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1. |
Record Nr. |
UNINA9911019414503321 |
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Autore |
Hurd Harry L (Harry Lee), <1940-> |
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Titolo |
Periodically correlated random sequences : spectral theory and practice / / Harry L. Hurd, Abolghassem Miamee |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley-Interscience, c2007 |
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ISBN |
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9786611094188 |
9781281094186 |
1281094188 |
9780470182833 |
0470182830 |
9780470182826 |
0470182822 |
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Descrizione fisica |
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1 online resource (382 p.) |
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Collana |
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Wiley series in probability and statistics |
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Altri autori (Persone) |
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MiameeAbolghassem <1944-> |
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Disciplina |
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Soggetti |
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Spectral theory (Mathematics) |
Sequences (Mathematics) |
Correlation (Statistics) |
Stochastic processes |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Includes bibliographical references (p. 337-350) and index. |
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Sommario/riassunto |
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Uniquely combining theory, application, and computing, this bookexplores the spectral approach to time series analysis The use of periodically correlated (or cyclostationary)processes has become increasingly popular in a range of researchareas such as meteorology, climate, communications, economics, andmachine diagnostics. Periodically Correlated Random Sequencespresents the main ideas of these processes through the use of basicdefinitions along with motivating, insightful, and illustrativeexamples. Extensive coverage of key concepts is provided, includingsecond-order theory, Hilbert spaces, Fourier theory, and thespectral theory of harmonizable sequences. The authors also providea paradigm for nonparametric time series analysis including testsfor the presence of PC structures. |
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Features of the book include: * An emphasis on the link between the spectral theory of unitaryoperators and the correlation structure of PC sequences * A discussion of the issues relating to nonparametric time seriesanalysis for PC sequences, including estimation of the mean, correlation, and spectrum * A balanced blend of historical background with modernapplication-specific references to periodically correlatedprocesses * An accompanying Web site that features additional exercises aswell as data sets and programs written in MATLAB® forperforming time series analysis on data that may have a PCstructure Periodically Correlated Random Sequences is an ideal text ontime series analysis for graduate-level statistics and engineeringstudents who have previous experience in second-order stochasticprocesses (Hilbert space), vector spaces, random processes, andprobability. This book also serves as a valuable reference forresearch statisticians and practitioners in areas of probabilityand statistics such as time series analysis, stochastic processes, and prediction theory. |
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2. |
Record Nr. |
UNINA9910141067203321 |
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Titolo |
2011 IEEE International Symposium on Mixed and Augmented Reality |
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Pubbl/distr/stampa |
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[Place of publication not identified], : IEEE, 2011 |
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ISBN |
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9781457721854 |
1457721856 |
9781457721847 |
1457721848 |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Sommario/riassunto |
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We present a system for accurate real-time mapping of complex and |
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arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision. |
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