Computer Sciences

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To further improve the DS’s reliable and resilient operation, in this Ph.D. work, the aim has been put towards development of an automated tool consisting of multiple modules to precisely investigate any type of data anomalies, followed by root cause finding.

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This dissertation considers various machine-learning-based signal processing architectures for equalization and detection of two- and three-dimensional magnetic recording signals for hard disk drives (HDDs). Recording in multiple dimensions on magnetic hard drives has been a challenge in the HDD industry. The objective of reading approaches for magnetic recording is to detect the highest density of information possible with an acceptable error rate.