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Chemical sensors for hostile environments [[electronic resource] ] : proceedings of the Chemical Sensors for Hostile Environments symposium, held at the 103rd Annual Meeting of the American Ceramic Society, April 22-25, 2001, in Indianapolis, Indiana / / edited by G.M. Kale, S.A. Akbar, M. Liu
| Chemical sensors for hostile environments [[electronic resource] ] : proceedings of the Chemical Sensors for Hostile Environments symposium, held at the 103rd Annual Meeting of the American Ceramic Society, April 22-25, 2001, in Indianapolis, Indiana / / edited by G.M. Kale, S.A. Akbar, M. Liu |
| Pubbl/distr/stampa | Westerville, Ohio, : American Ceramic Society, c2002 |
| Descrizione fisica | 1 online resource (128 p.) |
| Disciplina |
681.2
681/.2 |
| Altri autori (Persone) |
KaleG. M
AkbarSheikh A LiuM (Meilin) |
| Collana | Ceramic transactions |
| Soggetto topico |
Chemical detectors
Ceramics |
| ISBN |
1-280-67560-8
9786613652539 1-118-37103-8 1-118-37105-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chemical Sensors for Hostile Environments; Contents; Solid-State Electrochemical Sensors for Automotive Applications; Zirconia-Based Potentiometric NOx Sensor Utilizing Pt and Au Electrodes; Packaging Planar Exhaust Sensors for Hostile Exhaust Environments; Importance of Gas Diffusion in Semiconductor Gas Sensors; Durability of Thick-Film Ceramic Gas Sensors; Preparation and Characterization of Indium-Doped Calcium Zirconate for the Electrolyte in Hydrogen Sensors for Use in Molten Aluminum; Antimony Sensors for Molten Lead Using K-β-Al2O3 Solid Electrolytes
Preparation and Characterization of Iron Oxide-Zirconia Nanopowder for Its Use as an Ethanol Sensor MaterialSynthesis and Characterization of 2-3 Spinels as Material for Methane Sensors; Ammonia and Alcohol Gas Sensors Using Tungsten Oxide; Low-Temperature Gas Sensing Using Laser Activation; Synthesis of Gallium Oxide Hydroxide Crystals in Aqueous Solutions with or without Urea and Their Calcination Behavior; Index |
| Record Nr. | UNINA-9910830942603321 |
| Westerville, Ohio, : American Ceramic Society, c2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Chemical sensors for hostile environments [[electronic resource] ] : proceedings of the Chemical Sensors for Hostile Environments symposium, held at the 103rd Annual Meeting of the American Ceramic Society, April 22-25, 2001, in Indianapolis, Indiana / / edited by G.M. Kale, S.A. Akbar, M. Liu
| Chemical sensors for hostile environments [[electronic resource] ] : proceedings of the Chemical Sensors for Hostile Environments symposium, held at the 103rd Annual Meeting of the American Ceramic Society, April 22-25, 2001, in Indianapolis, Indiana / / edited by G.M. Kale, S.A. Akbar, M. Liu |
| Pubbl/distr/stampa | Westerville, Ohio, : American Ceramic Society, c2002 |
| Descrizione fisica | 1 online resource (128 p.) |
| Disciplina |
681.2
681/.2 |
| Altri autori (Persone) |
KaleG. M
AkbarSheikh A LiuM (Meilin) |
| Collana | Ceramic transactions |
| Soggetto topico |
Chemical detectors
Ceramics |
| ISBN |
1-280-67560-8
9786613652539 1-118-37103-8 1-118-37105-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chemical Sensors for Hostile Environments; Contents; Solid-State Electrochemical Sensors for Automotive Applications; Zirconia-Based Potentiometric NOx Sensor Utilizing Pt and Au Electrodes; Packaging Planar Exhaust Sensors for Hostile Exhaust Environments; Importance of Gas Diffusion in Semiconductor Gas Sensors; Durability of Thick-Film Ceramic Gas Sensors; Preparation and Characterization of Indium-Doped Calcium Zirconate for the Electrolyte in Hydrogen Sensors for Use in Molten Aluminum; Antimony Sensors for Molten Lead Using K-β-Al2O3 Solid Electrolytes
Preparation and Characterization of Iron Oxide-Zirconia Nanopowder for Its Use as an Ethanol Sensor MaterialSynthesis and Characterization of 2-3 Spinels as Material for Methane Sensors; Ammonia and Alcohol Gas Sensors Using Tungsten Oxide; Low-Temperature Gas Sensing Using Laser Activation; Synthesis of Gallium Oxide Hydroxide Crystals in Aqueous Solutions with or without Urea and Their Calcination Behavior; Index |
| Record Nr. | UNINA-9910841265003321 |
| Westerville, Ohio, : American Ceramic Society, c2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
| Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.] |
| Autore | Duin Robert |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (441 p.) |
| Disciplina |
620.0015118
681/.2 |
| Altri autori (Persone) | HeijdenFerdinand van der |
| Soggetto topico |
Engineering mathematics - Data processing
Measurement - Data processing Estimation theory - Data processing |
| ISBN |
1-280-26895-6
9786610268955 0-470-09015-4 1-60119-496-X 0-470-09014-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Classification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option
2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography 3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography 4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References 5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis 7.1.2 Multi-dimensional scaling |
| Record Nr. | UNINA-9910143693003321 |
Duin Robert
|
||
| Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
| Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.] |
| Autore | Duin Robert |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (441 p.) |
| Disciplina |
620.0015118
681/.2 |
| Altri autori (Persone) | HeijdenFerdinand van der |
| Soggetto topico |
Engineering mathematics - Data processing
Measurement - Data processing Estimation theory - Data processing |
| ISBN |
1-280-26895-6
9786610268955 0-470-09015-4 1-60119-496-X 0-470-09014-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Classification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option
2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography 3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography 4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References 5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis 7.1.2 Multi-dimensional scaling |
| Record Nr. | UNINA-9910830828303321 |
|
Duin Robert
|
||
| Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
| Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.] |
| Autore | Duin Robert |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (441 p.) |
| Disciplina |
620.0015118
681/.2 |
| Altri autori (Persone) | HeijdenFerdinand van der |
| Soggetto topico |
Engineering mathematics - Data processing
Measurement - Data processing Estimation theory - Data processing |
| ISBN |
1-280-26895-6
9786610268955 0-470-09015-4 1-60119-496-X 0-470-09014-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Classification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option
2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography 3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography 4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References 5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis 7.1.2 Multi-dimensional scaling |
| Record Nr. | UNINA-9910841314103321 |
|
Duin Robert
|
||
| Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others]
| Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others] |
| Autore | Heijden Ferdinand van der |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (431 pages) : illustrations |
| Disciplina | 681/.2 |
| Soggetto topico |
Engineering mathematics - Data processing
Measurement - Data processing Estimation theory - Data processing |
| ISBN |
1-119-15245-3
1-119-15244-5 1-119-15248-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910270899103321 |
|
Heijden Ferdinand van der
|
||
| Hoboken, New Jersey : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others]
| Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others] |
| Autore | Heijden Ferdinand van der |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (431 pages) : illustrations |
| Disciplina | 681/.2 |
| Soggetto topico |
Engineering mathematics - Data processing
Measurement - Data processing Estimation theory - Data processing |
| ISBN |
1-119-15245-3
1-119-15244-5 1-119-15248-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910811584503321 |
|
Heijden Ferdinand van der
|
||
| Hoboken, New Jersey : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cooperating embedded systems and wireless sensor networks [[electronic resource] /] / edited by Michel Banatre ... [et al.]
| Cooperating embedded systems and wireless sensor networks [[electronic resource] /] / edited by Michel Banatre ... [et al.] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (420 p.) |
| Disciplina |
681.2
681/.2 |
| Altri autori (Persone) | BanâtreMichel <1950-> |
| Collana | ISTE |
| Soggetto topico |
Embedded computer systems
Sensor networks |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-16479-1
9786612164798 0-470-61081-6 0-470-39345-9 |
| Classificazione |
ST 153
ST 200 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cooperating Embedded Systems and Wireless Sensor Networks; Table of Contents; Chapter 1. An Introduction to the Concept of Cooperating Objects and Sensor Networks; 1.1. Cooperating objects and wireless sensor networks; 1.2. Embedded WiSeNts; 1.3. Overview of the book; Chapter 2. Applications and Application Scenarios; 2.1. Summary; 2.2. Introduction; 2.3. Characteristics and requirements of applications; 2.4. State of the art projects; 2.5. Taxonomy of CO applications; 2.5.1. Control and Automation (CA); 2.5.2. Home and Office (HO); 2.5.3. Logistics (L); 2.5.4. Transportation (TA)
2.5.5. Environmental monitoring for emergency services (EM)2.5.6. Healthcare (H); 2.5.7. Security and Surveillance (SS); 2.5.8. Tourism (T); 2.5.9. Education and Training (ET); 2.6. Scenario description structure; 2.7. Application scenarios; 2.7.1. Forest fire detection scenario; 2.7.1.1. Introduction; 2.7.1.2. Scenario characteristics; 2.7.1.3. Functional specification; 2.7.1.4. Object decomposition; 2.7.1.5. Step-by-step scenario description; 2.7.1.6. System requirements; 2.7.2. Good Food; 2.7.2.1. Introduction; 2.7.2.2. Scenario characteristics; 2.7.2.3. User requirements 2.7.2.4. Functional specification2.7.2.5. Object decomposition; 2.7.2.6. Step-by-step scenario description; 2.7.2.7. System requirements; 2.7.3. CORTEX's Car Control; 2.7.3.1. Introduction; 2.7.3.2. Scenario characteristics; 2.7.3.3. User requirements; 2.7.3.4. Functional specification; 2.7.3.5. Object decomposition; 2.7.3.6. Step-by-step scenario description; 2.7.3.7. System requirements; 2.7.4. Hogthrob; 2.7.4.1. Introduction; 2.7.4.2. Scenario characteristics; 2.7.4.3. User requirements; 2.7.4.4. Functional specification; 2.7.4.5. Object decomposition 2.7.4.6. Step-by-step scenario description2.7.5. Smart surroundings; 2.7.5.1. Introduction; 2.7.5.2. Scenario characteristics; 2.7.5.3. System requirements; 2.7.6. Sustainable bridges; 2.7.6.1. Introduction; 2.7.6.2. Application characteristics; 2.7.6.3. System requirements; 2.7.6.4. Functional specification; 2.7.6.5. Object decomposition; 2.8. Conclusions; 2.9. List of abbreviations; 2.10. Bibliography; Chapter 3. Paradigms for Algorithms and Interactions; 3.1. Summary; 3.2. Introduction; 3.2.1. Aim of the chapter; 3.2.2. Organization of the chapter; 3.3. Definition of concepts 3.4. Wireless sensor networks for environmental monitoring3.4.1. Application scenarios; 3.4.2. Peculiarities of WSNs; 3.4.3. Medium Access Control; 3.4.3.1. Random Access Protocols; 3.4.3.2. Deterministic access protocols; 3.4.4. Routing and forwarding algorithms; 3.4.4.1. Location-based routing; 3.4.4.2. Data-centric routing; 3.4.4.3. Hierarchical-based routing; 3.4.5. Sensor data aggregation; 3.4.6. Clustering and backbone formation; 3.4.6.1. Clustering for ad hoc networks; 3.4.6.2. Clustering for WSNs; 3.4.7. Localization in ad hoc and WSNs; 3.4.7.1. Range-free localization 3.4.7.2. Range-based localization |
| Record Nr. | UNINA-9910139469103321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cooperating embedded systems and wireless sensor networks [[electronic resource] /] / edited by Michel Banatre ... [et al.]
| Cooperating embedded systems and wireless sensor networks [[electronic resource] /] / edited by Michel Banatre ... [et al.] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (420 p.) |
| Disciplina |
681.2
681/.2 |
| Altri autori (Persone) | BanâtreMichel <1950-> |
| Collana | ISTE |
| Soggetto topico |
Embedded computer systems
Sensor networks |
| ISBN |
1-282-16479-1
9786612164798 0-470-61081-6 0-470-39345-9 |
| Classificazione |
ST 153
ST 200 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cooperating Embedded Systems and Wireless Sensor Networks; Table of Contents; Chapter 1. An Introduction to the Concept of Cooperating Objects and Sensor Networks; 1.1. Cooperating objects and wireless sensor networks; 1.2. Embedded WiSeNts; 1.3. Overview of the book; Chapter 2. Applications and Application Scenarios; 2.1. Summary; 2.2. Introduction; 2.3. Characteristics and requirements of applications; 2.4. State of the art projects; 2.5. Taxonomy of CO applications; 2.5.1. Control and Automation (CA); 2.5.2. Home and Office (HO); 2.5.3. Logistics (L); 2.5.4. Transportation (TA)
2.5.5. Environmental monitoring for emergency services (EM)2.5.6. Healthcare (H); 2.5.7. Security and Surveillance (SS); 2.5.8. Tourism (T); 2.5.9. Education and Training (ET); 2.6. Scenario description structure; 2.7. Application scenarios; 2.7.1. Forest fire detection scenario; 2.7.1.1. Introduction; 2.7.1.2. Scenario characteristics; 2.7.1.3. Functional specification; 2.7.1.4. Object decomposition; 2.7.1.5. Step-by-step scenario description; 2.7.1.6. System requirements; 2.7.2. Good Food; 2.7.2.1. Introduction; 2.7.2.2. Scenario characteristics; 2.7.2.3. User requirements 2.7.2.4. Functional specification2.7.2.5. Object decomposition; 2.7.2.6. Step-by-step scenario description; 2.7.2.7. System requirements; 2.7.3. CORTEX's Car Control; 2.7.3.1. Introduction; 2.7.3.2. Scenario characteristics; 2.7.3.3. User requirements; 2.7.3.4. Functional specification; 2.7.3.5. Object decomposition; 2.7.3.6. Step-by-step scenario description; 2.7.3.7. System requirements; 2.7.4. Hogthrob; 2.7.4.1. Introduction; 2.7.4.2. Scenario characteristics; 2.7.4.3. User requirements; 2.7.4.4. Functional specification; 2.7.4.5. Object decomposition 2.7.4.6. Step-by-step scenario description2.7.5. Smart surroundings; 2.7.5.1. Introduction; 2.7.5.2. Scenario characteristics; 2.7.5.3. System requirements; 2.7.6. Sustainable bridges; 2.7.6.1. Introduction; 2.7.6.2. Application characteristics; 2.7.6.3. System requirements; 2.7.6.4. Functional specification; 2.7.6.5. Object decomposition; 2.8. Conclusions; 2.9. List of abbreviations; 2.10. Bibliography; Chapter 3. Paradigms for Algorithms and Interactions; 3.1. Summary; 3.2. Introduction; 3.2.1. Aim of the chapter; 3.2.2. Organization of the chapter; 3.3. Definition of concepts 3.4. Wireless sensor networks for environmental monitoring3.4.1. Application scenarios; 3.4.2. Peculiarities of WSNs; 3.4.3. Medium Access Control; 3.4.3.1. Random Access Protocols; 3.4.3.2. Deterministic access protocols; 3.4.4. Routing and forwarding algorithms; 3.4.4.1. Location-based routing; 3.4.4.2. Data-centric routing; 3.4.4.3. Hierarchical-based routing; 3.4.5. Sensor data aggregation; 3.4.6. Clustering and backbone formation; 3.4.6.1. Clustering for ad hoc networks; 3.4.6.2. Clustering for WSNs; 3.4.7. Localization in ad hoc and WSNs; 3.4.7.1. Range-free localization 3.4.7.2. Range-based localization |
| Record Nr. | UNINA-9910829891703321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cooperating embedded systems and wireless sensor networks [[electronic resource] /] / edited by Michel Banatre ... [et al.]
| Cooperating embedded systems and wireless sensor networks [[electronic resource] /] / edited by Michel Banatre ... [et al.] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (420 p.) |
| Disciplina |
681.2
681/.2 |
| Altri autori (Persone) | BanâtreMichel <1950-> |
| Collana | ISTE |
| Soggetto topico |
Embedded computer systems
Sensor networks |
| ISBN |
1-282-16479-1
9786612164798 0-470-61081-6 0-470-39345-9 |
| Classificazione |
ST 153
ST 200 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cooperating Embedded Systems and Wireless Sensor Networks; Table of Contents; Chapter 1. An Introduction to the Concept of Cooperating Objects and Sensor Networks; 1.1. Cooperating objects and wireless sensor networks; 1.2. Embedded WiSeNts; 1.3. Overview of the book; Chapter 2. Applications and Application Scenarios; 2.1. Summary; 2.2. Introduction; 2.3. Characteristics and requirements of applications; 2.4. State of the art projects; 2.5. Taxonomy of CO applications; 2.5.1. Control and Automation (CA); 2.5.2. Home and Office (HO); 2.5.3. Logistics (L); 2.5.4. Transportation (TA)
2.5.5. Environmental monitoring for emergency services (EM)2.5.6. Healthcare (H); 2.5.7. Security and Surveillance (SS); 2.5.8. Tourism (T); 2.5.9. Education and Training (ET); 2.6. Scenario description structure; 2.7. Application scenarios; 2.7.1. Forest fire detection scenario; 2.7.1.1. Introduction; 2.7.1.2. Scenario characteristics; 2.7.1.3. Functional specification; 2.7.1.4. Object decomposition; 2.7.1.5. Step-by-step scenario description; 2.7.1.6. System requirements; 2.7.2. Good Food; 2.7.2.1. Introduction; 2.7.2.2. Scenario characteristics; 2.7.2.3. User requirements 2.7.2.4. Functional specification2.7.2.5. Object decomposition; 2.7.2.6. Step-by-step scenario description; 2.7.2.7. System requirements; 2.7.3. CORTEX's Car Control; 2.7.3.1. Introduction; 2.7.3.2. Scenario characteristics; 2.7.3.3. User requirements; 2.7.3.4. Functional specification; 2.7.3.5. Object decomposition; 2.7.3.6. Step-by-step scenario description; 2.7.3.7. System requirements; 2.7.4. Hogthrob; 2.7.4.1. Introduction; 2.7.4.2. Scenario characteristics; 2.7.4.3. User requirements; 2.7.4.4. Functional specification; 2.7.4.5. Object decomposition 2.7.4.6. Step-by-step scenario description2.7.5. Smart surroundings; 2.7.5.1. Introduction; 2.7.5.2. Scenario characteristics; 2.7.5.3. System requirements; 2.7.6. Sustainable bridges; 2.7.6.1. Introduction; 2.7.6.2. Application characteristics; 2.7.6.3. System requirements; 2.7.6.4. Functional specification; 2.7.6.5. Object decomposition; 2.8. Conclusions; 2.9. List of abbreviations; 2.10. Bibliography; Chapter 3. Paradigms for Algorithms and Interactions; 3.1. Summary; 3.2. Introduction; 3.2.1. Aim of the chapter; 3.2.2. Organization of the chapter; 3.3. Definition of concepts 3.4. Wireless sensor networks for environmental monitoring3.4.1. Application scenarios; 3.4.2. Peculiarities of WSNs; 3.4.3. Medium Access Control; 3.4.3.1. Random Access Protocols; 3.4.3.2. Deterministic access protocols; 3.4.4. Routing and forwarding algorithms; 3.4.4.1. Location-based routing; 3.4.4.2. Data-centric routing; 3.4.4.3. Hierarchical-based routing; 3.4.5. Sensor data aggregation; 3.4.6. Clustering and backbone formation; 3.4.6.1. Clustering for ad hoc networks; 3.4.6.2. Clustering for WSNs; 3.4.7. Localization in ad hoc and WSNs; 3.4.7.1. Range-free localization 3.4.7.2. Range-based localization |
| Record Nr. | UNINA-9910840672603321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
