nils-herrmann/clean_scirepeval huggingface dataset:
Performance of the 802.11p Physical Layer in Vehicle-to-Vehicle Environments

A reliable robust wireless network of connected vehicles is desired to enable a number of future telematics and infotainment applications in the vehicular domain. To achieve this objective, vehicle-to-vehicle (V2V) communication is standardized by the IEEE 802.11p Dedicated Short Range Communications (DSRC) standard. Providing reliable communication performance in a highly dynamic time-varying V2V channel is a challenging task. To tackle this challenge, we propose a dynamic equalization scheme, on top of the existing DSRC technology, that significantly improves the packet error rate (PER) of data transmissions without changing the DSRC standard. We also show a hardware implementation of this scheme based on a field-programmable gate array (FPGA) to demonstrate its implementation feasibility. Furthermore, we extend our improved equalization scheme to various data rate options available in the DSRC standard, showing that the proposed scheme is sufficiently generic to support different types of V2V communication. Finally, we report the results of investigating the dependence of wireless communication performance (in terms of PER and throughput) on various design parameters such as packet length, payload size, and data rate.
Fusion performance in long-haul sensor networks with message retransmission and retrodiction

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks. We consider a class of such networks where sensors take measurements of one or more dynamic targets and send state estimates of the target(s) to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels render insufficient the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. The system can adopt certain retransmission-based transport protocols so that lost messages can be recovered over time. However, excess delay may be incurred that can potentially violate the deadline for reporting the estimate. For many applications, though, retrodiction/smoothing techniques can be applied so that the chances of incurring such excess delay are greatly reduced. In this work, we analyze the extent to which retrodiction, along with message retransmission, can improve the performance of delay-sensitive state estimation tasks. Results of numerical and simulation studies of an illustrative example and a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.
Timing recovery algorithm for magnetic amplifying magneto-optic system (MAMMOS)

Magnetic amplifying magneto-optical system (MAMMOS) technique has gained recent attention because of its potential to achieve high recording density. Timing recovery module plays a very important role in the performance of MAMMOS sampled-data detectors because it controls the time instants at which the continuous playback signal is sampled. In this paper, we report a new timing recovery algorithm for MAMMOS and investigate its performance through spin stand data.
A double decoding scheme to improve the PER performance of V2V communications

In this paper, we investigate to achieve low Packet Error Rate (PER) for Vehicle-to-Vehicle (V2V) communications within the IEEE 802.11p standard. We first present the best achievable PER with the assumption of perfect channel knowledge. Then we propose to approach the best achievable PER with a relaxed assumption of partial channel knowledge, i.e., knowing a few separated channels in a packet. In order to obtain partial channel knowledge in an 802.11p standard-compliant way, we propose a sub-packet decoding processing in which the successful decoding of the previous sub-packet will provide partial channel knowledge for decoding the following sub-packet. Then we design a double decoding scheme to achieve low PER in a standard-compliant way. The double decoding scheme utilizes the error detection information provided by CRC check in medium-access (MAC) layer and combines two different decoding methods in physical (PHY) layer. The results show that comparing to previous work, the proposed double decoding can improve PER significantly.
Subject identification from electroencephalogram (EEG) signals during imagined speech

We investigate the potential of using electrical brainwave signals during imagined speech to identify which subject the signals originated from. Electroencephalogram (EEG) signals were recorded at the University of California, Irvine (UCI) from 6 volunteer subjects imagining speaking one of two syllables, /ba/ and /ku/, at different rhythms without performing any overt actions. In this work, we assess the degree of subject-to-subject variation and the feasibility of using imagined speech for subject identification. The EEG data are first preprocessed to reduce the effects of artifacts and noise, and autoregressive (AR) coefficients are extracted from each electrode's signal and concatenated for subject identification using a linear SVM classifier. The subjects were identifiable to a 99.76% accuracy, which indicates a clear potential for using imagined speech EEG data for biométrie identification due to its strong inter-subject variation. Furthermore, the subject identification appears to be tolerant to differing conditions such as different imagined syllables and rhythms (as it is expected that the subjects will not imagine speaking the syllables at exactly the same rhythms from trial to trial). The proposed approach was also tested on a publicly available database consisting of EEG signals corresponding to Visual Evoked Potentials (VEPs) to test the applicability of the proposed method on a larger number of subjects, and it was able to classify 120 subjects with 98.96% accuracy.
What Is a "Good" Periocular Region for Recognition?

In challenging image acquisition settings where the performance of iris recognition algorithms degrades due to poor segmentation of the iris, image blur, specular reflections, and occlusions from eye lids and eye lashes, the periocular region has been shown to offer better recognition rates. However, the definition of a periocular region is subject to interpretation. This paper investigates the question of what is the best periocular region for recognition by identifying sub-regions of the ocular image when using near-infrared (NIR) or visible light (VL) sensors. To determine the best periocular region, we test two fundamentally different algorithms on challenging periocular datasets of contrasting build on four different periocular regions. Our results indicate that system performance does not necessarily improve as the ocular region becomes larger. Rather in NIR images the eye shape is more important than the brow or cheek as the image has little to no skin texture (leading to a smaller accepted region), while in VL images the brow is very important (requiring a larger region).
Differentially generated neural network classifiers are efficient

Differential learning for statistical pattern classification is based on the classification figure-of-merit (CFM) objective function. It is proved that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choice of hypothesis class as the training sample size grows large, while requiring the least classifier complexity necessary for Bayesian (i.e., minimum probability-of-error) discrimination. Differential learning almost always guarantees the best generalization allowed by the choice of hypothesis class for small training sample sizes.<<ETX>>
Effect of Retransmission and Retrodiction on Estimation and Fusion in Long-Haul Sensor Networks

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this paper, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can also degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Moreover, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.
A Framework for Binding and Retrieving Class-Specific Information to and from Image Patterns Using Correlation Filters

We describe a template-based framework to bind class-specific information to a set of image patterns and retrieve that information by matching the template to a query pattern of the same class. This is done by mapping the class-specific information to a set of spatial translations which are applied to the set of image patterns from which a template is designed, taking advantage of the properties of correlation filters. The bound information is retrieved during matching with an authentic query by estimating the spatial translations applied to the images that were used to design the template. In this paper, we focus on the problem of binding information to biometric signatures as an application of our framework. Our framework is flexible enough to allow spreading the information to be bound over multiple pattern classes which, in the context of biometric key-binding, enables multiclass and multimodal biometric key-binding. We demonstrate the effectiveness of the proposed scheme via extensive numerical results on multiple biometric databases.
Extended-Depth-of-Field Iris Recognition Using Unrestored Wavefront-Coded Imagery

Iris recognition can offer high-accuracy person recognition, particularly when the acquired iris image is well focused. However, in some practical scenarios, user cooperation may not be sufficient to acquire iris images in focus; 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 phase modulation mask. The conventional approach when using a camera system with such a phase mask is to restore the raw images acquired from the camera before feeding them into the iris recognition module. In this paper, we investigate the feasibility of skipping the image restoration step with minimal degradation in recognition performance while still increasing the depth of field of the whole system compared to an imaging system without a phase mask. By using a simulated wavefront-coded imagery, we present the results of two different iris recognition algorithms, namely, Daugman's iriscode and correlation-filter-based iris recognition, using more than 1000 iris images taken from the iris challenge evaluation database. We carefully study the effect of an off-the-shelf phase mask on iris segmentation and iris matching, and finally, to better enable the use of unrestored wavefront-coded images, we design a custom phase mask by formulating an optimization problem. Our results suggest that, in exchange for some degradation in recognition performance at the best focus, we can increase the depth of field by a factor of about four (over a conventional camera system without a phase mask) by carefully designing the phase masks.
Application of Soft-Decision Decoders to Non Narrow-Sense Reed-Solomon Codes

Reed-Solomon (RS) codes are defined in several different ways which are not exactly equivalent. Distinct algorithms for encoding and decoding of RS codes have been developed based on the different definitions. This paper clarifies the relationships among the several definitions of RS codes, especially focusing on the connection between non-binary BCH codes, which are not necessarily narrow-sense, and Generalized Reed- Solomon (GRS) codes. With the relationships, we introduce code space conversion which converts objects of a GRS code into corresponding objects of another GRS code with the same dimension. This transformation can be used for the flexible application of the decoder designed for a specific RS code. We show that the interpolation-based soft-decision RS decoders can be applied to non narrow-sense RS codes using code space conversion.
Application of pattern-output viterbi algorithm to algebraic soft-decision decoding over partial response channels

The algebraic soft-decision decoding algorithm (ASD) requires a reliability matrix as its input. In this paper, a new method to construct the reliability matrix over partial response (PR) channels of interest in magnetic recording is proposed by using recently introduced pattern-output Viterbi algorithm (POVA). A modified bit-level generalized minimum distance (BGMD) algorithm is also proposed with the POVA to achieve performance gains over PR channels that are as large as gains as over AWGN channels.
Recognition of Low-Resolution Faces Using Multiple Still Images and Multiple Cameras

We propose a new algorithm for recognition of low-resolution faces for cases when multiple images of the same face are available at matching. Specifically, this is the case of multiple-frame, or video, face recognition, and recognition with multiple cameras. Face recognition degrades when probe faces are of lower resolution than those available for training. There are two paradigms to alleviate this problem, but both have clear disadvantages. One is to use super-resolution algorithms to enhance the image, but as resolution decreases, super-resolution becomes more vulnerable to environmental variations, and it introduces distortions that affect recognition. On the other hand, it is possible to match in the low-resolution domain by downsampling the training set, but this is undesirable because features important for recognition depend on high frequency details that are erased by downsampling. We recently proposed a new framework for recognition that is different from these two paradigms, and we have shown that recognition is considerably improved for still-image face recognition. In this work, we show that the proposed method can be generalized to use a stream of frames, and produce even better recognition performance. The new algorithm incorporates the underlying assumptions of super-resolution methods with subspace distance metrics used for classification, simultaneously. We extend our previous formulation to use multiple frames, and we show that it can also be generalized to use multiple image formation processes, modeling different cameras. In this manuscript we consider two scenarios: face recognition with multiple frames, and the case of having a camera sensor network with dual cameras in a master-slave setting. Using the Multi-PIE database, our results show an increase of rank-1 recognition accuracy of about 6% for both cases compared to the single frame settings.
Signal reconstruction for multi-source variable-rate samples with autocorrelated errors in variables

Aggregating data from multiple sensors has become a critical requirement in cyberphysical systems (CPS) to increase the effective sampling rate for signal reconstruction. Depending on the application, these sensors can be geo-distributed, mobile, or only intermittently functional. These factors cause the aggregated sample set to be nonuniformly spaced with varying amounts of data collected per sensor. Due to the nature of how the timing or location measurements are made from the different sensors (e.g., indexed by GPS location), the samples may have significant errors in variables (EIV), where the location error from the different sensors follows an exponential autocorrelation function. In this work we demonstrate how to reconstruct signals using such noisy multi-source, variablerate (MSVR) data samples, and show that the proposed approach improves the error over existing EIV signal reconstruction algorithms.
A simple two-dimensional run-length-limited code for ultra-high-density magnetic-tape recording

Investigations of two-dimensional, run-length-limited codes have shown that they may be applied across multiple channels in several ways. This paper describes a new two-dimensional coding technique which not only utilizes constraints described by Marcellin (see  vol.10, no.1, p.254-266, 1992) to spread the k constraint across multiple channels, but also introduces a separate (d, k) constraint on bits recorded across channels, increasing either or both of signal-to-noise ratio and data density. A method for calculation of capacities is presented along with a low-cost, high speed code suitable for a ultra-high-density magnetic tape recording channel.
Example-based clear path detection assisted by vanishing point estimation

With the goal of avoiding obstacles on the road using only a single camera during autonomous driving, we propose an example-based clear path detection method that also considers the additional perspective cue from the estimated vanishing point. First, because of the benefit that the vanishing point provides knowledge for clear path detection, we apply an example-based method to estimate initial vanishing point candidates. Then, instead of building a pre-trained clear path model over the limited training set, we propose an example-based global image matching method to get an approximate idea of clear path candidate regions, and use a Gaussian Mixture Model (GMM) for local image patch modeling to further improve the clear path detection. Finally, we develop an iterative probabilistic refinement to improve performance of both components. Experimental results of real road scenes are presented to illustrate the effectiveness of the proposed method.
Signal Processing for Near 10 Tbit/in$^{2}$ Density in Two-Dimensional Magnetic Recording (TDMR)

Two-dimensional magnetic recording (TDMR) is a new magnetic recording paradigm that aims to record one bit of information in one or a few grains, with the goal of achieving a recording density of nearly 10 Tbit/in2. In addition to the usual noise, a TDMR channel experiences the problem that some bits are never recorded because of the randomness of grain size and location. Thus, it is believed that a key component of a TDMR channel is two-dimensional (2-D) signal processing along with a strong error correction code. In this study, the TDMR channel is investigated based on a random Voronoi grain model and a signal processing architecture is proposed. Here, a 2-D linear minimum mean squared error (LMMSE) equalizer and a low-density parity-check (LDPC) code are employed and the effects of unwritten bits are modeled by a Gaussian mixture model. In numerical simulations, the proposed architecture shows the feasibility of user bit densities near 10 Tbit/in2 for media with a 20 Tgrains/in2 grain density.
Field-programmable gate-array-based investigation of the error floor of low-density parity check codes for magnetic recording channels

Good performance of iterative detection and decoding using low-density parity check (LDPC) codes has stimulated great interest in the data storage industry. One major concern in using LDPC codes in the read channel is their error floor, which is still an open question. To investigate the performance of LDPC codes in low bit-error rates (BER/spl sim/10/sup -10/), we developed a high-throughput fully reconfigurable simulator using a field programmable gate array. Using this simulator, we are able to observe the performance of LDPC codes at low BER within a reasonably short time (10/sup -10/ within 1.5 h). We show the evaluation results for two types of LDPC codes.
Implementation-Friendly Constraint for Adaptive Finite Impulse Response Filters for Equalization

In read channels in magnetic recording systems, a finite impulse response (FIR) filter is often used as an equalizer, and the filter coefficients are adapted via a least mean squares (LMS) algorithm. This equalizer adaptation loop can interfere with the timing recovery (TR) loop and the automatic gain control (AGC) loop that are also present in such channels. In a previous paper by L. Du et al., a linear constraint is proposed for the LMS loop so that it is decoupled from the TR and the AGC loops. In this paper, we generalize that constraint, and propose a new constraint that offers the advantage of easier implementation.
Low complexity LDPC codes for partial response channels

This paper constructs and analyzes a class of regular LDPC codes with column weight of j=2, in contrast to the often-used j/spl ges/3 setting. These codes possess several significant features. First, they are free of 6-cycle, and can be easily constructed for a large range of code rates. Secondly, the parity check matrix of the code can be represented by a simple set, thus lending itself to a low complexity implementation. Thirdly, the proposed codes concatenated with proper precoder outperform j/spl ges/3 LDPC codes for partial response (PR) channels. Finally, they exhibit block error statistics significantly different from LDPC codes with j/spl ges/3, making them more compatible with Reed-Solomon error correction codes. The LDPC coded partial response (PR) channel is formulated as a dynamical model and analyzed using density evolution technique, which is used to explain the behavior of the concatenated system. A high rate (8/9) code with block size 4608 is constructed as an example, and its bit error rate (BER), block error statistics, and decoding convergence over ideal PR channel are investigated using simulation. The simulation results are consistent with the density evolution analysis, both indicating that LDPC codes with j=2 are attractive for partial response channels. PR targets for magnetic recording channel are used as examples to illustrate the performance of the proposed codes.
Strong shadow removal via patch-based shadow edge detection

Detecting objects in shadows is a challenging task in computer vision. For example, in clear path detection application, strong shadows on the road confound the detection of the boundary between clear path and obstacles, making clear path detection algorithms less robust. Shadow removal, relies on the classification of edges as shadow edges or non-shadow edges. We present an algorithm to detect strong shadow edges, which enables us to remove shadows. By analyzing the patch-based characteristics of shadow edges and non-shadow edges (e.g., object edges), the proposed detector can discriminate strong shadow edges from other edges in images by learning the distinguishing characteristics. In addition, spatial smoothing is used to further improve shadow edge detection. Numerical experiments show convincing results that shadows on the road are either removed or attenuated with few visual artifacts, which benefits the clear path detection. In addition, we show that the proposed method outperforms the state-of-art algorithms in different conditions.