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Algorithmic Foundations of Self-Organizing Programmable Matter

Abstract Imagine that we have a piece of matter that can change its physical properties like its shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Envisioning systems of nano-sensors devices, programmable matter consists of systems of simple computational elements, called particles, that can establish and release bonds, compute, and can actively move in a self-organized way. In this dissertation the feasibility of solving fundamental problems relevant for programmable matter is investigated. As a model for such self-organizing particle systems (SOPS), the geometric amoebot model is introduced. In this model, parti... (more)
Created Date 2017
Contributor Derakhshandeh, Zahra (Author) / Richa, Andrea (Advisor) / Sen, Arunabha (Advisor) / Xue, Guoliang (Committee member) / Scheideler, Christian (Committee member) / Arizona State University (Publisher)
Subject Computer science / Computer engineering / Information technology / Algorithmic Foundations / Distributed Computing / Object Coating / Programmable Matter / Self-Organizing Particle Systems / Shape Formation
Type Doctoral Dissertation
Extent 136 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis