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About 4,839 papers for “Merge algorithm”

Flocking for multi agent system : split and merge algorithm

Do, Bach Viet · DR-NTU (Nanyang Technological University) · 2010

The final year project is the extension of existing project with code name FAME.
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\nFAME is C# language game engine software application based on the open source
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\nrendering engine Orge3D developed to study and simulate steering behaviors of
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\nmulti autonomous agents.
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\nBefore this FYP, several steering behaviors has been studied and implemented
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\nincluding arrival, goal seeking, wandering and flocking, the most interesting
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\nbehavior. Flocking algorithm attempts to simulate the beautiful natural phenomenon
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\nof flocks of thousand birds, schools of countless fish or great herds of animals (In
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\ncomputer science, ―this gathering of mass individuals‖ are commonly termed
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\nflocking).Nonetheless, the problem of obstacle avoidance for flock of agents has not
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\nbeen studied before. In this project, the problem of a flock obstacle avoidance is
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\nanalyzed. Inspired by the nature of flocking avoidance behavior, the obstacle
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\navoidance should comprise of ways to split the whole flock to steer around the
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\nobstacle and merge back into the old flock afterward. This report is going to explain
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\nin the details algorithm developed during 8 months and show that it is able to
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\nproduce realistic animation and deliver strong performance.
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\nIn addition, in FAME, a steering agent is a combination of several steering behaviors,
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\ni.e, arrival behavior, goal seeking behavior, wandering behavior and flocking
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\nbehavior. Adding one more obstacle avoidance behavior increases chances to cause
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\nconflicts and the optimization of steering behaviors has also not been thoroughly
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\ninvestigated before. Thus, the problem of steering behaviors optimization and
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\nsolution are also discussed in this report.

Cited by 0

Parallel implementation the algorithm digital images segmentation split & merge.”

Andrea Del Pilar Reyes Mendoza · Universidad Industrial de Santander · 2011

En el Grupo de Investigación en Ingeniería Biomédica (GIIB), se han realizado trabajos de investigación con estudiantes de pregrado y posgrado en el campo del Procesamiento de Imágenes Digitales en la detección del Cáncer Cervical, sin embargo, se han presentado inconvenientes en la etapa de segmentación, ya que las imágenes a procesar son de gran tamaño y calidad, por lo que el tiempo empleado por los algoritmos utilizados para la segmentación de imágenes es muy elevado, y se tiene la dificultad para determinar la ubicación de la frontera final debido a que los recursos computacionales son insuficientes para el procesamiento requerido. Como alternativa de solución se propuso el uso de la Computación de Alto Rendimiento por medio de la arquitectura de computador paralelo Cluster, por ser una tecnología de bajo precio, acorde a los recursos económicos disponibles, y de esta forma poder disminuir la carga computacional y el tiempo de procesamiento para segmentar una imagen Cervico-Uterina. En la presente investigación se ha desarrollado un algoritmo paralelo para segmentar imágenes, basado en el método de regiones Split 8 Merge. El diseño del algoritmo se llevó a cabo siguiendo la metodología de lan Foster. Se implementaron tres prototipos del algoritmo con el fin de analizar la mejor forma de etiquetar las regiones, la eficiencia en el esquema de comunicaciones y el balanceo de carga al momento de agrupar y asignar tareas a los procesadores. Para determinar la homogeneidad de la región al momento de evaluar el criterio de homogeneidad se realizó mediante pruebas estadísticas. Por último se analizaron los resultados de las pruebas en cuanto al rendimiento y la eficiencia, obteniendo datos satisfactorios.

Cited by 0Open access

Stable Trajectory Clustering: An Efficient Split and Merge Algorithm

A. Rahmani, Mansoor Davoodi, Justin M. Calabrese · ArXiv.org · 2025

Clustering algorithms fundamentally group data points by characteristics to identify patterns. Over the past two decades, researchers have extended these methods to analyze trajectories of humans, animals, and vehicles, studying their behavior and movement across applications. \noindent This paper presents whole-trajectory clustering and sub-trajectory clustering algorithms based on DBSCAN line segment clustering, which encompasses two key events: split and merge of line segments. The events are utilized to capture object movement history based on the average Euclidean distance between line segments. In this framework, whole-trajectory clustering considers entire entities' trajectories, whereas sub-trajectory clustering employs a sliding window model to identify local similarity patterns. Many existing trajectory clustering algorithms respond to temporary anomalies in data by splitting trajectories, which often obscures otherwise consistent clustering patterns and leads to less reliable insights. To address this, we introduce the stable trajectory clustering algorithm, which leverages the mean absolute deviation concept to demonstrate that selective omission of transient deviations not only preserves the integrity of clusters but also improves their stability and interpretability. We evaluate all proposed algorithms on real trajectory datasets to illustrate their effectiveness and sensitivity to parameter variations.

Cited by 0Open access

A New Split and Merge Algorithm with Topological Maps

Luc Brun, Jean-Philippe Domenger · 1996

Usually, segmentation algorithms implementing the split and merge operations are restricted to a split stage followed by a merge stage. In this paper, we present a new split and merge algorithm combining alternatively split and merge operations at each recursive step. This algorithm is based on a data structure encoding topological maps in a discrete plane. The topogical maps of an image are defined by embeding the image and its boundaries in Khalimsky space. This data structure allows us to design efficient segmentation algorithm which are almost impossible to implement with usual data structure.

Cited by 0

Divide well to merge better: A novel clustering algorithm

Atiq Ur Rehman, Samir Brahim Belhaouari · Pattern Recognition · 2021

In this paper, a novel non-parametric clustering algorithm which is based on the concept of divide-and-merge is proposed. The proposed algorithm is based on two primary phases, after data cleaning: (i) the Division phase and (ii) the Merging phase. In the initial phase of division, the data is divided into an optimized number of small sub-clusters utilizing all the dimensions of the data. In the second phase of merging, the small sub-clusters obtained as a result of division are merged according to an advanced statistical metric to form the actual clusters in the data. The proposed algorithm has the following merits: (i) ability to discover both convex and non-convex shaped clusters, (ii) ability to discover clusters different in densities, (iii) ability to detect and remove outliers/noise in the data (iv) easily tunable or fixed hyperparameters (v) and its usability for high dimensional data. The proposed algorithm is extensively tested on 20 benchmark datasets including both, the synthetic and the real datasets and is found better/competing to the existing state-of-the-art parametric and non-parametric clustering algorithms.

Cited by 44Open access

Research on Models Merge Algorithm Based on Delaunay Triangulation

Cheng Peng · 2004

Models merging is widely used in many domains such as geography modeling, 3D design of highway and railway route and so on, so it is essential to design an efficient models merging algorithm. The objective of this paper is to research on models merging algorithm based on Delaunay triangulation. A method of obtaining model merged intersection lines quickly and an algorithm of searching triangles on the inside of the intersection lines fast are proposed. For the purpose of implementing Delaunay triangulation models mergence, two main steps, such as insert the merged intersection lines into the Initial Delaunay Triangulation (IDT), and cut the triangles inside of the intersection lines in IDT out, are adopted. The processes of models merging are expatiated detailedly. The proposed algorithms are implemented by using VC++ programming language and tested by some experimental data. The experimental result validated the correctness and feasibility about the proposed algorithms.

Cited by 0

Merge Non-Dominated Sorting Algorithm for Many-Objective Optimization

Moreno, Javier, Rodriguez, Daniel, Nebro, Antonio et al. · arXiv (Cornell University) · 2018

Many Pareto-based multi-objective evolutionary algorithms require to rank the solutions of the population in each iteration according to the dominance principle, what can become a costly operation particularly in the case of dealing with many-objective optimization problems. In this paper, we present a new efficient algorithm for computing the non-dominated sorting procedure, called Merge Non-Dominated Sorting (MNDS), which has a best computational complexity of $Θ(NlogN)$ and a worst computational complexity of $Θ(MN^2)$. Our approach is based on the computation of the dominance set of each solution by taking advantage of the characteristics of the merge sort algorithm. We compare the MNDS against four well-known techniques that can be considered as the state-of-the-art. The results indicate that the MNDS algorithm outperforms the other techniques in terms of number of comparisons as well as the total running time.

Cited by 0Open access

Coordinate Descent K-means Algorithm Based on Split-Merge

Fuheng Qu, Yuhang Shi, Yong Yang et al. · Computers, materials & continua/Computers, materials & continua (Print) · 2024

The Coordinate Descent Method for K-means (CDKM) is an improved algorithm of K-means. It identifies better locally optimal solutions than the original K-means algorithm. That is, it achieves solutions that yield smaller objec... | Find, read and cite all the research you need on Tech Science Press

Cited by 0Open access

A simple shuffle-based stable in-place merge algorithm

Mehmet Emin Dalkılıç, Elif Acar, Gorkem Tokatli · Procedia Computer Science · 2011

Sorting is one of the most fundamental problems in computer science. Divide-and-conquer based sorting algorithms use successive merging to combine sorted arrays; therefore performance of merging is critical for these types of applications. The classic merge algorithm uses an extra O(m+n) memory for combining arrays of size m and n. Some improvements on merge algorithms include in-place methods which reduce or eliminate additional memory space, thus getting more practical value. We present a new in-place, stable and shuffle-based merge algorithm which is simple to understand and program. The algorithm starts applying perfect shuffle on two sorted arrays. Then, using the knowledge that odd and evenindexed numbers are sorted among themselves, comparisons are made and then misplaced elements are relocated by applying successive inverse perfect shuffle and swap operations on blocks. We have implemented and compared the new algorithm with the classical merge algorithm and the shuffle-based algorithm of Ellis and Markov [Comp. J. 1 (2000)]. Through experiments we have observed that the new algorithm exhibits linear run time behaviour and has dramatic performance improvement compared to the Ellis and Markov’s algorithm [1].

Cited by 11Open access

Even Merge Algorithm and Its Application in Parallel Sorting Schemes

Manoj Kumar, Daniel, S. Hirschberg · 2013

Abstract-An algorithm is presented to merge two subfiles of size n/2 each, stored in the left and the right halves of a linearly connected processor array, in 3n /2 route steps and log n compare-exchange steps. This algorithm is extended to merge two horizontally adjacent subfiles of size m X n/2 each, stored in an m X n mesh-connected processor array in row-major order, in m + 2n route steps and log mn compare-exchange steps. These algorithms are faster than their counterparts proposed so far. Next, an algorithm is presented to merge two vertically aligned subfiles, stored in a mesh-connected processor array in row-major order. Finally, a sorting scheme is proposed that requires 11 n route steps and 2 log2 n compare-exchange steps to sort n2 elements stored in an n X n mesh-connected processor array. The previous best sorting algorithm requires 14 n route steps for practical values of n, 4 < n < 512 (i.e., mesh-connected processor arrays containing 16 to 262 144 processors). The merge algorithms for the mesh-connected processor array use wrap around connections. These connections can be avoided at the expense of some extra hardware. Index Terms-Linearly connected processor arrays, mesh-connected processor arrays, odd-even merge algorithm, SIMD machines.

Cited by 0

Comparison Of Merge Sort with A New Trimerge Sorting Algorithm

Jannatun Nayeem, Md. Abu Salek · Daffodil International University Journal of Science and Technology · 2010

The sorting problem is one of the most fundamental problems in computer science. This paper is concerned with a new Tri-merge sorting algorithm. This is a modification of Merge sort. It is competitive with the fastest shorting algorithms, especially when the number of elements to be sorted is too large. Compared with the preceding Merge sort, Tri-merge sort is more robust. It is not only faster on random inputs, but also avoids extreme comparisons. The empirical results show that Tri-merge sort is faster than Merge sort. This reduces the time complexity and makes the algorithm faster. For a large data, we try to implement this algorithm in well known programming language Java.

Cited by 0Open access

Graphically Contracted Function Construction with the Recursive Pairwise Merge Algorithm

Ron Shepard · The Journal of Physical Chemistry A · 2023

A new implementation of a recursive pairwise merge algorithm to construct a GCF from a list of CSF expansion coefficients is presented. The essential new feature is the preallocation of some work arrays used within the intermediate steps of the merge procedure. This results in roughly an order of magnitude improvement in overall efficiency and also approximately eliminates a factor of <i>n</i>, the molecular orbital dimension, from the original implementation. Initial application of this merge procedure to a series of H<sub><i>m</i></sub> molecules shows that the GCF wave functions can be represented well both with delocalized canonical Hartree-Fock orbitals and with localized molecular orbitals. For a given wave function complexity, as measured by the average facet count, <i>f̅</i>, the delocalized Hartree-Fock orbitals show smaller errors for small <i>f̅</i> values, while the localized orbitals show smaller errors for larger <i>f̅</i> values.

Cited by 0Open access

A fast parallel merge sorting algorithm based on LARPBS model

Hongjian Chen, Lu Wei · 2005

In this paper a fast parallel merge sorting algorithm based on LARPBS is presented.This method uses M~(1+e)(0e1) processors to merge the M~e() sorted sequence in O(lb()lb()M) time.With this merge algorithm,N~(1+e)() processors can be used to sort N elements in O((lb()lb()N)~2) time.

Cited by 0

Two parallel strategies of split-merge algorithm for image segmentation

Gong Xuejing, Linlin Ci, Yao Kang-ze · 2007

Split-merge algorithm is a general approach for image segmentation based on region growing. Firstly a new merge criterion is proposed to reduce the execution time of the algorithm by decreasing the number of iterations at the merge stage. Then, two strategies for its parallel implementation are discussed by analyzing their difference in load balance, communication complexity and scalability. In the end, the experimental data indicate the efficiency of the algorithm and the parallel strategies.

Cited by 4

Novel Hardware-Software Architecture for the Recursive Merge Filtering Algorithm

Piyush S. Jamkhandi, Amar Mukherjee, Kunal Mukherjee et al. · 2000

As reconfigurable devices move to the forefront of mainstream processing, the spectrum of application areas for such devices is also increasing. One such area is image processing. In this paper we present a novel hardware-software codesign architecture for the computation of the Discrete Wavelet Transform (DWT), based on a new Recursive Merge Filtering (RMF) algorithm. The architecture aims at reducing the overall data routing during the computation of the DWT. The method shows how data routing can be transformed into a series of index computations carried out on the reconfigurable device. The architecture applies hardware/software codesign principles for task division among the available processing resources and uses memory resources closer to the FPGA to avoid the main memory accesses and this reduces the processing time and access time. The paper also proposes ideas based on memory bank to further enhance the performance of the proposed architecture. The architecture is based on a dynamically reconfigurable device, the Xilinx XC6200 on the H.O.T Works Board used to carry out the data routing by a series of index computations. The complex computations are restricted to the main processor and the simple addition/subtraction tasks for data routing are based on the FPGA.

Cited by 1

A merge-first divide & conquer algorithm for ed delaunay triangulations

Cignoni P., Montani C., Scopigno R. · ISTI Open Portal · 1992

The paper deals with Delaunay triangulations in E?d space, a classic problem in computational geometry, from the point of view of the efficiency and the easy parallelization of the algorithms. The application field is the processing and visualization of large scattered datasets; in this field, the real time visualization of time-varying phenomena is a typical requirement. An extension in E?d of an algorithm originally proposed for E?2 Delaunay triangulations and two simple and effective speedup techniques is first proposed and a new algorithm based on a original interpretation of the well-known Divide and Conquer paradigm is then presented. Although the computational complexity of the algorithm does not improve the theoretical results reported in the literature, the technique is very efficient and present a quasi linear behaviour in real applications. Thanks to the use of a D&C technique, the algorithm can be easily parallelized on low grain parallel architectures (such as shared memory MIMD machines or networks of workstations). An evaluation of the performance on medium resolution datasets is reported.

Cited by 0Open access