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Record Nr. |
UNINA9910974471803321 |
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Autore |
Wesoowski Krzysztof |
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Titolo |
Introduction to digital communication systems / / Krzysztof Wesoowski |
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Pubbl/distr/stampa |
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Chichester, West Sussex, U.K. ; ; Hoboken, N.J., : J. Wiley, 2009 |
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ISBN |
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9786612279300 |
9781282279308 |
1282279300 |
9780470695197 |
0470695196 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (579 p.) |
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Disciplina |
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Soggetti |
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Digital communications |
Telecommunication |
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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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Introduction to Digital Communication Systems; Contents; Preface; About the Author; 1 Elements of Information Theory; 1.1 Introduction; 1.2 Basic Concepts; 1.3 Communication System Model; 1.4 Concept of Information and Measure of Amount of Information; 1.5 Message Sources and Source Coding; 1.5.1 Models of Discrete Memory Sources; 1.5.2 Discrete Memoryless Source; 1.5.3 Extension of a Memoryless Source; 1.5.4 Markov Sources; 1.5.5 Entropy of the Markov Source; 1.5.6 Source Associated with the Markov Source; 1.6 Discrete Source Coding; 1.6.1 Huffman Coding; 1.6.2 Shannon-Fano Coding |
1.6.3 Dynamic Huffman Coding1.6.4 Arithmetic Coding; 1.6.5 Lempel-Ziv Algorithm; 1.6.6 Case study: Source Coding in Facsimile Transmission; 1.7 Channel Models from the Information Theory Point of View; 1.7.1 Discrete Memoryless Channel; 1.7.2 Examples of Discrete Memoryless Channel Models; 1.7.3 Example of a Binary Channel Model with Memory; 1.8 Mutual Information; 1.9 Properties of Mutual Information; 1.10 Channel Capacity; 1.11 Decision Process and its Rules; 1.11.1 Idea of Decision Rule; 1.11.2 Maximum a Posteriori Probability (MAP) Decision Rule; 1.11.3 Maximum Likelihood Decision |
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1.12 Differential Entropy and Average Amount of Information for Continuous Variables1.13 Capacity of Band-Limited Channel with Additive White Gaussian Noise; 1.14 Implication of AWGN Channel Capacity for Digital Transmission; 1.15 Capacity of a Gaussian Channel with a Given Channel Characteristic; 1.16 Capacity of a Flat Fading Channel; 1.17 Capacity of a Multiple-Input Multiple-Output Channel; Problems; 2 Channel Coding; 2.1 Idea of Channel Coding; 2.2 Classification of Codes; 2.3 Hard- and Soft-Decision Decoding; 2.4 Coding Gain; 2.5 Block Codes; 2.5.1 Parity Check Matrix |
2.5.2 Generator Matrix2.5.3 Syndrome; 2.5.4 Hamming Codes; 2.5.5 The Iterated Code; 2.5.6 Polynomial Codes; 2.5.7 Codeword Generation for the Polynomial Codes; 2.5.8 Cyclic Codes; 2.5.9 Parity Check Polynomial; 2.5.10 Polynomial Codes Determined by Roots; 2.5.11 Syndrome Polynomial; 2.5.12 BCH Codes; 2.5.13 Reed-Solomon Codes; 2.5.14 Golay Codes; 2.5.15 Maximum Length Codes; 2.5.16 Code Modifications; 2.6 Nonalgebraic Decoding for Block Codes; 2.6.1 Meggitt Decoder; 2.6.2 Majority Decoder; 2.6.3 Information Set Decoding; 2.7 Algebraic Decoding Methods for Cyclic Codes |
2.8 Convolutional Codes and Their Description2.8.1 Convolutional Code Description; 2.8.2 Code Transfer Function; 2.8.3 Convolutional Codes with Rate k/n; 2.9 Convolutional Code Decoding; 2.9.1 Viterbi Algorithm; 2.9.2 Soft-Output Viterbi Algorithm (SOVA); 2.9.3 Error Probability Analysis for Convolutional Codes; 2.10 Concatenated Coding; 2.11 Case Studies: Two Examples of Concatenated Coding; 2.11.1 Concatenated Coding in Deep Space Communications; 2.11.2 Channel Coding in the DVB Satellite Segment; 2.12 Turbo Codes; 2.12.1 RSCC Code; 2.12.2 Basic Turbo Code Encoder Scheme |
2.12.3 RSCC Code MAP Decoding |
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Sommario/riassunto |
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Combining theoretical knowledge and practical applications, this advanced-level textbook covers the most important aspects of contemporary digital communication systems. Introduction to Digital Communication Systems focuses on the rules of functioning digital communication system blocks, starting with the performance limits set by the information theory. Drawing on information relating to turbo codes and LDPC codes, the text presents the basic methods of error correction and detection, followed by baseband transmission methods, and single- and multi-carrier digital modulations. The basi |
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2. |
Record Nr. |
UNINA9910495155803321 |
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Titolo |
Advances in Computational Intelligence : 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part II / / edited by Ignacio Rojas, Gonzalo Joya, Andreu Català |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (456 pages) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 12862 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Social sciences - Data processing |
Education - Data processing |
Computer vision |
Computer networks |
Computer systems |
Artificial Intelligence |
Computer Application in Social and Behavioral Sciences |
Computers and Education |
Computer Vision |
Computer Communication Networks |
Computer System Implementation |
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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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Nota di contenuto |
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Information fusion in Deep Learning for Biomedicine -- Intelligent Computing Solutions for SARS-CoV-2 Covid-19 (INClutions COVID-19) -- Advanced Topics in Computational Intelligence -- Biosignals Processing -- Deep Learning -- Meta-Learning and Other Automatic Learning Approaches in Intelligent Systems -- Artificial Intelligence and Biomedicine -- Convolutional Neural Networks: Beyond Traditional Solutions -- Bio-inspired Systems and Neuro-Engineering -- Agent-Based Models for Policy Design Towards a More Sustainable World -- |
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Randomization in Deep Learning -- Neural Networks for Time Series Forecasting -- Applications in Artificial Intelligence. |
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Sommario/riassunto |
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This two-volume set LNCS 12861 and LNCS 12862 constitutes the refereed proceedings of the 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, held virtually, in June 2021. The 85 full papers presented in this two-volume set were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections on Deep Learning for Biomedicine, Intelligent Computing Solutions for SARS-CoV-2 Covid-19, Advanced Topics in Computational Intelligence, Biosignals Processing, Neuro-Engineering and much more. |
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