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1. |
Record Nr. |
UNINA9910793614503321 |
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Autore |
Leidholm Nathan (Nathan Paul) |
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Titolo |
Elite Byzantine kinship, ca. 950-1204 : blood, reputation, and the genos / / by Nathan Leidholm [[electronic resource]] |
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Pubbl/distr/stampa |
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Leeds : , : Arc Humanities Press, , 2019 |
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ISBN |
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Descrizione fisica |
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1 online resource (x, 186 pages) : digital, PDF file(s) |
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Collana |
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Disciplina |
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Soggetti |
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Aristocracy (Social class) - Byzantine Empire |
Kinship - Byzantine Empire |
Elite (Social sciences) - Byzantine Empire |
History |
Byzantine Empire History 527-1081 |
Byzantine Empire History Comneni dynasty, 1081-1185 |
Byzantine Empire History Angeli, 1185-1204 |
Byzantine Empire Social conditions |
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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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Title from publisher's bibliographic system (viewed on 20 Nov 2020). |
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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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Defining 'the family' in Byzantine sources and the modern historiography -- The language of kinship -- Marriage impediments and the concept of family -- Interrogating consanguinity in a Byzantine context -- Family names and the politics of reputation -- Kinship and political developments of the eleventh and twelfth centuries. |
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Sommario/riassunto |
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By the end of the twelfth century, the Byzantine 'genos' was a politically effective social group based upon ties of consanguineous kinship, but, importantly, it was also a cultural construct, an idea that held very real power, yet defies easy categorization. This study explores the role and function of the Byzantine aristocratic family group, or 'genos', as a distinct social entity, particularly its political and cultural role, as it appears in a variety of sources in the tenth through twelfth centuries. |
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2. |
Record Nr. |
UNINA9910821465503321 |
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Autore |
Chiasserini Carla-Fabiana |
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Titolo |
Analytical modeling of wireless communication systems / / Carla-Fabiana Chiasserini, Marco Gribaudo, Daniele Manini |
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Pubbl/distr/stampa |
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London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2016 |
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©2016 |
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ISBN |
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1-119-30774-0 |
1-119-30773-2 |
1-119-30772-4 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (155 p.) |
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Collana |
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Stochastic Models in Computer Science and Telecommunication Networks Set ; ; Number 1 |
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Disciplina |
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Soggetti |
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Wireless communication systems |
Wireless communication systems - Design and construction |
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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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Cover; Title Page; Copyright; Contents; Preface; Introduction; List of Acronyms; PART 1: Sensor Networks; PART 2: Vehicular Networks; PART 3: Cellular Networks; Bibliography; Index; Other titles from ISTE in Networks and Telecommunications; EULA; 1: Fluid Models and Energy Issues; 2: Hybrid Automata for Transient Delay Analysis; 3: Safety Message Broadcasting; 4: Modeling Information Sharing; 5: Multi-RAT Algorithms; 1.1. The fluid-based approach; 1.2. Network scenario; 1.3. The sensor network model; 1.4. Results; 2.1. Event detection in WSNs; 2.2. Model for single-hop network topologies |
2.3. Solution technique2.4. Model for multi-hop network topologies; 2.5. Model validation and exploitation results; 2.6. Discussion; 3.1. System description; 3.2. Dissemination of safety messages; 3.3. Assumptions and notations; 3.4. Model outline; 3.5. Computation of the block probability; 3.6. Computation of the probability of first reception; 3.7. Performance evaluation; 4.1. System scenario; 4.2. Modeling information exchange in IVN; 4.3. Computation of the probability of successful information retrieval; 4.4. Model validation and exploitation; 5.1. RAT network; 5.2. Network model |
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5.3. Model solution5.4. Performance evaluation; 1.1.1. Sensor density and traffic generation; 1.1.2. Data routing; 1.1.3. Local and relay traffic rates; 1.1.4. Channel contention and data transmission; 1.1.5. Mean packet delivery delay; 1.1.6. Sensor active/sleep behavior; 1.3.1. A minimum energy routing strategy: computing u(r'|r); 1.3.2. Channel contention and data transmission: computing s(r) and PR(r); 1.3.3. Mean packet delivery delay: computing q(r); 1.4.1. Model validation; 1.4.2. Model exploitation; 1.4.3. Model solution complexity and accuracy; 2.1.1. The 802.15.4 MAC protocol |
2.2.1. Single message transfer2.2.2. Multiple message transfers; 2.3.1. Time discretization; 2.3.2. Transient solution; 2.3.3. Performance metrics computation; 3.2.1. The spatial differentiation approach; 3.2.2. The safety application; 3.6.1. A Gaussian approximation to the transient system behavior; 3.7.1. The impact of power capture; 3.7.2. The case of occupation probability ρ = 1; 3.7.3. The case of homogeneous occupation probability ρ < 1; 3.7.4. The case of inhomogeneous occupation probability; 3.7.5. The impact of the forwarding policy; 4.2.1. Model description; 5.1.1. Scenario |
5.1.2. RAT selection strategy5.2.1. Functional rates; 5.3.1. Analytical approach; 5.3.2. Computation of performance metrics; 5.4.1. Setting and results; 1.3.1.1. Computing єm(r, r')|; 1.3.1.2. Computing FkmE(e|r); 1.3.1.3. Computation of the minimum energy path (equation [1.9]); 1.3.1.4. Computing FmE(e|r); 1.3.1.5. Computing ps; r(r'|e); 1.3.1.6. Computing u(r'|r); 1.3.2.1. Computation of the mean number of transmissions freezing the backoff counter; 1.3.3.1. Computing q(r) for always active sensors; 1.3.3.2. Computing q(r) for active and sleeping sensors; 5.1.1.1. Network scenario |
5.2.1.1. Rates derivation |
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Sommario/riassunto |
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Wireless networks represent an inexpensive and convenient way to connect to the Internet. However, despite their applications across several technologies, one challenge still remains: to understand the behavior of wireless sensor networks and assess their performance in large-scale scenarios. When a large number of network nodes need to interact, developing suitable analytical models is essential to ensure the appropriate coverage and throughput of these networks and to enhance user mobility. This is intrinsically difficult due to the size and number of different network nodes and users. This book highlights some examples which show how this problem can be overcome with the use of different techniques. An intensive parameter analysis shows the reader how to the exploit analytical models for an effective development and management of different types of wireless networks. |
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