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Discretization is the process of converting continuous values into discrete values. It is a preprocessing step of several machine learning and data mining algorithms and the quality of discretization may drastically affect the performance of these algorithms. In this study we propose a discretization algorithm, namely line fitting-based discretization (lFIT), based on the Ramer--Douglas--Peucker algorithm. It is a static, univariate, unsupervised, splitting-based, global, and incremental discretization method where intervals are determined based on the Ramer--Douglas--Peucker algorithm and the quality of partitioning is assessed based on the standard error of the estimate. To evaluate the performance of the proposed method, a set of experiments are conducted on ten benchmark datasets and the achieved results are compared to those obtained by eight state-of-the-art discretization methods. Experimental results show that lFIT achieves higher predictive accuracy and produces less number of inconsistency while it generates larger number of intervals. The obtained results are also validated through Friedman's test and Holm's post hoc test which revealed the fact that lFIT produces discretization schemes that statistically comply both with supervised and unsupervised discretization methods.

Discretization is the process of converting continuous values into discrete values. It is a preprocessing step of several machine learning and data mining algorithms and the quality of discretization may drastically affect the performance of these algorithms. In this study we propose a discretization algorithm, namely line fitting-based discretization (lFIT), based on the Ramer--Douglas--Peucker algorithm. It is a static, univariate, unsupervised, splitting-based, global, and incremental discretization method where intervals are determined based on the Ramer--Douglas--Peucker algorithm and the quality of partitioning is assessed based on the standard error of the estimate. To evaluate the performance of the proposed method, a set of experiments are conducted on ten benchmark datasets and the achieved results are compared to those obtained by eight state-of-the-art discretization methods. Experimental results show that lFIT achieves higher predictive accuracy and produces less number of inconsistency while it generates larger number of intervals. The obtained results are also validated through Friedman's test and Holm's post hoc test which revealed the fact that lFIT produces discretization schemes that statistically comply both with supervised and unsupervised discretization methods.

In this paper, we propose a new ECG compression method using the fractal technique. The proposed approaches utilize the fact that ECG signals are a fractal curve. This algorithm consists of three steps: First, the original ECG signals are processed and they are converted into a 2-D array. Second, the Douglas-Peucker algorithm (DP) is used to detect critical points (compression phase). Finally, we used the fractal interpolation and the Iterated Function System (IFS) to generate missing points (decompression phase). The proposed (suggested) methodology is tested for different records selected from PhysioNet Database. The obtained results showed that the proposed method has various compression ratios and converges to a high value. The average compression ratios are between 3.19 and 27.58, and also, with a relatively low percentage error (PRD), if we compare it to other methods. Results depict also that the ECG signal can adequately retain its detailed structure when the PSNR exceeds 40 dB.

Topology optimization has seen rapid developments in its field with algorithms getting better and faster all the time. These new algorithms help reduce the lead time from concept development to a finished product. Simulation and post-processing of geometry are one of the major developmental costs. Post-processing of this geometry also takes up a lot of time and is dependent on the quality of the geometry output from the solver to make the product ready for rapid prototyping or final production. The work done in this thesis deals with the post-processing of the results obtained from topology optimization algorithms which output the result as a 2D image. A suitable methodology is discussed where this image is processed and converted into a CAD geometry all while minimizing deviation in geometry, compliance and volume fraction. Further on, a validation of the designs is performed to measure the extracted geometry's deviation from the post-processed result. The workflow is coded using MATLAB and uses an image-based post-processing approach. The proposed workflow is tested on several numerical examples to assess the performance, limitations and numerical instabilities. The code written for the entire workflow is included as an appendix and can be downloaded from the website:https://github.com/M87K452b/postprocessing-topopt.

WSNs which are the major component in the IoT mainly use interconnected intelligent wireless sensors. These wireless sensors sense monitor and gather data from their surroundings and then deliver them to users or access connected IoT devices remotely. One of the main issues in WSNs is that sensor nodes are generally powered by batteries, but because of the rugged environments, it is difficult to add energy. The other one may cause an unbalanced energy consumption among sensor nodes due to the uneven distribution of sensors. For these reasons, the death of nodes by the energy exhausting and the performance of the network may rapidly decrease. Hence, an efficient algorithm study for prolonging the network lifetime of WSNs is one of important challenges. In this paper, a chain-based wireless sensor network model is proposed to improve network performance with balanced energy consumption via the solution of the long-distance communication problem. The proposed algorithm is consisted of three phases: Segmentation, Chain Formation, and Data Collection. In segmentation phase, an optimal distance tolerance is determined, and then the network field is divided into small sub-regions according to its value. The chain formation is started from the sub-region far away from the sink, and then extended, and sensed data are collected along a chain and transmitted to a sink. Simulations have been performed to compare with PEGASIS and Enhanced PEGASIS using an OMNET++ simulator. The simulation results from this study showed that the proposed algorithm prolonged the network lifetime via the achievement of the balanced energy consumption compared to PEGASIS and Enhanced PEGASIS. The proposed algorithm can be used in any applications to improve network performance of WSNs.

Spatial visualization has always been a primary part of information visualization and analysis, especially in the era of big data. The map, the most fundamental components of spatial visualization, is a kind of simple, intuitive and popular way to show the visualization of geographic information. The traditional map is not convenient to overlay complex elements due to its own complex filled color and the actual geographical boundaries. We aim to cut offi dusty foliage of the maps, and deliver the main structure of the map visualization result. We proposes RectMap, a boundary-reserved map deformation approach for visualizing geographical map, which can maintain the mind map of original map. The proposed approach integrate traditional Douglas-Peucker algorithm and our Gridding algorithm. The Douglas-Peucker algorithm generates a simplified map, and the Gridding algorithm optimizes the initial simplified map. Case study and user study are further conducted to demonstrate the effiectiveness and usefulness of the new-style map.

The goal of this study is to assess the efficiency of Artificial Bee Colony (ABC) algorithm in finding the optimal solution of curve fitting problem specifically for medical images. Data of Computed Tomography (CT) images from two different patients were collected. The procedure of curve fitting for medical images include conversion of Digital and Communications in Medicine (DICOM) images to binary images, boundary and corner point detection, parameterization, and curve reconstruction by using ABC algorithm. Then, Sum Square Error (SSE) was used to calculate the distance of the fitted Cubic Bezier curve with the boundary of the original images. Based on the calculation and parameter tuning that had been done, the smallest error of both skulls is57.5754and28.8628, respectively. The finding of this study illustrated that the proposed method had efficiently produced fitted Bezier curve that resemble the original medical images. In addition, the used of Douglas Peucker algorithm helps to improve the performance of the proposed method since computational time can be minimized. This study had shown that the proposed method can be used as an alternative method in order to reconstruct or redesigned the medical images since it produces a small error. For future work, we are planning to explore and applied the ABC algorithm to reconstruct the missing part of the skull since it can reduce the time taken to produce the skull implant as well as reducing the cost of producing it.

Zero watermarking does not alter the original information contained in vector map data and provides perfect imperceptibility. The use of zero watermarking for data copyright protection has become a significant trend in digital watermarking research. However, zero watermarking encounters tremendous obstacles to its development and application because of its requirement to store copyright information with a third party and its difficulty in confirming copyright ownership. Aiming at the shortcomings of the existing zero watermarking technology, this paper proposes a new zero watermarking construction method based on the angular features of vector data that store the zero watermarking and copyright information on the blockchain after an XOR operation. When the watermark is being extracted, the copyright information can be extracted with the XOR operation to obtain the information stored on the blockchain. Experimental results show that the combination of zero watermarking and blockchain proposed in this paper gives full play to the advantages of the two technologies and protects the copyright of data in a lossless fashion. Compared with the traditional zero watermarking algorithms, the proposed zero watermarking algorithm exhibits stronger robustness. Moreover, the proposed data copyright protection framework with a combination of zero watermarking and blockchain can also be applied to other data types, such as images, audio, video, and remote sensing images.

We propose an algorithm for constructing orthogonal n-ary operations which is called a block composition algorithm here. Input data of the algorithm are two series of different arity operations being distributed by blocks. The algorithm consists of two parts: composition algorithm for constructing n-ary operations with orthogonal retracts from given blocks of operations and block-wise recursive algorithm for constructing orthogonal n-ary operations from obtained operations. Obtained results are illustrated by examples of orthogonal n-ary operations which are constructible by block-wise recursive algorithm and non-constructible by the well-known trivial recursive algorithm.

Clustering is an important technique in data mining. The innovative algorithm proposed in this paper obtains clusters by first identifying boundary points as opposed to existing methods that calculate core cluster points before expanding to the boundary points. To achieve this, an affine space-based boundary detection algorithm was employed to divide data points into cluster boundary and internal points. A connection matrix was then formed by establishing neighbor relationships between internal and boundary points to perform clustering. Our clustering algorithm with an affine space-based boundary detection algorithm accurately detected clusters in datasets with different densities, shapes, and sizes. The algorithm excelled at dealing with high-dimensional datasets.

There are significant challenges related to estimating the source term of the atmospheric release. Urged on by robots in performing emergency responding tasks, a fast and accurate algorithm for this inversion problem is indispensable. Sometimes the NM simplex algorithm is efficient in the optimization problem, but sometimes the quality of convergence is unacceptable as a numerical breakdown, even for smooth and well-behaved functions. In contrast, full convergence might be seen in parallel genetic algorithms with a comparative slower convergence. In this paper we combine the PGA and the NM simplex algorithm by initializing simplex from the final individual of PGA results and obtaining the best vertex through simplex algorithm thereafter. A numerical simulation of the proposed algorithm shows noteworthy improvement of efficiency and robustness, compared with the PGA or the NM algorithm only. HighlightsWe shortened the computation time running on an embedded system to less than half a second that meets the need to perform emergency tasks.The algorithm is tolerant of the measurement errors.We combined the PGA and NM algorithm which was not found in other papers yet.The PGA-NM hybrid algorithm could be developed and applied to other optimization problems in a similar way.

This study focuses on the improved initialization of initial centroids instead of random selection for the K-means algorithm. The random selection of initial seeds is a major drawback of the original Kmeans algorithm because it leads to less reliable result of clustering the data. The modified approach of the k-means algorithm integrates the computation of the weighted mean to improve the seeds initialization. This paper shows the comparison of K-Means and Modified K-Means algorithm, using the first simple dataset of four objects and the dataset for service vehicles. The two simple applications proved that the Modified K- Means of selecting initial centroids is more reliable than K-Means Algorithm. Clustering is better achieved in the modified k-means algorithm. Article DOI: http://dx.doi.org/10.20319/mijst.2016.22.4864 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

The original exact inference algorithm of the GP model runs very slow.We developed a recursive inference algorithm as an improvement.The new algorithm can obtain the same result in a shorter time.It works well for the real-time online prediction problems. Gaussian Process is a theoretically rigorous model for prediction problems. One of the deficiencies of this model is that its original exact inference algorithm is computationally intractable. Therefore, its applications are limited in the field of real-time online predictions. In this paper, a recursive prediction algorithm based on the Gaussian Process model is proposed. In recursive algorithms, the computational time of the next step can be greatly reduced by utilizing the intermediate results of the current step. The proposed recursive algorithm accelerates the prediction and avoids the loss of accuracy at the same time. Experiments are done on an ultra-short term electric load data set and the results are demonstrated to show the accuracy and efficiency of the new algorithm.

Objective: To improve the Edge Cover based Graph Coloring Algorithm (ECGCA) using independent set by incorporating parallel computing aspects in algorithm. Finding optimum time complexity is one of the main objectives of this paper. Methods/Statistical Analysis: This paper introduced some modification in ECGCA. Algorithm is implemented and tested using Java programming language. Java multithreading concept is used to achieve parallel computing in algorithm. DIMACS graph instances are used to test algorithm. Finding: Algorithm is tested on more than 75 DIMACS graph instances. To analyze the time complexity, execution time of algorithm in seconds is calculated by program. Algorithm is tested on different DIMACS graph instances. Test data is analyze in this paper and found that proposed algorithm executed in optimum time for large graphs. This paper also compared parallel algorithm and found that proposed parallel algorithm is less time complex than sequential algorithm. Most of the exact graph coloring algorithms are not suitable for large graph (more than 100 vertices) but proposed algorithm is tested on many large graphs and high execution success rate of algorithm is achieved. Application: This paper shows the experimental results of different type of application data. It means this algorithm can be used for maximum types of applications.

The traditional expectation maximization (EM) algorithm for the mixture model can explore the structural regularities of a network efficiently. But it always traps into local maxima. A deterministic annealing EM (DAEM) algorithm is put forward to solve this problem. However, it brings about the problem of convergence speed. A deterministic anti-annealing expectation maximization (DAAEM) algorithm not only prevents poor local optima, but also improves the convergence speed. Thus, the DAAEM algorithm is used to estimate parameters of the mixture model. This algorithm always sets its initial parameter β0 by experience, which maybe get trapped into meaningless results due to too small β0, or converge to local maxima more frequently due to too large β0. A parameter selection method for β0 is designed. In our method, the convergence rate of the DAAEM algorithm for mixture model is first derived from Jacobian matrix of the posterior probabilities. Then the theoretical lower bound of β0 is computed based on the convergence rate at meaningless points. In our experiments we select β0 by rounding up the lower bound to the nearest tenth. Experiments on real and synthetic networks demonstrate that the parameter selection method is valid, and the performance of the DAAEM algorithm beginning from the selected parameter is better than the EM and DAEM algorithms for mixture model. In addition, we find that the convergence rate of the DAAEM algorithm is affected by assortative mixing by degree of a network.

Given a 2.5D terrain and a query point p on or above it, we want to find the triangles of terrain that are visible from p. We present an approximation algorithm to solve this problem. We implement the algorithm and test it on real data sets. The experimental results show that our approximate solution is very close to the exact solution and compared to the other similar works, the computational cost of our algorithm is lower. We analyze the computational complexity of the algorithm. We consider the visibility testing problem where the goal is to test whether a given triangle of the terrain is visible or not with respect to p. We present an algorithm for this problem and show that the average running time of this algorithm is the same as the running time of the case where we want to test the visibility between two query points p and q. We also propose a randomized algorithm for providing an estimate of the portion of the visible region of a terrain for a query point. © 2016, Società Italiana di Fotogrammetria e Topografia (SIFET).

Block-matching motion estimation (BME) can efficiently reduce the temporal redundancy between the successive video sequences in video compression coding system. In this study, a fast BME algorithm using multilevel distortion search in Walsh–Hadamard domain is proposed to reduce the computational burden and speed up coding process. First, the proposed algorithm divides the block into several sub-blocks. Then, the Walsh–Hadamard transform is applied to these sub-blocks. Finally, the proposed algorithm calculates the partial block matching distortion by utilising a novel back diagonal search scheme which can quickly reject unnecessary candidate block in a multilevel manner. Experimental results show that the proposed algorithm effectively reduces the number of operations in block distortion calculation meanwhile maintains the best motion estimation matching quality. Compared with the full search, the proposed algorithm can reduce 87.19% computational complexity without any degradation of the peak signal to noise ratio. In addition, compared with the partial distortion search algorithm, successive elimination algorithm, multilevel successive elimination algorithm and the transform-domain successive elimination algorithm, the proposed algorithm can also save 68.27, 70.09, 37.81 and 37.44% computational complexity, respectively. Moreover, the proposed algorithm can also be easily incorporated into any block-based template search motion estimation algorithm.

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

The junction tree algorithm is currently the most popular algorithm for exact inference on Bayesian networks. To improve the time complexity of the junction tree algorithm, we need to find a triangulation with the optimal total table size. For this purpose, Ottosen and Vomlel have proposed a depth-first search (DFS) algorithm. They also introduced several techniques to improve the DFS algorithm, including dynamic clique maintenance and coalescing map pruning. Nevertheless, the efficiency and scalability of that algorithm leave much room for improvement. First, the dynamic clique maintenance allows to recompute some cliques. Second, in the worst case, the DFS algorithm explores the search space of all elimination orders, which has size n!, where n is the number of variables in the Bayesian network. To mitigate these problems, we propose an extended depth-first search (EDFS) algorithm. The new EDFS algorithm introduces the following two techniques as improvements to the DFS algorithm: (1) a new dynamic clique maintenance algorithm that computes only those cliques that contain a new edge, and (2) a new pruning rule, called pivot clique pruning. The new dynamic clique maintenance algorithm explores a smaller search space and runs faster than the Ottosen and Vomlel approach. This improvement can decrease the overhead cost of the DFS algorithm, and the pivot clique pruning reduces the size of the search space by a factor of O ( n 2 ) . Our empirical results show that our proposed algorithm finds an optimal triangulation markedly faster than the state-of-the-art algorithm does. A state-of-the-art algorithm for optimal triangulation is proposed.Evaluated the degree of impact for employing the total table size as optimality criterion.A novel pruning rule for triangulation algorithm is proposed.A fast dynamic clique maintenance algorithm is proposed.

In this paper the hybrid and modified versions of the PSO algorithm applied to improvement of the search characteristics of the classical PSO algorithm in the development problem of the SVM classifier have been offered and investigated. A herewith two hybrid versions of the PSO algorithm assume the use of the classical Grid Search (GS) algorithm and the Design of Experiment (DOE) algorithm accordingly, and the modified version of the PSO algorithm realizes the simultaneous search of the kernel function type, the parameters values of the kernel function, and also the regularization parameter value. Besides, the questions of applicability of the k nearest neighbors (kNN) algorithm in the development problem of the SVM classifier have been considered.

A semi-real-time pattern-matching algorithm consisting of an offline and online stage is proposed. The approach of the proposed algorithm is to perform a significant amount of the calculation required by pattern matching in the offline stage. This necessitates only a small amount of calculation in the online process to reject a great number of mismatched positions. The proposed algorithm first uses triangle inequality and orthogonal decomposition to derive the lower bounds of the distances between the pattern and the candidate windows of the base image. Then, mismatched candidate windows are rejected if their lower bounds exceed an adaptive threshold. The proposed method accelerates the online processing effectively while yielding the identical result as a full search algorithm. The proposed algorithm was compared with other state-of-the-art algorithms and the result confirms that the proposed algorithm has a distinct speed advantage over the other algorithms for online processing.

In finite element analysis, among the possible frameworks for stress-point integration algorithms in computational elastoplasticity, the implicit Closest Point Projection (CPP) algorithm is probably the most used one. The idea behind this algorithm is that all necessary variables, including the flow and hardening directions, are iteratively updated and enforced at the final solution. Therefore the algorithm is fully implicit and the final solution is independent of previous iterations. However, there are several possible implementations of the ideas behind the CPP algorithm. Even though asymptotic quadratic convergence may be obtained in all implementations if the algorithm is properly linearized, these different possibilities result in a different number of local iterations and in a different computational effort. Small stress-integration algorithms are frequently the only iterative core of large strain elastoplastic formulations. At the same time they are responsible for a large share of the overall computational time in finite element simulations and key in the overall robustness. In this work we present a new algorithm based on the ideas of the Closest Point Projection algorithm for anisotropic elastoplasticity with mixed hardening. We also compare our proposal with other possible implementations of the CPP algorithm for the same problem, namely the General CPP implementation and the Governing Parameter Method. We show that our proposal is in general more efficient. HighlightsWe propose an integration algorithm for anisotropic elastoplasticity.Our algorithm is fully implicit, based on the backward-Euler integration rule.We compare our proposal with the GCPP algorithm and the GPM.Our proposal needs significantly less iterations than GCPP and GPM.We show some demonstrative examples in small and large strains.

We introduced a new harmony memory initialization method.We introduced a new pitch adjustment method based on DE/best/1 mutation strategy.We comprehensively studied the parameter setting of our algorithm.We compared our algorithm with the state of the art variants of HS algorithm.We compared our algorithm with the state of the art variants of DE algorithm. The performance of the Harmony Search (HS) algorithm is highly dependent on the parameter settings and the initialization of the Harmony Memory (HM). To address these issues, this paper presents a new variant of the HS algorithm, which is called the DH/best algorithm, for the optimization of globally continuous problems. The proposed DH/best algorithm introduces a new improvisation method that differs from the conventional HS in two respects. First, the random initialization of the HM is replaced with a new method that effectively initializes the harmonies and reduces randomness. Second, the conventional pitch adjustment method is replaced by a new pitch adjustment method that is inspired by a Differential Evolution (DE) mutation strategy known as DE/best/1. Two sets of experiments are performed to evaluate the proposed algorithm. In the first experiment, the DH/best algorithm is compared with other variants of HS based on 12 optimization functions. In the second experiment, the complete CEC2014 problem set is used to compare the performance of the DH/best algorithm with six well-known optimization algorithms from different families. The experimental results demonstrate the superiority of the proposed algorithm in convergence, precision, and robustness.

This paper proposes a computationally efficient optimization algorithm for segmenting colour satellite images.CS algorithm incorporating Mantegna's and McCulloch's method for modeling levy flight is presented.PSO, DPSO, ABC and CS algorithms are compared with the proposed algorithm.All these optimization algorithms are exploited using three different objective functions.Performance assessment metrics demonstrated the improvement in the efficiency of the proposed algorithm. Satellite image segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. Several bio-inspired algorithms were developed to generate optimum threshold values for segmenting such images efficiently. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding. In this paper, we propose a computationally efficient image segmentation algorithm, called CSMcCulloch, incorporating McCulloch's method for l e ? v y flight generation in Cuckoo Search (CS) algorithm. We have also investigated the impact of Mantegna's method for l e ? v y flight generation in CS algorithm (CSMantegna) by comparing it with the conventional CS algorithm which uses the simplified version of the same. CSMantegna algorithm resulted in improved segmentation quality with an expense of computational time. The performance of the proposed CSMcCulloch algorithm is compared with other bio-inspired algorithms such as Particle Swarm Optimization (PSO) algorithm, Darwinian Particle Swarm Optimization (DPSO) algorithm, Artificial Bee Colony (ABC) algorithm, Cuckoo Search (CS) algorithm and CSMantegna algorithm using Otsu's method, Kapur entropy and Tsallis entropy as objective functions. Experimental results were validated by measuring PSNR, MSE, FSIM and CPU running time for all the cases investigated. The proposed CSMcCulloch algorithm evolved to be most promising, and computationally efficient for segmenting satellite images. Convergence rate analysis also reveals that the proposed algorithm outperforms others in attaining stable global optimum thresholds. The experiments results encourages related researches in computer vision, remote sensing and image processing applications.

The background, development, performance assessment, and analysis of a novel pattern recognition algorithm that is applicable to any set of binary images are discussed. The efficacy of the algorithm when applied to the problem of fingerprint recognition is quantified. The conclusion was that the algorithm is relatively poor as a fingerprint identification algorithm, averaging an equal error rate of approximately 19% as calculated by the rules specified in the Year 2000 Fingerprint Verification Competition. The positive attributes of the algorithm were its ultra-fast matching times, orientation independence, lack of rejection events, relative insensitivity to resolution difference, and one-way transformations. The mechanism of algorithm operation as applied to fingerprints was investigated using integral geometry. This investigation showed that the algorithm was an indirect measure of ridge width, which explained the algorithm’s relatively poor performance. Another set of experiments suggests that the algorithm may be well-suited to other pattern recognition problems, specifically cloud and precipitation particle recognition and camouflage recognition. In summary, the research extends the field of pattern recognition by developing, assessing the performance, and determining the mechanism of operation of a novel pattern recognition algorithm that is applicable to any set of binary images.

Strokes are considered an elementary unit of road networks and have been widely used in their analysis and application. However, most conventional stroke generation methods are based solely on a fixed angle threshold, which ignores road networks&rsquo, geometric and structural properties. To remedy this, this paper proposes an algorithm for generating strokes that takes into account these additional geometric and structural road network properties and that reduces the impact of stroke generation on road network quality. To this end, we introduce a model of feature-based information entropy and then utilize this model to calculate road networks&rsquo, information volume and both the elemental and neighborhood level. To make our experimental results more objective, we use the Douglas-Peucker algorithm to simplify the information change curve and to obtain the optimal angle threshold range for generating strokes for different road network structures. Finally, we apply this model to three different road networks, and the optimal threshold ranges are 54&deg, &ndash, 63&deg, (Chicago), 61&deg, (Moscow), 45&deg, 48&deg, (Monaco). And taking Monaco as an example, this paper conducts stroke selection experiments. The results demonstrate that our proposed algorithm has better connectivity and wider coverage than those based on a common angle threshold (60&deg, ).

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative samples from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP samples for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF), k-nearest neighbour algorithm (k-NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative samples from EEG signals compressing up to over 47% sample points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy (OCA). The proposed method outperforms the most recently reported methods in terms of OCA in the same epileptic EEG database.

One of the severe problems in a fingerprint-based system is retaining the fingerprint images. In this paper, we propose a method to minimize the fingerprint images size and retain the reference points. The method is divided into three parts, the first part is about digital image preprocessing that allows us to eliminate the noise, improve the image, convert it into a binary image, detect the skeleton and locate the reference point. The second part concerns the detection of critical points by the Douglas-Peucker method. The final part presents the methodology for the fingerprint curves reconstruction using the fractal interpolation curves. The experimental result shows the accuracy of this reconstruction method. The relative error (ER) is between 2.007% and 5.627% and the mean squared error (MSE) is between 0.126 and 0.009 at a small iterations number. On the other hand, for a greater number of iterations, the ER is between 0.415% and 1.64% and MSE is between 0.000124 and 0.0167. This clearly indicates that the interpolated curves and the original curves are virtually identical and exceedingly close.

Tree-structured compressive sensing (CS) shows that it is possible to recover tree-sparse signals using fewer measurements compared with conventional CS. However, performance guarantees rely heavily on the premise that an exact tree projection (ETP) algorithm is employed. Nevertheless, for a given sparsity, the condensing sort and select algorithm in the model-based compressive sampling matching pursuit (CoSaMP) algorithm can only yield an approximate tree projection. Therefore, in order to ensure reconstruction precision, the authors propose the combination of an ETP algorithm with the CoSaMP algorithm. Further, the hierarchical wavelet connected tree is also integrated into the ETP-CoSaMP algorithm to offset the high computational complexity of the ETP algorithm. Experimental results indicate that the hierarchical ETP based on CoSaMP algorithm (HETP-CoSaMP algorithm) enhances reconstruction accuracy while retaining reconstruction time that is comparable with that of the model-based CoSaMP algorithm.

There is ineffective classification problem in application of K-means clustering algorithm in massive data cluster analysis. This paper presents a K-means algorithm based on generalization threshold rough set optimization weight. Firstly, utilize attribute order described method, using the average distance calculation with Laplace method to optimize the generalization threshold of fuzzy rough set , then the Euclidean distance metric is used in the calculation of the similarity of K-means algorithm, introducing the variation coefficient into the cluster analysis, clustering the Euclidean distance weighted K-means algorithm totally based on data, finally, combine the rough set algorithm based on the generalization threshold optimization and K-means clustering algorithm, applied to medical and health data classification. The K-means algorithm based on generalization threshold rough set optimization weight presented by this paper has a better effect on medical and health data classification.

Hierarchical clustering (HC) algorithm can obtain good clustering results, but it needs large storage and computational complexity for large image processing. Anew color image segmentation algorithm based on mean shift and hierarchical clustering algorithm named MSHC is presented in this paper. MSHC algorithm preprocesses an input image by MS algorithm to form segmented regions that preserve the desirable discontinuity characteristics of image. The number of segmented regions, instead of the number of image pixels, is considered as the input data scale of HC algorithm. The proximity between each cluster is calculated to form the proximity matrix, and then ward algorithm is employed to obtain the final segmentation results. MSHC algorithm is employed on color image and medical image segmentation.

This paper studies the convergence of the hierarchical identification algorithm for bilinear-in-parameter systems. By replacing the unknown variables in the information vector with their estimates, a hierarchical least squares algorithm is derived based on the model decomposition. The proposed algorithm has higher computational efficiency than the over-parameterization model-based recursive least squares algorithm. The performance analysis shows that the parameter estimation errors converge to zero under persistent excitation conditions. The effectiveness of the proposed algorithm is verified by simulation examples.

Recently we developed a Bayesian-FBP (Filtered Backprojection) algorithm for CT image reconstruction. This algorithm is also referred to as the FBP-MAP (FBP Maximum a Posteriori) algorithm. This non-iterative Bayesian algorithm has been applied to real-time MRI, in which the k-space is under-sampled. This current paper investigates the possibility to extend this Bayesian-FBP algorithm by introducing more controlling parameters. Thus, our original Bayesian-FBP algorithm became a special case of the extended Bayesian-FBP algorithm. A cardiac patient data set is used in this paper to evaluate the extended Bayesian-FBP algorithm, and the result from a well-establish iterative algorithm with ${{\rm L}_1}$ -norms is used as the gold standard. If the parameters are selected properly, the extended Bayesian-FBP algorithm can outperform the original Bayesian-FBP algorithm.

We construct an algorithm for the numerical solution of nonlinear third-order boundary value problems. This algorithm is based on eight-point binary subdivision scheme. Proposed algorithm is stable and convergent and gives more accurate results than fourth-degree B-spline algorithm.

This paper proposes a hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. In this study, the proposed algorithm combines the reproduction step from weed algorithm and genetic algorithm. The reproduction step is applied to clone each bat population by fitness values and the genetic algorithm is applied in order to expand the population. The algorithm is evaluated on eighteen benchmark problems. The computational results of the proposed algorithm are compared with the methods in the literature which are self-adaptive differential evolution (DE), traditional DE algorithm, intersection mutation differential evolution (IMDE) algorithm, and the JDE self-adaptive algorithm. Findings show that the algorithm produces several solutions obtained by the previously published methods especially for the continuous unimodal function, the quartic function, the multimodal function and the discontinuous step function. In addition, the finding shows that the proposed algorithm can produce optimal solutions efficiently on benchmark instances within short computational time.

Facilitated by recent establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship trajectories are becoming increasingly available and the size of recorded trajectories is getting larger. Large sets of trajectories create problems of storing, transmitting and processing data. Using appropriate methods, an accurate representation of the original trajectories can be obtained by compressing redundant information, while maintaining the main characteristic elements. In this paper, a new scheme and the implementation of the Douglas-Peucker (DP) algorithm are presented, which can simplify AIS trajectories by extracting characteristic points. As for the simplification threshold, the solo parameter of the DP algorithm, a new AIS-based minimum ship domain evaluation method is proposed and acts as criteria for simplification threshold determination. Finally, a validation is made to examine the effectiveness of the DP simplification algorithm and the rationality of the simplification threshold. The result indicates that the DP algorithm can simplify AIS trajectories effectively; the simplification threshold is scientific and reasonable.

Fuzzy rule-based classification systems have been used extensively in data mining. This paper proposes a fuzzy rule- based classification algorithm based on a quantum ant optimization algorithm. A method of generating the hierarchical rules with different granularity hybridization is used to generate the initial rule set. This method can obtain an original rule set with a smaller number of rules. The modified quantum ant optimization algorithm is used to generate the optimal individual. Compared to other similar algorithms, the algorithm proposed in this paper demonstrates higher classification accuracy and a higher convergence rate. The algorithm is proved to be convergent on theory. Some experiments have been conducted on the algorithm, and the results proved that the algorithm is feasible.

most popular lines clipping algorithms are Cohen- Sutherland and Liang-Barsky line clipping algorithm. These algorithms are complex and the steps of calculation are very high. This paper proposes a simple new line clipping algorithm for 2D space which uses the parametric equation of the line. This algorithm further easily extended to the polygon clipping by considering the edge of the polygon as a line. The proposed algorithm is numerically tested for a numbers of random line segments and the results show the simplicity and less complex behavior of the algorithm. Keywordsclipping algorithm, Cohen-Sutherland line clipping algorithm, Liang-Barsky line clipping algorithm. 2D space

This paper illustrates an enhanced algorithm based on one of the swarm intelligence techniques for constructing the Phylogenetic tree (PT), which is used to study the relationship between species. The main scheme is to formulate a PT, an NP- complete problem through an evolutionary algorithm called Artificial Bee Colony (ABC). The tradeoff between the accuracy and the computational time taken for constructing the tree makes way for new variants of algorithms. A new variant of ABC algorithm is proposed to promote the convergence rate of general ABC algorithm through recommending a new formula for searching solution. In addition, a searching step has been included so that it constructs the tree faster with a nearly optimal solution. Experimental results are compared with the ABC algorithm, Genetic Algorithm and the state-of-the-art techniques like unweighted pair group method using arithmetic mean, Neighbour-joining and Relaxed Neighbor Joining. For results discussion, we used one of the standard dataset Treesilla. The results show that the Enhanced ABC (EABC) algorithm converges faster than others. The claim is supported by a distance metric called the Robinson-Foulds distance that finds the dissimilarity of the PT, constructed by different algorithms.

In risk assessment and predictive policing, biased data can yield biased results.

When using the traditional Douglas&ndash, Peucker (D&ndash, P) algorithm to simplify linear objects, it is easy to generate results containing self-intersecting errors, thus affecting the application of the D&ndash, P algorithm. To solve the problem of self-intersection, a new vector line simplification algorithm based on the D&ndash, P algorithm, monotonic chains and dichotomy, is proposed in this paper. First, the traditional D&ndash, P algorithm is used to simplify the original lines, and then the simplified lines are divided into several monotonic chains. Second, the dichotomy is used to search the intersection positions of monotonic chains effectively, and intersecting monotonic chains are processed, thus solving the self-intersection problems. Two groups of experimental data are selected based on large data sets. Results demonstrate that the proposed experimental method has advantages in algorithmic efficiency and accuracy when compared to the D&ndash, P algorithm and the Star-shaped algorithm.

A distributed algorithm is proposed which routes packets to minimise the expected latency in arbitrarily connected mesh networks, where each link has a set transmission rate. It is proven that the proposed algorithm converges to the global minimum. Initial simulations demonstrate the effectiveness of the proposed algorithm relative to current methods based on Dijkstra's algorithm.

Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we present an efficient polygonal data visualization method by organizing the simplification, tessellation and rendering operations into a single mesh generalization process. First, based on the sweep line method, we propose a topology embedded trapezoidal mesh data structure to organize the tessellated polygons. Second, we introduce horizontal and vertical generalization operations to simplify the trapezoidal meshes. Finally, we define a heuristic testing algorithm to efficiently preserve the topological consistency. The method is tested using three OpenStreetMap datasets and compared with the Douglas Peucker algorithm and the Binary Line Generalization tree-based method. The results show that the proposed method improves the rendering efficiency by a factor of six. Efficiency-sensitive mapping applications such as emergency mapping could benefit from this method, which would significantly improve their visualization performances.