Efficient Algorithms for Light Transmission, Focusing and Scattering Matrix Retrieval in Highly Diffusive 3D Random Media
dc.contributor.author | Guo, Han | |
dc.date.accessioned | 2019-02-07T17:54:37Z | |
dc.date.available | NO_RESTRICTION | |
dc.date.available | 2019-02-07T17:54:37Z | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/147580 | |
dc.description.abstract | Wavefront shaping provides an increasingly appealing avenue for imaging and other applications that require controlling electromagnetic waves passing through complex and disordered media. Indeed, these techniques allow researchers and engineers to exploit the properties of high-frequency waves, particularly optical ones, as they interact with these media to obtain nearly perfect transmission and a high degree of focusing. Here, we simulate the process of wave propagation in 3D random media using full-wave, integral equation-based computational electromagnetics schemes. We replicate many experimental observations relating to the existence of so-called open channels in non-absorbing random media and the distribution of their transmission coefficients. In addition, we develop new schemes for manipulating these waves, e.g. by focusing them onto one or multiple spots in the output plane. Furthermore, we leverage the computational methods to develop new schemes for characterizing random media, e.g. by computing their scattering and transmission matrices under a variety of conditions. Finally, we study the transmission properties of absorbing media and find a universal fluctuant pattern of their maximal transmission coefficients. | |
dc.language.iso | en_US | |
dc.subject | wave propagation and scattering | |
dc.subject | wavefront shaping technique | |
dc.subject | computational electromagnetics | |
dc.subject | applied optics | |
dc.subject | numerical simulation | |
dc.subject | random media | |
dc.title | Efficient Algorithms for Light Transmission, Focusing and Scattering Matrix Retrieval in Highly Diffusive 3D Random Media | |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | PhD | en_US |
dc.description.thesisdegreediscipline | Electrical Engineering | |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | |
dc.contributor.committeemember | Michielssen, Eric | |
dc.contributor.committeemember | Nadakuditi, Raj Rao | |
dc.contributor.committeemember | Schotland, John Carl | |
dc.contributor.committeemember | Grbic, Anthony | |
dc.subject.hlbsecondlevel | Computer Science | |
dc.subject.hlbsecondlevel | Electrical Engineering | |
dc.subject.hlbsecondlevel | Engineering (General) | |
dc.subject.hlbsecondlevel | Mathematics | |
dc.subject.hlbsecondlevel | Physics | |
dc.subject.hlbsecondlevel | Science (General) | |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | |
dc.subject.hlbtoplevel | Engineering | |
dc.subject.hlbtoplevel | Science | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/147580/1/hanguo_1.pdf | |
dc.identifier.orcid | 0000-0002-7206-8528 | |
dc.identifier.name-orcid | Guo, Han; 0000-0002-7206-8528 | en_US |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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