Kankhunthod, Kittipon
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Preferred name
Kankhunthod, Kittipon
Alternative Name
Kankhunthod, K.
Main Affiliation
Email
kittipon.ka@kmitl.ac.th
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Item type:Publication, Track Misregistration Estimation Technique Based on Hybrid K-Means and EM Algorithm in Bit-Patterned Media Recording Systems(2025-01-01); Track misregistration (TMR) in ultrahigh density bit-patterned media recording (BPMR) is a significant issue, severely degrading system performance. Although TMR can be managed by a servo control loop, this article proposes a hybrid TMR mitigation method based on K-means and expectation-maximization (EM) algorithms to enhance TMR prediction accuracy and improve bit-error-rate (BER) in multihead/multitrack BPMR systems. This method utilizes 2-D equalizer and 1-D generalized partial response (GPR) target coefficients for the soft-output Viterbi algorithm (SOVA) detector according to the estimated TMR level to mitigate this effect. Simulation results demonstrate that the proposed system significantly outperforms conventional systems, especially under high TMR conditions. The hybrid approach achieves high TMR estimation accuracy and delivers BER performance close to an ideal system with perfect TMR estimation, showing up to a 1.25 dB improvement in BER over systems without TMR mitigation. These findings underscore the effectiveness of the hybrid K-means-EM-based TMR estimator in enhancing system performance under various conditions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Track Misregistration Mitigation Using CNN-Based Method on Single-Reader/Two-Track Reading BPMR Systems(2023-01-01); One of the problems that cause a decrease in the performance of the ultra-high bit-patterned magnetic recording (BPMR) system is track misregistration (TMR). Since the gap between data tracks is extremely narrow, it easily affects keeping the reader in the desired position. Therefore, this paper proposes the track misregistration mitigation included the estimation and correction techniques on single-reader/two-track reading (SRTR) BPMR using only a readback signal. The TMR estimation technique uses the convolutional neural network (CNN) to estimate the TMR level by the histograms of the readback signal enabling minimization of the complexity of the CNN structure and amount of training time. The estimated TMR levels obtained from the proposed CNN-histogram-based method will then be utilized to detect the estimated recorded bit by the CNN-based data detector. The simulation shows that our proposed system provides better TMR prediction accuracy even though the system has to face higher media noise. Furthermore, the CNN-based data detectors perform superior to the partial response maximum likelihood (PRML) based data detector, especially in strong electronic noise situations and the severe imperfection of recording media. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mitigating Track Misregistration Using the DBSCAN Algorithm for Single-Reader/Two-Track Reading in Shingled Magnetic Recording Systems(2026-01-01); Shingled magnetic recording (SMR) systems can effectively reduce track width using the shingled writing technique, resulting in significantly higher areal density than conventional magnetic recording. However, track misregistration (TMR) still frequently occurs, leading to read errors and reduced signal processing performance. To address this, we propose a method for estimating TMR levels for single-reader/two-track reading in the SMR system using a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Furthermore, we also present a mitigation method for TMR effects using the DBSCAN algorithm. In the TMR-level estimation process, the equalized signal from the first equalizer is fed into the first DBSCAN algorithm. The estimated TMR level is then used to select the appropriate equalizer to equalize the readback signal. Finally, the DBSCAN-based detector is used to detect the equalized signal. Simulation results at an areal density of 2 Tb/in<sup>2</sup> demonstrate that our proposed method can accurately predict TMR levels, effectively mitigate TMR effects, and improve overall recording performance in terms of bit-error rate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Track Misregistration Prediction Scheme of Two-Track Reading with a Wide-Track Reader for Shingled Track Recording(2025-12-01); To enhance the areal density (AD) of magnetic recording technology, shingled magnetic recording (SMR), which overlaps adjacent tracks, has been proposed and extensively studied. The strong intertrack interference (ITI) is a major difficulty that needs to be overcome. Therefore, the two-track reading with a wide-track reader for the shingled track recording technique achieves the clear amplitude in two-track recording due to the longer bit length of magnetization over the regular single-track reading. Track misregistration (TMR); however, is one of the key concerns in this technique that may deteriorate the system’s performance, which refers to the misalignment between the center of the read head and the desired track. To address this issue, this study proposes the TMR prediction scheme and detector with the utilization of an Expectation-Maximization (EM) algorithm to process the readback signals obtained from the wide-track reader. Simulation results indicate that, at an AD of 2.0 Tb/in<sup>2</sup>, the EM-based TMR prediction method achieves strong prediction performance, while the EM-based data detector further enhances system performance by reducing the bit-error rate in shingled track recording systems.