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1. |
Record Nr. |
UNINA9910480117103321 |
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Autore |
Hiss G. |
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Titolo |
Imprimitive irreducible modules for finite quasisimple groups / / Gerhard Hiss, William J. Husen, Kay Magaard |
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Pubbl/distr/stampa |
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Providence, Rhode Island : , : American Mathematical Society, , 2014 |
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©2014 |
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ISBN |
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Descrizione fisica |
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1 online resource (114 p.) |
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Collana |
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Memoirs of the American Mathematical Society, , 1947-6221 ; ; Volume 234, Number 1104 (fourth of 5 numbers) |
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Disciplina |
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Soggetti |
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Algebraic fields |
Finite groups |
Semisimple Lie groups |
Electronic books. |
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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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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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""Cover""; ""Title page""; ""Acknowledgements""; ""Chapter 1. Introduction""; ""Chapter 2. Generalities""; ""2.1. Comments on the notation""; ""2.2. Conditions for primitivity""; ""2.3. Some results on linear groups of small degree""; ""2.4. Reduction modulo â?? and imprimitivity""; ""2.5. A result on polynomials""; ""Chapter 3. Sporadic Groups and the Tits Group""; ""Chapter 4. Alternating Groups""; ""Chapter 5. Exceptional Schur Multipliers and Exceptional Isomorphisms""; ""5.1. Description of the tables""; ""5.2. The proofs"" |
""9.3. Lusztig series""""9.4. Examples for the restriction to commutator subgroups""; ""Chapter 10. Exceptional groups""; ""10.1. The exceptional groups of type and ""; ""10.2. Explicit results on some exceptional groups""; ""Bibliography""; ""Back Cover"" |
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2. |
Record Nr. |
UNINA9910528217103321 |
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Autore |
Candy James V. |
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Titolo |
Model-based processing : an applied subspace identification approach / / James V. Candy |
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Pubbl/distr/stampa |
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Hoboken, NJ : , : John Wiley & Sons, Inc., , 2019 |
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ISBN |
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1-119-45777-7 |
1-119-45769-6 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (540 pages) |
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Collana |
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Disciplina |
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Soggetti |
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Signal processing - Digital techniques - Mathematics |
Automatic control - Mathematical models |
Invariant subspaces |
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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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Nota di bibliografia |
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Includes bibliographical references and index. |
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Sommario/riassunto |
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A bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems Model-Based Processing : An Applied Subspace Identification Approach provides expert insight on developing models for designing model-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles—all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development |
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problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features: Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters Practical processor designs including comprehensive methods of performance analysis Provides a link between model development and practical applications in model-based signal processing Offers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications Enables readers to bridge the gap from statistical signal processing to subspace identification Includes appendices, problem sets, case studies, examples, and notes for MATLAB Model-Based Processing: An Applied Subspace Identification Approach is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia. |
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