Relevant bibliographies by topics / Mapping Algorithm

Academic literature on the topic 'Mapping Algorithm'

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Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Mapping Algorithm"

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CHUNG, I.-HSIN, CHE-RUNG LEE, JIAZHENG ZHOU, and YEH-CHING CHUNG. "HIERARCHICAL MAPPING FOR HPC APPLICATIONS." Parallel Processing Letters 21, no. 03 (September 2011): 279–99. http://dx.doi.org/10.1142/s0129626411000229.

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As the high performance computing systems scale up, mapping the tasks of a parallel application onto physical processors to allow efficient communication becomes one of the critical performance issues. Existing algorithms were usually designed to map applications with regular communication patterns. Their mapping criterion usually overlooks the size of communicated messages, which is the primary factor of communication time. In addition, most of their time complexities are too high to process large scale problems. In this paper, we present a hierarchical mapping algorithm (HMA), which is capable of mapping applications with irregular communication patterns. It first partitions tasks according to their run-time communication information. The tasks that communicate with each other more frequently are regarded as strongly connected. Based on their connectivity strength, the tasks are partitioned into supernodes based on the algorithms in spectral graph theory. The hierarchical partitioning reduces the mapping algorithm complexity to achieve scalability. Finally, the run-time communication information will be used again in fine tuning to explore better mappings. With the experiments, we show how the mapping algorithm helps to reduce the point-to-point communication time for the PDGEMM, a ScaLAPACK matrix multiplication computation kernel, up to 20% and the AMG2006, a tier 1 application of the Sequoia benchmark, up to 7%.
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Villaseñor, Carlos, Nancy Arana-Daniel, Alma Alanis, Carlos Lopez-Franco, and Javier Gomez-Avila. "Multiellipsoidal Mapping Algorithm." Applied Sciences 8, no. 8 (July 27, 2018): 1239. http://dx.doi.org/10.3390/app8081239.

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The robotic mapping problem, which consists in providing a spatial model of the environment to a robot, is a research topic with a wide range of applications. One important challenge of this problem is to obtain a map that is information-rich (i.e., a map that preserves main structures of the environment and object shapes) yet still has a low memory cost. Point clouds offer a highly descriptive and information-rich environmental representation; accordingly, many algorithms have been developed to approximate point clouds and lower the memory cost. In recent years, approaches using basic and “simple” (i.e., using only planes or spheres) geometric entities for approximating point clouds have been shown to provide accurate representations at low memory cost. However, a better approximation can be implemented if more complex geometric entities are used. In the present paper, a new object-mapping algorithm is introduced for approximating point clouds with multiple ellipsoids and other quadratic surfaces. We show that this algorithm creates maps that are rich in information yet low in memory cost and have features suitable for other robotics problems such as navigation and pose estimation.
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Komatsu, Yuki, and Pierre Jais. "Practical Mapping Algorithm." Cardiac Electrophysiology Clinics 5, no. 2 (June 2013): 161–68. http://dx.doi.org/10.1016/j.ccep.2013.01.008.

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Aleomraninejad, Seyed, Kanokwan Sitthithakerngkiet, and Poom Kumam. "Hybrid subgradient algorithm for equilibrium and fixed point problems by approximation of nonexpansive mapping." Filomat 34, no. 5 (2020): 1721–29. http://dx.doi.org/10.2298/fil2005721a.

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In this paper anew algorithm considered on a real Hilbert space for finding acommonpoint in the solution set of a class of pseudomonotone equilibrium problem and the set of fixed points of nonexpansive mappings. We produce this algorithm by mappings Tk that are approximations of non-expansive mapping T. The strong convergence theorem of the proposed algorithms is investigated. Our results generalize some recent results in the literature.
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Vargas-Vera, Maria, and Miklos Nagy. "Architecture of DSSim." International Journal of Knowledge Society Research 6, no. 2 (April 2015): 65–82. http://dx.doi.org/10.4018/ijksr.2015040104.

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This paper presents the architecture of DSSim (DSSim stands for Similarity based on Dempster-Shafer) our multi-agent ontology mapping system. It describes several types of agents and their roles in the DSSim architecture. These agents are mapping agents which are able to perform either semantic or syntactic similarity. The authors' architecture is generic as no mappings need to be learned in advance and it could be easily extended by adding new mapping agents in the framework. These new mapping agents could run different similarity algorithms either semantic or syntactic. In this way, DSSim could assess which algorithm has a better performance. Additionally, this paper presents the main algorithms used in DSSim and discusses DSSim advantages and drawbacks.
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Vargas-Vera, Maria. "The Implementation of DSSim." International Journal of Knowledge Society Research 8, no. 3 (July 2017): 34–53. http://dx.doi.org/10.4018/ijksr.2017070103.

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This paper presents the decisions taken during the implementation of DSSim (DSSim stands for Similarity based on Dempster-Shafer) our multi-agent ontology mapping system. It describes several types of agents and their roles in the DSSim architecture. These agents are mapping agents which are able to perform either semantic or syntactic similarity. Our architecture is generic as no mappings need to be learned in advance and it could be easily extended by adding new mapping agents in the framework. The new added mapping agents could run different similarity algorithms (either semantic or syntactic). In this way, DSSim could assess which algorithm has a better performance. Additionally, this paper presents the algorithms used in our ontology alignment system DSSim.
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Zhang, Jingling, Yongfu Su, and Qingqing Cheng. "Hybrid Algorithm of Fixed Point for Weak Relatively Nonexpansive Multivalued Mappings and Applications." Abstract and Applied Analysis 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/479438.

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The purpose of this paper is to present the notion of weak relatively nonexpansive multi-valued mapping and to prove the strong convergence theorems of fixed point for weak relatively nonexpansive multivalued mappings in Banach spaces. The weak relatively nonexpansive multivalued mappings are more generalized than relatively nonexpansive multivalued mappings. In this paper, an example will be given which is a weak relatively nonexpansive multivalued mapping but not a relatively nonexpansive multivalued mapping. In order to get the strong convergence theorems for weak relatively nonexpansive multivalued mappings, a new monotone hybrid iteration algorithm with generalized (metric) projection is presented and is used to approximate the fixed point of weak relatively nonexpansive multivalued mappings. In this paper, the notion of multivalued resolvent of maximal monotone operator has been also presented which is a weak relatively nonexpansive multivalued mapping and can be used to find the zero point of maximal monotone operator.
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Turney, P. D. "The Latent Relation Mapping Engine: Algorithm and Experiments." Journal of Artificial Intelligence Research 33 (December 22, 2008): 615–55. http://dx.doi.org/10.1613/jair.2693.

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Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.
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Gavhane, Bhagyashree Ashok, and Prashant Vitthalrao Kathole. "Alternative RAM Mapping Algorithm for Embedded Memory Blocks in FPGA." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2542–49. http://dx.doi.org/10.31142/ijtsrd7172.

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SHEN, C. W., R. C. T. LEE, and Y. H. CHIN. "A PARALLEL NONLINEAR MAPPING ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 01, no. 01 (April 1987): 53–69. http://dx.doi.org/10.1142/s0218001487000059.

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In this paper, we shall present a parallel algorithm to perform nonlinear mapping which is useful for clustering analysis and pattern recognition. This parallel nonlinear mapping algorithm is slightly different from the original nonlinear mapping algorithm. Experimental results show that our parallel nonlinear mapping algorithm works quite well.
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Dissertations / Theses on the topic "Mapping Algorithm"

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Morovič, Ján. "To develop a universal gamut mapping algorithm." Thesis, University of Derby, 1998. http://hdl.handle.net/10545/200029.

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Pomerleau, François. "Registration algorithm optimized for simultaneous localization and mapping." Mémoire, Université de Sherbrooke, 2008. http://savoirs.usherbrooke.ca/handle/11143/1465.

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Building maps within an unknown environment while keeping track of the current position is a major step to accomplish safe and autonomous robot navigation. Within the last 20 years, Simultaneous Localization And Mapping (SLAM) became a topic of great interest in robotics. The basic idea of this technique is to combine proprioceptive robot motion information with external environmental information to minimize global positioning errors. Because the robot is moving in its environment, exteroceptive data comes from different points of view and must be expressed in the same coordinate system to be combined. The latter process is called registration. Iterative Closest Point (ICP) is a registration algorithm with very good performances in several 3D model reconstruction applications, and was recently applied to SLAM. However, SLAM has specific needs in terms of real-time and robustness comparatively to 3D model reconstructions, leaving room for specialized robotic mapping optimizations in relation to robot mapping. After reviewing existing SLAM approaches, this thesis introduces a new registration variant called Kd-ICP. This referencing technique iteratively decreases the error between misaligned point clouds without extracting specific environmental features. Results demonstrate that the new rejection technique used to achieve mapping registration is more robust to large initial positioning errors. Experiments with simulated and real environments suggest that Kd-ICP is more robust compared to other ICP variants. Moreover, the Kd-ICP is fast enough for real-time applications and is able to deal with sensor occlusions and partially overlapping maps. Realizing fast and robust local map registrations opens the door to new opportunities in SLAM. It becomes feasible to minimize the cumulation of robot positioning errors, to fuse local environmental information, to reduce memory usage when the robot is revisiting the same location. It is also possible to evaluate network constrains needed to minimize global mapping errors.
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Dunkelberg, Jr John S. "FEM Mesh Mapping to a SIMD Machine Using Genetic Algorithms." Digital WPI, 2001. https://digitalcommons.wpi.edu/etd-theses/1154.

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The Finite Element Method is a computationally expensive method used to perform engineering analyses. By performing such computations on a parallel machine using a SIMD paradigm, these analyses' run time can be drastically reduced. However, the mapping of the FEM mesh elements to the SIMD machine processing elements is an NP-complete problem. This thesis examines the use of Genetic Algorithms as a search technique to find quality solutions to the mapping problem. A hill climbing algorithm is compared to a traditional genetic algorithm, as well as a "messy" genetic algorithm. The results and comparative advantages of these approaches are discussed.
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Liu, Zhiyong Michael. "Mapping physical topology with logical topology using genetic algorithm." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ62245.pdf.

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Curotto, Molina Franco Andreas. "Graphslam algorithm implementation for solving simultaneous localization and mapping." Tesis, Universidad de Chile, 2016. http://repositorio.uchile.cl/handle/2250/139093.

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Ingeniero Civil Eléctrico
SLAM (Simultaneous Localization and Mapping) es el problema de estimar la posición de un robot (u otro agente), y simultáneamente, generar un mapa de su entorno. Es considerado un concepto clave en la robótica móvil, y fundamental para alcanzar sistemas verdaderamente autónomos. Entre las muchas soluciones que se han propuesto para resolver SLAM, los métodos basados en grafos han adquirido gran interés por parte de los investigadores en los últimos años. Estas soluciones presentan varias ventajas, como la habilidad de manejar grandes cantidades de datos, y conseguir la trayectoria completa del robot, en vez de solo la última posición. Una implementación particular de este método es el algoritmo GraphSLAM, presentado por primera vez por Thrun y Montemerlo en 2006. En esta memoria, el algoritmo GraphSLAM es implementado para resolver el problema de SLAM en el caso de dos dimensiones. En objetivo principal de esta memoria es proveer de una solución de SLAM ampliamente aceptada para la realización de pruebas comparativas con nuevos algoritmos de SLAM. La implementación usa el framework g2o como herramienta para la optimización de mínimos cuadrados no lineales. La implementación de GraphSLAM es capaz de resolver SLAM con asociación de datos conocida y desconocida. Esto significa que, incluso cuando el robot no tiene conocimiento del origen de las mediciones, éste puede asociar las mediciones a los estados correspondientes, mediante el uso de estimación probabilística. El algoritmo también usa un método basado en kernel para la estimación robusta ante outliers. Para mejorar el tiempo de cómputo del algoritmo, varias estrategias fueron diseñadas para verificar las asociaciones y ejecutar el algoritmo de manera eficiente. La implementación final se probó con datos simulados y reales, en el caso de asociación conocida y desconocida. El algoritmo fue exitoso en todas las pruebas, siendo capaz de estimar la trayectoria del robot y el mapa del entorno con un error pequeño. Las principales ventajas del algoritmo son su alta precisión, y su alto grado de configuración dado por la selección de parámetros. Las mayores desventajas son el tiempo de cómputo del algoritmo cuando la cantidad de datos es alta, y su incapacidad de eliminar falsos positivos. Finalmente, como trabajo futuro, se sugieren modificaciones para aumentar la velocidad de convergencia, y para eliminar falsos positivos.
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Wang, Qing. "Development, improvement and assessment of image classification and probability mapping algorithms." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1622.

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Remotely sensed imagery is one of the most important data sources for large-scale and multi-temporal agricultural, forestry, soil, environmental, social and economic applications. In order to accurately extract useful thematic information of the earth surface from images, various techniques and methods have been developed. The methods can be divided into parametric and non-parametric based on the requirement of data distribution, or into global and local based on the characteristics of modeling global trends and local variability, or into unsupervised and supervised based on whether training data are required, and into design-based and model-based in terms of the theory based on which the estimators are developed. The methods have their own disadvantages that impede the improvement of estimation accuracy. Thus, developing novel methods and improving the existing methods are needed. This dissertation focused on the development of a feature-space indicator simulation (FSIS), the improvement of geographically weighted sigmoidal simulation (GWSS) and k-nearest neighbors (kNN), and their assessment of land use and land cover (LULC) classification and probability (fraction) mapping of percentage vegetation cover (PVC) in Duolun County, Xilingol League, Inner Mongolia, China. The FSIS employs an indicator simulation in a high-dimensional feature space and expends derivation of indicator variograms from geographic space to feature space that leads to feature space indicator variograms (FSIV), to circumvent the issues existing in traditional indicator simulation in geostatistics. The GWSS is a stochastic and probability mapping method and considers a spatially nonstationary sample data and the local variation of an interest variable. The improved kNN, called Optimal k-nearest neighbors (OkNN), searches for an optimal number of nearest neighbors at each location based on local variability, and can be used for both classification and probability mapping. Three methods were validated and compared with several widely used approaches for LULC classification and PVC mapping in the study area. The datasets used in the study included a Landsat 8 image and a total of 920 field plots. The results obtained showed that 1) Compared with maximum likelihood classification (ML), support vector machine (SVM) and random forest (RF), the proposed FSIS classifier led to statistically significantly higher classification accuracy of six LULC types (water, agricultural land, grassland, bare soil, built-up area, and forested area); 2) Compared with linear regression (LR), polynomial regression (PR), sigmoidal regression (SR), geographically weighted regression (GWR), and geographically weighted polynomial regression (GWPR), GWSS did not only resulted in more accurate estimates of PVC, but also greatly reduced the underestimations and overestimation of PVC for the small and large values respectively; 3) Most of the red and near infrared bands relevant vegetation indices had significant contributions to improving the accuracy of mapping PVC; 4) OkNN resulted in spatially variable and optimized k values and higher prediction accuracy of PVC than the global methods; and 5) The range parameter of FSIVs was the major factor that spatially affected the classification accuracy of LULC types, but the FSIVs were less sensitive to the number of training samples. Thus, the results answered all six research questions proposed.
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Dash, Padmanava. "SeaWiFS Algorithm for Mapping Phycocyanin in Incipient Freshwater Cyanobacterial Blooms." Bowling Green State University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1120594611.

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Jiang, Dayou. "An exploration of BMSF algorithm in genome-wide association mapping." Kansas State University, 2013. http://hdl.handle.net/2097/15505.

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Master of Science
Department of Statistics
Haiyan Wang
Motivation: Genome-wide association studies (GWAS) provide an important avenue for investigating many common genetic variants in different individuals to see if any variant is associated with a trait. GWAS is a great tool to identify genetic factors that influence health and disease. However, the high dimensionality of the gene expression dataset makes GWAS challenging. Although a lot of promising machine learning methods, such as Support Vector Machine (SVM), have been investigated in GWAS, the question of how to improve the accuracy of the result has drawn increased attention of many researchers A lot of the studies did not apply feature selection to select a parsimonious set of relevant genes. For those that performed gene selections, they often failed to consider the possible interactions among genes. Here we modify a gene selection algorithm BMSF originally developed by Zhang et al. (2012) for improving the accuracy of cancer classification with binary responses. A continuous response version of BMSF algorithm is provided in this report so that it can be applied to perform gene selection for continuous gene expression dataset. The algorithm dramatically reduces the dimension of the gene markers under concern, thus increases the efficiency and accuracy of GWAS. Results: We applied the continuous response version of BMSF on the wheat phenotypes dataset to predict two quantitative traits based on the genotype marker data. This wheat dataset was previously studied in Long et al. (2009) for the same purpose but used only direct application of SVM regression methods. By applying our gene selection method, we filtered out a large portion of genes which are less relevant and achieved a better prediction result for the test data by building SVM regression model using only selected genes on the training data. We also applied our algorithm on simulated datasets which was generated following the setting of an example in Fan et al. (2011). The continuous response version of BMSF showed good ability to identify active variables hidden among high dimensional irrelevant variables. In comparison to the smoothing based methods in Fan et al. (2011), our method has the advantage of no ambiguity due to difference choices of the smoothing parameter.
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Baichbal, Shashidhar. "MAPPING ALGORITHM FOR AUTONOMOUS NAVIGATION OF LAWN MOWER USING SICK LASER." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1334587886.

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Phinjaroenphan, Panu, and s2118294@student rmit edu au. "An Efficient, Practical, Portable Mapping Technique on Computational Grids." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080516.145808.

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Grid computing provides a powerful, virtual parallel system known as a computational Grid on which users can run parallel applications to solve problems quickly. However, users must be careful to allocate tasks to nodes properly because improper allocation of only one task could result in lengthy executions of applications, or even worse, applications could crash. This allocation problem is called the mapping problem, and an entity that tackles this problem is called a mapper. In this thesis, we aim to develop an efficient, practical, portable mapper. To study the mapping problem, researchers often make unrealistic assumptions such as that nodes of Grids are always reliable, that execution times of tasks assigned to nodes are known a priori, or that detailed information of parallel applications is always known. As a result, the practicality and portability of mappers developed in such conditions are uncertain. Our review of related work suggested that a more efficient tool is required to study this problem; therefore, we developed GMap, a simulator researchers/developers can use to develop practical, portable mappers. The fact that nodes are not always reliable leads to the development of an algorithm for predicting the reliability of nodes and a predictor for identifying reliable nodes of Grids. Experimental results showed that the predictor reduced the chance of failures in executions of applications by half. The facts that execution times of tasks assigned to nodes are not known a priori and that detailed information of parallel applications is not alw ays known, lead to the evaluation of five nearest-neighbour (nn) execution time estimators: k-nn smoothing, k-nn, adaptive k-nn, one-nn, and adaptive one-nn. Experimental results showed that adaptive k-nn was the most efficient one. We also implemented the predictor and the estimator in GMap. Using GMap, we could reliably compare the efficiency of six mapping algorithms: Min-min, Max-min, Genetic Algorithms, Simulated Annealing, Tabu Search, and Quick-quality Map, with none of the preceding unrealistic assumptions. Experimental results showed that Quick-quality Map was the most efficient one. As a result of these findings, we achieved our goal in developing an efficient, practical, portable mapper.
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Books on the topic "Mapping Algorithm"

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Falkenhainer, Brian. The structure-mapping engine: Algorithm and examples. Urbana, Ill. (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Menemenlis, Dimitris. Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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Anderson, Jay Martin. Algorithms for computer cartography. Lancaster, Pa: Franklin and Marshall College, 1998.

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Beyond mapping: Concepts, algorithms, and issues in GIS. Ft. Collins, Colo., USA: GIS World, Inc., 1993.

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Lee, P. Mapping nested loop algorithms into multi-dimensional systolic arrays. New York: Courant Institute of Mathematical Sciences, New York University, 1988.

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Sokol'skaya, Elena, and Boris Kochurov. Geoecology of the city: models of environmental quality. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1205961.

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The monograph examines the features of studying the geoecological state of urbanized territories, reveals the use of integrated assessment and mapping in urban diagnostics, and finds a solution to geoecological problems on the example of world cities that are leading in the rating for quality of life. The components of an information and analytical model of the urban environment for assessing the geoecological situation are described; an algorithm for a comprehensive study of the geoecological state aimed at an adequate assessment of the quality of the urban environment. Special attention is paid to the methodology of geoecological assessment of the quality of the urban environment based on multifactor modeling, which allows making recommendations for improving the comfort of living of the population. It is intended for a wide range of specialists in the field of geoecology of the city, and can also be used as a textbook for students of environmental, natural-geographical, engineering specialties.
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Comparative gene finding: Models, algorithms, and implementation. London: Springer, 2010.

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V, Kalashnikov V., ed. Optimization with multivalued mappings: Theory, applications, and algorithms. New York: Springer, 2006.

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Benkeser, Christian. Power efficiency and the mapping of communication algorithms into VLSI. Konstanz: Hartung-Gorre, 2010.

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Morovič, Ján. To develop a universal gamut mapping algorithmte measuring machines. [Derby: University of Derby], 1998.

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Book chapters on the topic "Mapping Algorithm"

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Zhao, Jie, Wenlei Wang, Qiuming Cheng, and Yunqing Shao. "Nonlinear Mapping Algorithm." In Encyclopedia of Mathematical Geosciences, 1–4. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_226-1.

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Ewald, Roland. "Selection Mapping Generation." In Automatic Algorithm Selection for Complex Simulation Problems, 177–201. Wiesbaden: Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-8151-9_6.

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Boillat, Jacques E., and Peter G. Kropf. "A fast distributed mapping algorithm." In CONPAR 90 — VAPP IV, 405–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-53065-7_119.

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Mahdi, Tamer S., and Robert E. Mercer. "A Lexical Functional Mapping Algorithm." In Advances in Artificial Intelligence, 303–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47922-8_25.

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Wang, Xiao-guang, Xiang-wei Zheng, and Dian-jie Lu. "A Heuristic Virtual Network Mapping Algorithm." In Intelligent Computing Methodologies, 385–95. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09339-0_40.

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He, Ping, Tianyu Jing, Xiaohua Xu, Lei Zhang, and Huihui Lin. "Supervised Isometric Mapping Based Classification Algorithm." In Lecture Notes in Computer Science, 302–9. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46257-8_33.

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Mascioli, F. M. Frattale, and G. Martinelli. "A Constructive Algorithm for Binary Mapping." In ICANN ’93, 776. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_215.

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Kao, Ming-Yang, Jared Samet, and Wing-Kin Sung. "The Enhanced Double Digest Problem for DNA Physical Mapping." In Algorithm Theory - SWAT 2000, 383–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44985-x_33.

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Sticchi, Francesco. "Black Mirror and Nosedive: Becoming Algorithm." In Mapping Precarity in Contemporary Cinema and Television, 91–104. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63261-8_6.

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Binh, Huynh Thi Thanh, Bach Hoang Vinh, Nguyen Hong Nhat, and Le Hoang Linh. "Heuristic Algorithm for Virtual Network Mapping Problem." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 43–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13326-3_5.

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Conference papers on the topic "Mapping Algorithm"

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Osama, Hoda, Yasser Omar, and Amr Badr. "Mapping sorting algorithm." In 2016 SAI Computing Conference (SAI). IEEE, 2016. http://dx.doi.org/10.1109/sai.2016.7556025.

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Hegde, Kartik, Po-An Tsai, Sitao Huang, Vikas Chandra, Angshuman Parashar, and Christopher W. Fletcher. "Mind mappings: enabling efficient algorithm-accelerator mapping space search." In ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3445814.3446762.

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Wang, Chun-bo, Shao-hua Fu, and Zhong-yi Wei. "SAR image registration based on Susan algorithm." In International Symposium on Lidar and Radar Mapping Technologies. SPIE, 2011. http://dx.doi.org/10.1117/12.912333.

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Kraut, Jay. "A relative mapping algorithm." In 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2013. http://dx.doi.org/10.1109/ccece.2013.6567715.

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Tan, Junlang, Yihua Huang, and Kai Wang. "Logarithmic Tone Mapping Algorithm Based on Block Mapping Fusion." In 2018 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2018. http://dx.doi.org/10.1109/icalip.2018.8455806.

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Tong, Shi, and Yingyun Yang. "A novel tone mapping algorithm." In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2019. http://dx.doi.org/10.1109/itaic.2019.8785487.

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Brennan, Robert L. "Efficient embedded system algorithm mapping." In 2010 53rd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2010. http://dx.doi.org/10.1109/mwscas.2010.5548831.

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Jiang, Chunmao, Meiyu Xu, and Qian Shen. "An AUTOSAR ECU Mapping Algorithm." In 2014 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014) ). Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/sekeie-14.2014.11.

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Gorgoteanu, Damian, Lucian Stefanita Grigore, and Cristian Molder. "Mapping algorithm using SWARM robots." In 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2021. http://dx.doi.org/10.1109/ecai52376.2021.9515072.

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Chen, Lei, Shuhe Zhao, An Wang, and Yunxiao Luo. "An improved plane fitting based filtering algorithm for airborne lidar data." In International Symposium on Lidar and Radar Mapping Technologies. SPIE, 2011. http://dx.doi.org/10.1117/12.912740.

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Reports on the topic "Mapping Algorithm"

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Scannapieco, Anthony J. An Adaptive Mesh Algorithm: Mapping the Mesh Variables. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1304744.

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Pattipati, Krishna R., Peter B. Luh, Rong-Tay Lee, Samir Shah, and Somanth Deb. BM/C(3) Algorithm Mapping Onto Concurrent Processors. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada207877.

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Hendrickson, B., and R. Leland. An improved spectral graph partitioning algorithm for mapping parallel computations. Office of Scientific and Technical Information (OSTI), September 1992. http://dx.doi.org/10.2172/6970738.

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Steensland, Johan, and Jaideep Ray. A heuristic re-mapping algorithm reducing inter-level communication in SAMR applications. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/918288.

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Sinclair, Samantha, and Sandra LeGrand. Reproducibility assessment and uncertainty quantification in subjective dust source mapping. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41523.

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Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.
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Sinclair, Samantha, and Sandra LeGrand. Reproducibility assessment and uncertainty quantification in subjective dust source mapping. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41542.

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Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.
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Blundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40185.

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Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolution elevation models of canopy. The identification and mapping of canopy cover and micro-terrain features in areas of sparse vegetation is demonstrated with an elevation model for a region of western Montana, using algorithms for breaklines, elevation differencing, slope, terrain ruggedness, and breakline gradient direction. These algorithms were created at the U.S. Army Engineer Research Center – Geospatial Research Laboratory (ERDC-GRL) and can be accessed through an in-house tool constructed in the ENVI/IDL environment. After breakline processing, products from these algorithms are brought into a Geographic Information System as analytical layers and applied to a mobility routing model, demonstrating the effect of breaklines as obstacles in the calculation of optimal, off-road routes. Elevation model breakline analysis can serve as significant added value to micro-terrain feature and canopy mapping, obstacle identification, and route planning.
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Wilson, A. Dan. New methods, algorithms, and software for rapid mapping of tree positions in coordinate forest plots. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2000. http://dx.doi.org/10.2737/srs-rp-19.

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Wilson, A. Dan. New methods, algorithms, and software for rapid mapping of tree positions in coordinate forest plots. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2000. http://dx.doi.org/10.2737/srs-rp-19.

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Chang, P. A Differential Algebraic Integration Algorithm for Symplectic Mappings in Systems with Three-Dimensional Magnetic Field. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/833057.

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