Search results for "Image processing"

showing 10 items of 3285 documents

Combined spike-related functional MRI and multiple source analysis in the non-invasive spike localization of benign rolandic epilepsy.

2007

Abstract Objective To localize the irritative zone in children by combined spike-related fMRI and EEG multiple source analysis (MSA) in children with benign rolandic epilepsy. Methods Interictal spikes were averaged and localized using MSA, and source locations were displayed in the anatomical 3D-MRI in 11 patients (5–12 yrs, median 10). Interictal spikes were additionally recorded during the fMRI acquisition (EEG-fMRI), and the fMRI sequences were correlated off-line with the EEG spikes. Results MSA revealed an initial central dipole in all patients, including the face or hand area. A second dipolar source was mostly consistent with propagated activity. BOLD activations from EEG-fMRI, cons…

Benign Rolandic EpilepsyElectroencephalographyBrain mappingEpilepsyPhysiology (medical)medicineImage Processing Computer-AssistedHumansIctalChildCerebral CortexBrain MappingPrincipal Component Analysismedicine.diagnostic_testLateral sulcusElectroencephalographymedicine.diseaseEpilepsy RolandicMagnetic Resonance ImagingSensory Systemsnervous system diseasesRolandic epilepsyOxygennervous systemNeurologyChild PreschoolNeurology (clinical)PsychologyNeuroscienceInsulaClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
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A comparison of HDFS compact data formats: Avro versus Parquet

2017

In this paper, file formats like Avro and Parquet are compared with text formats to evaluate the performance of the data queries. Different data query patterns have been evaluated. Cloudera’s open-source Apache Hadoop distribution CDH 5.4 has been chosen for the experiments presented in this article. The results show that compact data formats (Avro and Parquet) take up less storage space when compared with plain text data formats because of binary data format and compression advantage. Furthermore, data queries from the column based data format Parquet are faster when compared with text data formats and Avro. Article in English. HDFS glaustųjų duomenų formatų palyginimas: Avro prieš Parquet…

Big DataComputer scienceBig dataEnergy Engineering and Power Technology02 engineering and technologyManagement Science and Operations Researchcomputer.software_genreColumn (database)020204 information systemsData query0202 electrical engineering electronic engineering information engineeringHDFSDatabasebusiness.industryPlain textMechanical Engineeringcomputer.file_formatAvroFile formatHiveParquetData formatHadoopBinary data020201 artificial intelligence & image processingbusinesscomputerMokslas – Lietuvos ateitis / Science – Future of Lithuania
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FASTA/Q data compressors for MapReduce-Hadoop genomics: space and time savings made easy

2021

Abstract Background Storage of genomic data is a major cost for the Life Sciences, effectively addressed via specialized data compression methods. For the same reasons of abundance in data production, the use of Big Data technologies is seen as the future for genomic data storage and processing, with MapReduce-Hadoop as leaders. Somewhat surprisingly, none of the specialized FASTA/Q compressors is available within Hadoop. Indeed, their deployment there is not exactly immediate. Such a State of the Art is problematic. Results We provide major advances in two different directions. Methodologically, we propose two general methods, with the corresponding software, that make very easy to deploy …

Big DataFASTQ formatComputer scienceBig data02 engineering and technologycomputer.software_genrelcsh:Computer applications to medicine. Medical informaticsBiochemistry03 medical and health sciencesSoftwareStructural BiologySpark (mathematics)0202 electrical engineering electronic engineering information engineeringData_FILESMapReduceMapReduce; hadoop; sequence analysis; data compressionMolecular Biologylcsh:QH301-705.5030304 developmental biologyFile system0303 health sciencesSettore INF/01 - InformaticaDatabasebusiness.industryMethodology ArticleApplied MathematicsSequence analysisGenomicsData compression; Hadoop; MapReduce; Sequence analysis; Algorithms; Big Data; Data Compression; Genomics; SoftwareComputer Science Applicationslcsh:Biology (General)Software deploymentHadoopData compressionlcsh:R858-859.7020201 artificial intelligence & image processingState (computer science)businesscomputerAlgorithmsSoftwareData compressionBMC Bioinformatics
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SOCIAL NETWORKS, BIG DATA AND TRANSPORT PLANNING

2016

[EN] The characteristics of people who are related or tied to each individual affects her activity-travel behavior. That influence is especially associated to social and recreational activities, which are increasingly important. Collecting high quality data from those social networks is very difficult using traditional travel surveys, because respondents are asked about their general social life, which is most demanding to remember that specific facts. On the other hand, currently there are different potential sources of transport data, which is characterized by the huge amount of information available, the velocity with it is obtained and the variety of format in which is presented. This s…

Big DataOperations researchTransport PlanningComputer scienceBig data02 engineering and technologyINGENIERÍA DEL TRANSPORTEINGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTESSocial life0502 economics and business0202 electrical engineering electronic engineering information engineeringTransporte y movilidad 34807 / C - Máster universitario en sistemas inteligentes de transporte 2283sortRecreation050210 logistics & transportationTransportation planningSocial networkMINERVA projectbusiness.industry05 social sciencesData scienceVariety (cybernetics)Social NetworksData quality020201 artificial intelligence & image processingbusinessLibro de Actas CIT2016. XII Congreso de Ingeniería del Transporte
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Mining customer requirements from online reviews: A product improvement perspective

2016

We propose a filtering model to predict helpfulness of reviews for product design.We provide a way to use the KANO model based on online reviews.We explore how to obtain insights from Big Data through knowledge-based view. Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the help…

Big data commerceEngineeringINF/01 - Computer ScienceInformation Systems and ManagementBig data02 engineering and technologyOnline reviewManagement Information SystemsKANO0502 economics and business0202 electrical engineering electronic engineering information engineeringProduct (category theory)Robustness (economics)Product designbusiness.industry05 social sciencesSettore IUS/10 - Diritto AmministrativoData scienceConjoint analysisProduct designConjoint analysiKano modelHelpfulnessNew product development050211 marketing020201 artificial intelligence & image processingbusinessInformation Systems
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Memristors in Nonlinear Network : Application to Information (Signal and Image) Processing

2021

Memristor is a two-terminal nonlinear dynamic electronic device. Typically, it is a passive nano-device whose conductivity is controlled by the flux, time-integral of the voltage across its terminals, or by the charge, time-integral of the current flowing through it, and it presents interesting features for versatile applications. This thesis considers memristor use as a neighborhood connection for 2D cellular nonlinear or neural network (CNN), essentially for information (image and signal) processing and electronic prosthesis. We develop a model of the memristor based 2D cellular nonlinear networks CNNs compatible to image applications by incorporating memristor in the adjacent neighborhoo…

BilateralityMemristor and modelsSignal and image processingRéseau 2 dimensions[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]Bilatéralité2 dimensional networks[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH]Propagation (réseau 1D)Fitzhugh-Nagumo cellsTraitement du signal et de l'imageFitzhugh-Nagumo cellulesPropagation (1D network)Memristor et models
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Computation of the area in the discrete plane: Green’s theorem revisited

2017

International audience; The detection of the contour of a binary object is a common problem; however, the area of a region, and its moments, can be a significant parameter. In several metrology applications, the area of planar objects must be measured. The area is obtained by counting the pixels inside the contour or using a discrete version of Green's formula. Unfortunately, we obtain the area enclosed by the polygonal line passing through the centers of the pixels along the contour. We present a modified version of Green's theorem in the discrete plane, which allows for the computation of the exact area of a two-dimensional region in the class of polyominoes. Penalties are introduced and …

Binary Objectcontour detectionPolyominoComputationGeometry0102 computer and information sciences02 engineering and technology01 natural sciencesconnectednessPick's theoremsymbols.namesake0202 electrical engineering electronic engineering information engineeringPick's theoremElectrical and Electronic EngineeringGreen's theoremMathematicsDigital picturesPixelMathematical analysisImage segmentationAtomic and Molecular Physics and OpticsComputer Science Applications[SPI.TRON]Engineering Sciences [physics]/Electronics010201 computation theory & mathematics[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Binary datasymbols[SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic020201 artificial intelligence & image processingpolyominoesGreen's theorem
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Fast Algorithms for Pseudoarboricity

2015

The densest subgraph problem, which asks for a subgraph with the maximum edges-to-vertices ratio d∗, is solvable in polynomial time. We discuss algorithms for this problem and the computation of a graph orientation with the lowest maximum indegree, which is equal to ⌈d∗⌉. This value also equals the pseudoarboricity of the graph. We show that it can be computed in O(|E| √ log log d∗) time, and that better estimates can be given for graph classes where d∗ satisfies certain asymptotic bounds. These runtimes are achieved by accelerating a binary search with an approximation scheme, and a runtime analysis of Dinitz’s algorithm on flow networks where all arcs, except the source and sink arcs, hav…

Binary search algorithmComputation0102 computer and information sciences02 engineering and technologyOrientation (graph theory)01 natural sciencesFlow (mathematics)010201 computation theory & mathematicsLog-log plotTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY0202 electrical engineering electronic engineering information engineeringGraph (abstract data type)020201 artificial intelligence & image processingUnit (ring theory)AlgorithmTime complexityMathematicsofComputing_DISCRETEMATHEMATICSMathematics2016 Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX)
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On the Non-uniform Redundancy in Grammatical Evolution

2016

This paper investigates the redundancy of representation in grammatical evolution (GE) for binary trees. We analyze the entire GE solution space by creating all binary genotypes of predefined length and map them to phenotype trees, which are then characterized by their size, depth and shape. We find that the GE representation is strongly non-uniformly redundant. There are huge differences in the number of genotypes that encode one particular phenotype. Thus, it is difficult for GE to solve problems where the optimal tree solutions are underrepresented. In general, the GE mapping process is biased towards short tree structures, which implies high GE performance if the optimal solution requir…

Binary treeComputer scienceBinary number0102 computer and information sciences02 engineering and technologyENCODE01 natural sciencesTree (graph theory)Tree structure010201 computation theory & mathematicsGrammatical evolution0202 electrical engineering electronic engineering information engineeringRedundancy (engineering)020201 artificial intelligence & image processingRepresentation (mathematics)Algorithm
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Multiscale modeling on biological systems

2018

Biochemistry & Molecular Biology010304 chemical physicsComputer scienceManagement scienceBiophysicsMEDLINE02 engineering and technologyCell BiologyModels TheoreticalMedical Biochemistry and MetabolomicsMOLECULAR BIOLOGY METHODS01 natural sciencesBiochemistryMultiscale modelingMedicinal and Biomolecular ChemistryTheoreticalModels0103 physical sciences0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingBiochemistry and Cell BiologyMolecular BiologyIntroductory Journal ArticleBiochemical and Biophysical Research Communications
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