Given two datasets A and B, their exclusive closest pairs (ECP) join is a one-to-one assignment of objects from the two datasets, such that (i) the closest pair (a, b) in A × B is in the result and (ii) the remai...
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the Document Analysis and Exploitation platform is a sophisticated technical environment that consists of a repository containing document images, implementations of document analysis algorithms, and the results of th...
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the Document Analysis and Exploitation platform is a sophisticated technical environment that consists of a repository containing document images, implementations of document analysis algorithms, and the results of these algorithms when applied to data in the repository. the use of a web services model makes it possible to set up document analysis pipelines that form the basis for reproducible protocols. Since the platform keeps track of all intermediate results, it becomes an information resource for the analysis of experimental data. this paper provides a tutorial on how to get started using the platform. It covers the technical details needed to overcome the initial hurdles and have a productive experience with DAE.
Semi-supervised learning methods constitute a category of machine learning methods which use labelled points together with unlabelled data to tune the classifier. the main idea of the semi-supervised methods is based ...
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Sustainable solutions must be explored to conserve natural resources such as soil and water, which are being depleted by both natural and anthropogenic *** proposed work aims to address water scarcity in agriculture b...
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In this paper we review the progress in the design of low-complexity digital correction structures and algorithms for time-interleaved ADCs over the last five years. We devise a discrete-time model, state the design p...
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In this paper we review the progress in the design of low-complexity digital correction structures and algorithms for time-interleaved ADCs over the last five years. We devise a discrete-time model, state the design problem, and finally derive the algorithms and structures. In particular, we discuss efficient algorithms to design time-varying correction filters as well as iterative structures utilizing polynomial based filters. Finally, we give an outlook to future research questions.
this book constitutes the thoroughly refereed post-conference proceedings of the 10thinternationalworkshop on Digital-forensics and Watermarking (IWDW 2011) held in Atlantic City, NJ, USA, during October 23-26, 2011...
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ISBN:
(数字)9783642322051
ISBN:
(纸本)9783642322044
this book constitutes the thoroughly refereed post-conference proceedings of the 10thinternationalworkshop on Digital-forensics and Watermarking (IWDW 2011) held in Atlantic City, NJ, USA, during October 23-26, 2011. the 37 revised full papers presented were carefully selected from 59 submissions. Conference papers are organized in 6 technical sessions, covering the topics of steganography and steganalysis, watermarking, visual cryptography, forensics, anti-forensics, fingerprinting, privacy and security.
this paper presents both standard and adaptive versions of regularized surface smoothing algorithms for 3D image enhancement. We incorporated both area decreasing flow and the median constraint as multiple regularizat...
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ISBN:
(纸本)3540239421
this paper presents both standard and adaptive versions of regularized surface smoothing algorithms for 3D image enhancement. We incorporated both area decreasing flow and the median constraint as multiple regularization functionals. the corresponding regularization parameters adaptively changes according to the local curvature value. the combination of area decreasing flow and the median constraint can efficiently remove various types of noise, such as Gaussian, impulsive, or mixed types. the adaptive version of the proposed regularized smoothing algorithm changes regularization parameters based on local curvature for preserving local edges and creases that reflects important surface information in 3D data. In addition to the theoretical expansion, experimental results show that the proposed algorithms can significantly enhance 3D data acquired by both laser range sensors and disparity maps from stereo images.
the proceedings contain 170 papers. the special focus in this conference is on European Society for Fuzzy Logic and Technology. the topics include: Higher degree fuzzy transform: application to stationary processes an...
ISBN:
(纸本)9783319668260
the proceedings contain 170 papers. the special focus in this conference is on European Society for Fuzzy Logic and Technology. the topics include: Higher degree fuzzy transform: application to stationary processes and noise reduction;sheffer stroke fuzzy implications;towards fuzzy type theory with partial functions;dynamic intuitionistic fuzzy evaluation of entrepreneurial support in countries;dynamic intuitionistic fuzzy evaluation of entrepreneurial support in countries;an interval valued hesitant fuzzy clustering approach for location clustering and customer segmentation;six sigma project selection using interval neutrosophic TOPSIS;integrated call center performance measurement using hierarchical intuitionistic fuzzy axiomatic design;prioritization of business analytics projects using interval type-2 fuzzy AHP;optimized fuzzy transform for image compression;compositions consistent withthe modus ponens property used in approximate reasoning;comparative study of type-1 and interval type-2 fuzzy systems in the fuzzy harmony search algorithm applied to benchmark functions;analysis of different proposals to improve the dissemination of information in university digital libraries;using fuzzy sets in a data-to-text system for business service intelligence;an approach to fault diagnosis using fuzzy clustering techniques;universal generalized net model for description of metaheuristic algorithms;global quality measures for fuzzy association rule bases;particle swarm optimization with fuzzy dynamic parameters adaptation for modular granular neural networks;edge detection based on ordered directionally monotone functions and learning in comparator networks.
the aim of this research was to predictive control of CO2 emissions by modelling the correlations between fuel nature structure (elementary composition) and CO2 emissions from a grate boiler. Back Propagation Neural N...
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the aim of this research was to predictive control of CO2 emissions by modelling the correlations between fuel nature structure (elementary composition) and CO2 emissions from a grate boiler. Back Propagation Neural Network (BPNN) coupled with Genetic algorithms (GA), which facilitates the learning algorithms to figure out the local minimum deviation, is employed to map the highly nonlinear relationships between elements such as C, H and O in fuels and final CO2 emission. A total of 15,000 training and testing data come from the recordings of a grate boiler within six months. And the predicted CO2 emissions based on fuel nature structure matched the measured data with fairly good agreement. Finally, the Box-Behnken experimental design methodology was used to extract the mathematical expression between elements in fuels and CO2 emission. Consequently, by knowing the C, H and O composition in fuels, the CO2 emission can be well forecasted, in such way, it is sensible to optimize the future fuel nature structure in order to achieve clean carbon footprint and control the CO2 emissions. (C) 2019 the Authors. Published by Elsevier Ltd.
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