nils-herrmann/clean_scirepeval huggingface dataset:
Two-track joint detection for two-dimensional magnetic recording (TDMR)

The use of array readers (i.e., multiple read elements positioned to read stored data on multiple adjacent tracks) is one of the recent concepts to improve the areal density of magnetic recording systems. Array readers provide diversity gain to deal with noise and enable the handling of inter-track interference (ITI) as well as multi-track detection. In this paper, an array-reader two-track (AR2T) detection system is studied. We present the design of 2-D equalizer that jointly processes array readback signal streams over two tracks. Such a 2-D equalizer can be designed to approximate a 1-D or a 2-D partial-response (PR) target. It is shown that 2-D PR target can result in less residual ITI. For the detection of signals corresponding to 2-D PR target, we propose a novel symbol-based detection algorithm that processes two input signal streams jointly. We also implement a pattern-dependent noise-predictive (PDNP) version of proposed detector. Simulation results show that more than 4 dB performance gain can be achieved by the AR2T detection system compared to the traditional single-reader single-track detection system under aggressive track density conditions.
A class of good quasi-cyclic low-density parity check codes based on progressive edge growth graph

We present a new class of quasi-cyclic low density parity check (QC-LDPC) codes, whose quasi-cyclic nature makes them attractive for implementation. Both regular and irregular QC-LDPC codes are designed by modifying the progressive edge growth (PEG) graph with a quasi-cyclic constraint. Simulations show that these QC-LDPC codes offer hardware-friendly parity check matrices and have as good error correction performance as random LDPC codes and other good QC-LDPC codes. In addition, the proposed QC-LDPC codes offer a much more flexible set of parameters compared to the traditional designs.
A comparison of detection methods in the presence of nonlinearities

In this paper we evaluate the detectability of nonlinear signals characteristic of high-density, magnetic recording channels read by a magneto-resistive (MR) head. We first introduce a signal model which performs nonlinear superposition of experimentally-collected MR-head transition responses. Feeding signals synthesized from this model through a detection simulator, we compare the performance of Decision Feedback Equalization (DFE), RAM DFE, Partial Response Maximum-likelihood detection with PR4, EPR4, and E/sup 2/PR4 targets, and Fixed Delay Tree Search with Decision Feedback (FDTS/DF) in the presence of time and amplitude nonlinearities. Signals are generated at a variety of signal-to-noise ratios (SNRs) and densities with runlength constraints of (0,3) and (1,7). We found that the most detrimental nonlinearities come from amplitude asymmetry characteristic of poorly biased MR heads, and such nonlinearities cause a loss of between 0.5 and 1 orders of magnitude in bit error rate. We found that of the schemes, FDTS/DF provided the great robustness against all nonlinearities in the presence of RLL(1,7) coding, and EPR4ML and E/sup 2/PR4ML preformed the best with RLL(0,3) coding.
Characterization and equalization of dropouts in the magnetic tape recording channel

We consider the issue of combating signal dropouts in magnetic tape recording. Dropouts are a major limiting factor in the performance of the channel as they cause long bursts of bit errors. To attack this problem, a model of the dropout event has been developed. Based on that model, a strategy has been identified in which an envelope detector followed by a feedforward equalizer is used. The equalization process is done in such a way as to address the three major effects caused by a dropout event: amplitude loss, pulse widening and peakshift. The crucial part of this scheme is the spacing profile estimator, which makes use of an envelope detector. With this scheme, the effects of the dropout are corrected at the expense of a delay in the channel. The model of the dropout and the correction scheme are all presented.
"Corefaces" - robust shift invariant PCA based correlation filter for illumination tolerant face recognition

In this paper we present a novel method for performing robust illumination-tolerant face recognition. We show that this method works well even when presented with partial test faces which are also captured under variable illumination and outperforms other competing face recognition algorithms. Our method is a hybrid PCA-correlation filter which links the best of two major approaches in face recognition; principal component analysis (PCA) for capturing the variability in a set of training images and advanced correlation filters which have attractive features such as illumination tolerance, shift-invariance, and can handle occlusions. We examine how these filters work and why our proposed method is able to perform better. We call our method 'Corefaces' as it seeks to model the 'core' face representation that remains relatively invariant to illumination variations. We show comparative results using the illumination subset of CMU-PIE database consisting of 65 people, and Yale-B illumination database and compare with other standard methods such as the illumination subspace method and Fisherfaces.
A model for self-organization in WTA networks and its application to map prediction problems

A mathematical model for long-term memory (LTM) reorganization in self-organizing winner-take-all (WTA) networks is developed. The model describes the temporal evolution of the density of neural LTM states using a diffusive partial differential equation. Solutions to this equation show that, in the long run, LTM states tend to cluster about the modes of the stimulating source's probability density function. This behavior is precisely what is required by many engineering problems involving maximum a posteriori (MAP) prediction. The connection between self-organizing WTA networks and MAP prediction is discussed. A simulated example demonstrating this connection is provided.<<ETX>>
Maximum-Margin Coupled Mappings for cross-domain matching

Biometrics systems typically work best in settings where probe samples are captured in the same manner as the training set. When biometrics are acquired under different conditions or with different sensors, naïve approaches to recognition perform poorly. Coupled mappings have been introduced for performing face recognition across different resolutions, and learn a common subspace between different domains. In this paper, we introduce Maximum-Margin Coupled Mappings (MMCM), which aims to learn projections such that there is a margin of separation between pairs of cross-domain data from the same class and pairs of cross-domain data from different classes. While coupled mapping techniques have traditionally been used for matching face images at different resolutions, we demonstrate that MMCM is effective for cross-sensor biometric matching as well.
Customizing the training dataset to an individual for improved heartbeat recognition performance in long-term ECG signals

This work presents an investigation of the potential benefits of customizing the analysis of long-term ECG signals, collected from individuals using wearable sensors, by incorporating small amount of data from these individuals in the training set of our classifiers. The global training dataset selected was from the MIT-BIH Arrhythmias Database. This proposal is validated on long-term ECG recordings collected via wearable technology in unsupervised environments, as well on the MIT-BIH Normal Sinus Rhythm Database. Results illustrate that heartbeat classification performance could improve significantly if short periods of data (e.g., data from the first 5-minutes of every 2 hours) from the specific individual are regularly selected and incorporated into the global training dataset for training a customized classifier.
Mitigating the Effects of Track Mis-Registration in Bit-Patterned Media

In bit-patterned media (BPM) aimed at magnetic recording densities of 1 Tbit/in2 and higher, adjacent tracks may become very close leading to significant inter-track interference (ITI). Read-head offset or track mis-registration (TMR) can further degrade the performance of the channel. To investigate the effects of ITI and TMR on the performance of the channel, we have developed a two-dimensional (2D) pulse response simulator for BPM. Simulation results using these pulse responses suggest that equalizers and detectors optimized for zero TMR may not perform well in the presence of TMR. To mitigate the effects of TMR, we propose a modified trellis for the Viterbi algorithm (VA). The modified VA (MVA) takes into account the ITI while computing branch metrics. Simulation results show that the MVA can improve the bit error rate in the presence of TMR.
Scrubbing with partial side information for radiation-tolerant memory

Memory systems used in space applications suffer from radiation-induced errors, either temporary upsets (soft errors) or permanent defects (hard errors or stuck-at errors). Scrubbing is a method to protect memory contents by periodically decoding the stored data to correct those soft and stuck-at errors then rewriting the corrected data back into memory. However, defective cells will remain and accumulate over time. Conventional coding disregards defective cells, however this may be inefficient for memory protection in space. In this study, alternative coding schemes for scrubbing are investigated, where the channel model depends on the cell states, defective or not, and the encoder uses channel state information (CSI) or side information. At every scrubbing, the error correcting code (ECC) decoder provides partial CSI back to the encoder and the encoder uses the CSI to improve the performance of memory systems with scrubbing. Information theoretic limits of the channel with partial CSI are investigated and several coding schemes are introduced to mitigate the effects of defective cells, particularly those caused by stuck-at defects. In addition, coding schemes with partial CSI are concatenated with binary Bose-Chaudhuri-Hocquenghem (BCH) codes to protect memory contents from both soft and stuck-at errors in space radiation environments. Numerical simulation results show that scrubbing with partial CSI improves reliability over the state-agnostic approaches.
Picket-Shift Codes for Bit-Patterned Media Recording With Insertion/Deletion Errors

We report the results of an investigation of bit-patterned media recording (BPMR) channels containing insertion/deletion errors that may be introduced because of the write synchronization problem. We first describe a simple channel model for insertion/deletion errors in BPM, as a result of write clock frequency offset. Based on this channel model, we propose a new error correction coding (ECC) scheme to correct insertion/deletion errors-picket shift. Simulation results show that picket shift performs significantly better than a no-ECC scheme in the presence of insertion/deletion errors.
Simultaneous super-resolution and feature extraction for recognition of low-resolution faces

Face recognition degrades when faces are of very low resolution since many details about the difference between one person and another can only be captured in images of sufficient resolution. In this work, we propose a new procedure for recognition of low-resolution faces, when there is a high-resolution training set available. Most previous super-resolution approaches are aimed at reconstruction, with recognition only as an after-thought. In contrast, in the proposed method, face features, as they would be extracted for a face recognition algorithm (e.g., eigenfaces, Fisher-faces, etc.), are included in a super-resolution method as prior information. This approach simultaneously provides measures of fit of the super-resolution result, from both reconstruction and recognition perspectives. This is different from the conventional paradigms of matching in a low-resolution domain, or, alternatively, applying a super-resolution algorithm to a low-resolution face and then classifying the super-resolution result. We show, for example, that recognition of faces of as low as 6 times 6 pixel size is considerably improved compared to matching using a super-resolution reconstruction followed by classification, and to matching with a low-resolution training set.
Analysis of dropout peakshift in magnetic tape recording

Dropout is the term used to describe sporadic and long-duration signal loss in a tape recording system due to media defects or debris generated by the head-media interface. The main mechanism believed to cause dropouts is spacing loss during writing and/or reading. Dropouts are a major limiting factor in the performance of the magnetic tape channel, which makes understanding them a critical issue for future density improvements. In this paper, we analyze the peakshift behavior of signals during a dropout, which constitutes a significant but unexpected side effect. This peakshift was found to be caused during the recording process. Both experimental analysis and modeling have been used in this study.
Dropout Compensation by Equalizer Selection and Kalman Filter-Based Timing Recovery

When occasional dropouts occur in magnetic tape recording systems because of defects and debris, the readback signal exhibits amplitude loss, pulse widening, and peak shifts, and the corresponding entire data block may be lost. In this paper, we develop a dropout compensation method that works in conjunction with the interpolated timing recovery method. In this method, we estimate the readback signal envelope and use it as a measure of the depth of a dropout. On the basis of the estimated envelope, an equalizer can be selected from a pretrained filter bank to transform the distorted transition response in a dropout to the desired partial response target. In addition, a Kalman filter-based scheme can be used to determine the phase and the frequency update gains in the timing recovery loop. We show that, by using Kalman filter-based timing recovery, more dropouts can be compensated
A multibit-per-cell memory model and nonbinary LDPC codes

Protecting nonvolatile memory systems in harsh radiation environments encountered in space missions is important and error correcting schemes can extend the lifetime of those memory systems. For example, recent research has shown that LDPC codes can extend the lifetime of nonvolatile memory under space radiation environment more than Bose-Chaudhuri-Hocquenghem (BCH) or Reed-Solomon (RS) codes at fixed codeword error rates. However, conventional memory models assume that bit errors are independent, but multibit errors were reported in satellite experiments. Moreover, memory feature sizes are shrinking and multibit-per-cell structures are becoming standard so radiation will increasingly lead to multibit errors. For these reasons, we can expect that the bit errors in memory systems will be correlated. In this work, we develop a mathematical multibit-per-cell memory model under a radiation environment. In this memory model, bit errors are correlated and the probability of errors depends on radiation parameters and time. For correlated bit errors, nonbinary codes can be more effective than binary codes. We will demonstrate that nonbinary LDPC code can outperform conventional BCH and RS codes in a correlated multibit error environment.
On decoding rules to minimize the probability of information bit errors

Decoding rules to minimize the probability of information bit errors in binary linear block codes are investigated. A novel decoding rule is proposed and compared to a recently proposed rule (see M. Elia and G. Prati, 1985) and to the standard rule, with respect to both the information bit error rate and the information word error rate. It is concluded that the proposed rule not only can be implemented much more efficiently that the standard rule, but also is superior with respect to either error rate. Some particular codes have been presented to show that the authors' rule can coincide or can differ from Elia and Prati's rule when applied to a given code.
Detecting occlusion from color information to improve visual tracking

Visual tracking in unconstrained environments often involves following an object that exhibits a number of appearance changes from factors such as scale change, rotation, and illumination. Effective tracking requires adapting a tracker to the object's changing appearance over time. When a target becomes occluded by other objects in the scene, a naive tracker may end up learning the appearance of the occluding object. Our work introduces a method of detecting occlusion by considering the color profile of the target to prevent inappropriate tracker updates while the target is occluded. We show improved overlap and central location precision with three visual trackers when adding our hue-based occlusion detection to each tracking system.
Extended Depth of Field Iris Recognition with Correlation Filters

Iris recognition can offer high-accuracy person recognition, particularly when the acquired iris image is well-focused. However, some practical applications require that the iris recognition system perform well even when the eyes are not in the focal plane of the camera lens. Therefore, iris recognition using camera systems with a large depth of field is very desirable. One approach to achieve extended depth of field is to use a wavefront coding system as proposed by Dowski and Cathey which uses a cubic phase modulation mask whose effect is a linear operation. The conventional approach is to restore the iris images from the camera outputs and then apply iris recognition algorithms to the restored iris images. Some correlation filters are invariant to linear operations and should maintain their recognition performance even when the training and testing images are the unrestored (i.e., blurred due to wavefront coding) images produced by the extended depth of field imaging system. In this work we investigate the recognition performance of correlation filters and compare it with the popular iriscode method when the input images are unrestored images produced by the wavefront coding imaging system. We present results of simulations done with more than 1000 such unrestored wavefront coded iris images taken from the ICE database.
Modeling and Two-Dimensional Equalization for Bit-Patterned Media Channels With Media Noise

In this paper, we first propose a simple but general analytical channel model for bit-patterned media (BPM) systems. It facilitates the generation of accurate readback signals with a large amount of media noise, as well as the analytical design of equalizer and channel partial response (PR) target. We further propose an analytical approach to jointly design the two-dimensional (2-D) equalizer with one-dimensional (1-D) channel PR target for BPM systems with media noise, according to the minimum mean square error (MMSE) criterion with monic constraint. Simulation results show that the proposed 2-D equalizer with 1-D target based approach achieves significant performance gain over the system designed without considering media noise.
Highly Parallel FPGA Emulation for LDPC Error Floor Characterization in Perpendicular Magnetic Recording Channel

Low-density parity-check (LDPC) codes offer a promising error correction approach for high-density magnetic recording systems due to their near-Shannon limit error-correcting performance. However, evaluation of LDPC codes at the extremely low bit error rates (BER) required by hard disk drive systems, typically around 10-12 to 10- 15, cannot be carried out on high-performance workstations using conventional Monte Carlo techniques in a tractable amount of time. Even field-programmable gate array (FPGA) emulation platforms take a few weeks to reach BER between 10-11 and 10-12. Thus, we implemented a highly parallel FPGA processing cluster to emulate a perpendicular magnetic recording channel, which enabled us to accelerate the emulation by > 100times over the fastest reported emulation. This increased throughput enabled us to characterize the performance of LDPC code BER down to near 10-14 and investigate its error floor.
Imagined Speech Classification with EEG Signals for Silent Communication: A Preliminary Investigation into Synthetic Telepathy

The objective of this work is to explore the potential use of electroencephalography (EEG) as a means for silent communication by way of decoding imagined speech from measured electrical brain waves. EEG signals were recorded at University of California, Irvine (UCI) from 7 volunteer subjects imagining two syllables, /ba/ and /ku/, without speaking or performing any overt actions. Our goal is to classify these imagined syllables and based on the resulting accuracy assess the feasibility of this task. In this research, the EEG data are preprocessed to reduce the effects of artifacts and noise, and autoregressive (AR) coefficients are extracted as features for imagined syllable classification using a k-Nearest Neighbor classifier. Initial results suggest that it is possible to identify imagined speech.
Multi-channel equalization for PWM channels in magneto-optical recording

We show by modeling and simulations that multitrack readback and equalization can lead to significant increases in the track density in magneto-optical recording without performance degradation due to two dimensional interference. We assume an architecture in which an isolated set of closely spaced tracks is read simultaneously. In an earlier paper, we described a multi-channel zero-forcing equalizer and a two-dimensional decision-feedback equalizer for this architecture, and presented their performance for PPM (pulse position modulation) optical recording channels. Since PWM (pulse width modulation) recording is more popular in magneto-optic (MO) disks, we model the PWM channel in this paper and show simulation results for the performance improvement obtained by using multi-channel equalization for data recorded in PWM format.
Raindrop detection and removal using salient visual features

Raindrops on vehicles' windshields can degrade the performance of in-vehicle vision systems. In this paper, we present a novel approach that detects and removes raindrops in the captured image when using a single in-vehicle camera. When driving in light or moderate rainy conditions, raindrops appear as small circlets on the windshield in each image frame. Therefore, by analyzing the color, texture and shape characteristics of raindrops in images, we first identify possible raindrop candidates in the regions of interest (ROI), which are small locally salient droplets in a raindrop saliency map. Then, a learning-based verification algorithm is proposed to reduce the number of false alarms (i.e., clear regions mis-detected as raindrops). Finally, we fill in the regions occupied by the raindrops using image inpainting techniques. Numerical experiments indicate that the proposed method is capable of detecting and reducing raindrops in various rain and road scenarios. We also quantify the improvement offered by the proposed method over the state-of-the-art algorithms aimed at the same problem and the benefits to the in-vehicle vision applications like clear path detection.