Full-Surface Data Storage

First, serial techniques are generalized to full-surface applications and the resulting systems analyzed for bit error rate (BER) performance under varying data density and noise levels. A multitrack Viterbi algorithm (MVA) performs well; however, the computational complexity limits the achievable data density. Equalization methods are introduced to increase achievable density. Iterative techniques based on greedy optimization are also studied. Though requiring relatively large computation, these iterative techniques have manageable memory requirements and can be used to implement soft-output algorithms. Finally, methods are developed that link maximum-likelihood (ML) data demodulation with ML image processing.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Weeks, William, IV

Contributors dc:contributor

• Blahut, Richard E.
• Steve Kang

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI9990187

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81364