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Let s and t be two points in the plane, how to compute the Euclidean shortest path between s and t which visits a sequence of segments given in the plane, is the problem to be discussed in this paper, especially, the situation of the ad- jacent segments intersect is the focus of our study. In this paper, we first analyze the degeneration applying rubber-band algorithm to solve the problem and introduce the algorithm for computing Euclidean shortest path with removing suffi- ciently small segments. Then based on rubber-band algorithm, we present a new algorithm for solving the degeneration and computing the ESP by crossing over two segments to deal with intersection and in our algorithm the adjacent seg- ments order can be changed when they intersect. Furthermore, we have implemented the two algorithms and have applied a large test data to test them. The experiments demonstrate that our algorithm is more efficient and effective, and it has the same time complexity as the rubber-band algorithm.

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. The time complexity of the exact algorithms is generally an exponential function of the scale of TSP. This work gives an approximate algorithm with a four-vertex-three-line inequality for the triangle TSP. The time complexity is O(n2) and it can generate an approximation less than 2 times of the optimal solution. The paper designs a simple algorithm with the inequality. The algorithm is compared with the double-nearest neighbor algorithm. The experimental results illustrate the algorithm find the better approximations than the double-nearest neighbor algorithm for most TSP instances.

The expectation maximization (EM) algorithm based on statistical property can reduce image noise and artifacts in CT reconstruction. Considering its slow convergence and unsmooth of reconstruction image, this paper proposed EM-TV algorithm which is combined with the minimization of total variation (TV) and a soft-threshold filtering. To make the data smooth and do not loss the information of the edge, first, using EM algorithm reconstruct an image; then its discrete difference transform (DDT) is calculated and the soft-threshold filtering is updated. Finally, experimental results and comparisons with the EM algorithm of Shepp–Logan phantom show that the proposed algorithm can not only make the image of reconstruction smooth and reduce the normalized mean square distance, but also speed up the convergence. And real projection data indicate that this algorithm is effective.

This paper presents a novel non-linear least squares enhanced proper orthogonal decomposition (POD)-based 4DVar algorithm (referred as NLS-4DVar) for the non-linear ensemble-based 4DVar. In the algorithm, the Gauss–Newton iterative method is employed to handle the non-quadratic non-linearity of the 4DVar cost function while the overall structure of the algorithm still resembles the original POD-4DVar algorithm. It is proved that the original POD-4DVar algorithm is a special case of the proposed NLS-4DVar algorithm under the assumption of the linear relationship between the model perturbations (MPs) and the simulated observation perturbations (OPs). Under the assumption it is also shown that the solution of POD-4DVar algorithm coincides with the solution of the proposed NLS-4DVar algorithm. On the contrary, if the linear relationship assumption is dropped, the solution of the POD-4DVar algorithm is only the first iteration of the proposed NLS-4DVar algorithm. As a result, our analysis provides an explanation for the degraded and inaccurate performance of the POD-4DVar algorithm when the underlying forecast model or (and) the observation operator is strongly non-linear. The potential merits and advantages of the proposed NLS-4DVar are demonstrated by a group of Observing System Simulation Experiments with Advanced Research WRF (ARW) using accumulated rainfall-observations. Keywords: non-linear ensemble, 4DVar, Gauss–Newton method, non-linear least squares, observing system simulation experiments (Published: 26 January 2015) Citation: Tellus A 2015, 67, 25340, http://dx.doi.org/10.3402/tellusa.v67.25340

When the chaotic characteristics of manufacturing quality level are studied, it is not practical to use chaotic methods because of the low speed of calculating the correlation integral. The original algorithm used to calculate the correlation integral is studied after a computer hardware upgrade. The result is that calculation of the correlation integral can be sped up only by improving the algorithm. This is accomplished by changing the original algorithm in which a single distance threshold-related correlation integral is obtained from one traversal of all distances between different vectors to a high-efficiency algorithm in which all of the distance threshold-related correlation integrals are obtained from one traversal of all of the distances between different vectors. For a time series with 3000 data points, this high-efficiency algorithm offers a 3.7-fold increase in speed over the original algorithm. Further study of the high-efficiency algorithm leads to the development of a super-high-efficiency algorithm, which is accomplished by changing the original and high-efficiency algorithms, in which the add-one operation of the Heaviside function is executed n times, such that the execution of the add-one operation occurs only once. The super-high-efficiency algorithm results in increases in the calculation speed by up to 109 times compared with the high-efficiency algorithm and by approximately 404 times compared with the original algorithm. The calculation speed of the super-high-efficiency algorithm is suitable for practical use with the chaotic method.

According to the traditional stabilized biconjugate gradient algorithm (BiCGSTAB) deficiency in data locality, this paper proposed a QTBiCGSTAB algorithm whose core idea is that recursively divides sparse matrix with quarter tree into sub-matrix and reorders them, to improve the hit ratio of cache and enhance the algorithm’s efficiency. And the idea is good for algorithm being parallized, that is proved by the numerical experiments later. It mainly shows, firstly, QTBiCGSTAB algorithm is more efficiency than BiCGSTAB, and the speedup would reach 1:330. The target division length would be influnced on the algorithm’s performance; Secondly, for large linear system, parallized QTBiCGSTAB is more efficiency than serial’s.

this paper, we have examined the comparative study of Floyd Warshall algorithm and the Rectangular algorithm. We have tested these two algorithms on random graphs generated by the Erdos - Renyi (ER) model. The evaluation of the algorithms for different probabilities show that the Floyd Warshall algorithm gives slightly better performance for dense graphs while the Rectangular algorithm works better for sparse graphs. Keywordsford algorithm, Dijkstra's algorithm, Floyd warshall algorithm, all pair shortest path algorithm, the rectangular algorithm, comparison of algorithms.

This paper develops a decomposition based least squares iterative identification algorithm for two-input single-output (TISO) systems. The basic idea is to decompose a TISO system into two subsystems and then to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm has less computational load. The simulation results indicate that the proposed algorithm is effective.

First in this paper, we will introduce a map matching algorithm based on fuzzy logic, and how we use it to fuse the navigation data. Then we will introduce a kind of iterative algorithm what we use to optimize the parameters of the map matching algorithm we mentioned before. So we can improve the precision of our map matching algorithm.

An algorithm which computes the minimum area triangle enclosing a convex polygon in linear time already exists in the literature. The paper describing the algorithm also proves that the provided solution is optimal and a lower complexity sequential algorithm cannot exist. However, only a high-level description of the algorithm was provided, making the implementation difficult to reproduce. The present note aims to contribute to the field by providing a detailed description of the algorithm which is easy to implement and reproduce, and a benchmark comprising 10,000 variable sized, randomly generated convex polygons for illustrating the linearity of the algorithm.

This paper presents an algorithm called “volume & neighbors” for finding elimination trees for multifrontal solver algorithm applied to three dimensional h -adaptive finite element method computations. The algorithm is described in a pseudo-code and explained on exemplary h -refined grids. The algorithm is implemented in three dimensional h -adaptive finite element method code and tested on a sequence of representative grids, namely uniform grid, grid with point singularity, grid with edge singularity and grid with face singularity. The number of floating point operations for the multifrontal solver algorithm working with the elimination trees generated by the volume & neighbors algorithm is compared with the number of floating-point operations resulting from execution of the state-of-the-art multifrontal direct solver MUMPS with state-of-the-art algorithms for constructing elimination trees like nested-dissection from METIS library and approximate minimum degree AMD algorithm as well as PORD algorithm. In all the cases the volume & neighbors algorithm outperforms other state-of-the art algorithms. The only exception is the case of the uniform grid, where the algorithm results in a similar number of FLOPs than nested dissection algorithm.

A computationally simple and efficient algorithm for compressed sensing is proposed. The algorithm, a simple combination of the orthogonal projection algorithm and of a novel quasi-Newton optimization scheme, is termed Quasi-Newton Iterative Projection (QNIP). There are two main advantages of the proposed algorithm. First, the computation of the proposed algorithm is very simple, which involves the application of the sampling matrix and its transpose at each iteration. Second, the algorithm appears to require a fewer number of iterations for convergence, whilst it provides a higher rate of perfect recovery compared with the reference algorithms. The performance of the proposed algorithm is validated via theoretical analysis as well as some numerical examples.

The Map Maker algorithm which converts survey data into geometric data with 2-dimensional Cartesian coordinates has been previously published. Analysis of the performance of this algorithm is continuing. The algorithm is suitable for generating 2D maps and it would be helpful to have this algorithm generalized to generate 3D and higher dimensional coordinates. The trigonometric approach of the Map Maker algorithm does not extend well into higher dimensions however this paper reports on an algebraic approach which solves the problem. A similar algorithm called the Coordinatizator algorithm has been published which converts survey data defining a higher dimensional space of measured sites into the lowest dimensionalcoordinatization accurately fitting the data. Therefore the Coordinatizator algorithm is not a projection transformation whereas the n-dimensional Map Maker algorithm is.

A novel approach for Image segmentation is proposed based on mean shift algorithm and normalized cuts algorithm. The normalized cuts algorithm gives good accuracy and better segmentation compared to all most of the existing methods. But it requires high computational power, also it takes huge time. The reason for this is it takes entire image as one matrix and computes. By using Mean Shift algorithm on the original image to partition it into sub graphs we can create image matrices with lower dimensions. The proposed algorithm first applied Mean Shift algorithm to obtain sub graphs. The normalized cuts algorithm is applied on the sub graphs. The experimental results also show that the results obtained by the proposed algorithm are better than results obtained by individual algorithms of Mean shift, normalized cuts.

Social network is a collection of heterogeneous multi-relational data represented by the graph, whose nodes represent object, whose edges represent relationships between nodes, and the weights represent the extent of the relationship between nodes. This paper gave a weighted K-means algorithm and introduced weighted K-means algorithm into social networks. Traditional k-means and most k-means variants are still computationally expensive for large datasets, however, the weighted K-means algorithm is to reduce the initial cluster centers blindness and randomness by eliminating noise point and narrowing the range of k values. Experiments datasets show that the weighted K-means algorithm significantly enhances the clustering quality. Therefore, the weighted K-means algorithm is effective and suitable for the social network. Algorithm’s error rate is smaller and accuracy is higher than that of traditional k-means algorithm.

The localization of anchor-free wireless sensor network’s serious cumulative error leads to low positioning accuracy. To solve this problem, this paper proposed a co-located positioning method based on asynchronous change of learning factor adaptive weights particle swarm optimization algorithm (SAAPSO) and Taylor algorithm. The first phase of localization is building relative coordinate system. The second phase is estimating the node’s initial position with SAAPSO algorithm and using Taylor algorithm to iterative calculate in the node’s initial position to obtain accurate result. In the third stage, the node with precise coordinates takes part in the localization of rest nodes. Simulation results show that: this positioning algorithm has smaller cumulative error and higher accuracy.

The iterative maximum-likelihood expectation-maximization (ML-EM) algorithm is an excellent algorithm for image reconstruction and usually provides better images than the filtered backprojection (FBP) algorithm. However, a windowed FBP algorithm can outperform the ML-EM in certain occasions, when the least-squared difference from the true image, that is, the least-squared error (LSE), is used as the comparison criterion. Computer simulations were carried out for the two algorithms. For a given data set the best reconstruction (compared to the true image) from each algorithm was first obtained, and the two reconstructions are compared. The stopping iteration number of the ML-EM algorithm and the parameters of the windowed FBP algorithm were determined, so that they produced an image that was closest to the true image. However, to use the LSE criterion to compare algorithms, one must know the true image. How to select the optimal parameters when the true image is unknown is a practical open problem. For noisy Poisson projections, computer simulation results indicate that the ML-EM images are better than the regular FBP images, and the windowed FBP algorithm images are better than the ML-EM images. For the noiseless projections, the FBP algorithms outperform the ML-EM algorithm. The computer simulations reveal that the windowed FBP algorithm can provide a reconstruction that is closer to the true image than the ML-EM algorithm. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 114–120, 2012 © 2012 Wiley Periodicals, Inc.

This paper provides a comprehensive empirical performance analysis of various trajectory compression algorithms for offline compression of trajectory data of sea borne objects which include boats, trawlers, ships etc. Our empirical study uses data from a surveillance application. The surveillance application receives data from various sensors which include GPS integrated sensors, Radars, AIS, S-AIS etc. The Algorithms under consideration include the classical Douglas Peucker Algorithm, SQUISH, OLDCAT, SPM. These algorithms have been compared with respect to the capability to preserve the spatio-temporal information and execution time.

To solve climbing robot path planning in spatial trusses with detecting points, a hierarchical algorithm is proposed according to the divide-and-conquer strategy. Path planning problem with large-scale detecting points is decomposed into a number of path planning sub-problems with small-scale detecting points by Density Peaks Clustering algorithm(DPC), and then all sub-problems with small-scale detecting points are resolved by Ant Colony Optimization algorithm(ACO). At last, all the paths obtained from each subproblem are merged, and local path is optimized by 2-Opt and 3-Opt algorithms. The hierarchical algorithm is tested on 5 different instances. Experimental results show that the proposed algorithm has the same accuracy and computational complexity can be decreased n squared compared with ACO algorithm. The efficiency has been significantly improved.

This dissertation exploits an Android base application gives clarification to assist parents to pathway their adolescent activity. It was implemented to locate the location based on real time map only when the user is found to be missing or kidnap cases. A parent's tool has to propel a application spot SMS to the child's tool to acquire the spot of the child. On the other side, the child's tool accept the acknowledgement and retort the GPS spot to the parent's tool upon request, but it was costly and was intricate as information's can be customized. This is based on Douglas peucker algorithm. This application provides the adolescent to be secure from the susceptible milieu by monitoring their commotion by exploring the android hook up in a smart handset of the adolescent tenuously.

The design of a haul road for an open-pit mine can significantly affect the cost associated with hauling ore and waste to the surface. This study proposes a new method for haul road design in open-pit mines to support efficient truck haulage operations. The road layout in open-pit mines was optimized by using raster-based least-cost path analysis, and the resulting zigzag road sections were simplified by applying the Douglas-Peucker algorithm. In addition, the road layout was modified by reflecting the radius of curvature suggested in the road design guides. Finally, a three-dimensional model reflecting the results of the road design was created by combining the road layout modification result with the slope of the open-pit mine and the bench design result. The application of the proposed method to an area containing gold deposits made it possible to design a haul road for open-pit mines such that it supported efficient truck haulage operations; furthermore, the time required for truck movement along the road could be estimated. The proposed method is expected to be useful for planning and designing open-pit mines and to facilitate the improvement of the road design function of existing mining software applications.

Because of the good local modification of the B spline curve, it is widely used to reconstruct the B-spline surface in reverse engineering. A algorithm of reconstructing quasi-uniform B-spline surface is designed based on optimization quadrilateral meshes of the cloud data model by reverse control points in the paper, and the definite basic concepts, solve the data points selection, nodes parameterization, chasing method solve the control points equation, etc. In VS++2008 environment, the algorithm was programmed and debugged with C++ language, and the running results are given. The implementation of the algorithm showed its feasibility and reliability, as well as the advantages of high speed and high accuracy.

With the technological development of global positioning and video surveillance systems and extensive utilization of social media platforms, large amounts of trajectories data are captured. Processing and analyzing of moving objects such as people, animals, and vehicles provides valuable information for industrial and academic studies. In this study, three different algorithms are investigated to find similarities of trajectory data. These algorithms are Euclidean distance based similarity measurement (ED), Dynamic-time Warping based similarity measurement (DTW) and Longest Common Subsequence based similarity measurement (LCSS). Tests are evaluated both on raw data and reduced data. Reduced data is obtained by using Douglas-Peucker algorithm. The tests for the evaluation of the similarity algorithms are performed on both a synthetic dataset and Geolife real trajectory dataset, with varying data sizes.

Fast iterative shrinkage threshold algorithm (FISTA) is an efficient first-order optimization algorithm for Linear inverse problems. However, the algorithm employed a fixed iterative step size which limits the speed of calculation, This paper proposes an adaptive fast iterative shrinkage threshold algorithm (FISTA) by using a Barzilai-Borwein (BB) operator. The proposed Algorithm uses the previous iteration information to update the step size which can speed up the rate of the iteration. The numerical experimental results of Compressed Sensing and Image Denoising demonstrate that the proposed algorithm has a faster convergence rate and improves the efficiency of the calculation.

We propose the novel data analysis algorithm which allows to identify exactly the position of a given point as exterior, interior, or boundary relatively to an intersection of the finite number of pattern sets. Due to the special structure of the problem under study, this algorithm can be realized not only by sequential, but also by parallel computing on the basis of appropriate model decomposition.

The paper designed a way to judge the motion trend of the press-fit curve. The algorithm fits the curve with the data from the “entering the frame” region and the “leaving the frame” region. We use the fitting result, two lines, to judge whether the trend of the motion is normal or not. Finally, we test the algorithm in the laboratory with limited data. We find the algorithm is useful and it can be applied in the industrial production.

Interactive segmentation algorithm has been paid more and more attention in the field of image segmentation. Random walk algorithm is a proposed interactive segmentation algorithm based on graph theory, this paper proposes an improved Random Walk algorithm, the algorithm is more real-time than the original algorithm, and also has good robustness. This method adds the new improved Watershed algorithm to pre segmentation processing, reconstruct the connected domain graph, and the segmentation function Gauss area using 2 norm entropy reduction as a function of energy, finally it was solved by Random Walk algorithm. This method is faster than the original algorithm, the accuracy is satisfactory, and can be used for multi-target segmentation which has good robustness to noise.

The mean shift algorithm (Mean Shift, MS) has been widely used because of the advantages of fewer iteration times and better real-time performance. In the other hand, because of the use of single color histogram representation of the target feature, the MS algorithm can't always track well in complex condition. Aiming at the problem that the traditional Mean-Shift algorithm is unstable when the background color is similar to the target color or there is partial occlusion. In this paper, the original color features in traditional mean shift algorithm is transformed into HSV color feature and the textural feature is integrated into it to improve the tracking performance in the case of the background color is similar to the target color, and the four neighborhood search method (4 areas with the same size are expanded around the candidate area) is applied to solve the problem of partial occlusion. The comparisons of experiments show that the algorithm of this paper has higher accuracy than the traditional MS algorithm and the background weighted MS algorithm in the above complex environment, besides, the proposed algorithm has a good operating efficiency.

The Successive Resultants Algorithm (SRA) is a root-finding algorithm for polynomials over \(\mathbb {F}_{p^n}\) and was introduced at ANTS in 2014 [19]. The algorithm is efficient when the characteristic p is small and \(n > 1\). In this paper, we abstract the core SRA algorithm to arbitrary finite fields and present three instantiations of our general algorithm, one of which is novel and makes use of a series of isogenies derived from elliptic curves with sufficiently smooth order.

Several clinical studies reveal the relationship between alterations in the topologies of the human retinal blood vessel, the outcrop and the disease evolution, such as diabetic retinopathy, hypertensive retinopathy, and macular degeneration. Indeed, the detection of these vascular changes always has gaps. In addition, the manual steps are slow, which may be subjected to a bias of the perceiver. However, we can overcome these troubles using computer algorithms that are quicker and more accurate. This paper presents and investigates a novel method for measuring the blood vessel diameter in the retinal image. The proposed method is based on a thresholding segmentation and thinning step, followed by the characteristic point determination step by the Douglas-Peucker algorithm. Thereafter, it uses the active contours to detect vessel contour. Finally, Heron’s Formula is applied to assure the calculation of vessel diameter. The obtained results for six sample images showed that the proposed method generated less errors compared to other techniques, which confirms the high performance of the proposed method.

Affine projection algorithm is a signal reuse algorithm, and it has a good convergence rate compared to other traditional adaptive filtering algorithm. There are two factors that affect the performance of the algorithm, which are step factor and the projection length. In the paper, we propose a new variable step size affine projection algorithm (VSS-APA). It dynamically changes the step size according to certain rules, so that it can get smaller steady-state error and faster convergence speed. Simulation results can prove that its performance is superior to the traditional affine projection algorithm and in the active noise control (ANC) applications, the new algorithm can get very good results.

Kinesthetic teaching provides an accessible way for non-experts to quickly and easily program a robot system by demonstration. A crucial aspect of this technique is to obtain an accurate approximation of the robot’s intended trajectory for the task, while filtering out spurious aspects of the demonstration. While several methods to this end have been proposed, they either rely on several demonstrations or on the user explicitly indicating relevant trajectory waypoints. We propose a method, based on the Douglas-Peucker line simplification algorithm that is able to extract the notablepoints of a trajectory from a single demonstration. Additionally, by utilizing velocity information in the task space, the method is able to achieve a level of precision that is sufficient for industrial assembly tasks. Along with this, we present a user study that shows that our method enables non-expert robot users to successfully program the robot for an assembly benchmark, and that they find this method intuitive.

The string matching problem is considered as one of the most interesting research areas in the computer science field because it can be applied in many essential different applications such as intrusion detection, search analysis, editors, internet search engines, information retrieval and computational biology. During the matching process two main factors are used to evaluate the performance of the string matching algorithm which are the total number of character comparisons and the total number of attempts. This study aims to produce an efficient hybrid exact string matching algorithm called Sinan Sameer Tuned Boyer Moore-Quick Skip Search (SSTBMQS) algorithm by blending the best features that were extracted from the two selected original algorithms which are Tuned Boyer-Moore and Quick-Skip Search. The SSTBMQS hybrid algorithm was tested on different benchmark datasets with different size and different pattern lengths. The sequential version of the proposed hybrid algorithm produces better results when compared with its original algorithms (TBM and Quick-Skip Search) and when compared with Maximum-Shift hybrid algorithm which is considered as one of the most recent hybrid algorithm. The proposed hybrid algorithm has less number of attempts and less number of character comparisons.

Unmanned Aerial Vehicles (UAVs) have been utilized in various applications in many fields in recent years. The paths the pilots flew can be measured and collected to be utilized to create routes for autonomous flight. However, there is a problem in that GPS errors result in the path being irregularly represented. The measured path can be optimized by using the Douglas-Peucker algorithm. Our research led to the proposal of a method to optimize this path by applying the Douglas-Peucker algorithm, which has been shown to be suitable for a two-dimensional path, in three-dimensional space. Optimization of the 3D path by the proposed method was possible by deleting unnecessary points from the three-dimensional space. Thus, the flight paths that were measured and collected can be utilized to define the autonomous flight path.

Nowadays many algorithms for mobile robot mapping in indoor environments have been created. In this work we use a Kinect 2.0 camera, a visible range cameras Beward B2720 and an infrared camera Flir Tau 2 for building 3D dense maps of indoor environments. We present the RGB-D Mapping and a new fusion algorithm combining visual features and depth information for matching images, aligning of 3D point clouds, a “loop-closure” detection, pose graph optimization to build global consistent 3D maps. Such 3D maps of environments have various applications in robot navigation, real-time tracking, non-cooperative remote surveillance, face recognition, semantic mapping. The performance and computational complexity of the proposed RGB-D Mapping algorithm in real indoor environments is presented and discussed.

Discrete Fourier Transform (DFT) plays a crucial role in signal processing. In this paper, a new fast algorithm is presented for two dimensional DFT with different lengths. This algorithm is derived using a technique for multidimensional integral point called ‘vector coding’. The new algorithm significantly reduces the multiplications and recursive stages compared with row-column algorithm, and also skip the data transposing. The algorithm can spread to multidimensional DFT. For two dimensional, compared with row-column algorithm, the vector coding algorithm has the same addition, but about three-quarters of multiplication, and reduce the recursive stages a half.This original article was incorrectly published with pages 5 and 6 missing. At the request of the Proceedings Editor, a corrected version of this article was published online 24 March 2017. This article has also been corrected in the printed version of the volume.

Image matching plays an important role in computer vision. The features extracted by SIFT algorithm have high stability invariant to scale, rotation and light, so it is the most popular algorithm for image matching. While SIFT algorithm also has its disadvantages of high dimensional data and time-consuming. To solve this problem, the traditional method employs PCA algorithm to reduce dimensionality of the descriptors. While PCA is a linear dimensionality reduction algorithm which means that it can only be used for linear distributed data. This paper employs the fuzzy K-means algorithm to improve it (referred to as FKPCA) and improved RANSAC algorithm to eliminate false matching points after matching with PCA-SIFT and FKPCA-SIFT. From the experimental results, compared with PCA-SIFT algorithm, it can be seen that FKPCA-SIFT can keep the high matching accuracy for image. Moreover, FKPCA-SIFT can also be applied to non-linear data to expand the scope of PCA-SIFT and provides a better reference platform for further research.

The problem of local minimum cannot be avoided when it comes to nonlinear optimization in the learning algorithm of neural network parameters, and the larger the optimization space is, the more obvious the problem becomes. This paper proposes a new type of hybrid learning algorithm for three-layered feed-forward neural networks. This algorithm is based on three-layered feed-forward neural networks with output layer function, namely linear function, combining a quasi Newton algorithm with adaptive decoupled step and momentum (QNADSM) and iterative least square method to export. Simulation proves that this hybrid algorithm has strong self-adaptive capability, small calculation amount and fast convergence speed. It is an effective engineer practical algorithm.

Ranking data points with respect to a given preference criterion is an example of a preference learning task. In this paper, we investigate the generalization performance of the regularized ranking algorithm associated with least square ranking loss in a reproducing kernel Hilbert space, and use the method of computing hold-out estimates for the proposed algorithm. Based on using the hold-out method, we obtain fast learning rate for this algorithm.

Let s and t be two points on the boundary of a simple polygon, how to compute the Euclidean shortest path between s and t which visits a sequence of segments given in the simple polygon is the problem to be discussed, especially, the situation of the adjacent segments intersect is the focus of our study. In this paper, we first analyze the degeneration applying rubber-band algorithm to solve the problem. Then based on rubber-band algorithm, we present an improved algorithm which can solve the degeneration by the method of crossing over two segments to deal with intersection and in our algorithm the adjacent segments order can be changed when they intersect. Particularly, we have implemented the algorithm and have applied a large of test data to test it. The experiments demonstrate that our algorithm is correct and efficient, and it has the same time complexity as the rubber-band algorithm.

Bat Algorithm is a recently-developed method in the field of computational intelligence. In this paper is presented an improved version of a Bat Meta-heuristic Algorithm, (IBACH), for solving integer programming problems. The proposed algorithm uses chaotic behaviour to generate a candidate solution in behaviors similar to acoustic monophony. Numerical results show that the IBACH is able to obtain the optimal results in comparison to traditional methods (branch and bound), particle swarm optimization algorithm (PSO), standard Bat algorithm and other harmony search algorithms. However, the benefits of this proposed algorithm is in its ability to obtain the optimal solution within less computation, which save time in comparison with the branch and bound algorithm (exact solution method). Index Terms—Bat algorithm; meta-heuristics; optimization; chaos; integer programming.

The diamond search (DS) algorithm is one of the most efficient block matching motion estimation algorithms by far and has already been applied in MPEG2/4. Through our research, we found that there is still some redundancy in the algorithm. In this paper, an improved new difference based search (DBS) algorithm is proposed. Simulation results demonstrate that the new algorithm outperforms the well-known diamond search (DS) algorithm and four step-searches (4SS). It obtains almost the same Peak Signal to Noise Ratio (PSNR) while requires less computations than the DS algorithm and 4SS algorithm.

This paper proposes a new algorithm to find a representation of the set of all non-dominated points of the bi-objective integer network flow problem. The algorithm solves a sequence of e-constraint problems with a branch-and-bound algorithm to find a subset of non-dominated points that represents the set of all non-dominated points well in the sense of coverage or uniformity. At each iteration of the algorithm, one non-dominated point, determined by solving one e-constraint problem, is added to the representation until it is guaranteed that the representation has the desired quality. Computational experiments on different problem types show the efficacy of the algorithm.

In this work we propose a derivative-free trust-region algorithm for unconstrained optimization. The algorithm is based on the ideas of Powell (Comput Optim Appl 53:527–555, 2012). We turn his general ideas into a step-form algorithm that avoids unnecessary reductions of the trust-region radius. The results related to the global convergence of the algorithm are presented in detail. We prove that, under reasonable hypotheses, any accumulation point of the sequence generated by the algorithm is stationary.

k-means is one of the most widely used partition based clustering algorithm. But the initial centroids generated randomly by the k-means algorithm cause the algorithm to converge at the local optimum. So to make k-means algorithm globally optimum, the initial centroids must be selected carefully rather than randomly. Though many researchers have already been carried out for the enhancement of k-means algorithm, they have their own limitations. In this paper a new method to formulate the initial centroids is proposed which results in better clusters equally for uniform and non-uniform data sets.

K-Means algorithm is a one of the most famous unsupervised clustering algorithm. It has many disadvantages, such as sensitivity to the initial clustering centers and computes all the data points multiple times when facing the increasing data volume. In order to overcome the above limitations, this paper proposes to make use of density idea to find k cluster centers by adjusting the threshold. Finally, we design and implementation of the K-Means algorithm on the modern Graphic Processing Unit (GPU). The ratio of distance between classes to distance within classes and speedup are used as evaluation criteria. The experiments indicate that the proposed algorithm significantly improves the stability and efficiency of K-Means algorithm.

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.