Two Dimensional Magnetic Recording (TDMR) is a promising technology for next generation magnetic storage systems based on a systems level framework involving sophisticated signal processing at the core. The TDMR channel suffers from severe jitter noise along with electronic noise that needs to be mitigated during signal detection and recovery. Recently, we developed noise prediction based techniques coupled with advanced signal detectors to work with these systems. However, it is important to understand the role of harmful patterns that can be avoided during the encoding process. In this paper, we investigate into the Voronoi based media model to study the harmful patterns over multi-track shingled recording systems. Through realistic quasi micromagnetic simulations studies, we identify 2D data patterns that contribute to high media noise. We look into the generic Voronoi model and present our analysis on multi-track detection with constrained coded data. We show that two dimensional (2D) constraints imposed on input patterns result in an order of magnitude improvement in the bit error rate for TDMR systems. The use of constrained codes can reduce the complexity of 2D intersymbol interference (ISI) signal detection since lesser 2D ISI span can be accommodated at the cost of a nominal code rate loss. However, a system must be designed carefully so that the rate loss incurred by a 2D constraint does not offset the detector performance gain due to more distinguishable readback signals.Index Terms-TDMR systems, 2D no isolated bit constraint, multi-track detection, bit error rate, GBP algorithm.

Selection of relevant features is an open problem inBrain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Abstract-Two dimensional magnetic recording (TDMR) achieves high areal densities by reducing the size of a bit comparable to the size of the magnetic grains resulting in two dimensional (2D) inter symbol interference (ISI) and very high media noise. Therefore, it is critical to handle the media noise along with the 2D ISI detection. In this paper, we tune the generalized belief propagation (GBP) algorithm to handle the media noise seen in TDMR. We also provide an intuition into the nature of hard decisions provided by the GBP algorithm. The performance of the GBP algorithm is evaluated over a Voronoi based TDMR channel model where the soft outputs from the GBP algorithm are used by a belief propagation (BP) algorithm to decode low-density parity check (LDPC) codes.

Two-dimensional magnetic recording (TDMR) is a promising technology for boosting areal densities using sophisticated signal processing algorithms within a systems framework. The read/write channel architectures have to effectively tackle 2D inter-symbol interference (ISI), 2D synchronization errors, media and electronic noise sources as well as thermal asperities resulting in burst erasures. 1D low-density parity check (LDPC) codes are well studied to correct large 1D burst errors/erasures. However, such 1D LDPC codes are not suitable for correcting 2D burst errors/erasures due to the 2D span of errors. In this paper, we propose construction of a native 2D LDPC code to effectively correct 2D burst erasures. We also propose a joint detectiondecoding engine based on the generalized belief propagation (GBP) algorithm to simultaneously handle 2D ISI, as well as correct bit/burst errors for TDMR channels. Our work is novel in two aspects: (a) We propose the construction of native 2D LDPC codes to correct large 2D burst erasures, (b) We develop a 2D joint signal detection-decoder engine that incorporates 2D ISI constraints, modulation code constrains along with LDPC decoding. The native 2D LDPC code can correct > 20% more burst erasures compared to the 1D LDPC code over a 2D page of detected bits. Also, the proposed algorithm is observed to achieve a signal-to-noise ratio (SNR) gain of > 0.5 dB in bit error rate (BER) performance (translating to 10% increase in areal densities around the 1.8 Tb/in 2 regime with grain sizes of 9 nm) as compared to a decoupled detector-decoder system configuration. The efficacy of our proposed algorithm and system architecture is evaluated by assessing areal density (AD) gains via simulations for a TDMR configuration comprising of a 2D generalized partial response (GPR) over the Voronoi media model assuming perfect 2D synchronization.