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1. |
Record Nr. |
UNISA990001278980203316 |
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Autore |
PRATELLESI, Marco |
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Titolo |
New journalism : teorie e tecniche del giornalismo multimediale / Marco Pratellesi |
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Pubbl/distr/stampa |
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[Milano] : Bruno Mondadori, 2008 |
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ISBN |
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Descrizione fisica |
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Collana |
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Disciplina |
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Soggetti |
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Giornalismo - Impiego [di] Elaboratori |
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Collocazione |
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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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2. |
Record Nr. |
UNINA9910462184303321 |
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Autore |
Arnold Carrie <1980-, > |
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Titolo |
Decoding anorexia : how breakthroughs in science offer hope for eating disorders / / Carrie Arnold |
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Pubbl/distr/stampa |
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New York, N.Y. : , : Routledge, , 2013 |
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ISBN |
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0-415-89866-8 |
1-283-71194-X |
0-203-08817-4 |
1-136-20158-0 |
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Descrizione fisica |
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1 online resource (ix, 204 p.) |
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Disciplina |
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Soggetti |
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Anorexia nervosa |
Eating disorders - Psychological aspects |
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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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Introduction: Off the Couch and Into the Brain -- 1. From Starving Saints to Dieting Divas -- 2. Interoception and the Insula -- 3. Climbing the Family Tree -- 4. Anorexia's Poster Children -- 5. When Anorexia Brings Friends -- 6. Starvation Becomes Obsession -- 7. Adapted to Flee Famine -- 8. Gym Rats -- 9. Stepping Up to the (Dinner) Plate -- 10. Oops, I Did It Again... -- 11. Standing at the Buffet of Life |
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Sommario/riassunto |
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"Decoding Anorexia is the first and only book to explain anorexia nervosa from a biological point of view. Its clear, user-friendly descriptions of the genetics and neuroscience behind the disorder is paired with first person descriptions and personal narratives of what biological differences mean to sufferers. Author Carrie Arnold, a trained scientist, science writer, and past sufferer of anorexia, speaks with clinicians, researchers, parents, other family members, and sufferers about the factors that make one vulnerable to anorexia, the neurochemistry behind the call of starvation, and why it's so hard to leave anorexia behind. She also addresses: - How environment is still important and influences behaviors - The characteristics of people at |
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high risk for developing anorexia nervosa - Why anorexics find starvation "rewarding" - Why denial is such a salient feature, and how sufferers can overcome it Carrie also includes interviews with key figures in the field that explains their work and how it contributes to our understanding of anorexia. Long thought to be a psychosocial disease of fickle teens, this book alters the way anorexia is understood and treated and gives patients, their doctors, and their family members hope"-- |
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3. |
Record Nr. |
UNINA9910139493903321 |
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Autore |
Najim Mohamed |
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Titolo |
Modeling, estimation and optimal filtration in signal processing [[electronic resource] /] / Mohamed Najim |
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Pubbl/distr/stampa |
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London, : ISTE |
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Hoboken, NJ, : J. Wiley & Sons, 2008 |
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ISBN |
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1-282-16500-3 |
9786612165009 |
0-470-61110-3 |
0-470-39368-8 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (410 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Electric filters, Digital |
Signal processing - Digital techniques |
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 and index. |
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Nota di contenuto |
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Modeling, Estimation and Optimal Filtering in Signal Processing; Table of Contents; Preface; Chapter 1. Parametric Models; 1.1. Introduction; 1.2. Discrete linear models; 1.2.1. The moving average (MA) model; 1.2.2. The autoregressive (AR) model; 1.3. Observations on stability, stationarity and invertibility; 1.3.1. AR model case; 1.3.2. ARMA model case; 1.4. The AR model or the ARMA model?; 1.5. Sinusoidal models; |
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1.5.1. The relevance of the sinusoidal model; 1.5.2. Sinusoidal models; 1.6. State space representations; 1.6.1. Definitions |
1.6.2. State space representations based on differential equation representation1.6.3. Resolution of the state equations; 1.6.4. State equations for a discrete-time system; 1.6.5. Some properties of systems described in the state space; 1.6.5.1. Introduction; 1.6.5.2. Observability; 1.6.5.3. Controllability; 1.6.5.4. Plurality of the state space representation of the system; 1.6.6. Case 1: state space representation of AR processes; 1.6.7. Case 2: state space representation of MA processes; 1.6.8. Case 3: state space representation of ARMA processes |
1.6.9. Case 4: state space representation of a noisy process1.6.9.1. An AR process disturbed by a white noise; 1.6.9.2. AR process disturbed by colored noise itself modeled by another AR process; 1.6.9.3. AR process disturbed by colored noise itself modeled by a MA process; 1.7. Conclusion; 1.8. References; Chapter 2. Least Squares Estimation of Parameters of Linear Models; 2.1. Introduction; 2.2. Least squares estimation of AR parameters; 2.2.1. Determination or estimation of parameters?; 2.2.2. Recursive estimation of parameters; 2.2.3. Implementation of the least squares algorithm |
2.2.4. The least squares method with weighting factor2.2.5. A recursive weighted least squares estimator; 2.2.6. Observations on some variants of the least squares method; 2.2.6.1. The autocorrelation method; 2.2.6.2. Levinson's algorithm; 2.2.6.3. The Durbin-Levinson algorithm; 2.2.6.4. Lattice filters; 2.2.6.5. The covariance method; 2.2.6.6. Relation between the covariance method and the least squares method; 2.2.6.7. Effect of a white additive noise on the estimation of AR parameters; 2.2.6.8. A method for alleviating the bias on the estimation of the AR parameters |
2.2.7. Generalized least squares method2.2.8. The extended least squares method; 2.3. Selecting the order of the models; 2.4. References; Chapter 3. Matched and Wiener Filters; 3.1. Introduction; 3.2. Matched filter; 3.2.1. Introduction; 3.2.2. Matched filter for the case of white noise; 3.2.3. Matched filter for the case of colored noise; 3.2.3.1. Formulation of problem; 3.2.3.2. Physically unrealizable matched filter; 3.2.3.3. A matched filter solution using whitening techniques; 3.3. The Wiener filter; 3.3.1. Introduction; 3.3.2. Formulation of problem; 3.3.3. The Wiener-Hopf equation |
3.3.4. Error calculation in a continuous physically non-realizable Wiener filter |
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Sommario/riassunto |
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The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the |
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