With highly demanding requirement in current emerging cloud storage and personal computers, hard disk drive recording with high stability and high volume has attached much attention in industry and academy. Recording media and recording head feasible for future high-density recording are both crucial to utilize magnetic recording with 1T bit/in2 recording density. Recoding media with FePt for high density and high stability was investigated in this thesis using FePt polymers with imprinting methods and FePt thin films with ion-beam bombardment technologies. The FePt polymers can be patterned using imprint at micro-and nano-scales. The micro-and nano-patterns could be retained on substrates after sintering at high temperatures. The high magnetic coercivity was proved with line and dot patterns at different scales. Recording heads with Al2O3based magnetic tunneling junction sensors were also studied in thesis. The magnetic tunneling junction sensors were proved to work stable at different temperatures varying from -30oC to 100oC. The long time running test up to 100 hours also proved the stability of the magnetic tunneling junction sensors working in extreme temperatures. Withstate-of-art patterning and depositing technologies, new ideas about using FePt polymer to work as magnetic recording media and using ion beam bombardments to tune the FePt magnetic properties were verified. The feasibility of using Al2O3 based magnetic tunneling junction sensors as recording head was also discussed. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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Hard disk drives (HDDs) are the most important information storage devices for computers. Positioning precision is crucial to today's increasingly high-speed, high-capacity, high data density, and miniaturized HDDs. Therefore, the demand for higher bandwidth servo systems that can quickly and precisely position the read/write head on a high track density becomes more pressing. Recently, the idea of applying dual-stage actuators to track servo systems has been studied. The push-pull piezoelectrically actuated devices have been developed as micro actuators for fine and fast positioning, while the voice coil motor functions as a large but coarse seeking. However, the current dual-stage actuator design uses piezoelectric patches only without passive damping. In this thesis, a dual-stage servo system using enhanced active-passive hybrid piezoelectric actuators is proposed. The actuators improve the existing dual-stage actuators for higher positioning precision and better shock resistance, due to the incorporation of passive damping in the design. The main aim is to develop this hybrid servo system not only to increase the speed of track seeking but also to improve precision of track following servos in HDDs. A new piezoelectrically actuated suspension with passive damping is designed and fabricated for precision positioning of the head while reducing undesirable vibrations. In order to evaluate positioning and track following performances for the dual-stage track servo systems, tasks carried out in the thesis are: (1) to investigate the damping abilities and transmissibilities of the active-passive hybrid micro actuators; (2) to investigate the shock resistance of the dual-stage piezoelectric actuators with passive damping; (3) to implement the synthesized active-passive suspension with enhanced piezoelectric actuators using a composite nonlinear feedback controller. / Chan, Kwong Wah. / "December 2006." / Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5512. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 84-90). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Graduation date: 1995

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains x, 134 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 97-99).

Thesis (Ph. D.)--University of California, Riverside, 2008. / Includes abstract. Available via ProQuest Digital Dissertations. Title from first page of PDF file (viewed March 10, 2010). Includes bibliographical references (p. 96-104). Also issued in print.

Thesis (Ph. D.)--University of California, Riverside, 2008. / Includes abstract. Available via ProQuest Digital Dissertations. Title from first page of PDF file (viewed March 10, 2010). Includes bibliographical references (p. 126-135). Also issued in print.

Thesis (Ph. D.)--University of California, San Diego, 2001. / Vita. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 88-91). Also available in electronic version.

The ability to accurately predict an impending hard disk failure is important for reliable storage system design. The facility provided by most hard drive manufacturers, called S.M.A.R.T. (self-monitoring, analysis and reporting technology), has been shown by current research to have poor predictive value. The problem of finding alternatives to S.M.A.R.T. for predicting disk failure is an area of active research. In this work, we present a rule discovery methodology, and show that it is possible to construct decision support systems that can detect such failures using information recorded from live disks. It is desired that any such prediction methodology should have high accuracy and must have ease of interpretability. Black box models can deliver highly accurate solutions but do not provide an understanding of events which explains the decision given by it. To this end we explore rule based classifiers for predicting hard disk failures from various disk events. We show that it is possible to learn easy to understand rules from disk events. Our evaluation shows that our system can be tuned either to have a high failure detection rate (i.e., classify a bad disk as bad) or to have a low false alarm rate (i.e., not classify a good disk as bad). We also propose a modification of MLRules algorithm for classification of data with imbalanced class distributions. The existing algorithm, assuming relatively balanced class distributions and equal misclassfication costs, performs poorly in classification of such datasets. The performance can be considerably improved by introducing cost- sensitive learning to the existing framework.