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This study introduces a new scheme for resolving the ambiguity in a pulse‐Doppler radar. While ambiguity resolution is typically performed before tracking, the proposed approach, Track‐Before‐Resolving, is resolving ambiguity utilising tracking filters. A specific Doppler ambiguity resolution algorithm is provided for demonstrating that the Track‐Before‐Resolving method is useful. In order to resolve the ambiguity, which is equivalent to finding the ambiguity number, this algorithm investigates the maximum probability calculated in the multiple model estimation, each with a tracking filter of a different ambiguity number. Simulation shows that the proposed algorithm performs effectively, even at low SNR. [ABSTRACT FROM AUTHOR]

With the increasing number of available satellites from multi‐global navigation satellite systems (GNSS) and their applications in complicated environments, an increased number of faults in measurements are inevitable. Fault detection and exclusion (FDE) is an effective way to reject observation faults in order to guarantee the integrity of a GNSS positioning and navigation system. We propose a modified between‐receiver single‐differencing (SD)‐based FDE algorithm for real‐time kinematic (RTK) positioning. First, a modified between‐receiver SD model‐based FDE is proposed for testing the estimated float ambiguities. This is helpful in rejecting observations with unreliable float ambiguities and obtaining reliable double‐differencing (DD) integer ambiguities, denoted as the SD float model in the sequel. Second, DD ambiguities are resolved by the DD model and used to update the previous SD float ambiguity. After that, a modified SD fix model, taking the DD ambiguities as the known parameters, is further developed to detect any unreliable or fault‐resolved ambiguities. To verify the modified SD model‐based FDE algorithm, a kinematic vehicle test is conducted. The fault detection test statistics of the SD float model and the SD fix model, time‐to‐first‐fix, ambiguity‐fix rate, SD residuals, and positioning performances are examined. The results show that the modified FDE method can detect and exclude fault satellites accurately and effectively, which is verified by the SD residuals of fault satellites. The ambiguity‐fix rate of the proposed modified FDE algorithm is improved by approximately 3% with 70 more epochs of fixes. Regarding the 3D positioning performance, the modified proposed SD‐based FDE algorithm can achieve 54.11%, 57.78%, and 73.11% improvements for standard deviation, root mean square, and mean bias, respectively, when compared with the processing strategy without the use of the proposed FDE. [ABSTRACT FROM AUTHOR]

In shadow detection area, deep learning‐based methods have achieved outstanding performance. However, the potential ambiguity in shadow images is still the main reason for the poor performance of the algorithms. By analysing the relationships among shadow edges, shadow false‐positive information, and shadow areas, an edge‐guided disambiguation module for better shadow detection performance is proposed. The edge‐guided disambiguation module consists two parts: 1) the shadow edge feature is used to guide the comprehensive shadow area feature generation of different resolution. 2) The shadow false‐positive features is applied to reduce ambiguity in the comprehensive shadow area features. In this way, the missed detections is added in the first step and the false detections is reduced in the second step. More accurate detection results is obtained. The comprehensive experiments are proceeded on the three public shadow detection datasets: SBU, UCF, and ISTD. The experimental results demonstrated the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

A new algorithm named N‐BANDS has been developed for the estimation of the combined effect of noise and rotational ambiguity in the bilinear decomposition of data matrices using the popular multivariate curve resolution–alternating least‐squares model. It is based on a nonlinear maximization and minimization of a component‐wise signal contribution function (SCF), with a single‐objective function and a separate module for applying a variety of constraints. The algorithm can be applied to multicomponent systems and efficiently estimates the extreme component profiles corresponding to maximum and minimum SCF in the presence of varying amounts of instrumental noise. Simulated systems mimicking multicomponent and multisample analytical calibration protocols have been studied, having uncalibrated interferents in the test samples. Different noise structures (independent and identically distributed, proportional and correlated) were added to the data matrices, with results that indicate an increase in the extension of feasible bands as the noise level increases. N‐BANDS has been developed as a new algorithm for estimating the combined effect of noise and rotational ambiguity in bilinear decompositions using multivariate curve resolution–alternating least‐squares, based on the maximization/minimization of component‐wise signal contribution functions, with a single objective function and a separate module of constraints. Multi component and multi sample simulated systems were studied with uncalibrated interferents in test samples. Noise addition results indicate an increase in the extension of feasible bands as the noise level increases. [ABSTRACT FROM AUTHOR]

Several studies have found that collaboration networks are scale‐free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale‐free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4 M ∼ 9.6 M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename‐initial‐based name matching, tend to produce degree distributions better fitted to power‐law slopes with the typical scaling parameter (2 < α < 3) than networks disambiguated by more accurate algorithm‐based methods. Such tendency is observed across collaboration networks generated under various conditions such as cumulative years, 5‐ and 1‐year sliding windows, and random sampling, and through simulation, found to arise due mainly to artefactual entities created by inaccurate disambiguation. This cautionary study calls for special attention from scholars analyzing network data in which entities such as people, organization, and gene can be merged or split by improper disambiguation. [ABSTRACT FROM AUTHOR]

An iterative adaptive approach (IAA) based algorithm is presented to realise coherent integration for the random pulse repetition interval radar. The proposed method firstly removes the effect of Doppler ambiguity via phase compensation, and then accomplishes target focusing via conducting the azimuth IAA and range inverse fast Fourier transform. Theoretical analysis and numerical experiments have verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Localization error occurrence due to a position ambiguity is a very critical problem in wireless distributed cooperative localization, which may, in turn, affect the reliability of the localization of the whole or a major portion of the network. In this paper, we formulate a two-dimensional cooperative localization model via graph partition in arbitrary topologies while considering the cooperative node's (or neighbor's) position ambiguity. Based on this model, we establish a robust algorithm, named weighted factor graph (FG)-based cooperative localization algorithm, by mapping local topologies into the FG and by incorporating the vector-addition error as the weight. The vector-addition error includes the measurement error and the neighbor's position ambiguity, simultaneously. The optimal weight is derived theoretically according to the statistic properties of the vector errors and the joint probability density function. Theoretical analysis and numerical results indicate that the proposed algorithm can acquire higher level of accuracy for localization in various topology scenarios in comparison with some typical algorithms. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests that assumption by analyzing coauthorship networks from five academic fields-biology, computer science, nanoscience, neuroscience, and physics-and an interdisciplinary journal, PNAS. Name instances in data sets of this study were disambiguated based on heuristics gained from previous algorithmic disambiguation solutions. We use disambiguated data as a proxy of ground-truth to test the performance of three types of initial-based disambiguation. Our results show that initial-based disambiguation can misrepresent statistical properties of coauthorship networks: It deflates the number of unique authors, number of components, average shortest paths, clustering coefficient, and assortativity, while it inflates average productivity, density, average coauthor number per author, and largest component size. Also, on average, more than half of top 10 productive or collaborative authors drop off the lists. Asian names were found to account for the majority of misidentification by initial-based disambiguation due to their common surname and given name initials. [ABSTRACT FROM AUTHOR]

ABSTRACT The key to high precision parameter estimation in global navigation satellite system (GNSS) applications is to properly deal with the integer valued carrier-phase ambiguities. The class of integer estimators fixes all ambiguities to integer values. This can also decrease the precision of the estimates of the non-ambiguity parameters, if the fixing is incorrect. The best integer-equivariant (BIE) estimator is optimal in the sense of minimizing the mean-squared error (MSE) of both the ambiguities and the real valued parameters, regardless of the precision of the float solution. However, the BIE estimator comprises a search in the integer space of ambiguities, whose complexity grows exponentially with the number of ambiguities. This search is not feasible for large-scale network solutions. To overcome this problem, a sequential BIE (SBIE) algorithm is proposed, which shows close to optimal performance with only linearly increasing complexity. Numerical simulations are used to verify the performance of the SBIE algorithm. Copyright © Spring 2014 Institute of Navigation. [ABSTRACT FROM AUTHOR]

This article explores the role of surface ambiguities in referring expressions, and how the risk of such ambiguities should be taken into account by an algorithm that generates referring expressions, if these expressions are to be optimally effective for a hearer. We focus on the ambiguities that arise when adjectives occur in coordinated structures. The central idea is to use statistical information about lexical co-occurrence to estimate which interpretation of a phrase is most likely for human readers, and to avoid generating phrases where misunderstandings are likely. Various aspects of the problem were explored in three experiments in which responses by human participants provided evidence about which reading was most likely for certain phrases, which phrases were deemed most suitable for particular referents, and the speed at which various phrases were read. We found a preference for ''clear'' expressions to ''unclear'' ones, but if several of the expressions are ''clear,'' then brief expressions are preferred over non-brief ones even though the brief ones are syntactically ambiguous and the non-brief ones are not; the notion of clarity was made precise using Kilgarriff's Word Sketches. We outline an implemented algorithm that generates noun phrases conforming to our hypotheses. [ABSTRACT FROM AUTHOR]

National exercises for the evaluation of research activity by universities are becoming regular practice in ever more countries. These exercises have mainly been conducted through the application of peer-review methods. Bibliometrics has not been able to offer a valid large-scale alternative because of almost overwhelming difficulties in identifying the true author of each publication. We will address this problem by presenting a heuristic approach to author name disambiguation in bibliometric datasets for large-scale research assessments. The application proposed concerns the Italian university system, comprising 80 universities and a research staff of over 60,000 scientists. The key advantage of the proposed approach is the ease of implementation. The algorithms are of practical application and have considerably better scalability and expandability properties than state-of-the-art unsupervised approaches. Moreover, the performance in terms of precision and recall, which can be further improved, seems thoroughly adequate for the typical needs of large-scale bibliometric research assessments. [ABSTRACT FROM AUTHOR]

We present subspace based detection algorithms for detecting moving targets applied to the data collected by an ultra wideband radar system. Combining the results of time reversal MUSIC algorithm and delay estimation MUSIC methods resulted in successful localization of targets. We have investigated methods to resolve the ambiguities in target detection and we performed experiments using two targets to test the effectiveness of the algorithms. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 237–244, 2010 [ABSTRACT FROM AUTHOR]

With the increasing number of digital documents, the ability to automatically classify those documents both efficiently and accurately is becoming more critical and difficult. One of the major problems in text classification is the high dimensionality of feature space. We present the ambiguity measure (AM) feature-selection algorithm, which selects the most unambiguous features from the feature set. Unambiguous features are those features whose presence in a document indicate a strong degree of confidence that a document belongs to only one specific category. We apply AM feature selection on a naïve Bayes text classifier. We favorably show the effectiveness of our approach in outperforming eight existing feature-selection methods, using five benchmark datasets with a statistical significance of at least 95% confidence. The support vector machine (SVM) text classifier is shown to perform consistently better than the naïve Bayes text classifier. The drawback, however, is the time complexity in training a model. We further explore the effect of using the AM feature-selection method on an SVM text classifier. Our results indicate that the training time for the SVM algorithm can be reduced by more than 50%, while still improving the accuracy of the text classifier. We favorably show the effectiveness of our approach by demonstrating that it statistically significantly (99% confidence) outperforms eight existing feature-selection methods using four standard benchmark datasets. [ABSTRACT FROM AUTHOR]

Estimating haplotype relative risks in a family-based study is complicated by phase ambiguity and the many parameters needed to quantify relative risks for all possible diplotypes. This problem becomes manageable if a particular haplotype has been implicated previously as relevant to risk. We fit log-linear models to estimate the risks associated with a candidate haplotype relative to the aggregate of other haplotypes. Our approach uses existing haplotype-reconstruction algorithms but requires assumptions about the distribution of haplotypes among triads in the source population. We consider three levels of stringency for those assumptions: Hardy-Weinberg Equilibrium (HWE), random mating, and no assumptions at all. We assessed our method's performance through simulations encompassing a range of risk haplotype frequencies, missing data patterns, and relative risks for either offspring or maternal genetic effects. The unconstrained model provides robustness to bias from population structure but requires excessively large sample sizes unless there are few haplotypes. Assuming HWE accommodates many more haplotypes but sacrifices robustness. The model assuming random mating is intermediate, both in the number of haplotypes it can handle and in robustness. To illustrate, we reanalyze data from a study of orofacial clefts to investigate a 9-SNP candidate haplotype of the IRF6 gene. [ABSTRACT FROM AUTHOR]

In this paper, the capability of the integer ambiguity method to find the absolute position of InSAR data is demonstrated. A radargrammetric model is used for the space triangulation. The method is demonstrated using ERS data of the Thames region and the results are compared to GPS measurements and a digital elevation model. The GPS stations, not visible in the image, are located on the InSAR data using a simple range-Doppler algorithm and an analysis of their positions is undertaken. The results are consistent with other methods but with the advantage that the radargrammetric model is very simple. The results also highlight the problems of integrating GPS data with InSAR data in urban areas where a GPS station may not be representative of the dominant scatterers within a resolution cell, and they suggest methods that might be applied in future to reduce this problem. The model should help the integration of InSAR data with other geometric survey data. [ABSTRACT FROM AUTHOR]

A genetic algorithm is applied to optimize the size and location of a sleeve, which reduces parasitic currents on the surface of the feed cable of a mobile handset antenna, so that the influence of the RF cable on the radiation pattern is minimized. The pattern is calculated by the electric-field integral equation (EFIE) method in each iteration of the genetic algorithm. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 25: 194–196, 2000 [ABSTRACT FROM AUTHOR]