George Alois Leonie Leenknegt

Disc defect classification for optical disc drives

Optical disc drives are subject to various disturbances and faults. A special type of fault is the so-called disc defect. In this paper we present an approach for disc defect classification. It is based on hierarchical clustering of measured signals that are affected by disc defects. The time-series are mapped into a feature space after which the feature vectors are clustered in a hierarchical fashion. Finally, signals are fitted onto the clusters to obtain single representations for each fault class. The resulting class descriptions can then be used for (on-line) classification of new disc defects. The approach is evaluated by applying it to a set of test data.

Recovering Data from Cracked Optical Discs using Hankel Iterative Learning Control

Optical discs, including Compact Discs (CDs), Digital Versatile Discs (DVDs), and Blu-ray Discs (BDs), can get cracked during storage and usage. Such cracks commonly lead to discontinuities in the data track, potentially preventing reading of the data on the disc. The aim of the present paper is to improve tracking performance of the optical disc drive in the presence of cracks. A Hankel Iterative Learning Control (ILC) algorithm is presented that can perfectly steer the lens during the crack towards the beginning of the track immediately after the crack, i.e., the actuator is steered appropriately during the crack crossing to compensate for the discontinuity in the data track. Experimental results confirm improved reading capabilities of cracked discs. The presented approach potentially enables the recovery of data from cracked discs that were previously considered as unreadable.