter, Alexandra
AU - Richa, Andrea
N1 - Funding Information:
This work was supported in part by NSF under Awards CCF-1353089 and CCF-1422603, and matching NSF REU awards; this work was conducted while the first author was an undergraduate student at ASU.
Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms.
AB - Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms.
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U2 - 10.1007/978-3-319-92435-9_14
DO - 10.1007/978-3-319-92435-9_14
M3 - Conference contribution
AN - SCOPUS:85049022175
SN - 9783319924342
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 188
EP - 203
BT - Unconventional Computation and Natural Computation - 17th International Conference, UCNC 2018, Proceedings
A2 - Stepney, Susan
A2 - Verlan, Sergey
PB - Springer Verlag
T2 - 17th International Conference on Unconventional Computation and Natural Computation, UCNC 2018
Y2 - 25 June 2018 through 29 June 2018
ER -