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Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration. [ABSTRACT FROM AUTHOR]

Background: The main obstacle encountered in microarray technology is how to mine the valuable information under the profiles and study the genes function.Objective: Maximal information coefficient (MIC) is a novel, non-parametric statistic that has been successfully applied to genome-wide association studies and differentially gene and miRNA expression analysis. However, the data used in these applications are not gold standard but real data.Methods: Therefore, this study attempts to test the feasibility of MIC for differentially expressed gene identification with simulation data.Results: Our experiments indicate that, MIC perfermance is better than Limma always, which is almost the same level of SAM, ROTS or DESeq2. However, the count of AUC < 0.5 of MIC is significantly smaller than the three methods, and MIC does not exhibit an abnormal phenomenon in which the AUC increases as the noise increases.Conclusions: Compared to the existing methods, our experiments show that MIC is not only in the first tier in identifying differentially expressed genes and noise immunity, but also shows better robustness and stronger data/environment adaptability. [ABSTRACT FROM AUTHOR]

This paper describes the design of an automatic parallelization framework. The kernel supplied at its front end was suggested as an instrument for parallel potential assessment. It was used to measure the maximum achievable speedups in the major set of the CHStone benchmark suite programs. In such framework, we suggested the liberation of parallelism incrementally. We proposed a data dependency heuristic-based transformation method to make true dependences dissociation. We generated an internal representation (IR2), where the Banerjee test conditions are met. Two among three of Banerjee test conditions came to be committed. In shared memory many/multicore platforms, the third condition could be satisfied by privatization. We would be able to choose the safe and the opportune pairwise (mapping-privatization) scheme among a number of threads mapping scenarios that become available in the IR2 structure. Instrumentation on a subset of CHStone benchmark was carried out as a validity proof of our proposal, and the results confirmed that our framework kernel is robust. [ABSTRACT FROM AUTHOR]

The minimum covariance determinant (MCD) method is a highly robust estimator of multivariate location and scatter, for which a fast algorithm is available. Since estimating the covariance matrix is the cornerstone of many multivariate statistical methods, the MCD is an important building block when developing robust multivariate techniques. It also serves as a convenient and efficient tool for outlier detection. The MCD estimator is reviewed, along with its main properties such as affine equivariance, breakdown value, and influence function. We discuss its computation, and list applications and extensions of the MCD in applied and methodological multivariate statistics. Two recent extensions of the MCD are described. The first one is a fast deterministic algorithm which inherits the robustness of the MCD while being almost affine equivariant. The second is tailored to high‐dimensional data, possibly with more dimensions than cases, and incorporates regularization to prevent singular matrices. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Robust Methods Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery [ABSTRACT FROM AUTHOR]

A novel methodology based deep belief networks and multiple models (DBNs-MMs) is presented to accomplish fault detection for complex systems. And firstly, historical datasets are collected and processed to train the DBNs, so that DBNs can be constructed to learn the nonlinear dynamic characteristics of complex system, and so a model with a specific architecture and some initial intelligence will be built up. Secondly, the operation condition and real-time measurement data are employed in DBNs-MMs to get a series of network outputs. Finally, the residuals can be obtained by comparing the measurement output and each DBN output. Then, the fault detection can be achieved by employing a properly adaptive threshold for each residual. Some faulty cases of the complex cryogenic propellant loading system have been used to demonstrate the effectiveness of the proposed fault detection methodology, and the result has shown its excellent performance. [ABSTRACT FROM AUTHOR]

In this short note comments are given on the discussion paper of Cerioli, A., Riani, M., Atkinson, A. C., and Corbellini, A., entitled "The power of monitoring: How to make the most of a contaminated multivariate sample." [ABSTRACT FROM AUTHOR]

Partially missing data sets are a prevailing problem in clustering analysis. We propose a hybrid algorithm combining fuzzy clustering with particle swarm optimization (PSO) for incomplete data clustering, and missing attributes are represented as intervals. Furthermore, we develop a neighbor interval reconstruction (NIR) method based on pre-classification results that estimates the nearest-neighbor interval of missing attribute using the nearest-neighbor rule, which avoids endpoints of intervals determined by different species information, thereby improving the accuracy of missing attribute intervals and enhancing the robustness of missing attribute imputation. Then, the PSO and fuzzy c-means hybrid algorithm are used for clustering the interval-valued data set, and the global optimization ability of the PSO can improve the accuracy of clustering results compared with gradient-based optimization methods. The experimental results for several UCI data sets show the superiority of the proposed NIR hybrid algorithm. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) under the separability assumption can provably be solved efficiently, even in the presence of noise, and has been shown to be a powerful technique in document classification and hyperspectral unmixing. This problem is referred to as near-separable NMF and requires that there exists a cone spanned by a small subset of the columns of the input nonnegative matrix approximately containing all columns. In this paper, we propose a preconditioning based on the minimum volume ellipsoid and semidefinite programming making the input matrix well-conditioned. This in turn can improve significantly the performance of near-separable NMF algorithms which is illustrated on the popular successive projection algorithm (SPA). The new preconditioned SPA is provably more robust to noise, and outperforms SPA on several synthetic data sets. We also show how an active-set method allows us to apply the preconditioning on large-scale real-world hyperspectral images. [ABSTRACT FROM AUTHOR]

The consensus clustering technique combines multiple clustering results without accessing the original data. Consensus clustering can be used to improve the robustness of clustering results or to obtain the clustering results from multiple data sources. In this paper, we propose a novel definition of the similarity between points and clusters. With an iterative process, such a definition of similarity can represent how a point should join or leave a cluster clearly, determine the number of clusters automatically, and combine partially overlapping clustering results. We also incorporate the concept of 'clustering fragment' into our method for increased speed. The experimental results show that our algorithm achieves good performances on both artificial data and real data. [ABSTRACT FROM AUTHOR]

A novel robust validity index is proposed for subtractive clustering (SC) algorithm. Although the SC algorithm is a simple and fast data clustering method with robust properties against outliers and noise; it has two limitations. First, the cluster number generated by the SC algorithm is influenced by a given threshold. Second, the cluster centers obtained by SC are based on data that have the highest potential values but may not be the actual cluster centers. The validity index is a function as a measure of the fitness of a partition for a given data set. To solve the first problem, this study proposes a novel robust validity index that evaluates the fitness of a partition generated by SC algorithm in terms of three properties: compactness, separation and partition index. To solve the second problem, a modified algorithm based on distance relations between data and cluster centers is designed to ascertain the actual centers generated by the SC algorithm. Experiments confirm that the preferences of the proposed index outperform all others. [ABSTRACT FROM AUTHOR]

We propose a robust estimation procedure for the analysis of longitudinal data including a hidden process to account for unobserved heterogeneity between subjects in a dynamic fashion. We show how to perform estimation by an expectation–maximization-type algorithm in the hidden Markov regression literature. We show that the proposed robust approaches work comparably to the maximum-likelihood estimator when there are no outliers and the error is normal and outperform it when there are outliers or the error is heavy tailed. A real data application is used to illustrate our proposal. We also provide details on a simple criterion to choose the number of hidden states. [ABSTRACT FROM PUBLISHER]

Abstract: Finding correspondences between two related feature point sets is a basic task in computer vision and pattern recognition. In this paper, we present a novel method for point pattern matching via spectral graph analysis. In particular, we aim to render the spectral matching algorithm more robust for positional jitter and outlier. A local structural descriptor, namely the spectral context, is proposed to describe the attribute domain of point sets, which is fundamentally different from the previous methods. Furthermore, the approximate distance order is defined and employed as the metric for geometric consistency of neighboring points in this work. By combining these two novel ingredients, we formulate feature point set matching as an optimization problem with one-to-one constraints. The correspondences are then obtained by maximizing the given objective function via the technique of probabilistic relaxation. Comparative experiments conducted on both synthetic and real data demonstrate the effectiveness of the proposed method, especially in the presence of positional jitter and outliers. [Copyright &y& Elsevier]

We proposed a robust mean change-point estimation algorithm in linear regression with the assumption that the errors follow the Laplace distribution. By representing the Laplace distribution as an appropriate scale mixture of normal distribution, we developed the expectation maximization (EM) algorithm to estimate the position of mean change-point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change-point. Finally, we applied our method to the classical Holbert data and detected a changepoint. [ABSTRACT FROM AUTHOR]

With rapid increase in disease variety, the role of image segmentation has been crucial in image guided surgery. Despite having a lot of existing methods, the robustness of an algorithm remains a concern with respect to the input image variety. This paper presents a state of art segmentation algorithms of “MICCAI Grand Challenge and Conference 2007, 2008 and 2009”. These algorithms are reported to have tested on real datasets used in “MICCAI Grand Challenge 2007, 2008 and 2009”. Due to the page constraint, selected papers based on some criteria are included in this review. In this work, we have implemented and evaluated all these methods on a particular data. The objective of this paper is to exhibit the divergence in performance if the input data is varied. [ABSTRACT FROM AUTHOR]

Abstract: Clustering is the process of grouping objects that are similar, where similarity between objects is usually measured by a distance metric. The groups formed by a clustering method are referred as clusters. Clustering is a widely used activity with multiple applications ranging from biology to economics. Each clustering technique has some advantages and disadvantages. Some clustering algorithms may even require input parameters which strongly affect the result. In most cases, it is not possible to choose the best distance metric, the best clustering method, and the best input argument values for an input data set. Therefore, multiple clusterings can be obtained by several distance metrics, several clustering methods, and several input argument values. And, multiple clusterings can be combined into a new and better quality final clustering. We propose a family of combining multiple clustering algorithms that are memory efficient, scalable, robust, and intuitive. Our new algorithms offer tremendous speed gain and low memory requirements by working at cluster level, while producing very good quality final clusters. Extensive experimental evaluations on some very challenging artificially generated and real data sets from a diverse set of domains establish the usefulness of our methods. [Copyright &y& Elsevier]

Coordinate measurement machines (CMMs) have been widely used in inspecting mechanical parts with higher accuracy. Both the distribution and the number of the sampled points on measurand have an important effect on the efficiency and quality of the measurement of CMMs. In this paper, an adaptive sampling method is proposed for inspection planning on CMM for free-form surface. The points are iteratively sampled from a form error model, which is constructed by superimposing appropriate form errors on the nominal data. Moreover, a modified algorithm is introduced to determine the deviation of two point sets for the improvement of the inspecting accuracy, and the inspection uncertainty is also analyzed. A comparison is performed between the proposed method and the two well-known sampling methods, which are the equi-parametric method and the patch mean Gaussian curvature-based method, both simulated and experiment results show the effectiveness and robustness of this method. [ABSTRACT FROM AUTHOR]

Abstract: The use of alternative measures to evaluate classifier performance is gaining attention, specially for imbalanced problems. However, the use of these measures in the classifier design process is still unsolved. In this work we propose a classifier designed specifically to optimize one of these alternative measures, namely, the so-called F-measure. Nevertheless, the technique is general, and it can be used to optimize other evaluation measures. An algorithm to train the novel classifier is proposed, and the numerical scheme is tested with several databases, showing the optimality and robustness of the presented classifier. [Copyright &y& Elsevier]

Carbon dioxide (CO2) fluxes over the temperate Eura-sian region have been estimated robustly for the period 2006-2008 using the time-dependent inversion com-ponent of the TRANSCOM protocol. Data from six CO2 measuring stations in temperate Eurasia, includ-ing the Indian station at Hanle along with 75 others from different parts of the world have been used. A careful analysis of a posteriori errors and model reso-lution shows that temperate Eurasia is a major resid-ual sink of CO2 with a value of 1.5 Gigatonnes of carbon/year. [ABSTRACT FROM AUTHOR]

The central challenge in robotic mapping is obtaining reliable data associations (or “loop closures”): state-of-the-art inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will still encounter errors, leading to system failure. We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, rather than characterizing loop closures as being “right” or “wrong”, we propose characterizing the error of those loop closures in a more expressive manner that can account for their non-Gaussian behavior. Our approach leads to an fully integrated Bayesian framework for dealing with error-prone data. Unlike earlier multiple-hypothesis approaches, our approach avoids exponential memory complexity and is fast enough for real-time performance. We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the “front-end” loop-validation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the real-world data sets that motivated this work. [ABSTRACT FROM AUTHOR]

One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. This paper introduces a novel fuzzy-possibilistic shell clustering model aiming at accurate detection of circles, spheres, and multidimensional spheroids in the presence of outlier data. The proposed fuzzy-possibilistic product partition c-spherical shell algorithm (FP3CSS) combines the probabilistic and possibilistic partitions in a qualitatively different way from previous, similar algorithms. The novel mixture partition is able to suppress the influence of extreme outlier data, which gives it net superiority in terms of robustness and accuracy, compared to previous algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license. [Copyright &y& Elsevier]

Abstract: Noise sensitivity is known as a key related issue of AdaBoost algorithm. Previous works exhibit that AdaBoost is prone to be overfitting in dealing with the noisy data sets due to its consistent high weights assignment on hard-to-learn instances (mislabeled instances or outliers). In this paper, a new boosting approach, named noise-detection based AdaBoost (ND-AdaBoost), is exploited to combine classifiers by emphasizing on training misclassified noisy instances and correctly classified non-noisy instances. Specifically, the algorithm is designed by integrating a noise-detection based loss function into AdaBoost to adjust the weight distribution at each iteration. A k-nearest-neighbor (k-NN) and an expectation maximization (EM) based evaluation criteria are both constructed to detect noisy instances. Further, a regeneration condition is presented and analyzed to control the ensemble training error bound of the proposed algorithm which provides theoretical support. Finally, we conduct some experiments on selected binary UCI benchmark data sets and demonstrate that the proposed algorithm is more robust than standard and other types of AdaBoost for noisy data sets. [Copyright &y& Elsevier]

The L-median is a robust estimator of multivariate location with good statistical properties. Several algorithms for computing the L-median are available. Problem specific algorithms can be used, but also general optimization routines. The aim is to compare different algorithms with respect to their precision and runtime. This is possible because all considered algorithms have been implemented in a standardized manner in the open source environment R. In most situations, the algorithm based on the optimization routine NLM (non-linear minimization) clearly outperforms other approaches. Its low computation time makes applications for large and high-dimensional data feasible. [ABSTRACT FROM AUTHOR]

Ensuring the efficient and robust operation of distributed computational infrastructures is critical, given that their scale and overall complexity is growing at an alarming rate and that their management is rapidly exceeding human capability. Clustering analysis can be used to find patterns and trends in system operational data, as well as highlight deviations from these patterns. Such analysis can be essential for verifying the correctness and efficiency of the operation of the system, as well as for discovering specific situations of interest, such as anomalies or faults, that require appropriate management actions. This work analyzes the automated application of clustering for online system management, from the point of view of the suitability of different clustering approaches for the online analysis of system data in a distributed environment, with minimal prior knowledge and within a timeframe that allows the timely interpretation of and response to clustering results. For this purpose, we evaluate DOC (Decentralized Online Clustering), a clustering algorithm designed to support data analysis for autonomic management, and compare it to existing and widely used clustering algorithms. The comparative evaluations will show that DOC achieves a good balance in the trade-offs inherent in the challenges for this type of online management. [ABSTRACT FROM AUTHOR]

This study presents a new approach for image clustering, which is based on a novel workspace derived from the empirical mode decomposition (EMD). The proposed algorithm exploits the EMD, which can decompose any non-linear and non-stationary data into a number of intrinsic mode functions (IMFs). The intermediate IMFs of the image histogram have very good characteristics and provide a robust workspace that is utilised in order to detect the clusters of an image in a fast way. The proposed method was applied to several images and the obtained results show good image clustering robustness and low computational time, overcoming the disadvantages of the existing image clustering algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This note extends to the continuous-time case the “tube-based” approach for the design of discrete-time robust model predictive control (MPC) algorithms developed in . This extension is of interest in view of the simplicity and popularity of the method as well as of the industrial relevance of continuous-time implementations of MPC. The proposed robust control law is composed of two terms: (1) a sampled-data MPC control law and (2) a continuous-time state feedback term. [Copyright &y& Elsevier]

Abstract: Prognostics aims at determining whether a failure of an engineered system (e.g., a nuclear power plant) is impending and estimating the remaining useful life (RUL) before the failure occurs. The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust; (ii) it wastes the resources for constructing the algorithms that are discarded; (iii) it requires the testing data in addition to the training data. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes. [Copyright &y& Elsevier]

Abstract: The advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of prediction, but the slowest in terms of time. It is very unfortunate that as fascinating and classic an area of statistics as model selection, with important practical applications, has received very little attention in terms of algorithmic design and engineering. In this paper, in order to partially fill this gap, we make the following contributions: (A) the first general algorithmic paradigm for stability-based methods for model selection; (B) reductions showing that all of the known methods in this class are an instance of the proposed paradigm; (C) a novel algorithmic paradigm for the class of stability-based methods for cluster validity, i.e., methods assessing how statistically significant is a given clustering solution; (D) a general algorithmic paradigm that describes heuristic and very effective speed-ups known in the literature for stability-based model selection methods. Since the performance evaluation of model selection algorithms is mainly experimental, we offer, for completeness and without even attempting to be exhaustive, a representative synopsis of known experimental benchmarking results that highlight the ability of stability-based methods for model selection and the computational resources that they require for the task. As a whole, the contributions of this paper generalize in several respects reference methodologies in statistics and show that algorithmic approaches can yield deep methodological insights into this area, in addition to practical computational procedures. [Copyright &y& Elsevier]

Abstract: This paper deals with subspace method aided data-driven design of robust fault detection and isolation systems. The basic idea is to identify a primary form of residual generators directly from test data and then make use of performance indices to make uniform the design of different type robust residuals. Four algorithms are proposed to design fault detection, isolation and identification residual generators. Each of them can achieve robustness to unknown inputs and sensitivity to sensor or actuator faults. Their existence conditions and multi-fault identification problem are briefly analyzed as well and the application of the method proposed is illustrated by a simulation study on the vehicle lateral dynamic system. [Copyright &y& Elsevier]

Co-training is a multiview semi-supervised learning algorithm to learn from both labeled and unlabeled data, which iteratively adopts a classifier trained on one view to teach the other view using some confident predictions given on unlabeled examples. However, as it does not examine the reliability of the labels provided by classifiers on either view, co-training might be problematic. Even very few inaccurately labeled examples can deteriorate the performance of learned classifiers to a large extent. In this paper, a new method named robust co-training is proposed, which integrates canonical correlation analysis (CCA) to inspect the predictions of co-training on those unlabeled training examples. CCA is applied to obtain a low-dimensional and closely correlated representation of the original multiview data. Based on this representation the similarities between an unlabeled example and the original labeled examples are determined. Only those examples whose predicted labels are consistent with the outcome of CCA examination are eligible to augment the original labeled data. The performance of robust co-training is evaluated on several different classification problems where encouraging experimental results are observed. [ABSTRACT FROM AUTHOR]

Abstract: To explore multi-way data, different methods have been proposed. Here, we study the popular PARAFAC (Parallel factor analysis) model, which expresses multi-way data in a more compact way, without ignoring the underlying complex structure. To estimate the score and loading matrices, an alternating least squares procedure is typically used. It is however well known that least squares techniques suffer from outlying observations, making the models useless when outliers are present in the data. In this paper, we present a robust PARAFAC method. Essentially, it searches for an outlier-free subset of the data, on which we can then perform the classical PARAFAC algorithm. An outlier map is constructed to identify outliers. Simulations and examples show the robustness of our approach. [Copyright &y& Elsevier]

Motivation: Binary datasets represent a compact and simple way to store data about the relationships between a group of objects and their possible properties. In the last few years, different biclustering algorithms have been specially developed to be applied to binary datasets. Several approaches based on matrix factorization, suffix trees or divide-and-conquer techniques have been proposed to extract useful biclusters from binary data, and these approaches provide information about the distribution of patterns and intrinsic correlations.Results: A novel approach to extracting biclusters from binary datasets, BiBit, is introduced here. The results obtained from different experiments with synthetic data reveal the excellent performance and the robustness of BiBit to density and size of input data. Also, BiBit is applied to a central nervous system embryonic tumor gene expression dataset to test the quality of the results. A novel gene expression preprocessing methodology, based on expression level layers, and the selective search performed by BiBit, based on a very fast bit-pattern processing technique, provide very satisfactory results in quality and computational cost. The power of biclustering in finding genes involved simultaneously in different cancer processes is also shown. Finally, a comparison with Bimax, one of the most cited binary biclustering algorithms, shows that BiBit is faster while providing essentially the same results.Availability: The source and binary codes, the datasets used in the experiments and the results can be found at: http://www.upo.es/eps/bigs/BiBit.htmlContact: dsrodbae@upo.esSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

One of the remaining problems in iris recognition is the implementation of its efficient hardware systems. The difficulty arises from the fact that the methods forming part of this type of authentication process are fairly complex. Furthermore, next generation algorithms for iris recognition will become even more complex to enhance the reliability and functionality of currently used solutions. An example of such an intensive processing task is iris image quality assessment and segmentation using video signal obtained at-a-distance and on-the-move. Thus, processing robustness and predictable analysis time in such dedicated architectures may be particularly important. This paper describes a hardware system designed to analyze iris biometric data developed as a part of the work conducted by the authors on the complete iris identification system (1:N). This specialized architecture is mainly composed of digital signal processors and field-programmable gate arrays. Several issues concerning efficient biometric sample processing are discussed. Algorithms for iris recognition previously developed by the authors on a PC platform were adapted to the described architecture. The obtained results are presented, and the developed system is compared with chosen commercially available iris authentication systems. [ABSTRACT FROM AUTHOR]

Abstract: Terrestrial lidar (TLiDAR) has been used increasingly over recent years to assess tree architecture and to extract metrics of forest canopies. Analysis of TLiDAR data remains a difficult task mainly due to the effects of object occlusion and wind on the quality of the retrieved results. We propose to link TLiDAR and tree structure attributes by means of an architectural model. The proposed methodology uses TLiDAR scans combined with allometric relationships to define the total amount of foliage in the crown and to build the tree branching structure. It uses the range (distance) and intensity information of the TLiDAR scans (i) to extract the stem and main branches of the tree, (ii) to reconstruct the fine branching structure at locations where the presence of foliage is very likely, and (iii) to use the availability of light as a criterion to add foliage in the center of the crown where TLiDAR information is sparse or absent due to occlusion effects. An optimization algorithm guides the model towards a realistic tree structure that fits the information gathered from TLiDAR scans and field inventory. The robustness and validity of the proposed model is assessed on five trees belonging to four different conifer species from natural forest environments. This approach addresses the data limitation of TLiDAR scans and aims to extract forest architectural metrics at different structural levels. [Copyright &y& Elsevier]

Abstract: There are many data clustering techniques available to extract meaningful information from real world data, but the obtained clustering results of the available techniques, running time for the performance of clustering techniques in clustering real world data are highly important. This work is strongly felt that fuzzy clustering technique is suitable one to find meaningful information and appropriate groups into real world datasets. In fuzzy clustering the objective function controls the groups or clusters and computation parts of clustering. Hence researchers in fuzzy clustering algorithm aim is to minimize the objective function that usually has number of computation parts, like calculation of cluster prototypes, degree of membership for objects, computation part for updating and stopping algorithms. This paper introduces some new effective fuzzy objective functions with effective fuzzy parameters that can help to minimize the running time and to obtain strong meaningful information or clusters into the real world datasets. Further this paper tries to introduce new way for predicting membership, centres by minimizing the proposed new fuzzy objective functions. And experimental results of proposed algorithms are given to illustrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

In this study, the authors present a novel data hiding scheme using the minimum distortion look-up table (LUT) embedding that achieves good distortion-robustness performance. LUT-based data hiding is a simple and efficient way to embed information into multimedia content for various applications, such as transaction tracking and database annotation. The authors find it possible to optimally reduce the data hiding-introduced distortion by designing the LUT according to the distribution of the host at a given robustness level. The authors first analyse the distortion introduced by LUT embedding and formulate its relationship with run constraints of LUT to construct an optimal coding problem. Subsequently, a Viterbi algorithm is presented to find the minimum distortion LUT. Then a new practical data hiding scheme using the optimal LUT is applied in the wavelet domain. Theoretical analysis and numerical results show that the new LUT design achieves not only less distortion but also more robustness than the traditional LUT-based data embedding schemes under common attacks such as Gaussian noise and JPEG compression. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new support vector machine (SVM) called dual margin Lagrangian support vectors machine (DMLSVM). Unlike other SVMs which use only support vectors to determine the separating hyperplanes, DMLSVM utilizes all the available training data for training the classifier, thus producing robust performance. The training data are weighted differently depending on whether they are in a marginal region or surplus region. For fast training, DMLSVM borrows its training algorithm from Lagrangian SVM (LSVM) and tailors the algorithm to its formulation. The convergence of our training method is rigorously proven and its validity is tested on a synthetic test set and UCI dataset. The proposed method can be used in a variety of applications such as a recommender systems for web contents of IPTV services. [ABSTRACT FROM AUTHOR]

Krishnapuram and Keller [IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98–110, May 1993] proposed a possibilistic c-means (PCM) clustering by relaxing the constraint in fuzzy c-means (FCMs) that the memberships of a data point across classes sum to 1. The PCM algorithm has a tendency to produce coincident clusters. This can be a merit of PCM as a good mode-seeking algorithm if initials and parameters are suitably chosen. However, the performance of PCM heavily depends on initializations and parameter selection. In this paper, we propose a mechanism of robust automatic merging. We then create an automatic merging possibilistic clustering method (AM-PCM), where the proposed algorithm does not only solve these parameter-selection and initialization problems but also obtains an optimal cluster number. The proposed AM-PCM algorithm first uses all data points as initial cluster centers and then automatically merges these surrounding points around each cluster mode such that it can self-organize data groups according to the original data structure. The AM-PCM can exhibit the robustness to parameter, noise, cluster number, different volumes, and initializations. The computational complexity of AM-PCM is also analyzed. Comparisons between AM-PCM and other clustering methods are made. Some numerical data and real datasets are used to show these good aspects of AM-PCM. Experimental results and comparisons actually demonstrate that the proposed AM-PCM is an effective and parameter-free robust clustering algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel technique for estimating normals on unorganized point clouds. Methods from robust statistics are used to detect the best local tangent plane for each point. Therefore the algorithm is capable to deal with points located in high curvature regions or near/on complex sharp features, while being highly robust with respect to noise and outliers. In particular, the presented method reliably recovers sharp features but does not require tedious manual parameter tuning as done by current methods. The key ingredients of our approach are a robust noise-scale estimator and a kernel density estimation (KDE) based objective function. In contrast to previous approaches the noise-scale estimation is not affected by sharp features and achieves high accuracy even in the presence of outliers. In addition, our normal estimation procedure allows detection and elimination of outliers. We confirm the validity and reliability of our approach on synthetic and measured data and demonstrate applications to point cloud denoising. [Copyright &y& Elsevier]

Presented herein is a robust approach to calibrating water quality models for water quality management using sparse field data. The calibration procedure adopts genetic algorithms (GAs) to inversely solve the governing equations, along with an alternating fitness method to maintain solution diversity. The proposed approach is illustrated with a total phosphorus model of the Triadelphia Reservoir in Maryland. A series of deterministic and stochastic alternating fitness GA schemes are implemented and compared with a standard GA. Significantly higher diversity is observed in the solutions obtained by the alternating fitness method than by the standard process. The diversified solutions obtained by the alternating fitness GA method are then classified into several patterns using a parameter pattern recognition model. The best solutions to each pattern are then chosen for further projection analyses, which generate a range of prediction results that provide decision makers with information for formulating sound pollution control schemes. [ABSTRACT FROM AUTHOR]

Study of symmetric or repeating patterns in scalar fields is important in scientific data analysis because it gives deep insights into the properties of the underlying phenomenon. Though geometric symmetry has been well studied within areas like shape processing, identifying symmetry in scalar fields has remained largely unexplored due to the high computational cost of the associated algorithms. We propose a computationally efficient algorithm for detecting symmetric patterns in a scalar field distribution by analysing the topology of level sets of the scalar field. Our algorithm computes the contour tree of a given scalar field and identifies subtrees that are similar. We define a robust similarity measure for comparing subtrees of the contour tree and use it to group similar subtrees together. Regions of the domain corresponding to subtrees that belong to a common group are extracted and reported to be symmetric. Identifying symmetry in scalar fields finds applications in visualization, data exploration, and feature detection. We describe two applications in detail: symmetry-aware transfer function design and symmetry-aware isosurface extraction. [ABSTRACT FROM AUTHOR]