nils-herrmann/clean_scirepeval huggingface dataset:
Symbol timing recovery for low-SNR partial response recording channels

Future very high density data storage systems will exhibit significantly more intersymbol interference (ISI) and significantly lower signal-to-noise ratio (SNR). Advanced signal detection algorithms, such as noise predictive maximum likelihood (NPML) and iterative soft decoding, are aimed at coping with such lower SNRs and higher ISI. However, at such low SNRs, because of their large residual timing jitter, current timing recovery schemes suffer frequently from loss of lock (the event where the estimated phase drift differs significantly from the actual phase drift for a significantly long duration leading to misindexing of the detected bits and thus error bursts) potentially offsetting the SNR gains provided by advanced detection methods. The main contribution of this paper is that by approximating the phase drifts present in recording systems as a piecewise linear phase drift model, we propose a novel timing recovery scheme, which is named frequency offset feedforward symbol timing recovery (FOSTR). For such a piecewise linear phase drift model, the problem of estimating the time-changing phase drift can be transformed to the problem of estimating the slopes (i.e., frequency offsets) and initial phase offsets of several linear ramps. The performance of the timing recovery based on this approach will be better than current methods in low SNR because the number of parameters to be estimated is significantly smaller. Bit-by-bit simulations with iterative soft decoding (low density parity check (LDPC) code of rate 16/17 and codeword size 4352 bits is used) show that FOSTR results in a significantly smaller residual timing jitter than that of the conventional decision-directed PLL-based feedback timing recovery schemes, although the adjusted ("adjusted" means the sectors suffering from loss of lock are not taken into account in bit error rate calculation) bit error rate (BER) performance of FOSTR is only about 0.6 dB better for a target adjusted BER of 1/spl times/10/sup -5/ than that of the conventional timing recovery scheme because iterative soft decoding is robust to residual timing jitter. However, the loss of lock rate (i.e., the fraction of sectors suffering from loss of lock) is significantly reduced by FOSTR.
Differential learning leads to efficient neural network classifiers

The authors outline a differential theory of learning for statistical pattern classification. The theory is based on classification figure-of-merit (CFM) objective functions, described by J. P. Hampshire II and A. H. Waibel ( 1990). They give the proof that differential learning is efficient, requiring the least classifier complexity and the smallest training sample size necessary to achieve Bayesian (i.e., minimum error) discrimination. A practical application of the theory is included in which a simple differentially trained linear neural network classifier discriminations handwritten digits of the AT&T DB1 database with a 1.3% error rate. This error rate is less than one half of the best previous result for a linear classifier on this optical character recognition (OCR) task.<<ETX>>
Performance of dropout correction on real magnetic tape waveforms with dropouts

The need to increase both linear and track densities in tape recording technology calls for more robustness against dropouts, the sporadic losses in signal amplitude, that are a principal source of errors in tape systems. A dropout correction scheme previously introduced by the authors enables more robust bit detection by restoring the signal afflicted by the dropout event. In this paper, we present the results of using this scheme on real oversampled waveforms from an experimental tape test stand. Real-time implementation using a peak detector has been simulated in this work, Albeit at a low density (1.85 channel bits per PW50), the emphasis of this study is to identify the types of error events caused by dropouts and to demonstrate the feasibility of dropout compensation even in the case of very challenging dropouts presented by the experimental data set. It is shown that this scheme can significantly reduce the frequency of dropout induced cycle slip error events that can be as long as the data block. In other cases, where cycle slip errors do not occur, the number of errors associated with dropout events is reduced, on average, by a factor of more than 2. The sensitivity of the method to the envelope detection method is examined, and this is identified as an opportunity for significantly improving the performance of the scheme.
Writing on dirty flash memory

The most important challenge in the scaling down of flash memory is its increased inter-cell interference (ICI). If side information about ICI is known to the encoder, the flash memory channel can be viewed as similar to Costa's “writing on dirty paper (dirty paper coding).” We first explain why flash memories are dirty due to ICI. We then show that “dirty flash memory” can be changed into “memory with defective cells” model by using only one pre-read operation. The asymmetry between write and erase operations in flash memory plays an important role in this change. Based on the “memory with defective cells” model, we show that additive encoding can significantly improve the probability of decoding failure by using the side information.
Matching highly non-ideal ocular images: An information fusion approach

We consider the problem of matching highly non-ideal ocular images where the iris information cannot be reliably used. Such images are characterized by non-uniform illumination, motion and de-focus blur, off-axis gaze, and non-linear deformations. To handle these variations, a single feature extraction and matching scheme is not sufficient. Therefore, we propose an information fusion framework where three distinct feature extraction and matching schemes are utilized in order to handle the significant variability in the input ocular images. The Gradient Orientation Histogram (GOH) scheme extracts the global information in the image; the modified Scale Invariant Feature Transform (SIFT) extracts local edge anomalies in the image; and a Probabilistic Deformation Model (PDM) handles nonlinear deformations observed in image pairs. The simple sum rule is used to combine the match scores generated by the three schemes. Experiments on the extremely challenging Face and Ocular Challenge Series (FOCS) database and a subset of the Face Recognition Grand Challenge (FRGC) database confirm the efficacy of the proposed approach to perform ocular recognition.
Model for demagnetization-induced noise in thin-film magnetic recording media

A linear, statistical model is described which predicts the power spectrum of measured noise in bulk-demagnetized (i.e. AC-erased) thin-film magnetic recording media. It is shown that the noise is the result of magnetic flux which is ascribed to erasure-induced transitions along the track length in the medium. The noise power spectrum for a rigid disk medium is shown to correspond to the power spectrum of Poisson-distributed induced transitions along the track length, while noise along the track width is sufficiently described in terms of a uniform, average magnetization with small variance. Experimental data from two thin-film disks are used with the model to estimate the Poisson parameter for each disk. It is demonstrated that AC-erased noise from particulate media can be considered as a limiting case of the Poisson model.
Field programmable gate array-based investigation of the error floor of low density parity check (LDPC) codes for magnetic recording channel

Low density parity check (LDPC) codes have shown 3 to 5 dB coding gain, at bit error rates (BER) around 10/sup -5/, over the uncoded partial response maximum-likelihood (PRML) channels. However, the error floor of LDPC codes is still an open question and is one of the major concerns in applying LDPC codes in high density receding channel. Due to the suboptimal nature of decoding schemes and the difficulties in code spectrum analysis, the performance analysis of LDPC code at very low BER region is very difficult, if not impossible. Therefore, the performance of LDPC codes is usually evaluated through Monte Carlo simulations. The sequential nature of the conventional C simulations limits their capability for BER investigation. It takes months to evaluate the performance of codes in partial response (PR) channel at 10/sup -9/ BER using optimized C code and a 2 GHz computer. To investigate the error floors, a high-throughput, fully-reconfigurable field programmable gate array (FPGA) platform was developed for the evaluation of LDPC codes in PR channel. In this paper, the architecture, main features and throughput of the channel simulator on FPGA was described. The error floor of one type of LDPC code is shown as an example.
Multi-Lane Pothole Detection from Crowdsourced Undersampled Vehicle Sensor Data

As smart vehicles have become more ubiquitous, the capability now exists to detect environmental road features (e.g., potholes, road incline angle, etc.) from their embedded sensor data. By aggregating data from multiple vehicles, crowdsourcing can be leveraged to detect environmental information with improved accuracy. We focus on using such data to detect and localize potholes on multi-lane roads. Extracting information from aggregated vehicle data is challenging due to undersampling sensors, sensor mobility, asynchronous sensor operation, sensor noise, vehicle and road heterogeneity, and GPS position error. GPS position error is particularly problematic in multi-lane environments since the position error is generally larger than standard lane widths. In this paper, we investigate these issues and develop a crowdsourced system to detect and localize potholes in multi-lane environments using accelerometer data from embedded vehicle sensors. Our crowdsourced system reduces the required network bandwidth by determining road incline and bank angle information in each vehicle to filter acceleration components that do not correspond to pothole conditions. We evaluate our system on simulated and real-world data, analyze tradeoffs in the number of vehicles and the amount of bandwidth required for accurate detection, and compare the results to the simpler single lane detection scenario.
Error floor investigation and girth optimization for certain types of low-density parity check codes

Low-density parity check (LDPC) codes with their near-Shannon capacity limit error correcting performance and iterative decoding algorithm are being evaluated for digital communications applications. For LDPC codes to be used in real systems, their error floors need to be investigated. In this paper, we evaluate the performance of disjoint difference set (DDS)-based LDPC codes (with column weights 3, 4, 5) and array code-based LDPC codes (with column weights 3, 4, 5) in the additive white Gaussian noise (AWGN) channel using a high-speed field programmable gate array (FPGA) simulation platform. The error floor regions (bit error rates down to 10/sup -12/) of those codes are presented. For better performance of array codes, a girth optimization method is proposed and the FPGA evaluation results are presented.
Modifying Viterbi Algorithm to Mitigate Intertrack Interference in Bit-Patterned Media

Bit-patterned media are currently being investigated to obtain magnetic recording densities of 1 Tbit/in2 and more. In bit-patterned media, the adjacent tracks may be as close as adjacent bits in a track resulting in significant intertrack interference (ITI). In this work, we propose a modified trellis for the Viterbi algorithm (VA) that can mitigate the effects of ITI. Simulation results show that the modified VA improves the bit error rate. Using a simple model for track misregistration (TMR), the performance of this modified VA in the presence of TMR is also investigated. Based on the simulation results, this modified VA also appears to be more tolerant to TMR than the conventional VA
Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals

In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MIT-BIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the “class-oriented” evaluation and an accuracy of 86.4% in the “subject-oriented” evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.
Adaptive Deep Metric Learning for Identity-Aware Facial Expression Recognition

A key challenge of facial expression recognition (FER) is to develop effective representations to balance the complex distribution of intra- and inter- class variations. The latest deep convolutional networks proposed for FER are trained by penalizing the misclassification of images via the softmax loss. In this paper, we show that better FER performance can be achieved by combining the deep metric loss and softmax loss in a unified two fully connected layer branches framework via joint optimization. A generalized adaptive (N+M)-tuplet clusters loss function together with the identity-aware hard-negative mining and online positive mining scheme are proposed for identity-invariant FER. It reduces the computational burden of deep metric learning, and alleviates the difficulty of threshold validation and anchor selection. Extensive evaluations demonstrate that our method outperforms many state-of-art approaches on the posed as well as spontaneous facial expression databases.
Field programmable gate array implementation of a generalized decoder for structured low-density parity check codes

This work describes a generalized decoder implementation for structured low-density parity check (LDPC) codes. The decoder features low logic consumption, efficient memory management, and full parameterization for reconfiguration. The goal is to provide a unified solution for fast evaluation of a broad class of structured LDPC codes utilizing the properties of field programmable gate arrays (FPGA): high speed and configurability. As a fully reconfigurable core, it is ready to be used in different applications to lower the design to market time. The throughput and resource consumptions are evaluated.
Locally Rewritable Codes for Resistive Memories

Resistive memories, such as phase change memories and resistive random access memories, have attracted significant research interest because of their scalability, non-volatility, fast speed, and rewritability. However, their write endurance needs to be improved substantially for large-scale deployment of resistive memories. In addition, their write power consumption is much higher than the power consumption of read operation. Inspired by locally repairable codes (LRCs) recently introduced for distributed storage systems, we propose locally rewritable codes (LWCs) for resistive memories. We define a novel parameter of rewriting locality , which can be connected to repair locality of LRC. As small values of repair locality of LRC enable fast repair in distributed storage systems, small values of rewriting locality of LWC are able to reduce the problems of write endurance and write power consumption. We show how a small value of rewriting locality can improve write endurance and power consumption by deriving the upper bounds on writing cost. Also, we point out the dual relation of LRC and LWC, which indicates that the existing construction methods of LRC can be applied to construct LWC. Finally, we investigate the construction of LWC with error correcting capability for random errors.
Modulation coding for flash memories

The aggressive scaling down of flash memories has threatened data reliability since the scaling down of cell sizes gives rise to more serious degradation mechanisms such as cell-to-cell interference and lateral charge spreading. The effect of these mechanisms has pattern dependency and some data patterns are more vulnerable than other ones. In this paper, we will categorize data patterns taking into account degradation mechanisms and pattern dependency. In addition, we propose several modulation coding schemes to improve the data reliability by transforming original vulnerable data patterns into more robust ones.
Application of Image Processing to Characterize Patterning Noise in Self-Assembled Nano-Masks for Bit-Patterned Media

In bit-patterned media (BPM), media noise is expected to be dominated by fabrication imperfections and to manifest as island size and spacing fluctuations. Previous work has shown that media noise has a significant impact on the error rates of BPM channels. Thus, having a realistic model of media noise is essential to evaluate the performance of the BPM channels. In this work, we apply image processing techniques to a scanning electron microscope image of a candidate BPM nano-mask, produced by a self-assembly technique, to estimate likely size and location fluctuations of the islands. These statistics may also be useful to characterize the samples of candidate BPM masks. We assume that these observed fluctuations are representative of patterning noise that might result from such a mask. The results indicate that these fluctuations are correlated and can be modeled by Gaussian random processes. Using estimated characteristics of islands size and location fluctuations, we model the correlated media noise and investigate the performance of BPM channels in the presence of such correlated media noise.
Correlation Filters for Object Alignment

Alignment of 3D objects from 2D images is one of the most important and well studied problems in computer vision. A typical object alignment system consists of a landmark appearance model which is used to obtain an initial shape and a shape model which refines this initial shape by correcting the initialization errors. Since errors in landmark initialization from the appearance model propagate through the shape model, it is critical to have a robust landmark appearance model. While there has been much progress in designing sophisticated and robust shape models, there has been relatively less progress in designing robust landmark detection models. In this paper we present an efficient and robust landmark detection model which is designed specifically to minimize localization errors thereby leading to state-of-the-art object alignment performance. We demonstrate the efficacy and speed of the proposed approach on the challenging task of multi-view car alignment.
Zero-Aliasing Correlation Filters for Object Recognition

Correlation filters (CFs) are a class of classifiers that are attractive for object localization and tracking applications. Traditionally, CFs have been designed in the frequency domain using the discrete Fourier transform (DFT), where correlation is efficiently implemented. However, existing CF designs do not account for the fact that the multiplication of two DFTs in the frequency domain corresponds to a circular correlation in the time/spatial domain. Because this was previously unaccounted for, prior CF designs are not truly optimal, as their optimization criteria do not accurately quantify their optimization intention. In this paper, we introduce new zero-aliasing constraints that completely eliminate this aliasing problem by ensuring that the optimization criterion for a given CF corresponds to a linear correlation rather than a circular correlation. This means that previous CF designs can be significantly improved by this reformulation. We demonstrate the benefits of this new CF design approach with several important CFs. We present experimental results on diverse data sets and present solutions to the computational challenges associated with computing these CFs. Code for the CFs described in this paper and their respective zero-aliasing versions is available at http://vishnu.boddeti.net/projects/correlation-filters.html
Performance analysis and simplification of digital zero phase start (DZPS) employed in data storage timing recovery systems

Digital zero phase start (DZPS) (Buch B., US patent 6,191,906, 2001) is a method used in timing recovery for data storage systems to jump-start the acquisition process. The DZPS sums readback signal samples and uses the arctan function. We propose a simplified DZPS, which only averages the readback signal samples and the arctan function operation can be omitted. We investigate the statistics of the two methods and find that the simplified DZPS degrades the phase estimate mean square error negligibly.
Tentpoles Scheme: A Data-Aided Channel Estimation Mechanism for Achieving Reliable Vehicle-to-Vehicle Communications

802.11p-based Dedicated Short Range Communications (DSRC) is considered a promising wireless technology for enhancing transportation safety and improving highway efficiency. One major challenge of IEEE 802.11p technology development ensuring its communication reliability in highly dynamic Vehicle-to-Vehicle (V2V) environments. In this paper, by investigating the characteristics of V2V channels through empirical measurements, we show that the decreased reliability of V2V communication is because the IEEE 802.11p design does not have sufficient number of training symbols in time domain and pilot subcarriers in frequency domain to enable estimation of the fast-changing V2V channels accurately. To tackle this challenge, we propose a new approach called Tentpoles scheme, which utilizes a small subset of data symbols and subcarriers protected by a strong Error Correction Code (ECC) to track and estimate V2V channel variations. Since this subset of data symbols and subcarriers provide dual-use, our Tentpoles scheme not only improves the accuracy of channel estimation and communication reliability, but has only a small impact on effective throughput. Through extensive simulations in synthetic V2V channels and emulation experiments using empirical measurements, our proposed Tentpoles scheme is shown to significantly outperform other V2V channel estimation and equalization methods in terms of communication reliability, at the cost of only moderate loss of throughput.
Face class code based feature extraction for face recognition

In face recognition, the goal is to assign a class label for a test image of a subject from N classes in the database, when binary classifiers are used, the commonly used method is the one-per-class (OPC) i.e., one classifier per subject. A drawback of the OPC method is that when the number of classes is large, it takes very long time to make a classification decision. In place of the computationally-demanding OPC method, we propose a new feature extraction method "face class code" (FCC) based on binary classifiers. For example, correlation filters and support vector machines can be used to generate feature vectors to deal with large number of classes. The FCC method encodes each class label into a binary string, and we design classifiers to discriminate '1' or '0' for each bit in the sequence, to determine the class label. Thus, we will need as few as [log/sub 2/(N)] binary classifiers to achieve an N-class recognition problem. This binary coding framework also opens the whole world of error control codes (ECC), which can be used to improve the recognition performance. The proposed method is verified through experiments on the PIE database and the AR database.