nils-herrmann/clean_scirepeval huggingface dataset:
Crowdsourcing undersampled vehicular sensor data for pothole detection

The increased availability of embedded vehicle sensors allows for the detection of road features such as potholes. Despite being a promising approach, current vehicle embedded sensors operate at low frequencies and undersample sensor signals, thus degrading detection accuracy. One emerging solution is to crowdsource such undersampled sensor data from multiple vehicles to increase the detection accuracy. Aggregating sensor data from multiple vehicles, nonetheless, is a challenging task given the heterogeneity among vehicles, asynchronous sensor operation, GPS error, and sensor noise. Additionally, there may be bandwidth restrictions in vehicular networks which limit the amount of data available for aggregation. We investigate these issues by focusing on the problem of pothole detection. To quantify the detection accuracies and effects of real-world limitations, we design and evaluate three crowdsourcing pothole detection schemes involving vehicles and the Cloud. We also address the issue of lack of extensive model training data by demonstrating that a detection model applicable to real-world systems can be derived using simulated data. We validate our pothole detection methods using 38.1 km of real-world data collected from driving on roads in Warren,
Evaluation of Low-Density Parity-Check Codes on Perpendicular Magnetic Recording Model

Low-density parity-check (LDPC) codes have shown superior error-correcting performance in a variety of data storage system studies, including traditional longitudinal magnetic recording systems. However, perpendicular magnetic recording systems (of increasing interest) exhibit impairments different from longitudinal magnetic recording systems, and thus present new challenges for error-correcting codes. In this effort, we evaluate a structured LDPC code using a perpendicular magnetic recording channel model that includes impairments such as transition noise, nonlinear transition shift, transition percolation, and baseline wander (BLW). The channel model, as well as the LDPC encoder and the decoder are implemented in field-programmable gate array (FPGA) hardware. The LDPC coded system is evaluated down to bit error rate (BER) of 10-12 and frame error rate (FER) of 10-8. The impact of individual impairments on coding performance is studied separately. The soft output Viterbi algorithm (SOVA) + LDPC system maintains its superior error-correcting performance under the perpendicular recording channel
A high-throughput, field programmable gate array implementation of soft output Viterbi algorithm for magnetic recording

A high throughput, reconfigurable field programmable gate array (FPGA) implementation of the soft output Viterbi algorithm (SOVA) is described. Such a SOVA module provides the soft information needed by an iterative soft decoder used with Turbo codes and low density parity check (LDPC) codes. The implementation of the SOVA algorithm runs at 100 Mb/s on Xilinx Virtex II 2000 FPGA. Using a higher capacity FPGA and repeating the circuit doubles the throughput. The design can be reconfigured for different partial response (PR) targets. The design requires about 5 kb of memory for the EPR4 channel and may be integrated on a single chip with serially implemented LDPC decoder.
Duality between erasures and defects

We investigate the duality of the binary erasure channel (BEC) and the binary defect channel (BDC). This duality holds for channel capacities, capacity achieving schemes, minimum distances, and upper bounds on the probability of failure to retrieve the original message. In addition, the relations between BEC, BDC, binary erasure quantization (BEQ), and write-once memory (WOM) are described. From these relations we claim that the capacity of the BDC can be achieved by Reed-Muller (RM) codes under maximum a posterior (MAP) decoding. Also, polar codes with a successive cancellation encoder achieve the capacity of the BDC. Inspired by the duality between the BEC and the BDC, we introduce locally rewritable codes (LWC) for resistive memories, which are the counterparts of locally repairable codes (LRC) for distributed storage systems. The proposed LWC can improve endurance limit and power efficiency of resistive memories.
Low-density Parity-check codes with run length limited (RLL) constraints

This paper addresses a new scheme by Vasic and Pedagani to combine low-density parity-check (LDPC) codes with run length limited (RLL) constraints. In this method, the RLL constraints are embedded into the LDPC codewords by deliberately flipping the bits of LDPC codewords that violate the RLL constraints. It is important to keep the number of flipped bits small in order to not overburden the LDPC decoder. In this paper, we introduce a method to control the number of flipped bits by using pseudorandom sequences. We present a new low-complexity iterative decoding and detection scheme to correct both the flipped bits and channel errors in a partial response channel. Analyses and simulation results show that the proposed method has good performance and reasonable complexity for (0,k) RLL constraints.
Stacked correlation filters for biometric verification

Correlation filters (CFs) are a well-known pattern classification approach used in biometrics. A CF is a spatial-frequency array that is specifically synthesized from a set of training patterns to produce a sharp correlation output peak at the location of the best match for an authentic image comparison and no such peak for an impostor image comparison. The underlying premise when using CFs is that this correlation output peak behavior on training data ideally extends to testing data. Yet in 1:1 verification scenarios, where there is limited training data available to represent pattern distortions, the correlation output from an authentic comparison can be difficult to discern from the correlation output from an impostor. In this paper we introduce Stacked Correlation Filters (SCFs), a simple and powerful approach to address this problem by training an additional set of classifiers which learn to differentiate correlation outputs from authentic and impostor match pairs. This is done by training a series of stacked modular CFs with each layer refining the output of the previous layer. Our basic premise is that since correlation outputs have an expected shape, an additional CF can be trained to recognize such shape and refine the final output. As previous works with CFs have only focused on individual filter design or application, which assumes the CF to provide a sharp peak, this is a new CF paradigm that can benefit many existing CF designs and applications.
Zero-aliasing correlation filters

Traditional correlation filters are designed and implemented via the frequency domain, where the correlation of two signals may be computed efficiently. However, when the discrete Fourier transform (DFT) of length N is used, multiplication in the frequency domain results in an N-point circular correlation, rather than a linear correlation. The resulting correlation filter output is therefore corrupted by the aliasing effects of circular correlation. One solution is to design and implement the correlation filter directly in the space domain. However, this is more computationally intense. Recent literature has discussed ways in which to minimize circular correlation effects, but the effects are not completely removed. We propose a new frequency domain method for completely eliminating circular correlation effects when designing correlation filters. We demonstrate this idea with the well-known minimum average correlation energy (MACE) filter and show how the reformulated MACE filter in the frequency domain outperforms the original formulation of the MACE filter.
Automatic target recognition of multiple targets from two classes with varying velocities using correlation filters

Correlation filters (CFs) can detect multiple targets in one scene making them well-suited for automatic target recognition (ATR) applications. We present a method to efficiently compute the Quadratic CF (QCF) capable of detecting multiple targets from two classes. We use a Kalman filter framework to combine information from successive correlation outputs in a probabilistic way integrating the ATR tasks of detection, recognition, and tracking.
BaseLine Wander Compensation by Per-Survivor-Processing

Baseline wander (BLW) is one of major sources of bit errors in perpendicular recording. In this paper, we present a method to compensate for BLW by per-survivor processing (PSP), i.e., along each surviving path in the Viterbi algorithm (VA). We apply the PSP method in VA in timing recovery loop and SOVA for iterative decoding. Simulation results show that the cycle slip rate and bit error rate are lowered and approach ideal case where there is no BLW
Investigation of human identification using two-lead Electrocardiogram (ECG) signals

In this paper, we investigate the applicability of Electrocardiogram (ECG) signals for human identification. Wavelet Transform (WT) and Independent Component Analysis (ICA) methods are applied to extract morphological features that appear to offer excellent discrimination among subjects. The proposed method is aimed at the two-lead ECG configuration that is routinely used in long-term continuous monitoring of heart activity. The information from the two ECG leads is fused to achieve improved subject identification. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database , Ml T-BIH Normal Sinus Rhythm Database  and Long-Term ST Database , in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Excellent rank-1 recognition rates (as high as 99.6%) were achieved based on single heartbeats. The proposed method exhibits good identification accuracies not just with the normal ECG signals, but also in the presence of various arrhythmias. This work adds to the growing evidence that ECG signals can be useful for human identification.
Wavelet packet correlation methods in biometrics

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimizing some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations which provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object recognition task. We present results by implementing a biometric recognition system using the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.
Channel Modeling and Equalizer Design for Staggered Islands Bit-Patterned Media Recording

In this paper, we first present a recording physics based analytical channel model for bit-patterned media recording (BPMR) systems with staggered islands configuration. We further propose an analytical approach to jointly design the equalizer with partial response (PR) target for BPMR systems, by using the minimum mean-squared error (MMSE) criterion with monic constraint, taking into account inter-track interference (ITI) and media noise. Simulation results show that the proposed approach performs better than the system designed without considering ITI and media noise. We further investigate the performance of staggered array against regular array islands with bit-aspect ratios (BAR) of 1 and 2, and with different amount of media noise, inter-symbol interference (ISI), and ITI. We found that staggered array islands with BAR of 2 offer better bit error rate (BER) performance and better tolerance to media noise.
Binary SOVA and Nonbinary LDPC Codes for Turbo Equalization in Magnetic Recording Channels

Nonbinary low-density parity check (LDPC) codes can provide coding gains over binary LDPC codes at the cost of increased complexity. For magnetic recording channels, optimal detectors for the nonbinary codes are symbol-based, which are significantly more complex than binary detectors. In this paper, we investigate the feasibility of using much less complex bit-based soft output Viterbi algorithm (SOVA) as a channel detector for nonbinary LDPC codes and performing turbo iterations between the SOVA and the nonbinary LDPC decoder. We show that performance gains can still be obtained by the turbo iterations by using the bit-based SOVA, although the performance gains are suboptimal to the symbol-based detectors. This scheme can be useful in low-complexity nonbinary LDPC coding systems.
Low-density parity-check codes with variable rate and randomized constraints for advanced magnetic tape recording

Variable rate and randomized run length limited (VR/sup 2/ RLL) constraint is used in some magnetic tape recording systems. The variable rate encoding algorithm monitors the incoming data stream for conditions such as run length limit (RLL) constraints. If one of the conditions is violated, a bit is inserted into the data stream. The purpose of the VR/sup 2/ RLL constraint is to ensure that the encoded data contains adequate tracking and amplitude information for reliable data detection and prevent the preamble pattern from occurring during the data field. Although the VR/sup 2/ approach to RLL constraint is effective, it has the disadvantages of variable block length and error propagation. Furthermore, VR/sup 2/ is not suitable for soft decision decoding needed by low-density parity check (LDPC) codes being investigated because of their good performance, low complexity and inherent parallel decoding architecture. LDPC codes usually exhibit excellent error correction performance and the LDPC decoder may be able to recover the codeword even if we deliberately introduce some errors in the codeword in order to satisfy RLL constraints. Based on this observation, the paper proposes a scheme of combining LDPC codes with the VR/sup 2/ RLL constraint based on bit flipping. In addition to providing soft decision decoding to achieve large coding gain, the proposed scheme has the advantages of no error propagation and fixed block length.
Vision-based potential collision detection for reversing vehicle

This paper deals with the detection of potential collision for a reversing vehicle. The collision detection relies on monitoring the surroundings of reversing vehicle by means of on-board cameras mounted on the side window and rear side of the vehicle. We first detect the stationary vehicle(s) in the region of interest (ROI) using histogram of oriented gradients (HOG) features and support vector machine (SVM) based classification. This is followed by the use of a Gaussian Mixture Model (GMM) to detect dynamic vehicle(s) in the ROI. Trajectories and speeds of the dynamic vehicles are predicted through the particular position of the detected vehicles in each frame and through their observed trajectory and speed patterns. Finally a potential collision is predicted based on the trajectories and speeds of both vehicles. The experimental results indicate that the proposed framework performs well in different traffic scenarios and can contribute to the advancement for improving the safety of car driving.
A neural network architecture for path planning

The potential field approach to path planning is computationally intensive but highly parallel. The authors propose a general neural network architecture called the wave expansion neural network (WENN), specifically designed to implement such potential field operations useful for path planning. The authors describe WENN-based neural network systems which implement certain types of artificial potential fields for path planning. The neural network takes the workspace which needs to be navigated as input and generates artificial potential fields which are then subsequently used to find a path. Neural network implementation results in greatly reduced computations.<<ETX>>
Comparison of Kernel Class-dependence Feature Analysis (KCFA) with Kernel Discriminant Analysis (KDA) for Face Recognition

Kernel methods have been applied to many linear feature analysis classifiers to generate nonlinear classifiers for improved classification performance. The recently proposed kernel class-dependence feature analysis (KCFA) method extends linear correlation filter technology to kernel correlation filters, greatly improving the classification performance. In this paper, we compare the KCFA method with the kernel discriminant analysis {KDA) method and show that the KCFA and the KDA result in the same representation subspace and the relationship between them is similar to the relationship between the orthogonal and the simplex signal representations in digital communications. We present face recognition results to illustrate that the KCFA method is preferable to the KDA method.
Timing Recovery Loop Delay Compensation by Optimal Loop Gains

Future data storage systems must cope with higher densities and lower signal-to-noise ratios. Decision-aided timing recovery loops will likely need a longer delay in the sequence detector to make reliable bit decisions. Loop delay degrades the dynamics of the phase-locked loop (PLL). In this paper we present a process to determine optimal gains for PLL with delay assuming that the timing drift is well modeled by a second order random walk. Simulation shows that the loss of lock rate from the proposed method is similar to the delay compensation method in [1, 2], with both approaches offering much lower loss of lock rates than conventional PLL without delay compensation. The simulation also shows there is an optimal delay. Method in [1, 2] adds components to the PLL, whereas no extra hardware is needed for the proposed method.
Efficient boosting for synthesizing a minimally compact reduced complexity correlation filter bank for biometric identification

This paper addresses how to efficiently select which training images to use from an enrollment video sequence to train a correlation filter based face recognition system. Efficient enrollment and the selective use of training images from a video sequence of face images is a very vital component that determines the success of any face recognition system. We describe an efficient boosting algorithm for synthesizing a minimal set of filters that capture the different facial variations during the enrollment phase such that the resulting filter bank can also maintain good generalization and discrimination for recognition and verification. This is done by determining a fitness metric for each filter that determines the amount of facial variation capacity represented by that filter. If that capacity is exceeded by using more training images than needed for that filter then the resulting filter quality is compromised and discrimination performance can degrade leading to lower acceptance and rejection rates. We use advanced correlation filters that have shown to exhibit built-in illumination tolerance.
Arrhythmia detection and classification using morphological and dynamic features of ECG signals

Computer-assisted cardiac arrhythmia detection and classification can play a significant role in the management of cardiac disorders. In this paper, we propose a new approach for arrhythmia classification based on a combination of morphological and dynamic features. Wavelet Transform (WT) and Independent Component Analysis (ICA) are applied separately to each heartbeat to extract corresponding coefficients, which are categorized as ‘morphological’ features. In addition, RR interval information is also obtained characterizing the ‘rhythm’ around the corresponding heartbeat providing ‘dynamic’ features. These two different types of features are then concatenated and Support Vector Machine (SVM) is utilized for the classification of heartbeats into 15 classes. The procedure is applied to the data from two ECG leads independently and the two results are fused for the final decision. Compare the two classification results and the classification result is kept if the two are identical or the one with greater classification confidence is picked up if the two are inconsistent. The proposed method was tested over the entire MIT-BIH Arrhythmias Database  and it yields an overall accuracy of 99.66% on 85945 heartbeats, better than any other published results.
Dual segmented Kalman filters based symbol timing recovery for low-SNR partial response data storage channels

We present a new tracking algorithm to perform timing recovery for low-SNR partial response data storage channels. This approach, designated "dual segmented Kalman filters based symbol timing recovery (DSK-STR)", is based on the conventional phase-locked loop (PLL) based timing recovery scheme. However, DSK-STR uses two intertwined PLLs and the gains of these PLLs are updated at each symbol instant according to a modified Kalman filter algorithm. The performance of DSK-STR is investigated via Monte-Carlo simulations and it is seen that DSK-STR offers a 3.5 dB signal-to-noise ratio (SNR) gain over the conventional PLL-based timing recovery scheme for a target loss of lock rate of 1/spl times/10/sup -5/ in longitudinal magnetic recording channels.
Additive encoding low-density parity-check (AE-LDPC) codes for two-dimensional magnetic recording (TDMR)

In this paper, a novel coding with side information at the encoder (CSIE) scheme is developed for two-dimensional magnetic recording (TDMR) channel. The write errors in the TDMR channel are modeled with random Voronoi grains, and the encoder uses the grain states as side information for additive encoding (AE) of the codewords of a low-density parity-check (LDPC) code. Getting the complete side information about all the grain states for encoding may be impractical because of the large number of read-after-write tests required. As a more practical alternative, coding with partial side information is also investigated with the AE-LDPC coding scheme, where partial side information can be extracted from the distribution of the grains instead of read-after-write tests. The proposed coding schemes are evaluated by numerical simulations using an idealized TDMR channel model. Simulation results show that the AE-LDPC provides an improved customer density with a sufficient reliability.
Mitigation of Noise Correlation for Two-Dimensional Detection in Array-Reader Based Magnetic Recording

Array-reader based two-dimensional magnetic recording (TDMR) is considered as a key technology for next-generation magnetic recording due to its advantages such as diversity gain against noise and inter-track interference suppression. In this work, a three-reader two-track (3R2T) system is studied where two two-dimensional equalizers and a joint detector are used to detect data from two tracks. Analysis shows strong cross-correlation between the noise components at the two equalizers' outputs. Two approaches are presented to handle the cross-correlation in the equalizer and detector, respectively. Simulation results demonstrate that significant performance gain can be achieved using the two proposed approaches.