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Journal articles on the topic 'Tiling (Mathematics) Data processing'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Rodríguez, Gabriel, Louis-Noël Pouchet, and Juan Touriño. "Representing Integer Sequences Using Piecewise-Affine Loops." Mathematics 9, no. 19 (September 24, 2021): 2368. http://dx.doi.org/10.3390/math9192368.

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A formal, high-level representation of programs is typically needed for static and dynamic analyses performed by compilers. However, the source code of target applications is not always available in an analyzable form, e.g., to protect intellectual property. To reason on such applications, it becomes necessary to build models from observations of its execution. This paper details an algebraic approach which, taking as input the trace of memory addresses accessed by a single memory reference, synthesizes an affine loop with a single perfectly nested reference that generates the original trace. This approach is extended to support the synthesis of unions of affine loops, useful for minimally modeling traces generated by automatic transformations of polyhedral programs, such as tiling. The resulting system is capable of processing hundreds of gigabytes of trace data in minutes, minimally reconstructing 100% of the static control parts in PolyBench/C applications and 99.99% in the Pluto-tiled versions of these benchmarks. As an application example of the trace modeling method, trace compression is explored. The affine representations built for the memory traces of PolyBench/C codes achieve compression factors of the order of 106 and 103 with respect to gzip for the original and tiled versions of the traces, respectively.

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Kempeneers, Pieter, Tomas Kliment, Luca Marletta, and Pierre Soille. "Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure." Remote Sensing 14, no. 2 (January 15, 2022): 398. http://dx.doi.org/10.3390/rs14020398.

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This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.

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3

Yassour, M., T. Kaplan, A. Jaimovich, and N. Friedman. "Nucleosome positioning from tiling microarray data." Bioinformatics 24, no. 13 (June 27, 2008): i139—i146. http://dx.doi.org/10.1093/bioinformatics/btn151.

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4

Andonov, R., S. Balev, S. Rajopadhye, and N. Yanev. "Optimal semi-oblique tiling." IEEE Transactions on Parallel and Distributed Systems 14, no. 9 (September 2003): 944–60. http://dx.doi.org/10.1109/tpds.2003.1233716.

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Amaral, Marcelo, David Chester, Fang Fang, and Klee Irwin. "Exploiting Anyonic Behavior of Quasicrystals for Topological Quantum Computing." Symmetry 14, no. 9 (August 26, 2022): 1780. http://dx.doi.org/10.3390/sym14091780.

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The concrete realization of topological quantum computing using low-dimensional quasiparticles, known as anyons, remains one of the important challenges of quantum computing. A topological quantum computing platform promises to deliver more robust qubits with additional hardware-level protection against errors that could lead to the desired large-scale quantum computation. We propose quasicrystal materials as such a natural platform and show that they exhibit anyonic behavior that can be used for topological quantum computing. Different from anyons, quasicrystals are already implemented in laboratories. In particular, we study the correspondence between the fusion Hilbert spaces of the simplest non-abelian anyon, the Fibonacci anyons, and the tiling spaces of the one-dimensional Fibonacci chain and the two-dimensional Penrose tiling quasicrystals. A concrete encoding on these tiling spaces of topological quantum information processing is also presented by making use of inflation and deflation of such tiling spaces. While we outline the theoretical basis for such a platform, details on the physical implementation remain open.

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6

Olasz, A., B. Nguyen Thai, and D. Kristóf. "A NEW INITIATIVE FOR TILING, STITCHING AND PROCESSING GEOSPATIAL BIG DATA IN DISTRIBUTED COMPUTING ENVIRONMENTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 111–18. http://dx.doi.org/10.5194/isprs-annals-iii-4-111-2016.

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Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. <br><br> This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (<a href="https://github.com/posseidon/IQLib/"target="_blank">https://github.com/posseidon/IQLib/</a>) developed in the frame of the IQmulus EU FP7 research and development project (<a href="http://www.iqmulus.eu"target="_blank">http://www.iqmulus.eu</a>). The data distribution framework has no limitations on programming language environment and can execute scripts (and workflows) written in different development frameworks (e.g. Python, R or C#). It is capable of processing raster, vector and point cloud data. The above-mentioned prototype is presented through a case study dealing with country-wide processing of raster imagery. Further investigations on algorithmic and implementation details are in focus for the near future.

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7

Olasz, A., B. Nguyen Thai, and D. Kristóf. "A NEW INITIATIVE FOR TILING, STITCHING AND PROCESSING GEOSPATIAL BIG DATA IN DISTRIBUTED COMPUTING ENVIRONMENTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 111–18. http://dx.doi.org/10.5194/isprsannals-iii-4-111-2016.

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Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. <br><br> This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (<a href="https://github.com/posseidon/IQLib/"target="_blank">https://github.com/posseidon/IQLib/</a>) developed in the frame of the IQmulus EU FP7 research and development project (<a href="http://www.iqmulus.eu"target="_blank">http://www.iqmulus.eu</a>). The data distribution framework has no limitations on programming language environment and can execute scripts (and workflows) written in different development frameworks (e.g. Python, R or C#). It is capable of processing raster, vector and point cloud data. The above-mentioned prototype is presented through a case study dealing with country-wide processing of raster imagery. Further investigations on algorithmic and implementation details are in focus for the near future.

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8

Jaworski, John, and Elizabeth Bliss. "Data Processing Mathematics." Mathematical Gazette 71, no. 458 (December 1987): 334. http://dx.doi.org/10.2307/3617092.

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9

Richards, B. "Data processing mathematics." Data Processing 28, no. 3 (April 1986): 162. http://dx.doi.org/10.1016/0011-684x(86)90015-8.

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10

Storch, Iago, Bruno Zatt, Luciano Agostini, Guilherme Correa, and Daniel Palomino. "Memory-aware Workload Balancing Technique based on Decision Trees for Parallel HEVC Video Coding." Journal of Integrated Circuits and Systems 15, no. 3 (December 28, 2020): 1–9. http://dx.doi.org/10.29292/jics.v15i3.96.

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Video coding applications demand high computational effort to achieve high compression rates at a low perceptual quality expense. In order to reach acceptable encoding time for such applications, modern video coding standards have been em-ploying parallelism approaches to exploit multiprocessing plat-forms, such as the tiling tool from HEVC standard. When employing Tiles, each frame is divided into rectangular-shaped regions which can be encoded independently. However, alt-hough it is possible to distribute the data equally among the processing units when using Tiles, balancing the workload among processing units poses great challenges. Therefore, this paper proposes a workload balancing technique aiming to speed up the HEVC parallel encoding using Tiles. Different from other literature works, the proposed solution uses a novel approach employing static uniform tiling to avoid memory management difficulties that may emerge when dynamic tiling solutions are employed. The proposed technique relies on workload distribution history of previous frames to predict the workload distribution of the current frame. Then, the pro-posed technique balances the workload among Tiles by em-ploying a workload reduction scheme based on decision trees in the coding process. Experimental tests show that the pro-posed solution outperforms the standard uniform tiling and it is competitive with related works in terms of speedup. Moreo-ver, the solution optimizes resources usage in multiprocessing platforms, presents a negligible coding efficiency loss and avoids increasing memory bandwidth usage by 9.8%, on aver-age, when compared to dynamic tiling solutions, which can impact significantly the performance in memory-constrained platforms.

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11

Jimenez, M., J. M. Llaberia, and A. Fernandez. "A cost-effective implementation of multilevel tiling." IEEE Transactions on Parallel and Distributed Systems 14, no. 10 (October 2003): 1006–20. http://dx.doi.org/10.1109/tpds.2003.1239869.

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12

He, K., X. Li, J. Zhou, X. W. Deng, H. Zhao, and J. Luo. "NTAP: for NimbleGen tiling array ChIP-chip data analysis." Bioinformatics 25, no. 14 (May 25, 2009): 1838–40. http://dx.doi.org/10.1093/bioinformatics/btp320.

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13

Ding, Shuang, Xiao Diao Huang, and Chun Jian Yu. "Post Processing for Five-Axis Machine Tools with Pose Error Compensation." Advanced Materials Research 1037 (October 2014): 283–87. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.283.

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This paper describes the enhancement of the machining precision of 5-axis machine tools. Most post-processors did not consider the pose errors so that the processing error was increased invisibly. A post processing method with pose error compensation was proposed for table/spindle-tilting type machine tools.12 position errors and 12 rotation errors were compensated by the proposed method. Mathematical model contained pose errors which converted cutter location data into machine motion coordinates was established based on inverse kinematics and the analytical solutions of NC code were derived. The tool paths of the freeform surface machining were posted with the presented method. And the simulation experiment was carried out which verified the feasibility of the post processing method with pose error compensation. Comparison of processing error shows that the post processing with pose error compensation can effectively improve the machining accuracy by approximately 50%.

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14

Piccolboni, Antonio. "Multivariate Segmentation in the Analysis of Transcription Tiling Array Data." Journal of Computational Biology 15, no. 7 (September 2008): 845–56. http://dx.doi.org/10.1089/cmb.2007.0141.

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15

Judy, Jennifer Toolan, and Hongkai Ji. "TileProbe: modeling tiling array probe effects using publicly available data." Bioinformatics 25, no. 18 (July 9, 2009): 2369–75. http://dx.doi.org/10.1093/bioinformatics/btp425.

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16

Villa, G., S. Mas, X. Fernández-Villarino, J. Martínez-Luceño, J. C. Ojeda, B. Pérez-Martín, J. A. Tejeiro, C. García-González, E. López-Romero, and C. Soteres. "THE NEED OF NESTED GRIDS FOR AERIAL AND SATELLITE IMAGES AND DIGITAL ELEVATION MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 131–38. http://dx.doi.org/10.5194/isprs-archives-xli-b2-131-2016.

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Usual workflows for production, archiving, dissemination and use of Earth observation images (both aerial and from remote sensing satellites) pose big interoperability problems, as for example: non-alignment of pixels at the different levels of the pyramids that makes it impossible to overlay, compare and mosaic different orthoimages, without resampling them and the need to apply multiple resamplings and compression-decompression cycles. These problems cause great inefficiencies in production, dissemination through web services and processing in “Big Data” environments. Most of them can be avoided, or at least greatly reduced, with the use of a common “nested grid” for mutiresolution production, archiving, dissemination and exploitation of orthoimagery, digital elevation models and other raster data. “Nested grids” are space allocation schemas that organize image footprints, pixel sizes and pixel positions at all pyramid levels, in order to achieve coherent and consistent multiresolution coverage of a whole working area. A “nested grid” must be complemented by an appropriate “tiling schema”, ideally based on the “quad-tree” concept. In the last years a “de facto standard” grid and Tiling Schema has emerged and has been adopted by virtually all major geospatial data providers. It has also been adopted by OGC in its “WMTS Simple Profile” standard. In this paper we explain how the adequate use of this tiling schema as common nested grid for orthoimagery, DEMs and other types of raster data constitutes the most practical solution to most of the interoperability problems of these types of data.

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17

Villa, G., S. Mas, X. Fernández-Villarino, J. Martínez-Luceño, J. C. Ojeda, B. Pérez-Martín, J. A. Tejeiro, C. García-González, E. López-Romero, and C. Soteres. "THE NEED OF NESTED GRIDS FOR AERIAL AND SATELLITE IMAGES AND DIGITAL ELEVATION MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 131–38. http://dx.doi.org/10.5194/isprsarchives-xli-b2-131-2016.

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Usual workflows for production, archiving, dissemination and use of Earth observation images (both aerial and from remote sensing satellites) pose big interoperability problems, as for example: non-alignment of pixels at the different levels of the pyramids that makes it impossible to overlay, compare and mosaic different orthoimages, without resampling them and the need to apply multiple resamplings and compression-decompression cycles. These problems cause great inefficiencies in production, dissemination through web services and processing in “Big Data” environments. Most of them can be avoided, or at least greatly reduced, with the use of a common “nested grid” for mutiresolution production, archiving, dissemination and exploitation of orthoimagery, digital elevation models and other raster data. “Nested grids” are space allocation schemas that organize image footprints, pixel sizes and pixel positions at all pyramid levels, in order to achieve coherent and consistent multiresolution coverage of a whole working area. A “nested grid” must be complemented by an appropriate “tiling schema”, ideally based on the “quad-tree” concept. In the last years a “de facto standard” grid and Tiling Schema has emerged and has been adopted by virtually all major geospatial data providers. It has also been adopted by OGC in its “WMTS Simple Profile” standard. In this paper we explain how the adequate use of this tiling schema as common nested grid for orthoimagery, DEMs and other types of raster data constitutes the most practical solution to most of the interoperability problems of these types of data.

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18

Karimi, Mohammad H., and Davud Asemani. "Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation." ISA Transactions 53, no. 3 (May 2014): 834–44. http://dx.doi.org/10.1016/j.isatra.2013.11.015.

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19

Zacher, Benedikt, Pei Kuan, and Achim Tresch. "Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data." BMC Bioinformatics 11, no. 1 (2010): 194. http://dx.doi.org/10.1186/1471-2105-11-194.

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20

Olanda, R., M. Perez, and X. Benavent. "Tiling of the Wavelet Lowpass Subbands for Progressive Browsing of Images." IEEE Signal Processing Letters 13, no. 11 (November 2006): 680–83. http://dx.doi.org/10.1109/lsp.2006.879468.

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21

Wang, Jia, Tao Sun, Li Zhou, Yuan Zhi Zhang, and Yuan Yuan Gao. "A 18.4M Triangles/s 122.6 mW Tile Co-Processor for Embedded GPU Systems." Applied Mechanics and Materials 462-463 (November 2013): 1050–54. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.1050.

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This paper presents an efficient and accurate tile co-processor architecture which can be used in the tile based rendering systems. The design involves two key components, the vertex processing unit and the triangle tiling unit. The former part is used to get the vertices transformed, clipped and projected to generate the triangle list which located in the view frustum while the latter one reads in the triangle data and determines the tile list which indicates tiles that each triangle covers. A modified Bounding BOX (BBOX) test pipeline and a mask screening technology for different overlap types is proposed and employed in the design in order to get faster triangle binning with lower power consumption. The proposed architecture works at the frequency of 270 MHz, gains 18.4 M triangles tiling/sec with a power consumption less than 122.6 mW. The chip is implemented in 0.13 um CMOS technology and consumes 2.5 x 2.5 mm2 totally.

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22

Ivanek, J. "Mathematics for data processing and computing." European Journal of Operational Research 23, no. 3 (March 1986): 414–15. http://dx.doi.org/10.1016/0377-2217(86)90314-0.

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23

Frey, Brendan J., Quaid D. Morris, and Timothy R. Hughes. "GenRate: A Generative Model that Reveals Novel Transcripts in Genome-Tiling Microarray Data." Journal of Computational Biology 13, no. 2 (March 2006): 200–214. http://dx.doi.org/10.1089/cmb.2006.13.200.

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24

Kumkov, Sergey I., Vyacheslav S. Nikitin, Tatyana N. Ostanina, and Valentin M. Rudoy. "Interval processing of electrochemical data." Journal of Computational and Applied Mathematics 380 (December 2020): 112961. http://dx.doi.org/10.1016/j.cam.2020.112961.

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25

Taskesen, Erdogan, Renee Beekman, Jeroen de Ridder, Bas J. Wouters, Justine K. Peeters, Ivo P. Touw, Marcel J. T. Reinders, and Ruud Delwel. "HAT: Hypergeometric Analysis of Tiling-arrays with application to promoter-GeneChip data." BMC Bioinformatics 11, no. 1 (2010): 275. http://dx.doi.org/10.1186/1471-2105-11-275.

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Ashcroft, W. A. "Mathematics for Seismic Data Processing and Interpretation." Physics of the Earth and Planetary Interiors 58, no. 2-3 (December 1989): 269–70. http://dx.doi.org/10.1016/0031-9201(89)90063-0.

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27

Tang, Hui, and Terry M. Therneau. "Statistical Metrics for Quality Assessment of High-Density Tiling Array Data." Biometrics 66, no. 2 (July 23, 2009): 630–35. http://dx.doi.org/10.1111/j.1541-0420.2009.01298.x.

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28

Alexandre, Cyprien, Rosa Johary, Thibault Catry, Pascal Mouquet, Christophe Révillion, Solofo Rakotondraompiana, and Gwenaelle Pennober. "A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts." Remote Sensing 12, no. 2 (January 10, 2020): 252. http://dx.doi.org/10.3390/rs12020252.

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In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

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29

Cesaroni, M., D. Cittaro, A. Brozzi, P. G. Pelicci, and L. Luzi. "CARPET: a web-based package for the analysis of ChIP-chip and expression tiling data." Bioinformatics 24, no. 24 (October 21, 2008): 2918–20. http://dx.doi.org/10.1093/bioinformatics/btn542.

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30

Nicolas, Pierre, Aurélie Leduc, Stéphane Robin, Simon Rasmussen, Hanne Jarmer, and Philippe Bessières. "Transcriptional landscape estimation from tiling array data using a model of signal shift and drift." Bioinformatics 25, no. 18 (June 26, 2009): 2341–47. http://dx.doi.org/10.1093/bioinformatics/btp395.

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31

K., Anandhan, Ajay Shanker Singh, and Thirunavukkarasu K. "An Automatic Rice Plant Disease Detection Model Built With Unstructured Data Using IMDT Tiling and CNN Cognitive Object Detection." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (December 31, 2022): 65–75. http://dx.doi.org/10.17762/ijritcc.v10i12.5887.

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Nowadays agriculture and processes are getting more intelligent mechanisms to improve the yield and reduce manual work. Smart agriculture provides numerous modern ideologies to farmers. But still, farmers face one important issue crop disease. Many researchers provide plenty of ways to recover and tackle the situation to come out of this problem. Therefore, they proceed with image processing to identify diseases from rice plant images. Farmers mainly face problems to take proper images for classification. Because of various reasons like various environmental factors, farmers ignorance, field size, capturing angle, device limitations, etc. are affecting the quality of the disease detection system, and these factors degrade overall performance. For this problem, introducing the Intelligent multi-dimensional tiling (IMDT) technique with an advanced convolution neural network with cognitive object detection (CNN-COD). IMDT technique developed with an intelligent expert system that adjusts input image size, capturing angles and other factors automatically. This advanced tiling technique supports to do the cropping and fluttering of input images for resizing. And CNN-COD model was used to calculate rice leaf width size and rescaled at the time of image segmentation with the Residual network (ResNet) model. Created dynamic tiled images are uniformly and scaled dimensional objects. These input values are used to train the CNN-COD rice plant disease, prediction model. Our proposed models were appraised on more than 4960 images which contain 8 various types of rice crop diseases. The experimental result portrayed out the CNN-COD model receives significant improvement in objection detection and image classification for the rice plant disease detection system. Mean average precision (MAP) values compared the CNN-COD model with the YOLOv4 model it got improved by 3.7% with the tiled input dataset.

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32

Tagiev, F. K., M. A. Fradkina, N. G. Chernoguz, and I. L. Shain. "Adaptive noise-immune data-processing system." Measurement Techniques 30, no. 9 (September 1987): 833–36. http://dx.doi.org/10.1007/bf00865883.

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33

Usuki, Shin, and Kenjiro Takai Miura. "Nano-Micro Geometric Modeling Using Microscopic Image." Key Engineering Materials 523-524 (November 2012): 345–49. http://dx.doi.org/10.4028/www.scientific.net/kem.523-524.345.

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In recent years, there are a lot of active researches on nano-micro manufacturing and metrology, since not only industrial fields but also medical fields require higher accuracy with respect to miniaturizing size of the target. However, we cannot make an effective use of three dimensional measurement data for the nano-micro design and manufacturing due to a wide variety of instruments, resolutions, and noises. In fact, the nano-micro geometric modeling is at an early stage of development in spite of its importance for the next generation. In order to find a solution to this problem, we propose to combine the multi-resolution processing with the microscopic images for high speed and non-destructive geometric modeling as well as for the homogeneous modeling from micro features to macro ones. This research includes measurement data tiling between different instruments, high resolution optical microscopic imaging, focus judgment of three dimensional microscopic data, and large scale point crowd processing. These built models are potentially applied to in-line inspections and numerical simulations. Therefore, the nano-micro geometric modeling contribute to further developments of ultra precise manufacturing and the biotechnology.

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34

Scherr, A. L. "Distributed data processing." IBM Systems Journal 38, no. 2.3 (1999): 354–74. http://dx.doi.org/10.1147/sj.382.0354.

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35

Boehm, J., and K. Liu. "NOSQL FOR STORAGE AND RETRIEVAL OF LARGE LIDAR DATA COLLECTIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 577–82. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-577-2015.

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Developments in LiDAR technology over the past decades have made LiDAR to become a mature and widely accepted source of geospatial information. This in turn has led to an enormous growth in data volume. The central idea for a file-centric storage of LiDAR point clouds is the observation that large collections of LiDAR data are typically delivered as large collections of files, rather than single files of terabyte size. This split of the dataset, commonly referred to as tiling, was usually done to accommodate a specific processing pipeline. It makes therefore sense to preserve this split. A document oriented NoSQL database can easily emulate this data partitioning, by representing each tile (file) in a separate document. The document stores the metadata of the tile. The actual files are stored in a distributed file system emulated by the NoSQL database. We demonstrate the use of MongoDB a highly scalable document oriented NoSQL database for storing large LiDAR files. MongoDB like any NoSQL database allows for queries on the attributes of the document. As a specialty MongoDB also allows spatial queries. Hence we can perform spatial queries on the bounding boxes of the LiDAR tiles. Inserting and retrieving files on a cloud-based database is compared to native file system and cloud storage transfer speed.

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SHIMADA, Osamu, Akihiko SUGIYAMA, and Toshiyuki NOMURA. "A Low Complexity Noise Suppressor with Hybrid Filterbanks and Adaptive Time-Frequency Tiling." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 1 (2010): 254–60. http://dx.doi.org/10.1587/transfun.e93.a.254.

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Ames, W. F. "Underwater acoustic data processing." Mathematics and Computers in Simulation 31, no. 6 (February 1990): 594. http://dx.doi.org/10.1016/0378-4754(90)90066-r.

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Aksenov, E. A., A. I. Lopatin, and O. N. Khazov. "CAMAC modules for processing metrological-system data." Measurement Techniques 30, no. 11 (November 1987): 1055–56. http://dx.doi.org/10.1007/bf00865053.

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Guintivano, Jerry, Michal Arad, Kellie LK Tamashiro, Todd D. Gould, and Zachary A. Kaminsky. "BioTile, A Perl based tool for the identification of differentially enriched regions in tiling microarray data." BMC Bioinformatics 14, no. 1 (2013): 76. http://dx.doi.org/10.1186/1471-2105-14-76.

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Tompkins, Diane L., Linda B. Bourque, and Virginia A. Clark. "Processing Data: The Survey Example." American Statistician 47, no. 3 (August 1993): 235. http://dx.doi.org/10.2307/2684986.

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Tang, Yuzhe, and Bugra Gedik. "Autopipelining for Data Stream Processing." IEEE Transactions on Parallel and Distributed Systems 24, no. 12 (December 2013): 2344–54. http://dx.doi.org/10.1109/tpds.2012.333.

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Hyun, Seung Ho, Bogun Jin, and Seung Jae Lee. "A novel Bad Data Processing algorithm for analog data in substation automation systems." Applied Mathematics and Computation 205, no. 2 (November 2008): 824–31. http://dx.doi.org/10.1016/j.amc.2008.05.137.

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Reyes-Herrera, Paula H., and Elisa Ficarra. "Computational Methods for CLIP-Seq Data Processing." Bioinformatics and Biology Insights 8 (January 2014): BBI.S16803. http://dx.doi.org/10.4137/bbi.s16803.

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RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.

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Golovan, A. A., and N. B. Vavilova. "Satellite navigation. Raw data processing for geophysical applications." Journal of Mathematical Sciences 146, no. 3 (October 2007): 5920–30. http://dx.doi.org/10.1007/s10958-007-0406-9.

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Kahsay, Robel, Jeet Vora, Rahi Navelkar, Reza Mousavi, Brian C. Fochtman, Xavier Holmes, Nagarajan Pattabiraman, et al. "GlyGen data model and processing workflow." Bioinformatics 36, no. 12 (April 23, 2020): 3941–43. http://dx.doi.org/10.1093/bioinformatics/btaa238.

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Abstract Summary Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources. Availability and implementation GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license. Supplementary information Supplementary data are available at Bioinformatics online.

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Garcia de la Nava, J., S. van Hijum, and O. Trelles. "PreP: gene expression data pre-processing." Bioinformatics 19, no. 17 (November 20, 2003): 2328–29. http://dx.doi.org/10.1093/bioinformatics/btg318.

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Wiaux, Y., J. D. McEwen, and P. Vielva. "Complex Data Processing: Fast Wavelet Analysis on the Sphere." Journal of Fourier Analysis and Applications 13, no. 4 (April 11, 2007): 477–93. http://dx.doi.org/10.1007/s00041-006-6917-9.

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Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, Furqan Aziz, Bader Alouffi, M. Irfan Uddin, and Ahmed AlGhamdi. "Efficient Processing of Image Processing Applications on CPU/GPU." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/4839876.

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Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.

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Metre, K. V. "Location based Continuous Query Processing over Geo-streaming Data." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 106–14. http://dx.doi.org/10.17762/turcomat.v12i1s.1583.

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In recent years, many data-intensive and location based applications have emerged that need to process stream data in applications such as network monitoring, telecommunications data management, and sensor networks. Unlike regular queries, a continuous query exists for certain period of time and need to be continuously processed during this time. The algorithms used for data processing for the traditional database systems are not suited to tackle complex and various continuous queries over dynamic streaming data. The indexing for finite queries is preferred to indexing on infinite data to avoid expensive operations of index maintenance. Previous related work focused on moving queries on static objects or static queries on moving object. But now-a-days queries as well as objects are dynamic. So, hybrid indexing for queries significantly reduces the space costs and scales well with the increasing data. To deal with the speed of unbounded data, it is necessary to use data parallelism in query processing. The data parallelism in query processing offers better performance, availability and scalability.

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Et.al, Anjana Yadav. "Improving the Performance of Multidimensional Clinical Data for OLAP using an Optimized Data Clustering approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 3269–75. http://dx.doi.org/10.17762/turcomat.v12i3.1575.

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Medicine is a fresh way to utilize for curing, analyzing and detecting the diseases through data clustering with OLAP (Online Analytical Processing). The large amount of multidimensional clinical data is reduced the efficiency of OLAP query processing by enhancing the query accessing time. Hence, the performance of OLAP model is improved by using data clustering in which huge data is divided into several groups (clusters) with cluster heads to achieve fast query processing in least time. In this paper, a Dragon Fly Optimization based Clustering (DFOC) approach is proposed to enhance the efficiency of data clustering by generating optimal clusters from multidimensional clinical data for OLAP. The results are evaluated on MATLAB 2019a tool and shown the better performance of DFOC against other clustering methods ACO, GA and K-Means in terms of intra-cluster distance, purity index, F-measure, and standard deviation

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