Relevant bibliographies by topics / Space-filling curve

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Space-filling curve'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Space-filling curve"

1

Zhang, Yuefeng. "Space-filling curve ordered dither." Computers & Graphics 22, no. 4 (August 1998): 559–63. http://dx.doi.org/10.1016/s0097-8493(98)00043-0.

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T.V, Sushma, and Roopa M. "Hilbert space filling curve using scilab." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 129. http://dx.doi.org/10.14419/ijet.v7i1.9.9748.

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Space filling curve is used widely for linear mapping of multi-dimensional space. This provides a new line of thinking for various applications in image processing, Image compression being the most widely used. The paper highlights the locality preserving property of Hilbert Space filling curve which is essential in numerous applications such asin image compression, numerical analysis of a large aray of data, parallel processing and so on. A simplistic approach forusingHilbert Space filling curve using Scilab code has been presented.
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Ye, Ruisong, and Li Liu. "A Matrix Iterative Approach to Systematically Generate Hilbert-type Space-filling Curves." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 12 (December 30, 2015): 6281–94. http://dx.doi.org/10.24297/ijct.v14i12.1741.

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Hilbert-type space-filling curve has attracted much interest thanks to its mathematical importance and extensive applications in signal processing. In this paper, we construct the complete six Hilbert-type space-filling curves form amatrix point of view. The address matrix for each considered Hilbert-type space-filling curve can be easily generated by a recursive manner. Besides the six Hilbert-type space-filling curves, we also construct their corresponding variation versions. The merit of the matrix approach is that the iterative algorithm is easy to implement and can be generalized to produce any other Hilbert-type space-filling curves and their variation versions.
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Janakirama, Siva, K. Thenmozhi, Sundararaman Rajagopala, Har Narayan Upadhyay, John Bosco Balaguru Rayappan, and Rengarajan Amirtharaj. "Space Filling Curve for Data Filling: An Embedded Security Approach." Research Journal of Information Technology 6, no. 3 (March 1, 2014): 188–97. http://dx.doi.org/10.3923/rjit.2014.188.197.

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Forman, Noah. "Brownian bricklayer: A random space-filling curve." Statistics & Probability Letters 143 (December 2018): 43–46. http://dx.doi.org/10.1016/j.spl.2018.07.010.

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Luitjens, J., M. Berzins, and T. Henderson. "Parallel space-filling curve generation through sorting." Concurrency and Computation: Practice and Experience 19, no. 10 (2007): 1387–402. http://dx.doi.org/10.1002/cpe.1179.

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Sagan, Hans. "A geometrization of Lebesgue’s space-filling curve." Mathematical Intelligencer 15, no. 4 (September 1993): 37–43. http://dx.doi.org/10.1007/bf03024322.

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Kapil, Sajan, Prathamesh Joshi, Hari Vithasth Yagani, Dhirendra Rana, Pravin Milind Kulkarni, Ranjeet Kumar, and K. P. Karunakaran. "Optimal space filling for additive manufacturing." Rapid Prototyping Journal 22, no. 4 (June 20, 2016): 660–75. http://dx.doi.org/10.1108/rpj-03-2015-0034.

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Purpose In additive manufacturing (AM) process, the physical properties of the products made by fractal toolpaths are better as compared to those made by conventional toolpaths. Also, it is desirable to minimize the number of tool retractions. The purpose of this study is to describe three different methods to generate fractal-based computer numerical control (CNC) toolpath for area filling of a closed curve with minimum or zero tool retractions. Design/methodology/approach This work describes three different methods to generate fractal-based CNC toolpath for area filling of a closed curve with minimum or zero tool retractions. In the first method, a large fractal square is placed over the outer boundary and then rest of the unwanted curve is trimmed out. To reduce the number of retractions, ends of the trimmed toolpath are connected in such a way that overlapping within the existing toolpath is avoided. In the second method, the trimming of the fractal is similar to the first method but the ends of trimmed toolpath are connected such that the overlapping is found at the boundaries only. The toolpath in the third method is a combination of fractal and zigzag curves. This toolpath is capable of filling a given connected area in a single pass without any tool retraction and toolpath overlap within a tolerance value equal to stepover of the toolpath. Findings The generated toolpath has several applications in AM and constant Z-height surface finishing. Experiments have been performed to verify the toolpath by depositing material by hybrid layered manufacturing process. Research limitations/implications Third toolpath method is suitable for the hybrid layered manufacturing process only because the toolpath overlapping tolerance may not be enough for other AM processes. Originality/value Development of a CNC toolpath for AM specifically hybrid layered manufacturing which can completely fill any arbitrary connected area in single pass while maintaining a constant stepover.
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McClure, Mark. "Self-Similar Structure in Hilbert's Space-Filling Curve." Mathematics Magazine 76, no. 1 (February 1, 2003): 40. http://dx.doi.org/10.2307/3219131.

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Korchmáros, G., L. Storme, and T. Szőnyi. "Space-filling subsets of a normal rational curve." Journal of Statistical Planning and Inference 58, no. 1 (March 1997): 93–110. http://dx.doi.org/10.1016/s0378-3758(96)00063-8.

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Dissertations / Theses on the topic "Space-filling curve"

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Reeder, John. "Hilbert Space Filling Curve (HSFC) Nearest Neighbor Classifier." Honors in the Major Thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/794.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf
Bachelors
Engineering and Computer Science
Computer Engineering
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Lentfort, Christian. "Decentralized Indexing of Presentities over n-Dimensional Context Information." Thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-16902.

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Modern context-aware applications no longer justify their decisions based only on their own information but on the decisions and information of other applications in a similar context.  Acquiring context information of other entities in an distributed system is difficult task when using the current content centric solutions such as DHTs.  This project aims to build a distributed index that provides storage for the so called Presentities solely based on the state of their context information.  Furthermore, the stored Presentities must be efficiently accessible even if only some information of their current context is available. To fulfill these requirements the PAST DHT was extended to support range queries and modified to use points on a space-filling curve as index values. The simulation of the system has shown very good accuracy rates, on average 99%, for range queries by maintaining a logarithmic relationship to the amount of required messages sent in the DHT.  Problems have emerged from the lack of load balancing implemented into the used DHT, but it is still the case that the proposed method of using space-filling curves to build a context centric decentralized index is both sufficient and effective. Keywords: context awareness, indexing, space-flling curves, Hilbert curve,Pastry, PAST
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Nguyen, Giap. "Courbes remplissant l'espace et leur application en traitement d'images." Thesis, La Rochelle, 2013. http://www.theses.fr/2013LAROS423/document.

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Les courbes remplissant l'espace sont connues pour la capacité d'ordonner les points multidimensionnels sur une ligne en tout conservant la localité, i.e. les points proches sont toujours proches sur la ligne. La conservation de la localité est beaucoup recherchée dans plusieurs applications. La courbe de Hilbert est la courbe remplissant l'espace qui conserve le mieux la localité. Cette courbe est originalement proposée en 2D, i.e. n'est qu'applicable aux points dans un espace 2D. Pour une perspective d'application dans le cas multidimensionnel, nous proposons dans cette thèse une généralisation de la courbe de Hilbert. La courbe généralisée est définie en s'appuyant sur la propriété essentielle de la courbe de Hilbert qui crée son niveau de conservation de la localité : l'adjacence. Ainsi, elle évite la dépendance du motif primitif RBG qui est le seul motif primitif de la courbe étendu par les recherches précédentes. Le résultat est donc une famille de courbe conservant bien la localité. L'optimisation de la conservation de la localité est aussi abordée pour permettre de retrouver la courbe qui conserve le mieux la localité. Pour cet objectif, nous proposons une mesure de la conservation de la localité. En s'appuyant sur les paramètres, cette mesure peut adapter aux différentes situations applicatives comme le changement de métrique ou de taille de localité. La construction est une partie importante de la thèse, elle est la base du calcul de l'index utilisé dans l'application. Pour un calcul de l'index rapide, la courbe de Hilbert autosimilaire est utilisée. La courbe de Hilbert satisfaisant les conditions de la courbe fait l'objet du chapitre 4. La courbe généralisée est enfin appliquée dans la recherche d'image. Il s'agit d'une recherche par le contenu où chaque image est caractérisée par un vecteur multidimensionnel. Les images sont ordonnées par la courbe sur une ligne ; ainsi, la recherche est simplifiée en une recherche sur une liste ordonnée. En donnant une image d'entrée, les images similaires sont celles correspondantes aux index voisins de l'index de l'image d'entrée. La conservation de la localité garantit que ces index correspondent aux images similaires
The space-filling curves are known for the ability to order the multidimensional points on a line while preserving the locality, i.e. the close points are closely ordered on the line. The locality preserving is wished in many applications. Hilbert curve is the best locality preserving space-filling curve. This curve is originally proposed in 2D, i.e. it is only applied to points in a 2D space. For application in the multidimensional case, we propose in this thesis a generalization of Hilbert curve. Generalized curve is based on the essential property of Hilbert curve that creates its level of locality preserving: the adjacency. Thus, it avoids the dependence on the pattern RBG, which is the only pattern of the curve extended by previous researches. The result is a family of curves preserving well the locality. The optimization of the locality preserving is also addressed to find out the best locality preserving curve. For this purpose, we propose a measure of the locality preserving. Based on the parameters, this measure can adapt to different application situations such as the change of metric or locality size. The curve construction is an important part of the thesis. It is the basis of the index calculation used in application. For a rapid index calculation, the self-similar Hilbert curves is used. They are Hilbert curves satisfying the self-similar conditions specified in chapitre 4. The generalized curve is finally applied in image search. It is the question of the content-based image search (CBIR) where each image is characterized by a multidimensionalvector. Images are ordered by the curve of a line, and the search is simplified to the search on an ordered list. By giving an input image, similar images are those corresponding to neighbors of the index of the input. The locality preserving ensures that these indexes correspond to similar images
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Weston, David John. "Shape matching using space-filling curves." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537572.

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Irgin, Umit. "Analysis Of Koch Fractal Antennas." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610644/index.pdf.

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Fractal is a recursively-generated object describing a family of complex shapes that possess an inherent self-similarity in their geometrical structure. When used in antenna engineering, fractal geometries provide multi-band characteristics and lowering resonance frequencies by enhancing the space filling property. Moreover, utilizing fractal arrays, controlling side lobe-levels and radiation patterns can be realized. In this thesis, the performance of Koch curve as antenna is investigated. Since fractals are complex shapes, there is no well&ndash
established for mathematical formulation to obtain the radiation properties and frequency response of Koch Curve antennas directly. The Koch curve antennas became famous since they exhibit better frequency response than their Euclidean counterparts. The effect of the parameters of Koch geometry to antenna performance is studied in this thesis. Moreover, modified Koch geometries are generated to obtain the relation between fractal properties and antenna radiation and frequency characteristics.
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Leifsson, Patrik. "Fractal sets and dimensions." Thesis, Linköping University, Department of Mathematics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7320.

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Fractal analysis is an important tool when we need to study geometrical objects less regular than ordinary ones, e.g. a set with a non-integer dimension value. It has developed intensively over the last 30 years which gives a hint to its young age as a branch within mathematics.

In this thesis we take a look at some basic measure theory needed to introduce certain definitions of fractal dimensions, which can be used to measure a set's fractal degree. Comparisons of these definitions are done and we investigate when they coincide. With these tools different fractals are studied and compared.

A key idea in this thesis has been to sum up different names and definitions referring to similar concepts.

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Lawder, Jonathan. "The application of space-filling curves to the storage and retrieval of multi-dimensional data." Thesis, Birkbeck (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249662.

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Indexing of multi-dimensional data has been the focus of a considerable amount of research effort over many years but no generally agreed paradigm has emerged to compare with the impact of the B-Tree, for example, on the indexing of one-dimensional data. At the same time, the need for efficient methods is ever more important in an environment where databases become larger and more complex in their structures. Mapping multi-dimensional data to one dimension, thus enabling one-dimensional access methods to be exploited, has been suggested in the literature but for the most part interest has been confined to the Z-order curve. The possibility of using other curves, such as the Hilbert and Gray-code curves, whose characteristics differ from those of the Z-order curve, has also been suggested. In this thesis we design and implement a working file store which is underpinned by the principle of mapping multi-dimensional data to one of a variety of space-filling curves and their variants. Data is then indexed using a B+ Tree which remains compact, regardless of the volume and number of dimensions. The implementation has entailed developing algorithms for mapping data to one dimension and, most importantly, developing algorithms to facilitate the querying of data in a flexible way. We focus on the Hilbert curve but also consider other curves and propose new alternative algorithms for querying data mapped to the Z-order curve. The current implementation accommodates data in up to sixteen dimensions but the approach is generic and not limited to this number. We report on preliminary testing of the implementation, which provides very encouraging results. We also undertake a brief exploration of the application of space-filling curves to the indexing of spatial data.
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Buchin, Kevin [Verfasser]. "Organizing Point Sets : Space-Filling Curves, Delaunay Tessellations of Random Point Sets, and Flow Complexes / Kevin Buchin." Berlin : Freie Universität Berlin, 2008. http://d-nb.info/1022697765/34.

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Pawlowski, Filip igor. "High-performance dense tensor and sparse matrix kernels for machine learning." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN081.

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Dans cette thèse, nous développons des algorithmes à haute performance pour certains calculs impliquant des tenseurs denses et des matrices éparses. Nous abordons les opérations du noyau qui sont utiles pour les tâches d'apprentissage de la machine, telles que l'inférence avec les réseaux neuronaux profonds. Nous développons des structures de données et des techniques pour réduire l'utilisation de la mémoire, pour améliorer la localisation des données et donc pour améliorer la réutilisation du cache des opérations du noyau. Nous concevons des algorithmes parallèles à mémoire séquentielle et à mémoire partagée.Dans la première partie de la thèse, nous nous concentrons sur les noyaux tenseurs denses. Les noyaux tenseurs comprennent la multiplication tenseur-vecteur (TVM), la multiplication tenseur-matrice (TMM) et la multiplication tenseur-tendeur (TTM). Parmi ceux-ci, la MVT est la plus liée à la largeur de bande et constitue un élément de base pour de nombreux algorithmes. Nous proposons une nouvelle structure de données qui stocke le tenseur sous forme de blocs, qui sont ordonnés en utilisant la courbe de remplissage de l'espace connue sous le nom de courbe de Morton (ou courbe en Z). L'idée clé consiste à diviser le tenseur en blocs suffisamment petits pour tenir dans le cache et à les stocker selon l'ordre de Morton, tout en conservant un ordre simple et multidimensionnel sur les éléments individuels qui les composent. Ainsi, des routines BLAS haute performance peuvent être utilisées comme micro-noyaux pour chaque bloc. Les résultats démontrent non seulement que l'approche proposée est plus performante que les variantes de pointe jusqu'à 18%, mais aussi que l'approche proposée induit 71% de moins d'écart-type d'échantillon pour le MVT dans les différents modes possibles. Enfin, nous étudions des algorithmes de mémoire partagée parallèles pour la MVT qui utilisent la structure de données proposée. Nos résultats sur un maximum de 8 systèmes de prises montrent une performance presque maximale pour l'algorithme proposé pour les tenseurs à 2, 3, 4 et 5 dimensions.Dans la deuxième partie de la thèse, nous explorons les calculs épars dans les réseaux de neurones en nous concentrant sur le problème d'inférence profonde épars à haute performance. L'inférence sparse DNN est la tâche d'utiliser les réseaux sparse DNN pour classifier un lot d'éléments de données formant, dans notre cas, une matrice de caractéristiques sparse. La performance de l'inférence clairsemée dépend de la parallélisation efficace de la matrice clairsemée - la multiplication matricielle clairsemée (SpGEMM) répétée pour chaque couche dans la fonction d'inférence. Nous introduisons ensuite l'inférence modèle-parallèle, qui utilise un partitionnement bidimensionnel des matrices de poids obtenues à l'aide du logiciel de partitionnement des hypergraphes. Enfin, nous introduisons les algorithmes de tuilage modèle-parallèle et de tuilage hybride, qui augmentent la réutilisation du cache entre les couches, et utilisent un module de synchronisation faible pour cacher le déséquilibre de charge et les coûts de synchronisation. Nous évaluons nos techniques sur les données du grand réseau du IEEE HPEC 2019 Graph Challenge sur les systèmes à mémoire partagée et nous rapportons jusqu'à 2x l'accélération par rapport à la ligne de base
In this thesis, we develop high performance algorithms for certain computations involving dense tensors and sparse matrices. We address kernel operations that are useful for machine learning tasks, such as inference with deep neural networks (DNNs). We develop data structures and techniques to reduce memory use, to improve data locality and hence to improve cache reuse of the kernel operations. We design both sequential and shared-memory parallel algorithms. In the first part of the thesis we focus on dense tensors kernels. Tensor kernels include the tensor--vector multiplication (TVM), tensor--matrix multiplication (TMM), and tensor--tensor multiplication (TTM). Among these, TVM is the most bandwidth-bound and constitutes a building block for many algorithms. We focus on this operation and develop a data structure and sequential and parallel algorithms for it. We propose a novel data structure which stores the tensor as blocks, which are ordered using the space-filling curve known as the Morton curve (or Z-curve). The key idea consists of dividing the tensor into blocks small enough to fit cache, and storing them according to the Morton order, while keeping a simple, multi-dimensional order on the individual elements within them. Thus, high performance BLAS routines can be used as microkernels for each block. We evaluate our techniques on a set of experiments. The results not only demonstrate superior performance of the proposed approach over the state-of-the-art variants by up to 18%, but also show that the proposed approach induces 71% less sample standard deviation for the TVM across the d possible modes. Finally, we show that our data structure naturally expands to other tensor kernels by demonstrating that it yields up to 38% higher performance for the higher-order power method. Finally, we investigate shared-memory parallel TVM algorithms which use the proposed data structure. Several alternative parallel algorithms were characterized theoretically and implemented using OpenMP to compare them experimentally. Our results on up to 8 socket systems show near peak performance for the proposed algorithm for 2, 3, 4, and 5-dimensional tensors. In the second part of the thesis, we explore the sparse computations in neural networks focusing on the high-performance sparse deep inference problem. The sparse DNN inference is the task of using sparse DNN networks to classify a batch of data elements forming, in our case, a sparse feature matrix. The performance of sparse inference hinges on efficient parallelization of the sparse matrix--sparse matrix multiplication (SpGEMM) repeated for each layer in the inference function. We first characterize efficient sequential SpGEMM algorithms for our use case. We then introduce the model-parallel inference, which uses a two-dimensional partitioning of the weight matrices obtained using the hypergraph partitioning software. The model-parallel variant uses barriers to synchronize at layers. Finally, we introduce tiling model-parallel and tiling hybrid algorithms, which increase cache reuse between the layers, and use a weak synchronization module to hide load imbalance and synchronization costs. We evaluate our techniques on the large network data from the IEEE HPEC 2019 Graph Challenge on shared-memory systems and report up to 2x times speed-up versus the baseline
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Izciler, Fatih. "3d Object Recognition From Range Images." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614915/index.pdf.

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Recognizing generic objects by single or multi view range images is a contemporary popular problem in 3D object recognition area with developing technology of scanning devices such as laser range scanners. This problem is vital to current and future vision systems performing shape based matching and classification of the objects in an arbitrary scene. Despite improvements on scanners, there are still imperfections on range scans such as holes or unconnected parts on images. This studyobjects at proposing and comparing algorithms that match a range image to complete 3D models in a target database.The study started with a baseline algorithm which usesstatistical representation of 3D shapesbased on 4D geometricfeatures, namely SURFLET-Pair relations.The feature describes the geometrical relationof a surface-point pair and reflects local and the global characteristics of the object. With the desire of generating solution to the problem,another algorithmthat interpretsSURFLET-Pairslike in the baseline algorithm, in which histograms of the features are used,isconsidered. Moreover, two other methods are proposed by applying 2D space filing curves on range images and applying 4D space filling curves on histograms of SURFLET-Pairs. Wavelet transforms are used for filtering purposes in these algorithms. These methods are tried to be compact, robust, independent on a global coordinate frame and descriptive enough to be distinguish queries&rsquo
categories.Baseline and proposed algorithms are implemented on a database in which range scans of real objects with imperfections are queries while generic 3D objects from various different categories are target dataset.
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Books on the topic "Space-filling curve"

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Sagan, Hans. Space-filling curves. New York: Springer-Verlag, 1994.

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Bader, Michael. Space-Filling Curves. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31046-1.

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Sagan, Hans. Space-Filling Curves. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-0871-6.

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Sergeyev, Yaroslav D., Roman G. Strongin, and Daniela Lera. Introduction to Global Optimization Exploiting Space-Filling Curves. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8042-6.

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service), SpringerLink (Online, ed. Space-Filling Curves: An Introduction with Applications in Scientific Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Sergeyev, Yaroslav D. D., Roman G. Strongin, and Daniela Lera. Introduction to Global Optimization Exploiting Space-Filling Curves. Springer, 2013.

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Sergeyev, Yaroslav D., Roman G. Strongin, and Daniela Lera. Introduction to Global Optimization Exploiting Space-Filling Curves. Springer, 2013.

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Book chapters on the topic "Space-filling curve"

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Sagan, Hans. "Hilbert’s Space-Filling Curve." In Universitext, 9–30. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-0871-6_2.

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Sagan, Hans. "Peano’s Space-Filling Curve." In Universitext, 31–47. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-0871-6_3.

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Sagan, Hans. "Sierpiński’s Space-Filling Curve." In Universitext, 49–68. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-0871-6_4.

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Sagan, Hans. "Lebesgue’s Space-Filling Curve." In Universitext, 69–83. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-0871-6_5.

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Sagan, Hans. "Schoenberg’s Space-Filling Curve." In Universitext, 119–30. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-0871-6_7.

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Sagan, Hans. "A Geometrization of Lebesgue’s Space-Filling Curve." In Mathematical Conversations, 185–91. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0195-0_17.

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Nguyen, Giap, Patrick Franco, and Jean-Marc Ogier. "Space-Filling Curve for Image Dynamical Indexing." In Computer and Information Sciences III, 311–19. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4594-3_32.

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Owczarek, Valentin, Patrick Franco, and Rémy Mullot. "Space-Filling Curve: A Robust Data Mining Tool." In Advances in Intelligent Systems and Computing, 663–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32520-6_49.

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Terry, Justin, Bela Stantic, Paolo Terenziani, and Abdul Sattar. "Variable Granularity Space Filling Curve for Indexing Multidimensional Data." In Advances in Databases and Information Systems, 111–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23737-9_9.

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Kudabalage, Ashan Eranga, Le Van Dang, Leran Du, and Yumi Ueda. "Region-Based Space Filling Curve for Medical Image Scanning." In Research in Intelligent and Computing in Engineering, 973–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7527-3_93.

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Conference papers on the topic "Space-filling curve"

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McVay, J., N. Engheta, and A. Hoorfar. "Three-dimensional Hilbert space-filling-curve plasmonics." In 2006 IEEE Antennas and Propagation Society International Symposium. IEEE, 2006. http://dx.doi.org/10.1109/aps.2006.1710640.

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Uher, Vojtech, Petr Gajdos, and Vaclav Snasel. "Towards the Gosper Space Filling Curve Implementation." In 2017 3rd IEEE International Conference on Cybernetics (CYBCONF). IEEE, 2017. http://dx.doi.org/10.1109/cybconf.2017.7985819.

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Xu, Pan, Cuong Nguyen, and Srikanta Tirthapura. "Onion Curve: A Space Filling Curve with Near-Optimal Clustering." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00119.

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Nair, Siddharth H., Arpita Sinha, and Leena Vachhani. "Hilbert's space-filling curve for regions with holes." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263684.

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Guo, Hongyi, Min Chen, Xi Liu, and Mengmeng Xie. "Genome Compression based on Hilbert Space Filling Curve." In 2015 International Conference on Management, Education, Information and Control. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/meici-15.2015.294.

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O'Shaughnessy, Stephen. "Image-based Malware Classification: A Space Filling Curve Approach." In 2019 IEEE Symposium on Visualization for Cyber Security (VizSec). IEEE, 2019. http://dx.doi.org/10.1109/vizsec48167.2019.9161583.

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Ren, Zhuojun, Guang Chen, and Wenke Lu. "Space Filling Curve Mapping for Malware Detection and Classification." In CSSE 2020: 2020 3rd International Conference on Computer Science and Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3403746.3403924.

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Gottstein, Cyprien, Philippe Raipin Parvedy, Michel Hurfin, Thomas Hassan, and Thierry Coupaye. "Inverse Space Filling Curve Partitioning Applied to Wide Area Graphs." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101417.

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Abstract:
The most recent developments in graph partitioning research often consider scale-free graphs. Instead we focus on partitioning geometric graphs using a less usual strategy: Inverse Spacefilling Partitioning (ISP). ISP relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. We extend ISP to apply it to a new context where the targets are now Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.
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Sasidharan, Aparna, John M. Dennis, and Marc Snir. "A General Space-filling Curve Algorithm for Partitioning 2D Meshes." In 2015 IEEE 17th International Conference on High-Performance Computing and Communications; 2015 IEEE 7th International Symposium on Cyberspace Safety and Security; and 2015 IEEE 12th International Conference on Embedded Software and Systems. IEEE, 2015. http://dx.doi.org/10.1109/hpcc-css-icess.2015.192.

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Bohm, Christian, Martin Perdacher, and Claudia Plant. "Cache-oblivious loops based on a novel space-filling curve." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840585.

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