Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Rex Buddenberg, Steven Iatrou. Includes bibliographical references (p. 119-120). Also available online.

Thesis (M.S.)--Rutgers University, 2009. / "Graduate Program in Microbiology and Molecular Genetics." Includes bibliographical references (p. 63-71).

Thesis (Ph. D.)--Stanford University, 1986. / Cover title. "June 1986." Includes bibliographical references.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: XML; path expression; axis; order; indexing; inlined; compression; XPath; lossless. Includes bibliographical references (p. 45-46).

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed February 12, 2010). Thesis advisor: Dr. Toni Kazic. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 161-171). Available also in a digital version from Dissertation Abstracts.

Thesis--PlanB (M.S.)--University of Wisconsin--Stout, 2002. / Includes bibliographical references.

As semiconductor device scaling is reaching the 45 nm node, the need for novel device concept, architecture and new materials has never been so pressing as today. Flash memories, the driving force of semiconductor memory market in recent years, also face the same or maybe more severe challenges to meet the demands for high-density, low-cost, low-power, high-speed, better endurance and longer retention time. As traditional continuous floating gate flash struggles to balance the trade-off between high speed and retention requirement, nanocrystal (NC) floating gate flash has attracted more and more interest recently due to its advantages over traditional flash memories in many areas such as better device scaling, lower power consumption and improved charge retention. However, there are still two major challenges remaining for embedded NC synthesis: the deposition method and the size and distribution control. Nowadays using bio-nano techniques such as DNA, virus or protein for NC synthesis and assembly has become a hot topic and feasible for actual electronic device fabrication. In this dissertation a new method for NC deposition wherein a colloidal suspension of commercially-available NCs was organized using a self-assembled chaperonin array. The chaperonin array was applied as a scaffold to mediate NCs into an assembly with uniform spatial distribution on Si wafers. By using this method, we demonstrated that colloidal PbSe and Co NCs in suspension can self-assemble into ordered arrays with a high density of up to 10¹²cm⁻². MOSCAP and MOSFET memory devices were successfully fabricated with the chaperonin protein mediated NCs, showing promising memory functions such as a large charge storage capacity, long retention time and good endurance. The charge storage capacity with respect to material work function, NC size and density was explored. In addition to NC engineering, the tunnel barrier was engineered by replacing traditional SiO₂ by high-k material HfO₂, giving a higher write/erase speed with a reduced effective oxide thickness (EOT). Suggestions for future research in this direction are presented in the last part of this work. / text

When building storage systems that aim to simultaneously provide robustness, scalability, and efficiency, one faces a fundamental tension, as higher robustness typically incurs higher costs and thus hurts both efficiency and scalability. My research shows that an approach to storage system design based on a simple principle—separating data from metadata—can yield systems that address elegantly and effectively that tension in a variety of settings. One observation motivates our approach: much of the cost paid by many strong protection techniques is incurred to detect errors. This observation suggests an opportunity: if we can build a low-cost oracle to detect errors and identify correct data, it may be possible to reduce the cost of protection without weakening its guarantees. This dissertation shows that metadata, if carefully designed, can serve as such an oracle and help a storage system protect its data with minimal cost. This dissertation shows how to effectively apply this idea in three very different systems: Gnothi—a storage replication protocol that combines the high availability of asynchronous replication and the low cost of synchronous replication for a small-scale block storage; Salus—a large-scale block storage with unprecedented guarantees in terms of consistency, availability, and durability in the face of a wide range of server failures; and Exalt—a tool to emulate a large storage system with 100 times fewer machines. / text

This thesis makes progress towards the fundamental understanding of heterogeneous and dynamic information systems and the way that we store and process massive data-sets. Reliable large-scale data storage: Distributed storage systems for large clusters typically use replication to provide reliability. Recently, erasure codes have been used to reduce the large storage overhead of three-replicated systems. However, traditional erasure codes are associated with high repair cost that is often considered an unavoidable price to pay. In this thesis, we show how to overcome these limitations. We construct novel families of erasure codes that are optimal under various repair cost metrics, while achieving the best possible reliability. We show how these modern storage codes significantly outperform traditional erasure codes. Low-rank approximations for large-scale data processing: A central goal in data analytics is extracting useful and interpretable information from massive data-sets. A challenge that arises from the distributed and large-scale nature of the data at hand, is having algorithms that are good in theory but can also scale up gracefully to large problem sizes. Using ideas from prior work, we develop a scalable lowrank optimization framework with provable guarantees for problems like the densest k-subgraph (DkS) and sparse PCA. Our experimental findings indicate that this low-rank framework can outperform the state-of-the art, by offering higher quality and more interpretable solutions, and by scaling up to problem inputs with billions of entries. / text