Candy james v (26 Ergebnisse)

Hardcover. Zustand: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Soft cover. Zustand: Very Good. Some mild shelf-wear. Contents clean and tight. Very good.

hardcover. Zustand: Very Good. very good bookin very good dust jacket; slight edge wear to dj. 8vo. 240 pp.

Zustand: As New. Like New condition. A near perfect copy that may have very minor cosmetic defects.

Hardcover. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

Zustand: New. pp. 608.

Zustand: New. In English.

Zustand: New. In English.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Gut. Zustand: Gut | Seiten: 386 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Hardcover. Zustand: Brand New. 2nd edition. 601 pages. 10.00x7.00x1.50 inches. In Stock.

Zustand: New. 2019. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.

Zustand: New. Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control. Num Pages: 640 pages, illustrations. BIC Classification: TJK; UYS. Category: (P) Professional & Vocational. Dimension: 167 x 244 x 40. Weight in Grams: 1028. . 2016. 2nd Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Hardcover. Zustand: Brand New. 511 pages. 9.00x6.00x1.25 inches. In Stock.

Buch. Zustand: Neu. Neuware - 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 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.…

Zustand: New. pp. xxi + 677 Illus.

Zustand: New. In English.

Hardcover. Zustand: Brand New. 1st edition. 677 pages. 9.50x6.00x1.25 inches. In Stock.

Buch. Zustand: Neu. Neuware - A unique treatment of signal processing using a model-based perspectiveSignal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool.Model-Based Signal Processing develops the model-based approach in a unified manner and follows it through the text in the algorithms, examples, applications, and case studies. The approach, coupled with the hierarchy of physics-based models that the author develops, including linear as well as nonlinear representations, makes it a unique contribution to the field of signal processing.The text includes parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space models as some of the model sets with its focus on how they may be used to solve signal processing problems. Special features are provided that assist readers in understanding the material and learning how to apply their new knowledge to solving real-life problems.\* Unified treatment of well-known signal processing models including physics-based model sets\* Simple applications demonstrate how the model-based approach works, while detailed case studies demonstrate problem solutions in their entirety from concept to model development, through simulation, application to real data, and detailed performance analysis\* Summaries provided with each chapter ensure that readers understand the key points needed to move forward in the text as well as MATLAB(r) Notes that describe the key commands and toolboxes readily available to perform the algorithms discussed\* References lead to more in-depth coverage of specialized topics\* Problem sets test readers' knowledge and help them put their new skills into practiceThe author demonstrates how the basic idea of model-based signal processing is a highly effective and natural way to solve both basic as well as complex processing problems. Designed as a graduate-level text, this book is also essential reading for practicing signal-processing professionals and scientists, who will find the variety of case studies to be invaluable.…

Zustand: New. Model-Based Signal Processing develops the "model-based approach" to signal processing for a variety of useful model sets including the popularly termed "physics-based" models. It presents a unique viewpoint of signal processing from the model-based perspective. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 677 pages, illustrations. BIC Classification: TJK; UYS. Category: (P) Professional & Vocational. Dimension: 164 x 243 x 36. Weight in Grams: 1106. . 2005. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…