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Network virtualization is considered as a key technology for the future network. The emergence of software-defined network (SDN) provides a platform for the research and development of network virtualization. One of the key challenges in network virtualization is virtual network embedding (VNE). Some of the previous VNE algorithms perform virtual node embedding, which combines the nodes’ resource attributes and local topology attributes by arithmetic operations. On the one hand, it is not easy to distinguish the topological differences between SN and VN only by simple topology metrics. On the other hand, it is easy to ignore the different weight impacts of different metrics using only arithmetic operations, which will lead to an unbalanced embedding solution. To deal with these issues, we propose a novel node-ranking approach based on topology-differentiating (VNE-NRTD) for SDN-based virtual network embedding. Owing to the topological difference between SN and VN, different node metrics are used to quantify the substrate nodes and virtual nodes, respectively. Then, the nodes are ranked using the modified set pair analysis (SPA) method to avoid the unbalanced embedding solution. On this basis, we introduce the global bandwidth of the network topology into node-ranking to further improve the efficiency of node embedding. The simulation results show that the VNE-NRTD algorithm proposed in this paper outperforms other latest heuristic algorithms in terms of the VNR acceptance ratio, long-term average R/C ratio, substrate node utilization, and substrate link utilization.

This paper mainly studies the influence of dynamic cognitive diagnosis on personalized learning. Considering the influence of knowledge correlation factors and human brain memory factors on learning activities, a knowledge tracing model integrating learning factors is proposed. Firstly, based on the exercise-knowledge association information, the model maps learners and exercises to the knowledge space with clear meaning. Then, the evolution process of learners’ knowledge learning is quantitatively modeled in the knowledge space by integrating the classical learning curve and forgetting curve theory of pedagogy. On the other hand, considering the influence of topic semantics in the learning process, a knowledge tracing model integrating topic semantics is proposed in this paper. Firstly, the model designs a dynamic enhanced memory network to store the common information of knowledge and describes the learners’ dynamic mastery of knowledge. Secondly, the depth representation method of exercise resources is proposed to mine the text personality information and integrate it into the process of learners’ knowledge change modeling. Through a large number of experiments on exercise records, it is verified that the proposed model has accurate prediction performance and knowledge tracing interpretability.

Nonnegative matrix factorization (NMF) is a promising approach for local feature extraction in face recognition tasks. However, there are two major drawbacks in almost all existing NMF-based methods. One shortcoming is that the computational cost is expensive for large matrix decomposition. The other is that it must conduct repetitive learning, when the training samples or classes are updated. To overcome these two limitations, this paper proposes a novel incremental nonnegative matrix factorization (INMF) for face representation and recognition. The proposed INMF approach is based on a novel constraint criterion and our previous block strategy. It thus has some good properties, such as low computational complexity, sparse coefficient matrix. Also, the coefficient column vectors between different classes are orthogonal. In particular, it can be applied to incremental learning. Two face databases, namely FERET and CMU PIE face databases, are selected for evaluation. Compared with PCA and some state-of-the-art NMF-based methods, our INMF approach gives the best performance.

We consider the problem of a two-dimensional rectangular cavity in a PEC half plane covered by layers of material with uniform thickness. The rectangular geometry allows for an application of Fourier methods to solve the problem. The paper will also discuss how to compute the far field scattering once the solution is found. The fast mode matching approximation technique developed by Morgan and Schwering will be applied to this layered cavity problem setting. Numerical results will show that using the Fourier solution on the layered problem combined with Morgan and Schwering's technique can produce good approximations while using much less computation time than would be required for the entire solution.

In this paper, we have presented a new permutation-substitution image encryption architecture using chaotic maps and Tompkins-Paige algorithm. The proposed encryption system includes two major parts, chaotic pixels permutation and chaotic pixels substitution. A logistic map is used to generate a bit sequence, which is used to generate pseudorandom numbers in Tompkins-Paige algorithm, in 2D permutation phase. Pixel substitution phase includes two process, the tent pseudorandom image (TPRI) generator and modulo addition operation. All parts of the proposed chaotic encryption system are simulated. Uniformity of the histogram of the proposed encrypted image is justified using the chi-square test, which is less than 𝜒 2 (255, 0.05). The vertical, horizontal, and diagonal correlation coefficients, as well as their average and RMS values for the proposed encrypted image are calculated that is about 13% less than previous researches. To quantify the difference between the encrypted image and the corresponding plain-image, three measures are used. These are MAE, NPCR, and UACI, which are improved in our proposed system considerably. NPCR of our proposed system is exactly the ideal value of this criterion. The key space of our proposed method is large enough to protect the system against any Brute-force and statistical attacks.

Made available in DSpace on 2013-08-28T14:12:58Z (GMT). No. of bitstreams: 1 WOS000270785500001.pdf: 3096186 bytes, checksum: 7fbd01a7e8e037c0219b1d98aac0c8bb (MD5) Made available in DSpace on 2013-09-30T18:10:14Z (GMT). No. of bitstreams: 2 WOS000270785500001.pdf: 3096186 bytes, checksum: 7fbd01a7e8e037c0219b1d98aac0c8bb (MD5) WOS000270785500001.pdf.txt: 41939 bytes, checksum: 99b6d5e0f7b68e7ff6b6d3ecbeccf32c (MD5) Previous issue date: 2009-01-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:23:33Z No. of bitstreams: 2 WOS000270785500001.pdf: 3096186 bytes, checksum: 7fbd01a7e8e037c0219b1d98aac0c8bb (MD5) WOS000270785500001.pdf.txt: 41939 bytes, checksum: 99b6d5e0f7b68e7ff6b6d3ecbeccf32c (MD5) Made available in DSpace on 2014-05-20T13:23:33Z (GMT). No. of bitstreams: 2 WOS000270785500001.pdf: 3096186 bytes, checksum: 7fbd01a7e8e037c0219b1d98aac0c8bb (MD5) WOS000270785500001.pdf.txt: 41939 bytes, checksum: 99b6d5e0f7b68e7ff6b6d3ecbeccf32c (MD5) Previous issue date: 2009-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Thematic Project Although GPS kinematic relative positioning can provide high accuracy, GPS observables, like any other kind of measurement, are not free of errors. Indeed, they have several kinds of errors. In this paper, we show how to construct a functional mathematical model within the context of a Kalman Filter in order to eliminate most of these errors. Furthermore, we discuss how the multipath effect, a kind of error not modeled in the functional model, can be corrected using the proposed wavelet method. The behavior of the double difference functional model in the kinematic mode is also demonstrated and analyzed aiming to provide better insight into the problem. The results obtained from the multipath experiments were very promising and are presented here. Copyright (C) 2009 Eniuce Menezes de Souza et al. Maringa State Univ UEM, Dept Stat, Maringa, Parana, Brazil FCT UNESP, Cartog Dept, Presidente Prudente, Brazil FCT UNESP, Dept Math, Presidente Prudente, Brazil FCT UNESP, Cartog Dept, Presidente Prudente, Brazil FCT UNESP, Dept Math, Presidente Prudente, Brazil FAPESP: 03/12770-3 Thematic Project: 06/04008-2

The deflection of compliant mechanism (CM) which involves geometrical nonlinearity due to large deflection of members continues to be an interesting problem in mechanical systems. This paper deals with an analytical investigation of large deflections in compliant mechanisms. The main objective is to propose a convenient method of solution for the large deflection problem in CMs in order to overcome the difficulty and inaccuracy of conventional methods, as well as for the purpose of mathematical modeling and optimization. For simplicity, an element is considered which is a cantilever beam out of linear elastic material under vertical end point load. This can further be used as a building block in more complex compliant mechanisms. First, the governing equation has been obtained for the cantilever beam; subsequently, the Adomian decomposition method (ADM) has been utilized to obtain a semianalytical solution. The vertical and horizontal displacements of a cantilever beam can conveniently be obtained in an explicit analytical form. In addition, variations of the parameters that affect the characteristics of the deflection have been examined. The results reveal that the proposed procedure is very accurate, efficient, and convenient for cantilever beams, and can probably be applied to a large class of practical problems for the purpose of analysis and optimization.

This paper is intended to present a lossless image compression method based on multiple-tables arithmetic coding (MTAC) method to encode a gray-level imagef. First, the MTAC method employs a median edge detector (MED) to reduce the entropy rate off. The gray levels of two adjacent pixels in an image are usually similar. A base-switching transformation approach is then used to reduce the spatial redundancy of the image. The gray levels of some pixels in an image are more common than those of others. Finally, the arithmetic encoding method is applied to reduce the coding redundancy of the image. To promote high performance of the arithmetic encoding method, the MTAC method first classifies the data and then encodes each cluster of data using a distinct code table. The experimental results show that, in most cases, the MTAC method provides a higher efficiency in use of storage space than the lossless JPEG2000 does.

Nonlinearities in spacecraft attitude determination problem has been studied intensively during the past decades. Traditionally, multiplicative extended Kalman filter_MEKF_algorithm has been a good solution for most nominal space missions. But in recent years, advances in space missions deserve a revisit of the issue. Though there exist a variety of advanced nonlinear filtering algorithms, most of them are prohibited for actual onboard implementation because of their overload computational complexity. In this paper, we address this difficulty by developing a new algorithm framework based on the marginal filtering principle, which requires only 4 sigma points to give a complete 6-state attitude and angular rate estimation. Moreover, a new strategy for sigma point construction is also developed to further increase the efficiency and numerical accuracy. Incorporating the presented framework and novel sigma points, we proposed a new, nonlinear attitude and rate estimator, namely, the Marginal Geometric Sigma Point Filter. The new algorithm is of the same precision as traditional unscented Kalman filters, while keeping a significantly lower computational complexity, even when compared to the reduced sigma point algorithms. In fact, it has truly rivaled the efficiency of MEKF, even when simple closed-form solutions are involved in the latter.

Teleoperated robotic systems are those in which human operators control remote robots through a communication network. The deployment and integration of teleoperated robot’s systems in the medical operation have been hampered by many issues, such as safety concerns. Elliptic curve cryptography (ECC), an asymmetric cryptographic algorithm, is widely applied to practical applications because its far significantly reduced key length has the same level of security as RSA. The efficiency of ECC on GF (p) is dictated by two critical factors, namely, modular multiplication (MM) and point multiplication (PM) scheduling. In this paper, the high-performance ECC architecture of SM2 is presented. MM is composed of multiplication and modular reduction (MR) in the prime field. A two-stage modular reduction (TSMR) algorithm in the SCA-256 prime field is introduced to achieve low latency, which avoids more iterative subtraction operations than traditional algorithms. To cut down the run time, a schedule is put forward when exploiting the parallelism of multiplication and MR inside PM. Synthesized with a 0.13 um CMOS standard cell library, the proposed processor consumes 341.98k gate areas, and each PM takes 0.092 ms.

This paper investigates how to change the disassortativity of the whole network by connecting nodes of different types in two communities. A model connecting two multi-center networks is studied to see if analytical results are achievable. There are three main methods to connect two multi-center subnetworks depending on whether the connecting nodes are centers: (1) connect the centers of one sub-network to noncenter nodes of the other sub-network, (2) connect the centers of the two sub-networks together, and (3) connect non-center nodes of the two sub-networks. The results show that the disassortative property of a single multicenter network can be maintained in scenarios (1) and (2) above, but the disassortativity is changed in (3). In conclusion, either assortativity or disassortativity is achievable by connecting nodes with different degree properties in an ideal network constructed from two communities with similar network topology.

The static behavior of self-anchored cable-stayed suspension bridge under vertical load is described with the continuum method. Based on the partition generalized variation principle, considering the compression-bending coupling effect of the main girder and the tower, the large displacement incomplete generalized potential energy functional of three-span self-anchored cable-stayed suspension bridge is established. Then, the basic differential equations of self-anchored cable-stayed suspension bridge are derived through constraint variation. Taking a self-anchored cable-stayed suspension bridge with main span 100 m, for example, the results by the proposed analytic method agree with that of numerical analysis. Therefore, the basic differential equations proposed in this paper could be applied to the preliminary analysis of self-anchored cable-stayed suspension bridge. The equations also provide a theoretical basis to describe the static behavior of this type of bridge.

This paper presents a method determining neighborhoods of the image pixels automatically in adaptive denoising. The neighborhood is named stationary neighborhood (SN). In this method, the noisy image is considered as an observation of a nonlinear time series (NTS). Image denoising must recover the true state of the NTS from the observation. At first, the false neighbors (FNs) in a neighborhood for each pixel are removed according to the context. After moving the FNs, we obtain an SN, where the NTS is stationary and the real state can be estimated using the theory of stationary time series (STS). Since each SN of an image pixel consists of elements with similar context and nearby locations, the method proposed in this paper can not only adaptively find neighbors and determine size of the SN according to the characteristics of a pixel, but also be able to denoise while effectively preserving edges. Finally, in order to show the superiority of this algorithm, we compare this method with the existing universal denoising algorithms.

The flight dynamic equations in mathematics for aircraft response to the adverse weathers, such as wind shear, atmospheric turbulence, and in-flight icing, are nonlinear and unsteady. To effectively analyze the performance degradation and variations in stability of commercial aircraft that encountered these weather hazards, the nonlinear and dynamic (i.e., time dependent) aerodynamic models based on flight data would be needed. In the present paper, a numerical modeling method based on a fuzzy-logic algorithm will be presented to estimate the aerodynamic models for a twin-jet transport by using the flight data from the flight data recorder (FDR). The aerodynamic models having the capability to generate continuous stability derivatives, especially for sensitivity study of unknown factors in adverse weather conditions, will be demonstrated in this paper.

The reasonability of artificial multi-point ground motions and the identification of abnormal records in seismic array observations, are two important issues in application and analysis of multi-point ground motion fields. Based on the dynamic time warping (DTW) distance method, this paper discusses the application of similarity measurement in the similarity analysis of simulated multi-point ground motions and the actual seismic array records. Analysis results show that the DTW distance method not only can quantitatively reflect the similarity of simulated ground motion field, but also offers advantages in clustering analysis and singularity recognition of actual multi-point ground motion field.

This paper presents a novel prior knowledge-based Green's kernel for support vector regression (SVR). After reviewing the correspondence between support vector kernels used in support vector machines (SVMs) and regularization operators used in regularization networks and the use of Green's function of their corresponding regularization operators to construct support vector kernels, a mathematical framework is presented to obtain the domain knowledge about magnitude of the Fourier transform of the function to be predicted and design a prior knowledge-based Green's kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function makes it suitable for signals corrupted with noise that includes many real world systems. We conduct several experiments mostly using benchmark datasets to compare the performance of our proposed technique with the results already published in literature for other existing support vector kernel over a variety of settings including different noise levels, noise models, loss functions, and SVM variations. Experimental results indicate that knowledge-based Green's kernel could be seen as a good choice among the other candidate kernel functions.

It is known that many image enhancement methods have a tradeoff between noise suppression and edge enhancement. In this paper, we propose a new technique for image enhancement filtering and explain it in human visual perception theory. It combines kernel regression and local homogeneity and evaluates the restoration performance of smoothing method. First, image is filtered in kernel regression. Then image local homogeneity computation is introduced which offers adaptive selection about further smoothing. The overall effect of this algorithm is effective about noise reduction and edge enhancement. Experiment results show that this algorithm has better performance in image edge enhancement, contrast enhancement, and noise suppression.

This paper addresses the cryptanalysis of a secure communication scheme proposed by Wu (2006), where the information signal is modulated into a system parameter of a unified chaotic system. It is demonstrated that a parameter observer can be designed to identify the parameter determined by the transmitted information and then the transmitted information can be obtained. Compared with the existing analysis using adaptive observer, the cryptanalysis based on parameter observer is much simpler and needs less structure information of the transmitter system. With numerical simulations, it is shown that the parameter observer has stronger practicality and robustness. Furthermore, it is still possible to obtain the transmitted information, even if the derivative of the transmitted signal is unknown.

This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD) from a two-dimensional (2D) image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN) as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Structure of data set is of critical importance in identifying clusters, especially the density difference feature. In this paper, we present a clustering algorithm based on density consistency, which is a filtering process to identify same structure feature and classify them into same cluster. This method is not restricted by the shapes and high dimension data set, and meanwhile it is robust to noises and outliers. Extensive experiments on synthetic and real world data sets validate the proposed the new clustering algorithm.

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

Nonnegative matrix factorization (NMF) is a popular tool for analyzing the latent structure of nonnegative data. For a positive pairwise similarity matrix, symmetric NMF (SNMF) and weighted NMF (WNMF) can be used to cluster the data. However, both of them are not very efficient for the ill-structured pairwise similarity matrix. In this paper, a novel model, called relationship matrix nonnegative decomposition (RMND), is proposed to discover the latent clustering structure from the pairwise similarity matrix. The RMND model is derived from the nonlinear NMF algorithm. RMND decomposes a pairwise similarity matrix into a product of three low rank nonnegative matrices. The pairwise similarity matrix is represented as a transformation of a positive semidefinite matrix which pops out the latent clustering structure. We develop a learning procedure based on multiplicative update rules and steepest descent method to calculate the nonnegative solution of RMND. Experimental results in four different databases show that the proposed RMND approach achieves higher clustering accuracy.

Feature extraction plays an important role in preprocessing procedure in dealing with small sample size problems. Considering the fact that LDA, LPP, and many other existing methods are confined to one case of the data set. To solve this problem, we propose an efficient method in this paper, named global between maximum and local within minimum. It not only considers the global structure of the data set, but also makes the best of the local geometry of the data set through dividing the data set into four domains. This method preserves relations of the nearest neighborhood, as well as demonstrates an excellent performance in classification. Superiority of the proposed method in this paper is manifested in many experiments on data visualization, face representative, and face recognition.

This paper suggests an upside-down tree-based orthogonal matching pursuit (UDT-OMP) compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree structure of the wavelet coefficients of signal besides its sparsity in wavelet basis. Compared with conventional greedy pursuit algorithms: orthogonal matching pursuit (OMP) and tree-based orthogonal matching pursuit (TOMP), signal-to-noise ratio (SNR) using UDT-OMP is significantly improved.

In most synthesis evaluation systems and decision-making systems, data are represented by objects and attributes of objects with a degree of belief. Formally, these data can be abstracted by the form (objects; attributes; P), wherePrepresents a kind degree of belief between objects and attributes, such that,Pis a basic probability assignment. In the paper, we provide a kind of probability information system to describe these data and then employ rough sets theory to extract probability decision rules. By extension of Dempster-Shafer evidence theory, we can get probabilities of antecedents and conclusion of probability decision rules. Furthermore, we analyze the consistency of probability decision rules. Based on consistency of probability decision rules, we provide an inference method to finish inference of probability decision rules, which can be used to decide the class of a new objectx′. The conclusion points out that the inference method of the paper not only deals with precise information, but also imprecise or uncertain information as well.

Published version of an article in the journal: Mathematical problems in engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2012/619162 Many linguistic aggregation methods have been proposed and applied in the linguistic decision- making problems. In practice, experts need to assess a number of values in a side of reference domain higher than in the other one; that is, experts use unbalanced linguistic values to express their evaluation for problems. In this paper, we propose a new linguistic aggregation operator to deal with unbalanced linguistic values in group decision making, we adopt 2-tuple representation model of linguistic values and linguistic hierarchies to express unbalanced linguistic values, and moreover,we present the unbalanced linguistic ordered weighted geometric operator to aggregate unbalanced linguistic evaluation values; a comparison example is given to show the advantage of ourmethod.

The extraction of affine invariant features plays an important role in many fields of image processing. In this paper, the original image is transformed into new images to extract more affine invariant features. To construct new images, the original image is cut in two areas by a closed curve, which is called general contour (GC). GC is obtained by performing projections along lines with different polar angles. New image is obtained by changing gray value of pixels in inside area. The traditional affine moment invariants (AMIs) method is applied to the new image. Consequently, cutting affine moment invariants (CAMIs) are derived. Several experiments have been conducted to evaluate the proposed method. Experimental results show that CAMIs can be used in object classification tasks.

The main purpose of the paper is to optimize state feedback parameters using intelligent method, GA, Hermite-Biehler, and chaos algorithm. GA is implemented for local search but it has some deficiencies such as trapping into a local minimum and slow convergence, so the combination of Hermite-Biehler and chaos algorithm has been added to GA to avoid its deficiencies. Dividing search space is usually done by distributed population genetic algorithm (DPGA). Moreover, using generalized Hermite-Biehler Theorem can find the domain of parameters. In order to speed up the convergence at the first step, Hermite-Biehler method finds some intervals for controller, in the next step the GA will be added, and, finally, chaos disturbance will help the algorithm to reach a global minimum. Therefore, the proposed method can optimize the parameters of the state feedback controller.

We present a new method to estimate noise for a single-slice sinogram of low-dose CT based on the homogenous patches centered at a special pixel, called center point, which has the smallest variance among all sinogram pixels. The homogenous patch, composed by homogenous points, is formed by the points similar to the center point using similarity sorting, similarity decreasing searching, and variance analysis in a very large neighborhood (VLN) to avoid manual selection of parameter for similarity measures.Homogenous pixels in the VLN allow us find the largest number of samples, who have the highest similarities to the center point, for noise estimation, and the noise level can be estimated according to unbiased estimation.Experimental results show that for the simulated noisy sinograms, the method proposed in this paper can obtain satisfied noise estimation results, especially for sinograms with relatively serious noises.

The classification of erythrocyte plays an important role in clinic diagnosis. In terms of the fact that the shape deformability of red blood cell brings more difficulty in detecting and recognize for operating automatically, we believed that the recovered 3D shape surface feature would give more information than traditional 2D intensity image processing methods. This paper proposed a combined approach for complex surface segmentation of red blood cell based on shape-from-shading technique and multiscale surface fitting. By means of the image irradiance equation under SEM imaging condition, the 3D height field could be recovered from the varied shading. Afterwards the depth maps of each point on the surfaces were applied to calculate Gaussian curvature and mean curvature, which were used to produce surface-type label image. Accordingly the surface was segmented into different parts through multiscale bivariate polynomials function fitting. The experimental results showed that this approach was easily implemented and promising.

This paper presents a new and fast multiphase image segmentation model for color images. We propose our model by incorporating the globally convex image segmentation method and the split Bregman method into the piecewise constant multiphase Vese-Chan model for color images. We have applied our model to many synthetic and real color images. Numerical results show that our model can segment color images with multiple regions and represent boundaries with complex topologies, including triple junctions. Comparison with the Vese-Chan model demonstrates the efficiency of our model. Besides, our model does not require a priori denoising step and is robust with respect to noise.

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.

Limiting factors for the precise orbit determination (POD) of low-earth orbit (LEO) satellite using dual-frequency GPS are nowadays mainly encountered with the in-flight phase error modeling. The phase error is modeled as a systematic and a random component each depending on the direction of GPS signal reception. The systematic part and standard deviation of random part in phase error model are, respectively, estimated by bin-wise mean and standard deviation values of phase postfit residuals computed by orbit determination. By removing the systematic component and adjusting the weight of phase observation data according to standard deviation of random component, the orbit can be further improved by POD approach. The GRACE data of 1–31 January 2006 are processed, and three types of orbit solutions, POD without phase error model correction, POD with mean value correction of phase error model, and POD with phase error model correction, are obtained. The three-dimensional (3D) orbit improvements derived from phase error model correction are 0.0153 m for GRACE A and 0.0131 m for GRACE B, and the 3D influences arisen from random part of phase error model are 0.0068 m and 0.0075 m for GRACE A and GRACE B, respectively. Thus the random part of phase error model cannot be neglected for POD. It is also demonstrated by phase postfit residual analysis, orbit comparison with JPL precise science orbit, and orbit validation with KBR data that the results derived from POD with phase error model correction are better than another two types of orbit solutions generated in this paper.

This paper proposes one possibility to generalize the morphological operations (particularly, dilation, erosion, opening, and closing) to color images. First, properties of a desirable generalization are stated and a brief review is done on former approaches. Then, the method is explained, which is based on a total ordering of the colors in an image induced by its color histogram; this is valid for just one image and may present problems in smoothly coloured images. To solve these drawbacks a refinement consisting of smoothing the histogram and using a joint histogram of several images is presented. Results of applying the so-defined morphological operations on several sets of images are shown and discussed.

We apply semigroup property of enhanced Chebyshev polynomials to present an anonymous authentication protocol. This paper aims at improving security and reducing computational and storage overhead. The proposed scheme not only has much lower computational complexity and cost in the initialization phase but also allows the users to choose their passwords freely. Moreover, it can provide revocation of lost or stolen smart card, which can resist man-in-the-middle attack and off-line dictionary attack together with various known attacks.

Image motion deblurring with unknown blur kernel is an ill-posed problem. This paper proposes a blind motion deblurring approach that solves blur kernel and the latent image robustly. For kernel optimization, an edge mask is used as an image prior to improve kernel update, then an edge selection mask is adopted to improve image update. In addition, an alternative iterative method is introduced to perform kernel optimization under a multiscale scheme. Moreover, for image restoration, a total-variation-(TV-) based algorithm is proposed to recover the latent image via nonblind deconvolution. Experimental results demonstrate that our method obtains accurate blur kernel and achieves better deblurring results than previous works.

Distributional Similarity has attracted considerable attention in the field of natural language processing as an automatic means of countering the ubiquitous problem of sparse data. As a logographic language, Chinese words consist of characters and each of them is composed of one or more radicals. The meanings of characters are usually highly related to the words which contain them. Likewise, radicals often make a predictable contribution to the meaning of a character: characters that have the same components tend to have similar or related meanings. In this paper, we utilize these properties of the Chinese language to improve Chinese word similarity computation. Given a content word, we first extract similar words based on a large corpus and a similarity score for ranking. This rank is then adjusted according to the characters and components shared between the similar word and the target word. Experiments on two gold standard datasets show that the adjusted rank is superior and closer to human judgments than the original rank. In addition to quantitative evaluation, we examine the reasons behind errors drawing on linguistic phenomena for our explanations.

The quaternion wavelet transform is a new multiscale analysis tool. Firstly, this paper studies the standard orthogonal basis of scale space and wavelet space of quaternion wavelet transform in spatialL2(R2), proves and presents quaternion wavelet’s scale basis function and wavelet basis function concepts in spatial scale spaceL2(R2;H), and studies quaternion wavelet transform structure. Finally, the quaternion wavelet transform is applied to image denoising, and generalized Gauss distribution is used to model QWT coefficients’ magnitude distribution, under the Bayesian theory framework, to recover the original coefficients from the noisy wavelet coefficients, and so as to achieve the aim of denoising. Experimental results show that our method is not only better than many of the current denoising methods in the peak signal to noise ratio (PSNR), but also obtained better visual effect.

3D measurement and reconstruction of rigid microstructures rely on the precise matches of structural feature points (SFPs) between microscopic images. However, most of the existing algorithms fail in extracting and tracking at microscale due to the poor quality of the microscopic images. This paper presents a novel framework for extracting and matching SFPs in microscopic images under two stereo microscopic imaging modes in our system, that is, fixed-positioning stereo and continuous-rotational stereo modes, respectively. A 4-DOF (degree of freedom) micro visual measurement system is developed for 3D projective structural measurement of rigid microstructures using the SFPs obtained from microscopic images by the proposed framework. Under the fixed-positioning stereo mode, a similarity-pictorial structure algorithm is designed to preserve the illumination invariance in SFPs matching, while a method based on particle filter with affine transformation is developed for accurate tracking of multiple SFPs in image sequence under the continuous-rotational stereo mode. The experimental results demonstrate that the problems of visual distortion, illumination variability, and irregular motion estimation in micro visual measurement process can be effectively resolved by the proposed framework.

In this paper, a hybrid scheme is proposed to find the reliable point-correspondences between two images, which combines the distribution of invariant spatial feature description and frequency domain alignment based on two-stage coarse to fine refinement strategy. Firstly, the source and the target images are both down-sampled by the image pyramid algorithm in a hierarchical multi-scale way. The Fourier-Mellin transform is applied to obtain the transformation parameters at the coarse level between the image pairs; then, the parameters can serve as the initial coarse guess, to guide the following feature matching step at the original scale, where the correspondences are restricted in a search window determined by the deformation between the reference image and the current image; Finally, a novel matching strategy is developed to reject the false matches by validating geometrical relationships between candidate matching points. By doing so, the alignment parameters are refined, which is more accurate and more flexible than a robust fitting technique. This in return can provide a more accurate result for feature correspondence. Experiments on real and synthetic image-pairs show that our approach provides satisfactory feature matching performance.

Reconstruction from few views is an important problem in medical imaging and applied mathematics. In this paper, a combined energy minimization is proposed for image reconstruction.l2energy of the image gradient is introduced in the lower density region, and it can accelerate the reconstruction speed and improve the results. Total variation of the image is introduced in the higher density region, and the image features can be preserved well. Nonlinear conjugate gradient method is introduced to solve the problem. The efficiency and accuracy of our method are shown in several numerical experiments.

This paper proposes a reversible watermarking technique for natural images. According to the similarity of neighbor coefficients’ values in wavelet domain, most differences between two adjacent pixels are close to zero. The histogram is built based on these difference statistics. As more peak points can be used for secret data hiding, the hiding capacity is improved compared with those conventional methods. Moreover, as the differences concentricity around zero is improved, the transparency of the host image can be increased. Experimental results and comparison show that the proposed method has both advantages in hiding capacity and transparency.

A novel adaptive fuzzy min-max neural network classifier called AFMN is proposed in this paper. Combined with principle component analysis and adaptive genetic algorithm, this integrated system can serve as a supervised and real-time classification technique. Considering the loophole in the expansion-contraction process of FMNN and GFMN and the overcomplex network architecture of FMCN, AFMN maintains the simple architecture of FMNN for fast learning and testing while rewriting the membership function, the expansion and contraction rules for hyperbox generation to solve the confusion problems in the hyperbox overlap region. Meanwhile, principle component analysis is adopted to finish dataset dimensionality reduction for increasing learning efficiency. After training, the confidence coefficient of each hyperbox is calculated based on the distribution of samples. During classifying procedure, utilizing adaptive genetic algorithm to complete parameter optimization for AFMN can also fasten the entire procedure than traversal method. For conditions where training samples are insufficient, data core weight updating is indispensible to enhance the robustness of classifier and the modified membership function can adjust itself according to the input varieties. The paper demonstrates the performance of AFMN through substantial examples in terms of classification accuracy and operating speed by comparing it with FMNN, GFMN, and FMCN.

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6 level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP) is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

The Forward Least-Squares Approximation (FLSA) SVM is a newly-emerged Least-Square SVM (LS-SVM) whose solution is extremely sparse. The algorithm uses the number of support vectors as the regularization parameter and ensures the linear independency of the support vectors which span the solution. This paper proposed a variant of the FLSA-SVM, namely, Reduced FLSA-SVM which is of reduced computational complexity and memory requirements. The strategy of “contexts inheritance” is introduced to improve the efficiency of tuning the regularization parameter for both the FLSA-SVM and the RFLSA-SVM algorithms. Experimental results on benchmark datasets showed that, compared to the SVM and a number of its variants, the RFLSA-SVM solutions contain a reduced number of support vectors, while maintaining competitive generalization abilities. With respect to the time cost for tuning of the regularize parameter, the RFLSA-SVM algorithm was empirically demonstrated fastest compared to FLSA-SVM, the LS-SVM, and the SVM algorithms.

Information system (IS) project selection is of critical importance to every organization in dynamic competing environment. The aim of this paper is to develop a hybrid multicriteria group decision making approach based on intuitionistic fuzzy theory for IS project selection. The decision makers’ assessment information can be expressed in the form of real numbers, interval-valued numbers, linguistic variables, and intuitionistic fuzzy numbers (IFNs). All these evaluation pieces of information can be transformed to the form of IFNs. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate IS project in group decision making environment. Finally, a numerical example for information system projects selection is given to illustrate application of hybrid multi-criteria group decision making (MCGDM) method based on intuitionistic fuzzy theory and TOPSIS method.

Video panoramic image stitching is in general challenging because there is small overlapping between original images, and stitching processes are therefore extremely time consuming. We present a new algorithm in this paper. Our contribution can be summarized as a multiple-constraint corner matching process and the resultant faster image stitching. The traditional Random Sample Consensus (RANSAC) algorithm is inefficient, especially when stitching a large number of images and when these images have quite similar features. We first filter out many inappropriate corners according to their position information. An initial set of candidate matching-corner pairs is then generated based on grayscales of adjacent regions around each corner. Finally we apply multiple constraints, e.g., their midpoints, distances, and slopes, on every two candidate pairs to remove incorrectly matched pairs. Consequently, we are able to significantly reduce the number of iterations needed in RANSAC algorithm so that the panorama stitching can be performed in a much more efficient manner. Experimental results demonstrate that (i) our corner matching is three times faster than normalized cross-correlation function (NCC) rough match in traditional RANSAC algorithm and (ii) panoramas generated from our algorithm feature a smooth transition in overlapping image areas and satisfy human visual requirements.

Nonsubsampled Contourlet transform (NSCT) has properties such as multiscale, localization, multidirection, and shift invariance, but only limits the signal analysis to the time frequency domain. Fractional Fourier transform (FRFT) develops the signal analysis to fractional domain, has many super performances, but is unable to attribute the signal partial characteristic. A novel image fusion algorithm based on FRFT and NSCT is proposed and demonstrated in this paper. Firstly, take FRFT on the two source images to obtain fractional domain matrices. Secondly, the NSCT is performed on the aforementioned matrices to acquire multiscale and multidirection images. Thirdly, take fusion rule for low-frequency subband coefficients and directional bandpass subband coefficients to get the fused coefficients. Finally, the fused image is obtained by performing the inverse NSCT and inverse FRFT on the combined coefficients. Three modes images and three fusion rules are demonstrated in the proposed algorithm test. The simulation results show that the proposed fusion approach is better than the methods based on NSCT at the same parameters.

Multiattribute decision making (MADM) is one of the central problems in artificial intelligence, specifically in management fields. In most cases, this problem arises from uncertainty both in the data derived from the decision maker and the actions performed in the environment. Fuzzy set and high-order fuzzy sets were proven to be effective approaches in solving decision-making problems with uncertainty. Therefore, in this paper, we investigate the MADM problem with completely unknown attribute weights in the framework of interval-valued intuitionistic fuzzy (IVIF) set (IVIFS). We first propose a new definition of IVIF entropy and some calculation methods for IVIF entropy. Furthermore, we propose an entropy-based decision-making method to solve IVIF MADM problems with completely unknown attribute weights. Particular emphasis is put on assessing the attribute weights based on IVIF entropy. Instead of the traditional methods, which use divergence among attributes or the probabilistic discrimination of attributes to obtain attribute weights, we utilize the IVIF entropy to assess the attribute weights based on the credibility of the decision-making matrix for solving the problem. Finally, a supplier selection example is given to demonstrate the feasibility and validity of the proposed MADM method.