Turbo-Detection for Multilayer Magnetic Recording Using Deep Neural Network-Based Equalizer and Media Noise Predictor

Sayyafan, Amirhossein; Aboutaleb, Ahmed; Belzer, Benjamin J.; Sivakumar, Krishnamoorthy; Greaves, S.J.; Chan, Kheong Sann

doi:10.1109/tmag.2021.3122136

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…An MLP has been used to handle many problems, e.g., ITI, TMR, and so on, in magnetic recording channels [6,24,25,26]. Because the MLP is a simple neural network and can solve a nonlinear problem, this paper then utilizes it to solve the SA problem.…”

Section: Sa Mitigation Methodsmentioning

confidence: 99%

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An MLP-based skew angle mitigation method for bit-patterned magnetic recording

Koonkarnkhai,

Kankhunthod,

Kovintavewat

2024

IEICE Electron. Express

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Skew angle (SA) is one of the crucial problems in a bit-patterned magnetic recording (BPMR) system. Practically, during the read process, the read head may be tilted up to 30 degrees ( • ) away from the target track. Without the SA detection and correction mechanism, the performance of the BPMR system will be unbearable. This paper proposes a SA mitigation strategy for a BPMR system based on a multilayer perceptron (MLP) so as to estimate and rectify the SA. Specifically, the MLP-based SA estimator extracts the SA amount directly from the readback signal, which will then be used in the MLP-based detector to produce the estimated recording bits. Simulation results show that the proposed method can estimate the SA embedded in the readback signal more precisely than the conventional one, which just uses a look-up table to detect the SA from the target coefficients. Particularly, for the system operating at an areal density (AD) of 3 terabits per square inch with SA = 15 • and 30 • , the suggested system performs better than the system without the SA mitigation method.

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Section: Sa Mitigation Methodsmentioning

confidence: 99%

“…[4]. Many research works have been conducted to handle the 2D interference, e.g., multi-head multi-track detection [5], neural network-based equalizer and detector [6,7], turbo equalization [5,8], equalizer and target design [9], modified detector [4], arrangement of magnetic island [10,11] and modulation code [12,13].…”

Section: Introductionmentioning

confidence: 99%

An MLP-based skew angle mitigation method for bit-patterned magnetic recording

Koonkarnkhai,

Kankhunthod,

Kovintavewat

2024

IEICE Electron. Express

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show abstract

“…The authors in [14] designed a threelayer feedforward neural network to extract driver behavior features and used real driving behavior data to achieve autonomous driving of vehicles. The authors in [15] improved the three-layer into a multi-layer, designed a multi-layer feedforward neural network, and built a driving behavior neural network model with the relevant nonlinear function approximation, which improved the convergence speed; however, the prediction results were not satisfactory when the size of the training data was small.…”

Section: Related Workmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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Emotions are closely related to driving behavior, and drivers with different emotions have different degrees of bad driving behavior. In order to explore the relationship between emotions and driving violations, a prediction model based on an emotional style transfer network is proposed. First, inspired by the idea of generative adversarial networks (GAN), the eigenvalues of emotions are extracted. Secondly, the one-way propagation method of the GAN network is improved to cyclic generation, which avoids the problems of non-convergence and long periods in the data training process, improving the utilization of training data. Thirdly, a driving violation prediction model is designed. In this model, the emotion factors are designed as time-related sequences, and by improving the Long Short-Term Memory (LSTM) model, the encoding and decoding processes of the time-related sequences are added to form the context, which improves the accuracy of prediction. Finally, the experimental and simulation data show that the proposed model has significant advantages in loss value, accuracy rate, macro-average score, and other indicators. At the same time, an emotion-induction scheme is given to reduce the possibility of driving violations. Furthermore, the proposed model can provide a theoretical basis for the impact of emotions on driving safety.

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Three-dimensional magnetic recording

Greaves

2023

Journal of Magnetism and Magnetic Materials

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Turbo-Detection for Multilayer Magnetic Recording Using Deep Neural Network-Based Equalizer and Media Noise Predictor

Cited by 7 publications

References 27 publications

An MLP-based skew angle mitigation method for bit-patterned magnetic recording

An MLP-based skew angle mitigation method for bit-patterned magnetic recording

Driving Violation Prediction Based on an Emotional Style Transfer Network

Three-dimensional magnetic recording

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