ren elizabeth (25 risultati)

Condizione: good. This is a retired library book with typical wear markings. May or may not have library stickers attached which will not be removed.

paperback. Condizione: Very Good. Fantasio; Ventrue, Eve; Hedman, Justine Marie; Hedman, Kailey Elizabeth; Boudreau, Chantal (illustratore). Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!

paperback. Condizione: Very Good. Fantasio; Ventrue, Eve; Hedman, Justine Marie; Hedman, Kailey Elizabeth; Boudreau, Chantal (illustratore). Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!

Hardcover. Condizione: Used - Very Good. Cover edges show some minor wear from reading and storage. Pages are clean; there is no writing, highlighting or margin notes.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: Very Good. Size: 8vo - over 7¾" - 9¾" tall.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: Very good.

Condizione: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Condizione: As New. Unread book in perfect condition.

Condizione: New. Fantasio; Ventrue, Eve; Hedman, Justine Marie; Hedman, Kailey Elizabeth; Boudreau, Chantal (illustratore).

Condizione: New.

Condizione: New. In.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Single Issue Magazine. Condizione: Fair. Lauck, Peter; Dawes, Edwin; Bugg, Robert; Cooper, Mario; Giesen, Herman; Wicks, Ren; Nikolai (illustratore). First Edition. 114 pages. Articles: Wanted - A Miracle in Greece - staving off economic collapse and Communist inroads; Hollywood Headache - The movie moguls are atwitter these days - audiences down, censors upset, costs up, etc. - nice color photos; Tuned in on the Surgeon - doctors now witness operations via television; The Abundant Henry Wallace - study of a man who'd like to be a key in the 1948 political scene; Heartless Harvest (conclusion) - plight of the country's migrant field workers; Behind Your Stick of Gum - men brave death in tropic jungles to gather its basic ingredients - with photos; They Can't Fool the Revenue Man - few succeed in income-tax evasion; Young Man of Fashion - Mr. Charlie James and his super dress designs - article with great color photos. Fiction: Sweet Water (part 1 of 5); Charlie Squires and the Seven Marchettis; Banner Over First; The Split Hickory; A Wonderful Mixer; Wait and See. Includes ads for: Parker pens (featuring Lily Pons); RKO movies; De Beers - featuring Miss Jean Bullitt of Chestnut Hill, Philadelphia; Philco radio-phonographs; Plymouth cars; Firestone; Dodge cars; Mansfield Jogs shoes; Chevrolet cars; Cory all-glass coffee brewers - two pages!; Miller beer; DeSoto cars; Vintage cartoonPepsi ad involving large excavator; Fantastic two-page color ad for movie "Unconquered" starring Gary Cooper; American Airlines; Caterpillar - great color-photo ad with dozer and scraper; Westinghouse record players; Kodak film; Remington electric shaver ad features Bing Crosby and Barry Fitzgerald in scene from movie "Welcome Stranger"; Are You Mitty?: Gillette razors; Budweiser; National Guard - with large photo of President Truman; Lucky Strike cigarettes (back cover). Somewhat above-average external wear. Covers holding by one staple. A worthy vintage copy.

Condizione: New. PRINT ON DEMAND pp. 320.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With increasing availability of computation power, digital signal analysis algorithms have the potential of evolving from the common framewise operational method to samplewise operations which offer more precision in time. This thesis discusses a set of methods with samplewise operations: local signal approximation via Recursive Least Squares (RLS) where a mathematical model is fit to the signal within a sliding window at each sample. Thereby both the signal models and cost windows are generated by Autonomous Linear State Space Models (ALSSMs). The modeling capability of ALSSMs is vast, as they can model exponentials, polynomials and sinusoidal functions as well as any linear and multiplicative combination thereof. The fitting method offers efficient recursions, subsample precision by way of the signal model and additional goodness of fit measures based on the recursively computed fitting cost. Classical methods such as standard Savitzky-Golay (SG) smoothing filters and the Short-Time Fourier Transform (STFT) are united under a common framework.First, we complete the existing framework. The ALSSM parameterization and RLS recursions are provided for a general function. The solution of the fit parameters for different constraint problems are reviewed. Moreover, feature extraction from both the fit parameters and the cost is detailed as well as examples of their use. In particular, we introduce terminology to analyze the fitting problem from the perspective of projection to a local Hilbert space and as a linear filter. Analytical rules are given for computation of the equivalent filter response and the steady-state precision matrix of the cost.After establishing the local approximation framework, we further discuss two classes of signal models in particular, namely polynomial and sinusoidal functions. The signal models are complementary, as by nature, polynomials are suited for time-domain description of signals while sinusoids are suited for the frequency-domain.For local approximation of polynomials, we derive analytical expressions for the steady-state covariance matrix and the linear filter of the coefficients based on the theory of orthogonal polynomial bases. We then discuss the fundamental application of smoothing filters based on local polynomial approximation. We generalize standard SG filters to any ALSSM window and introduce a novel class of smoothing filters based on polynomial fitting to running sums. 286 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -With increasing availability of computation power, digital signal analysis algorithms have the potential of evolving from the common framewise operational method to samplewise operations which offer more precision in time. This thesis discusses a set of methods with samplewise operations: local signal approximation via Recursive Least Squares (RLS) where a mathematical model is fit to the signal within a sliding window at each sample. Thereby both the signal models and cost windows are generated by Autonomous Linear State Space Models (ALSSMs). The modeling capability of ALSSMs is vast, as they can model exponentials, polynomials and sinusoidal functions as well as any linear and multiplicative combination thereof. The fitting method offers efficient recursions, subsample precision by way of the signal model and additional goodness of fit measures based on the recursively computed fitting cost. Classical methods such as standard Savitzky-Golay (SG) smoothing filters and the Short-Time Fourier Transform (STFT) are united under a common framework.First, we complete the existing framework. The ALSSM parameterization and RLS recursions are provided for a general function. The solution of the fit parameters for different constraint problems are reviewed. Moreover, feature extraction from both the fit parameters and the cost is detailed as well as examples of their use. In particular, we introduce terminology to analyze the fitting problem from the perspective of projection to a local Hilbert space and as a linear filter. Analytical rules are given for computation of the equivalent filter response and the steady-state precision matrix of the cost.After establishing the local approximation framework, we further discuss two classes of signal models in particular, namely polynomial and sinusoidal functions. The signal models are complementary, as by nature, polynomials are suited for time-domain description of signals while sinusoids are suited for the frequency-domain.For local approximation of polynomials, we derive analytical expressions for the steady-state covariance matrix and the linear filter of the coefficients based on the theory of orthogonal polynomial bases. We then discuss the fundamental application of smoothing filters based on local polynomial approximation. We generalize standard SG filters to any ALSSM window and introduce a novel class of smoothing filters based on polynomial fitting to running sums.Books on Demand GmbH, Überseering 33, 22297 Hamburg 286 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With increasing availability of computation power, digital signal analysis algorithms have the potential of evolving from the common framewise operational method to samplewise operations which offer more precision in time. This thesis discusses a set of methods with samplewise operations: local signal approximation via Recursive Least Squares (RLS) where a mathematical model is fit to the signal within a sliding window at each sample. Thereby both the signal models and cost windows are generated by Autonomous Linear State Space Models (ALSSMs). The modeling capability of ALSSMs is vast, as they can model exponentials, polynomials and sinusoidal functions as well as any linear and multiplicative combination thereof. The fitting method offers efficient recursions, subsample precision by way of the signal model and additional goodness of fit measures based on the recursively computed fitting cost. Classical methods such as standard Savitzky-Golay (SG) smoothing filters and the Short-Time Fourier Transform (STFT) are united under a common framework.First, we complete the existing framework. The ALSSM parameterization and RLS recursions are provided for a general function. The solution of the fit parameters for different constraint problems are reviewed. Moreover, feature extraction from both the fit parameters and the cost is detailed as well as examples of their use. In particular, we introduce terminology to analyze the fitting problem from the perspective of projection to a local Hilbert space and as a linear filter. Analytical rules are given for computation of the equivalent filter response and the steady-state precision matrix of the cost.After establishing the local approximation framework, we further discuss two classes of signal models in particular, namely polynomial and sinusoidal functions. The signal models are complementary, as by nature, polynomials are suited for time-domain description of signals while sinusoids are suited for the frequency-domain.For local approximation of polynomials, we derive analytical expressions for the steady-state covariance matrix and the linear filter of the coefficients based on the theory of orthogonal polynomial bases. We then discuss the fundamental application of smoothing filters based on local polynomial approximation. We generalize standard SG filters to any ALSSM window and introduce a novel class of smoothing filters based on polynomial fitting to running sums.

Taschenbuch. Condizione: Neu. Using Local State Space Model Approximation for Fundamental Signal Analysis Tasks | Elizabeth Ren | Taschenbuch | Series in Signal and Information Processing | Englisch | 2023 | Hartung-Gorre | EAN 9783866287921 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.