Page-oriented volume holographic memories (POVHM) is a quadratically nonlinear channel because of the intensity detection at its output. To combat the two-dimensional intersymbol interference in a high-capacity POVHM, equalization of the output intensity array requires identification of the quadratic and possibly spatially varying impulse response of the channel. Although conventional adaptive filtering schemes are devised for identification of linear channels, they also require the length of the impulse response to be known in advance. In this work, we develop multistage quadratic normalized least mean square (LMS) (MS-QNLMS) adaptive filtering and multistage Volterra normalized LMS (MS-VNLMS) filtering to estimate the channel under quadratic nonlinearity, which do not require the support or length of the impulse response to be known a priori. By employing extensive numerical experiments, we provide performance and convergence comparisons of the proposed schemes with respect to a true-order quadratic estimator. We also show that MS-QNLMS filtering has less computational complexity and converges faster and more robust to various channel parameters as compared to MS-VNLMS.
Reliable verification and identification can be achieved by fusing hard and soft information from multiple classifiers. Correlation filter based classifiers have shown good performance in biometric verification applications. In this paper, we develop a method of fusing soft information from multiple correlation filters. Usually, correlation filters are designed to produce a strong peak in the correlation filter output for authentics whereas no such peak should be produced for impostors. Traditionally, the peak-to-sidelobe-ratio (PSR) has been used to characterize the strength of the peak and thresholds are set on the PSR in order to determine whether the test image is an authentic or an impostor. In this paper, we propose to fuse multiple correlation output planes, by appending them for classification by a Support Vector Machine (SVM), to improve the performance over traditional PSR based classification. Multiple Unconstrained Optimal Tradeoff Synthetic Discriminant Function (UOTSDF) filters having varying degrees of discrimination and distortion tolerance are employed here to create a feature vector for classification by a SVM, and this idea is evaluated on the plastic distortion set of the NIST 24 fingerprint database. Results on this database provide an Equal Error Rate (EER) of 1.36% when we fuse correlation planes, in comparison to an average EER of 3.24% using the traditional PSR based classification from a filter, and 2.4% EER on fusion of PSR scores from the same filters using SVM, which demonstrates the advantages of fusing the correlation output planes over the fusion of just the peak-to-sidelobe-ratios (PSRs).
In this paper, we develop a channel model and investigate the performance of equalization and detection schemes for the magnification error-dominated volume holographic storage channel.
Like other coherent optical storage channels, a volume holographic storage channel is a quadratic channel since its output is a function of the magnitude-square of the light incident on the photo-detector array. Because of such intensity detection, the sign/phase information at the output is destroyed which makes the estimation of the amplitude response difficult. In this paper, we present a new method called modified least mean squares (MLMS) method to estimate the amplitude channel response of a volume holographic storage channel in the presence of quadratic non-linearity. Numerical simulations show that MLMS is very effective in estimating the amplitude response of such channels under even severe loss of sign information.
KEYWORDS: Spatial light modulators, Charge-coupled devices, Holography, Volume holography, Convolution, CCD cameras, Data modeling, Data storage, Systems modeling, Binary data
Data pages retrieved from a volume holographic storage channel (VHSC) suffer generally from two degradations: inter-symbol interference (ISI) and noise. ISI can be combatted by equalizers which are designed based on careful modeling of the VHSC. In this paper, we present an efficient model for volume holographic storage channels. Numerical simulations show that the developed model outputs are accurate.
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