Channel coding in communication networks [[electronic resource] ] : from theory to turbocodes / / edited by Alain Glavieux

• Record Nr.: UNINA9910143313603321
• Pubbl/distr/stampa: London ; ; Newport Beach, CA, : ISTE, 2007
• ISBN: 1-280-84766-2; 9786610847662; 0-470-61242-8; 0-470-39455-2; 1-84704-574-X
• Descrizione fisica: 1 online resource (438 p.)
• Collana: Digital signal and image processing series
• Altri autori (Persone): GlavieuxAlain
• Disciplina: 003.54; 003/.54; 621.3821
• Soggetti: Coding theory; Error-correcting codes (Information theory); Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Channel Coding in Communication Networks; Table of Contents; Homage to Alain Glavieux; Chapter 1. Information Theory; 1.1. Introduction: the Shannon paradigm; 1.2. Principal coding functions; 1.2.1. Source coding; 1.2.2. Channel coding; 1.2.3. Cryptography; 1.2.4. Standardization of the Shannon diagram blocks; 1.2.5. Fundamental theorems; 1.3. Quantitative measurement of information; 1.3.1. Principle; 1.3.2. Measurement of self-information; 1.3.3. Entropy of a source; 1.3.4. Mutual information measure; 1.3.5. Channel capacity; 1.3.6. Comments on the measurement of information; 1.4. Source coding1.4.1. Introduction; 1.4.2. Decodability, Kraft-McMillan inequality; 1.4.3. Demonstration of the fundamental theorem; 1.4.4. Outline of optimal algorithms of source coding; 1.5. Channel coding; 1.5.1. Introduction and statement of the fundamental theorem; 1.5.2. General comments; 1.5.3. Need for redundancy; 1.5.4. Example of the binary symmetric channel; 1.5.4.1. Hamming's metric; 1.5.4.2. ; Decoding with minimal Hamming distance; 1.5.4.3. Random coding; 1.5.4.4. Gilbert-Varshamov bound; 1.5.5. A geometrical interpretation; 1.5.6. Fundamental theorem: Gallager's proof; 1.5.6.1. Upper bound of the probability of error1.5.6.2. Use of random coding; 1.5.6.3. Form of exponential limits; 1.6. Channels with continuous noise; 1.6.1. Introduction; 1.6.2. A reference model in physical reality: the channel with Gaussian additive noise; 1.6.3. Communication via a channel with additive white Gaussian noise; 1.6.3.1. Use of a finite alphabet, modulation; 1.6.3.2. Demodulation, decision margin; 1.6.4. Channel with fadings; 1.7. Information theory and channel coding; 1.8. Bibliography; Chapter 2. Block Codes; 2.1. Unstructured codes; 2.1.1. The fundamental question of message redundancy2.1.2. Unstructured codes; 2.1.2.1. Code parameters; 2.1.2.2. Code, coding and decoding; 2.1.2.3. Bounds of code parameters; 2.2. Linear codes; 2.2.1. Introduction; 2.2.2. Properties of linear codes; 2.2.2.1. Minimum distance and minimum weight of a code; 2.2.2.2. Linear code base, coding; 2.2.2.3. Singleton bound; 2.2.3. Dual code; 2.2.3.1. Reminders of the Gaussian method; 2.2.3.2. Lateral classes of a linear code C; 2.2.3.3. Syndromes; 2.2.3.4. Decoding and syndromes; 2.2.3.5. Lateral classes, syndromes and decoding; 2.2.3.6. Parity check matrix and minimum code weight2.2.3.7. Minimum distance of C and matrix H; 2.2.4. Some linear codes; 2.2.5. Decoding of linear codes; 2.3. Finite fields; 2.3.1. Basic concepts; 2.3.2. Polynomial modulo calculations: quotient ring; 2.3.3. Irreducible polynomial modulo calculations: finite field; 2.3.4. Order and the opposite of an element of F2[X]/(p(X)); 2.3.4.1. Order; 2.3.4.2. Properties of the order; 2.3.4.3. Primitive elements; 2.3.4.4. Use of the primitives; 2.3.4.5. How to find a primitive; 2.3.4.6. Exponentiation; 2.3.5. Minimum polynomials; 2.3.6. The field of nth roots of unity
• Sommario/riassunto: This book provides a comprehensive overview of the subject of channel coding. It starts with a description of information theory, focusing on the quantitative measurement of information and introducing two fundamental theorems on source and channel coding. The basics of channel coding in two chapters, block codes and convolutional codes, are then discussed, and for these the authors introduce weighted input and output decoding algorithms and recursive systematic convolutional codes, which are used in the rest of the book. Trellis coded modulations, which have their primary applications in hi

Event-Trigger Dynamic State Estimation for Practical WAMS Applications in Smart Grid / / by Zhen Li, Sen Li, Tyrone Fernando, Xi Chen

• Record Nr.: UNINA9910407734203321
• Autore: Li Zhen
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• ISBN: 3-030-45658-7
• Edizione: [1st ed. 2020.]
• Descrizione fisica: 1 online resource (294 pages)
• Disciplina: 621.3191
• Soggetti: Electronic circuits; Signal processing; Image processing; Speech processing systems; Energy systems; Circuits and Systems; Signal, Image and Speech Processing; Energy Systems
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Introduction -- Event-trigger Design for Linear Filtering Event-trigger Strategies -- State Estimation of Doubly Fed Induction Generator (DFIG) Wind Turbine (WT) in Smart Grid -- Event-trigger Particle Filter Design under Limited Communication Bandwidth -- Event-trigger Heterogeneous Nonlinear Filter Design under Limited Computational Burden -- Event-trigger Robust Nonlinear Filter Design under Non-Gaussian Noises -- Event-trigger Robust Nonlinear Filter Design with Packet Dropout -- Discussion on Other Practical Design.
• Sommario/riassunto: This book describes how dynamic state estimation application in wide-area measurement systems (WAMS) are crucial for power system reliability, to acquire precisely power system dynamics. The event trigger DSE techniques described by the authors provide a design balance between the communication rate and estimation performance, by selectively sending the innovational data. The discussion also includes practical problems for smart grid applications, such as the ; non-Gaussian process/measurement noise, packet dropout, computation burden of accurate DSE, robustness to the system variation, etc. Readers will learn how the event trigger DSE can facilitate the effective reduction of communication rates, with guaranteed accuracy under a variety of practical conditions in smart grid applications. Focuses on dynamic state estimation (DSE) design for practical smart grid applications; Summarizes the event trigger strategy design for DSE.; Enables designs that reduce the communication rate and achieve balance between the bandwidth and accuracy.