Sungbin Im

A Compressed Sensing Approach for Modeling the Super-Resolution Near-Field Structure Disc System With a Sparse Volterra Filter

In this paper, we investigate the compressed sensing (CS) algorithms for modeling a super-resolution near-field structure (super-RENS) disc system with a sparse Volterra filter. It is well known that the super-RENS disc system has severe nonlinear inter-symbol interference (ISI). A nonlinear system with memory can be well described with the Volterra series. Furthermore, CS can restore sparse or compressed signals from measurements. For these reasons, we employ the CS algorithms to estimate a sparse super-RENS read-out channel. The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read-out channel and that observable nonlinear interactions take place among restricted components in the read-out channel.

Domain Bloom in Super-Resolution Near-Field Structure Read-Out Signals

Super-resolution near-field structure (super-RENS) read-out samples are affected by a nonlinear and noncausal channel, which results in intersymbol interference. Some of these nonlinearities are caused by domain bloom or asymmetry, which is an imperfection in the disc. In this study, we investigate the effect of the domain bloom on the super-RENS read-out signal. For this, we employ the asymmetric symbol conversion scheme to generate asymmetric symbols corresponding to a bit pattern and apply the linear modeling approach to model the read-out channel with a finite impulse response filter. The modeling performance is verified with the causal and noncausal filters in terms of normalized mean square error. The performance of the approach considered here is compatible with that of the Volterra filter, whereas the approach maintains a lower complexity than the Volterra filter.

Performance of the Contraction Mapping-Based Iterative Two-Dimensional Equalizer for Bit-Patterned Media

Bit-patterned media (BPM) storage is one of the promising technologies to overcome the limitations of the conventional magnetic recording. However, a high-density BPM storage has intertrack interference, intersymbol interference and noise, which severely degrade the system performance. In this paper, we present a simple iterative two-dimensional (2-D) equalizer based on the contraction mapping theorem to mitigate these adverse effects. In the simulation, we demonstrate the bit separation characteristics of the one-dimensional (1-D) and proposed 2-D equalizers and evaluate the bit-error-rate (BER) performance of the proposed equalizer comparing with the conventional equalizers. According to the simulation results, the proposed equalizer is a promising candidate with maintaining proper complexity for a high-density BPM storage.

An iterative two-dimensional equalizer for bit patterned media based on contraction mapping

Patterned media storage (PMS) is one of the promising technologies to overcome the limitations of the conventional magnetic recording. For a high areal density PMS, both inter-track interference (ITI) and inter-symbol interference (ISI), and noise severely degrade the bit error rate (BER) performance of the system. In this paper, we present a simple iterative two-dimensional equalizer, which is based on the contraction mapping theorem, to mitigate these adverse effects. The performance of the proposed equalizer is evaluated using computer simulations for various conditions. Simulation results suggest that the proposed equalizer is a good candidate for ultra-high capacity PMS with a moderate complexity.

Characterization of Quadratic Nonlinearity between Motion Artifact and Acceleration Data and its Application to Heartbeat Rate Estimation

Accelerometers are applied to various applications to collect information about movements of other sensors deployed at diverse fields ranging from underwater area to human body. In this study, we try to characterize the nonlinear relationship between motion artifact and acceleration data. The cross bicoherence test and the Volterra filter are used as the approaches to detection and modeling. We use the cross bicoherence test to directly detect in the frequency domain and we indirectly identify the nonlinear relationship by improving the performance of eliminating motion artifact in heartbeat rate estimation using a nonlinear filter, the second-order Volterra filter. In the experiments, significant bicoherence values are observed through the cross bicoherence test between the photoplethysmogram (PPG) signal contaminated with motion artifact and the acceleration sensor data. It is observed that for each dataset, the heartbeat rate estimation based on the Volterra filter is superior to that of the linear filter in terms of average absolute error. Furthermore, the leave one out cross-validation (LOOCV) is employed to develop an optimal structure of the Volterra filter for the total datasets. Due to lack of data, the developed Volterra filter does not demonstrate significant difference from the optimal linear filter in terms of t-test. Through this study, it can be concluded that motion artifact may have a quadaratical relationship with acceleration data in terms of bicoherence and more experimental data are required for developing a robust and efficient model for the relationship.

Performance evaluation on a polyphase filter bank structure based anti-jamming system

Satellite communication has a broadband property and wide area coverage. It is also vulnerable to jamming because of the utilization of open channels. In this paper, a satellite transponder adopts a polyphase filter bank structure, which allows flexible frequency management while a nulling scheme was applied to remove narrowband interference signal of a filter bank sub-channel. For this satellite transponder, jamming, polyphase filter bank and nulling scheme are introduced, and in the simulation, we evaluate the performance of the anti-jamming system in terms of BER and PSNR by transceiving random data and images under various jamming environments.

A contraction mapping based two-dimensional equalizer and its application to holographic data storage systems

As the limits of the magnetic storage systems appear, the holographic data storage (HDS) devices with high data transfer rate and high recording density are emerging as attractive candidates for next-generation optical storage devices. In this paper, to effectively improve the detection performance that is degraded by the two-dimensional inter-symbol interference under the HDS channel environment and the pixel misalignment, an iterative two-dimensional equalization scheme is proposed based on the contraction mapping. In order to evaluate the performance of the proposed scheme, for various holographic channel environments we measure the BER performance using computer simulation and compare the proposed one with the conventional threshold detection scheme, which verifies the superiority of the proposed scheme.

Application of an iterative 2D equalizer to holographic data storage systems

At the present time when the limits of the magnetic storage systems appear, the holographic data storage (HDS) devices with high data transfer rate and high recording density are emerging as attractive candidates for next-generation optical storage devices. In this paper, to effectively improve the detection performance that is degraded by the two-dimensional inter-symbol interference under the HDS channel environment and the pixel misalignment, an iterative two-dimensional equalization scheme is proposed based on the contraction mapping theorem. In order to evaluate the performance of the proposed scheme, for various holographic channel environments we measure the BER performance using computer simulation and compare the proposed one with the conventional threshold detection scheme, which verifies the superiority of the proposed scheme.

Nonlinear Modeling for Super-RENS Disc Using a Simplicial Canonical Piecewise-Linear Adaptive Filter

A correct and accurate model of an optical storage system is very important in the development and performance evaluation of various data detection algorithms. In this paper, we present a nonlinear modeling of a super-resolution near-field structure (super-RENS) read-out signal using the simplicial canonical piecewise-linear (SCPWL) model. The validity of this model is tested using radio frequency (RF) signal samples obtained from a super-RENS disc. The experiment results on modeling indicate that the SCPWL model can be efficiently utilized for the nonlinear modeling of the super-RENS systems.