nils-herrmann/clean_scirepeval huggingface dataset:
Two-Dimensional Generalized Partial Response Equalizer for Bit-Patterned Media

The use of bit-patterned media is one of the approaches being investigated to extend magnetic recording densities to 1 Tbit/in2 and beyond. In patterned media, track pitch may be small causing adjacent tracks to have significant interference on the replay waveform from the main data track. To mitigate the effect of such inter-track interference (ITI), we propose the use of a two-dimensional (2D) generalized partial response (GPR) equalizer. We select both the equalizer and the partial response target using the minimum mean squared error (MMSE) criterion. However, we avoid the need for a 2D Viterbi algorithm by imposing a constraint on the 2D target that forces the adjacent track contributions (in the ideal case) to zero. Simulation results show that this 2D equalizer significantly improves the bit error rate (BER). In this work, the effect of a 2D GPR equalizer on the performance of a patterned media system in the presence of track misregistration (TMR) is also investigated. Based on the simulation results, the 2D equalization method appears to be more tolerant to TMR than the conventional GPR The main drawback of the proposed method is the need for simultaneously acquiring the signals from three adjacent tracks.
Pattern-Flipping Chase-Type Decoders with Error Pattern Extracting Viterbi Algorithm over Partial Response Channels

Towards the goal of achieving better error correction performance in data storage systems, iterative soft decoding of low density parity check (LDPC) codes and soft-decision decoding of Reed-Solomon (RS) codes have started receiving increasing research attention. However, even with increased computing power, complexities of soft-decision decoding algorithms are still too high for real products which require high throughput and small hardware area. Another problem is that the performance gains of those approaches are smaller for magnetic recording channels than they are for memoryless additive white Gaussian noise (AWGN) channels. We propose a new soft-decision decoding algorithm (based on the Chase algorithm), which takes advantage of pattern reliability instead of symbol reliability or bit reliability. We also present a modified Viterbi algorithm that provides probable error patterns with corresponding reliabilities. Simulation results of the proposed algorithms over the partial response (PR) channel show attractive performance gains. The proposed algorithm dramatically reduces the number of iterations compared to the conventional Chase2 algorithm over the PR channel.
Performance and Complexity of 32 k-bit Binary LDPC Codes for Magnetic Recording Channels

Recently, 32 k-bit sector size for hard disk drives is being investigated to take advantage of the superior performance of long error correcting codes. Meanwhile, low-density parity-check (LDPC) codes have been actively investigated for obtaining coding gains over conventional Reed-Solomon (RS) codes mainly for 4 k-bit sectors. In this paper, the coding gain of a 32 k-bit LDPC code over a 4 k-bit LDPC code, a 32 k-bit RS code, and a 4 k-bit RS code in magnetic recording channels is investigated. The decoding complexity of 32 k-bit LDPC codes and 4 k-bit LDPC codes is also discussed. It is important to evaluate whether the coding gains are enough to justify the increased complexity. Using the 32 k-bit LDPC code, 0.8-dB gain over the 32 k-bit RS code or the 4 k-bit LDPC code (the two schemes coincidentally showed similar performance) at 32 k-bit block error rate (BLER) 10-3, and 1.6-dB gain over the 4 k-bit RS code were obtained. It is shown that 32 k-bit LDPC codes require a larger number of iterations than the 4 k-bit LDPC codes. It is also shown that there is much room to improve the design of 32 k-bit LDPC codes than the code used in the simulation. To illustrate the potential, quasicyclic LDPC codes with column weights up to 13 with girth 6 are investigated.
Symmetric Information Rate of an Ideal Readback Two Dimensional Magnetic Recording (TDMR) Channel with Random Voronoi Grains

Two-dimensional magnetic recording (TDMR) is a data storage paradigm that has recently been proposed to record more than one channel bit to one magnetic grain in order to achieve 10 Tbit/in² of user bit density on a magnetic recording medium with 20 Tgrains/in². In addition to conventional noise in magnetic recording, the TDMR channel also suffers from write errors due to the randomness in size and placement of the grains; however aggressive two-dimensional (2D) signal processing schemes may enable reliable data retrieval. In this study, the symmetric information rate (SIR) of a TDMR channel model based on random Voronoi grains, named the ideal readback TDMR channel model, is analyzed. Using numerical simulations, a lower bound of the SIR is evaluated for a half grain per channel bit ideal readback TDMR channel. Although approximately half of the channel bits may not be recorded in this channel, the resulting lower bound of the SIR is 0.46 ~ 0.51 information bits per grain. This result indicates the potential of TDMR to achieve 10 Tbit/in² of user bit density on a magnetic medium with 20 Tgrains/in².
Why error measures are sub-optimal for training neural network pattern classifiers

Pattern classifiers that are trained in a supervised fashion are typically trained with an error measure objective function such as mean-squared error (MSE) or cross-entropy (CE). These classifiers can in theory yield Bayesian discrimination, but in practice they often fail to do so. The authors explain why this happens and identify a number of characteristics that the optimal objective function for training classifiers must have. They show that classification figures of merit (CFM/sub mono/) possess these optimal characteristics, whereas error measures such as MSE and CE do not. The arguments are illustrated with a simple example in which a CFM/sub mono/-trained low-order polynomial neural network approximates Bayesian discrimination on a random scalar with the fewest number of training samples and the minimum functional complexity necessary for the task. A comparable MSE-trained net yields significantly worse discrimination on the same task.<<ETX>>
Automatic recognition of multiple targets with varying velocities using quadratic correlation filters and Kalman filters

Automatic target recognition (ATR) systems require detection, recognition, and tracking algorithms. The classical approach is to treat these three stages separately. In this paper, we investigate a correlation filter (CF)-based approach that combines these tasks for enhanced ATR. We present a Kalman filter framework to combine information from successive correlation outputs in a probabilistic way. Our contribution is a framework that is able to locate multiple targets with different velocities at unknown positions providing enhanced ATR with only a marginal increase in computation over other CF ATR algorithms.
Competitive learning with generalized winner-take-all activation

Competitive learning paradigms are usually defined with winner-take-all (WTA) activation rules. The paper develops a mathematical model for competitive learning paradigms using a generalization of the WTA activation rule (g-WTA). The model is a partial differential equation (PDE) relating the time rate of change in the ;density' of weight vectors to the divergence of a vector field called the neural flux. Characteristic trajectories are used to study solutions of the PDE model over scalar weight spaces. These solutions show how the model can be used to design competitive learning algorithms which estimate the modes of unknown probability density functions.
Systematic Random Error Correcting and All Undirectional Error Detecting Codes

t-random-error correcting and all-unidirectional-error detecting (t-EC/AUED) codes obtained by appending a suffix to each codeword of a linear block code are presented. Although this technique has been previously used to construct such codes, a different approach is proposed that involves starting with a nonoptimal t-EC linear code to obtain more efficient t-EC/AUED codes. Specifically, t-EC/AUED codes constructed from an even-weight t-EC linear code are shown to require a shorter suffix than when a general t-EC linear code is used. The t-EC/AUED codes obtained in this manner are thoroughly compared to the best known codes, and are shown to require fewer bits in almost all cases.
Improved iris segmentation based on local texture statistics

High performance human identification using iris biometrics requires the development of automated algorithms for robust segmentation of the iris region given an ocular image. Many studies have shown that iris segmentation is one of the most crucial element of iris recognition systems. While many iris segmentation techniques have been proposed, most of these methods try to leverage gradient information in the ocular images to segment the iris, rendering them unsuitable for scenarios with very poor quality images. In this paper, we present an iris segmentation algorithm, which unlike the traditional edge-based approaches, is based on the local statistics of the texture region in the iris and as such is more suited for segmenting poor quality iris images. Our segmentation algorithm builds upon and adapts the seminal work on Active Contours without Edges [6] for iris segmentation. We demonstrate the performance of our algorithm on the ICE  and FOCS  databases.
Performance comparison of equalization and low-pass coding for holographic storage

We evaluated the comparative use of equalization and low-pass codes to tackle the ISI problem in a 2D-ISI channel, specifically holographic storage channel. We presented PRML equalization results with 6 different low-pass codes having various low-pass constraints. Detailed channel simulations were performed and the SNR gains through equalization or coding were translated into equivalent density gains using standard scaling laws. We showed that equalization tends to perform better than low-pass codes from the standpoint of overall density gain. We found that for both optical noise dominated and electronic noise dominated cases, equalization performs better. This is mostly because the density gains of low-pass codes were usually overwhelmed by the penalty paid in terms of the code-rate.
Correlation filters with controlled scale response

Correlation filtering methods are becoming increasingly popular for image recognition and location. The recent introduction of optimal tradeoff circular harmonic function filters allowed the user to specify the response of a correlation filter to in-plane rotation distortion. In this paper we introduce a new correlation filter design that can provide a user-specified response to in-plane scale distortion. The design is based on the Mellin radial harmonic (MRH) transform and incorporates multiple harmonics into the correlation filter for improved discrimination capability. Additionally, the filter design minimizes the average correlation energy in order to achieve sharp correlation peaks, and thus we refer to these filters as minimum average correlation energy Mellin radial harmonic (MACE-MRH) filters. We present underlying theory, a MACE-MRH filter design method, and numerical simulation results.
Performance Evaluation of Partial Response Targets for Perpendicular Recording Using Field Programmable Gate Arrays

We present the results of performance evaluation of partial response (PR) targets for perpendicular recording by simulation using a field programmable gate array (FPGA). Previous research proposed several PR targets for perpendicular recording channels. We evaluated the bit error rate (BER) performance of those PR targets. Different PR targets led to not only different BERs for given signal-to-noise ratios (SNRs), but also different slopes of BER versus SNR curves. The result can be explained in terms of error event distributions of the corresponding PR targets. In the FPGA simulation, additive white Gaussian noise was used, but no jitter noise was introduced. The soft output Viterbi algorithm is used as the channel detector. A rate-8/9 low-density parity check code with code length 4923 and column weight 3 is used as the error correcting code. Two channel iterations are applied. We attain BERs as low as 10-12. Our results also show that the BER behavior of the PR4 target is significantly different from that of the [1 2 2 1] target
Maximum Margin Correlation Filter: A New Approach for Localization and Classification

Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most of them, the SVM classifier assumes that the object to be classified is centered in the query image, which might not always be valid, e.g., when locating and classifying a particular class of vehicles in a large scene. In this paper, we introduce a new classifier called Maximum Margin Correlation Filter (MMCF), which, while exhibiting the good generalization capabilities of SVM classifiers, is also capable of localizing objects of interest, thereby avoiding the need for image centering as is usually required in SVM classifiers. In other words, MMCF can simultaneously localize and classify objects of interest. We test the efficacy of the proposed classifier on three different tasks: vehicle recognition, eye localization, and face classification. We demonstrate that MMCF outperforms SVM classifiers as well as well known correlation filters.
Convergence analysis of iterative soft decoding in partial response channels

In this paper, we apply density evolution to investigate the convergence of iterative soft decoding in partial response (PR) channels and to predict the bit-error-rate (BER) performance of concatenated PR systems. The convergence analysis and computer simulations reveal that column weight-2 low-density parity check (LDPC) codes are similar to recursive systematic convolutional (RSC) codes in terms of extrinsic information transfer characteristics, BER performance, and block-error statistics, while the decoding complexity of column weight-2 LDPC codes is significantly smaller than that of RSC codes.
On state fusers over long-haul sensor networks

We consider a network of sensors wherein the state estimates are sent from sensors to a fusion center to generate a global state estimate. The underlying fusion algorithm affects the performance measure QCC(τ) (with subscripts CC indicating the effects of the communications and computing quality) of the global state estimate computed within the allocated time τ. We present a probabilistic performance bound on QCC(τ) as a function of the distributions of state estimates, communications parameters as well as the fusion algorithm. We present simulations of simplified scenarios to illustrate the qualitative effects of different fusers, and system-level simulations to complement the analytical results.
A comparative evaluation of iris and ocular recognition methods on challenging ocular images

Iris recognition is believed to offer excellent recognition rates for iris images acquired under controlled conditions. However, recognition rates degrade considerably when images exhibit impairments such as off-axis gaze, partial occlusions, specular reflections and out-of-focus and motion-induced blur. In this paper, we use the recently-available face and ocular challenge set (FOCS) to investigate the comparative recognition performance gains of using ocular images (i.e., iris regions as well as the surrounding peri-ocular regions) instead of just the iris regions. A new method for ocular recognition is presented and it is shown that use of ocular regions leads to better recognition rates than iris recognition on FOCS dataset. Another advantage of using ocular images for recognition is that it avoids the need for segmenting the iris images from their surrounding regions.
Investigation of Two-Dimensional Magnetic Recording (TDMR) With Position and Timing Uncertainty at 4 Tb/in$^{2}$

The feasibility of two-dimensional magnetic recording (TDMR) at 4 Tb/in2 customer areal-density is investigated via numerical simulations. In addition to the coding overhead needed to correct possible errors in a TDMR channel, the overhead required to obtain accurate position and timing estimates is taken into account in determining achievable customer density. The TDMR channel model used for this investigation is based on a random Voronoi granular medium, a two-dimensional (2-D) Gaussian read sensitivity function with random position and timing offsets and additive white Gaussian noise (AWGN). Read channel signal processing consists of a 2-D linear minimum mean square error (LMMSE) equalizer, a Gaussian mixture model (GMM) based log likelihood ratio (LLR) extractor and a low-density parity-check (LDPC) decoder. This work suggests that reliable performance may be achieved on media with 14 × 1012 grains/in2 by TDMR at a net customer areal-density of 0.29 bits per grain, including the overhead required for the LDPC code and the position and the timing fields.
Reduced-complexity biometric recognition using 1-D cross-sections of correlation filters

Correlation filters are an attractive image processing technique for object recognition. They can provide the necessary recognition accuracy for many applications, but it would be desirable to reduce the complexity of the correlation filter algorithm (in terms of computation and storage space). This is especially true for biometric identification tasks, where multiple correlation filters must be tested against a single image. We propose an algorithm for match metric computation that trades a (usually minor) degradation in accuracy for an orders-of-magnitude complexity reduction. This algorithm analyzes ID cross-sections of the frequency domain in which the filter is applied. We compare our proposed technique to the standard technique using a dataset of face images.
Soft-Decision Decoding of Reed-Solomon Codes Using Successive Error-and-Erasure Decoding

We propose a soft-decision decoding algorithm of Reed-Solomon (RS) codes using successive error-and-erasure decoding. Extensive simulations are conducted to show the possible performance gain of the proposed method. We derive a formula for performance estimation based on ordered statistics of symbol reliability, which matches well with the results of the simulation. The proposed method with almost the same average complexity as a conventional hard-decision decoder outperforms Koetter-Vardy (KV) algorithm and Chase2-GMD algorithm (CGA).
Support vector machines based data detection for holographic data storage systems

The nonlinear nature of holographic data storage systems (HDSS) suggests that nonlinear equalization and detection techniques may be beneficial. The complexity involved in nonlinear methods does not often make them practical solutions. Support vector machines (SVMs) are recently being studied for pattern recognition applications. We investigated linear SVM detection and observed that the bit error rate (BER) using SVM for data detection on linear minimum mean squared error (LMMSE) equalized holographically recorded and retrieved 2D data pages is about 17% better than the simple threshold detection on unequalized pages.
Multidimensional overlap-add and overlap-save for correlation and convolution

When two signals or images to be correlated or convolved are of different sizes, overlap-add (OLA) and overlap-save (OLS) algorithms are often employed to reduce computational complexity. In this paper, we investigate the use of these algorithms for multidimensional correlations required in many image and video matching applications. In contrast to conventional wisdom, we show that OLA is more computationally efficient and OLS is more memory efficient. We present theoretical expressions for computational complexity for an arbitrary dimensionality and validate these expressions experimentally. We also discuss parameter selection from both memory and computational perspectives.
Enhanced Video-Based Target Detection using Multi-Frame Correlation Filtering

Most existing video-based target detection systems employ state-space models to keep track of an explicit number of individual targets. We introduce a framework for enhancing target detection in video by applying probabilistic models to the soft information in correlation outputs before thresholding. We show how to efficiently compute arrays of posterior target probabilities for every position in the scene conditioned on all current and past frames of a video sequence. These arrays can then be thresholded in the typical manner to yield more reliable target detections. Because the framework avoids the formation of explicit tracks, it is well suited for handling scenes with unknown numbers of targets at unknown positions. Simulation results on forward-looking infrared (FLIR) video sequences show that our proposed framework can significantly reduce the false-alarm rate of a bank of correlation filters while requiring only a marginal increase in computation.
Turbo decoding for optical data storage

Turbo codes have been investigated for magnetic recording channels as potential candidates to succeed the current Viterbi-based partial response maximum likelihood (PRML) approaches. In contrast, relatively less attention has been devoted to the investigation of turbo decoding methods for optical data storage channels. In this paper, we present the simulation results of applying turbo decoding to an optical data storage channel. Our results focus on a variety of practical optical storage channel aspects including storage density, partial response targets, and the type of precoder used.
Optimal tradeoff circular harmonic function correlation filter methods providing controlled in-plane rotation response

Correlation methods are becoming increasingly attractive tools for image recognition and location. This renewed interest in correlation methods is spurred by the availability of high-speed image processors and the emergence of correlation filter designs that can optimize relevant figures of merit. In this paper, a new correlation filter design method is presented that allows one to optimally tradeoff among potentially conflicting correlation output performance criteria while achieving desired correlation peak value behavior in response to in-plane rotation of input images. Such controlled in-plane rotation response is useful in image analysis and pattern recognition applications where the sensor follows a pre-arranged trajectory while imaging an object. Since this new correlation filter design is based on circular harmonic function (CHF) theory, we refer to the resulting filters as optimal tradeoff circular harmonic function (OTCHF) filters. Underlying theory, OTCHF filter design method, and illustrative numerical results are presented.
Coding scheme for 3D vertical flash memory

Recently introduced 3D vertical flash memory is expected to be a disruptive technology since it overcomes scaling challenges of conventional 2D planar flash memory by stacking up cells in the vertical direction. However, 3D vertical flash memory suffers from a new problem known as fast detrapping, which is a rapid charge loss problem. In this paper, we propose a scheme to compensate the effect of fast detrapping by intentional inter-cell interference (ICI). In order to properly control the intentional ICI, our scheme relies on a coding technique that incorporates the side information of fast detrapping during the encoding stage. This technique is closely connected to the well-known problem of coding in a memory with defective cells. Numerical results show that the proposed scheme can effectively address the problem of fast detrapping.