This article presents a concatenated Bahl-Cocke-Jelinek-Raviv (BCJR) detector, low-density
parity-check (LDPC) decoder, and deep neural network (DNN) architecture for a turbo …

This article considers deep neural network (DNN)-based turbo-detection for multilayer
magnetic recording (MLMR), an emerging hard disk drive (HDD) technology that uses …

It is an important research direction of mental health discipline in the current era to evaluate
and analyze college students' mental health by using deep learning methods and form …

Data detection in magnetic recording (MR) channels can be viewed as an image processing
problem, proceeding from the 2-D image of readback bits, to higher level abstractions of …

In this article, we demonstrate a convolutional neural network (CNN)-based data detection
channel on real data from a commercial hard disk drive (HDD) employed with two …

Convolution neural network (CNN) is used as the data detection channel for two-
dimensional magnetic recording (TDMR). We demonstrate that by only feeding interfered …

This paper describes challenges, solutions, and prospects for data recovery in multilayer
magnetic recording (MLMR)—the vertical stacking of magnetic media layers to increase …

In this article, we present a systematic study of using a machine learning (ML) data detection
channel consisting of a convolutional neural network (CNN) for data recovery in a two …

Dynamic bit encoding and decoding of the magnetic recording process remain a challenge
in that the process is restrained by the balance between reading and writing performance of …

We investigate several architectures for a convolutional neural network (CNN) media noise
estimator (MNE) that sends media noise estimates to a three-track Bahl–Cocke–Jelenik …