08316nam 2200709 450 991083102330332120230125222151.01-282-65652-X97866126565210-470-59067-X0-470-59066-110.1002/9780470590676(CKB)2670000000032877(EBL)554991(SSID)ssj0000423610(PQKBManifestationID)11276740(PQKBTitleCode)TC0000423610(PQKBWorkID)10468413(PQKB)11689439(MiAaPQ)EBC554991(CaBNVSL)mat08039845(IDAMS)0b00006485f0dcda(IEEE)8039845(PPN)261933078(CaSebORM)9780470399835(OCoLC)644164236(EXLCZ)99267000000003287720171024d2008 uy engur|n|---|||||txtccrMultiple-input multiple-output channel models theory and practice /Nelson Costa, Simon Haykin1st editionHoboken, New Jersey :Wiley,c2010.[Piscataqay, New Jersey] :IEEE Xplore,[2010]1 online resource (248 p.)Adaptive and cognitive dynamic systems: signal processing, learning, communications and control ;65Description based upon print version of record.0-470-39983-X Includes bibliographical references and index.Preface -- Chapter 1: Introduction -- 1.1 Historical Perspective -- 1.1.1 Electromagnetism -- 1.1.2 The Hertz Transmitter -- 1.1.3 Tesla and Wireless Power -- 1.1.4 Lodge and Tunable Circuits -- 1.1.5 Marconi and Trans-Atlantic Communication -- 1.2 MIMO Communications -- 1.3 MIMO Channel Models -- 1.3.1 The Channel Model Spectrum -- 1.3.2 Wideband MIMO Channel Models -- 1.4 Software Defined Radio -- 1.5 Overview -- 1.5.1 Chapter 2: Multiple Antenna Channels and Correlation -- 1.5.2 Chapter 3: Correlative Models -- 1.5.3 Chapter 4: Cluster Models -- 1.5.4 Chapter 5: Channel Sounding -- 1.5.5 Chapter 6: Experimental Validation -- 1.5.6 Appendices: Background and Definitions -- Chapter 2: Multiple Antenna Channels and Correlation -- 2.1 The Radio Channel: Definitions -- 2.1.1 The Physical Channel -- 2.1.2 The Analytical Channel -- 2.2 Channel Classifications -- 2.2.1 Linear Time-Invariant Channels -- 2.2.2 Time-Invariant Narrowband Channels -- 2.2.3 Time-Varying Wideband Channels and Bello's Model -- 2.2.4 The Tapped-Delay Line Model and the Physical Channel -- 2.2.5 Narrowband Diversity Channels -- 2.2.6 The Narrowband MIMO Channel -- 2.2.7 The Wideband MIMO Channel -- 2.2.8 The Wideband MIMO Channel Recast Using Tensors -- 2.3 Summary of Channel Classifications -- 2.4 Second-Order Statistics of Multiple Antenna Channels -- 2.4.1 Second-Order Statistics of the Vector Channel -- 2.4.2 Second-Order Statistics of the Narrowband MIMO Channel -- 2.5 Second-order Statistics of the Wideband MIMO Channel -- 2.5.1 Eigenvalue Decomposition of the Wideband Correlation Matrix -- 2.6 Spatial Structure of Multiple Antenna Channels -- 2.6.1 SIMO Channels and Beamformers -- 2.6.2 MIMO Beamformers -- 2.7 Summary and Discussion -- 2.7.1 Channel Classifications -- 2.7.2 Multi-Antenna Channels -- 2.7.3 Spatial Structure and the APS -- 2.8 Notes and References -- 2.8.1 Channel Classifications -- 2.8.2 Second-Order Statistics of Multi-Antenna Channels -- 2.8.3 The Spatial Structure of Multi-Antenna Channels.Chapter 3: Correlative Models -- 3.1 Vector Channel Synthesis from the Vector Correlation Matrix -- 3.2 Matrix Channel Synthesis from the Narrowband Correlation Matrix -- 3.2.1 Number of Model Parameters -- 3.3 One-Sided Correlation for Narrowband MIMO Channels -- 3.4 The Kronecker Model -- 3.4.1 The Narrowband Kronecker Model -- 3.4.2 The Wideband Kronecker Model -- 3.4.3 Notes on the Narrowband and Wideband Kronecker Models -- 3.5 The Weichselberger Model -- 3.5.1 The Vector Mode Model -- 3.5.2 H-matrix From Structured Vector Modes -- 3.6 The Structured Model -- 3.6.1 H-Tensor Synthesis from the Wideband Correlation Tensor -- 3.6.2 One-Sided Correlation for Wideband MIMO Channels. -- 3.6.3 Approximating the Wideband Correlation Matrix -- 3.6.4 Number of Parameters Comparison -- 3.7 Summary and Discussion -- 3.7.1 The Kronecker Model -- 3.7.2 The Weichselberger Model -- 3.7.3 The Structured Model -- 3.8 Notes and References -- 3.8.1 Correlative Models -- 3.8.2 Tensor Decomposition -- Chapter 4: Cluster Models -- 4.1 What is a Cluster? -- 4.2 The Saleh-Valenzuela Model -- 4.2.1 Model Summary -- 4.2.2 Model Implementation -- 4.2.3 Some Typical Parameters -- 4.3 Clusters in Time and Space -- 4.3.1 Azimuth, Elevation, and Delay Spreads -- 4.4 The Extended Saleh-Valenzuela Model -- 4.5 The COST 273 Model -- 4.5.1 Generic Channel Model -- 4.5.2 Environments -- 4.5.3 Receiver, Transmitter Placement -- 4.5.4 COST 273 Procedure -- 4.5.5 Features Not Yet Implemented and Omissions -- 4.5.6 Advantages/Disadvantages: COST 273 -- 4.6 The Random Cluster Model (RCM) -- 4.6.1 General Description -- 4.6.2 Determining the Environment PDF -- 4.6.3 Advantages/Disadvantages: The RCM -- 4.7 Summary and Discussion -- 4.8 Notes and References -- Chapter 5: Channel Sounding -- 5.1 Introduction -- 5.2 The WMSDR -- 5.2.1 Transmission -- 5.2.2 Reception -- 5.2.3 Timing and Carrier Offsets -- 5.3 Narrowband Channel Sounding -- 5.3.1 Periodic Pulse Sounding -- 5.3.2 Narrowband Single-Input, Single-Output Channel Sounding.5.3.3 Narrowband MIMO Channel Sounding -- 5.4 Wideband Sounding: Correlative Sounding -- 5.4.1 ML-sequences -- 5.4.2 Cross-Correlation Using the FFT -- 5.4.3 Digital Matched Filters -- 5.5 Wideband Sounding: Sampled Spectrum Channel Sounding -- 5.6 Switched-array Architectures -- 5.7 Timing and Carrier Recovery -- 5.7.1 Digital Timing Recovery Methods -- 5.7.2 Phase Recovery Using a Decision Directed Feedback Loop -- 5.8 Summary and Discussion -- 5.9 Notes and References -- Chapter 6: Experimental Verifications -- 6.1 Validation Metrics -- 6.1.1 Channel Capacity -- 6.1.2 The Diversity and Correlation Metrics -- 6.1.3 The Demmel Condition Number -- 6.1.4 The Environmental Characterization Metric -- 6.1.5 Correlation Matrix Difference Metric -- 6.2 WMSDR Experimental Setup -- 6.2.1 Terminology -- 6.2.2 Measurement Description -- 6.3 BYU Wideband Channel Sounder Experimental Setup -- 6.3.1 BYU Transmitter Set -- 6.3.2 BYU Receiver Set -- 6.3.3 Measurement Description -- 6.4 Experimental Results -- 6.4.1 Capacity Measure: Methodology -- 6.4.2 Results: MIMO APS and Spatial Structure -- 6.4.3 Results: Wideband Correlation Matrices -- 6.5 Discussion -- 6.5.1 Accuracy of the Results -- 6.5.2 Sources of Error -- 6.6 Summary and Discussion -- 6.7 Notes and References -- Appendix A: An Introduction to Tensor Algebra -- Appendix B: Proof of Theorems from Chapter 3 -- Appendix C: COST 273 Model Summary -- Glossary -- Bibliography -- Index. A complete discussion of MIMO communications, from theory to real-world applications The emerging wireless technology Wideband Multiple-Input, Multiple-Output (MIMO) holds the promise of greater bandwidth efficiency and wireless link reliability. This technology is just now being implemented into hardware and working its way into wireless standards such as the ubiquitous 802.11g, as well as third- and fourth-generation cellular standards. Multiple-Input Multiple-Output Channel Models uniquely brings together the theoretical and practical aspects of MIMO communications, revealing hoAdaptive and cognitive dynamic systems: signal processing, learning, communications and control ;65MIMO systemsWireless communication systemsMIMO systems.Wireless communication systems.621.382621.384Costa Nelson1975-1650875Haykin Simon S.1931-8857CaBNVSLCaBNVSLCaBNVSLBOOK9910831023303321Multiple-input multiple-output channel models4000478UNINA02180nam 2200385 450 99633715330331620240214234722.0(CKB)4100000007806484(NjHacI)994100000007806484(EXLCZ)99410000000780648420240214d2019 uy 0itaur|||||||||||txtrdacontentcrdamediacrrdacarrierMogadiscio 1948 Un eccidio di italiani fra decolonizzazione e guerra fredda /Annalisa Urbano, Antonio VarsoriBologna, Italy :Società editrice il Mulino Spa,2019.1 online resource (296 pages)88-15-35086-1 Il trattato di pace del 1947 aveva previsto la rinuncia dell'Italia alle proprie colonie, che già durante la guerra erano passate sotto l'amministrazione militare britannica. Londra, che mirava a imporre la propria influenza sull'ex impero italiano, in Somalia aveva incoraggiato la nascita di un movimento nazionalista. L'Italia aveva puntato invece al "ritorno in Africa", sostenendo gruppi filo-italiani. Le tensioni si acuirono: l'11 gennaio 1948, nel corso di incidenti circa cinquanta italiani e una decina di somali vennero uccisi, mentre le autorità britanniche si rivelarono incapaci di mantenere l'ordine. Nonostante la gravità dei fatti e le reazioni immediate, l'eccidio di Mogadiscio sarebbe ben presto caduto nell'oblio. Sulla base di un'ampia documentazione, il volume ricostruisce quella tragica vicenda, inserendola nel contesto degli eventi coevi: dalle elezioni del 18 aprile '48 allo scontro Est-Ovest, alle difficili relazioni italo-britanniche, alla realtà politica e sociale somala, al processo di decolonizzazione.Mogadiscio 1948ItaliansSomaliaHistory20th centuryMassacresItaliansHistoryMassacres.904.7Urbano Annalisa769578Varsori AntonioNjHacINjHaclBOOK996337153303316Mogadiscio 19483911922UNISA03483nam 22005415 450 991033779540332120200630042321.03-030-17435-210.1007/978-3-030-17435-4(CKB)4100000008153881(MiAaPQ)EBC5771194(DE-He213)978-3-030-17435-4(PPN)236525700(EXLCZ)99410000000815388120190507d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCorporate Social Responsibility in Finland Origins, Characteristics, and Trends /by Laura Olkkonen, Anne Quarshie1st ed. 2019.Cham :Springer International Publishing :Imprint: Palgrave Pivot,2019.1 online resource (116 pages) illustrations3-030-17434-4 Includes bibliographical references and index.Introduction -- Finnish paternalism at the start of the industrial revolution -- The Nordic welfare state as a backdrop for CSR -- The dawn of stakeholder thinking in Nordic countries -- The public sector: hard and soft regulation -- The private sector: an on-going transformation -- The nonprofit sector and civil society: conflict and collaboration -- Media and communication environment -- Positioning the CSR performance of Finnish companies -- Diffusion of global CSR trends in Finland -- CSR profession in Finland -- On-going challenges -- Conclusion.This book introduces a Finnish approach to corporate social responsibility (CSR) and embeds it within a broader discussion on the Nordic roots of business responsibility and stakeholder thinking. The first part of the book traces the origins of Finnish CSR from paternalism at the beginning of industrialization to the start of the welfare state. The second part discusses the characteristics of Finnish CSR in light of the cultural and societal context and structure, and the third part introduces current trends and challenges. Each section of the book includes case examples that illustrate Finnish CSR from different perspectives. The book will be of use to scholars and students with an interest in the Nordic approach to CSR.Social responsibility of businessIndustrial management—Environmental aspectsNonprofit organizationsCorporate Social Responsibilityhttps://scigraph.springernature.com/ontologies/product-market-codes/526010Sustainability Managementhttps://scigraph.springernature.com/ontologies/product-market-codes/515040Non-Profit Organizations and Public Enterpriseshttps://scigraph.springernature.com/ontologies/product-market-codes/527090Social responsibility of business.Industrial management—Environmental aspects.Nonprofit organizations.Corporate Social Responsibility.Sustainability Management.Non-Profit Organizations and Public Enterprises.658.408658.408094897Olkkonen Lauraauthttp://id.loc.gov/vocabulary/relators/aut954547Quarshie Anneauthttp://id.loc.gov/vocabulary/relators/autBOOK9910337795403321Corporate Social Responsibility in Finland2159140UNINA06442nam 22008295 450 991062929810332120240222143021.09783031046483(electronic bk.)978303104647610.1007/978-3-031-04648-3(MiAaPQ)EBC7131944(Au-PeEL)EBL7131944(CKB)25280520400041(DE-He213)978-3-031-04648-3(PPN)266353932(EXLCZ)992528052040004120221104d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPython for Probability, Statistics, and Machine Learning /by José Unpingco3rd ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (524 pages)Print version: Unpingco, José Python for Probability, Statistics, and Machine Learning Cham : Springer International Publishing AG,c2023 9783031046476 Includes bibliographical references and index.Introduction -- Part 1 Getting Started with Scientific Python -- Installation and Setup -- Numpy -- Matplotlib -- Ipython -- Jupyter Notebook -- Scipy -- Pandas -- Sympy -- Interfacing with Compiled Libraries -- Integrated Development Environments -- Quick Guide to Performance and Parallel Programming -- Other Resources -- Part 2 Probability -- Introduction -- Projection Methods -- Conditional Expectation as Projection -- Conditional Expectation and Mean Squared Error -- Worked Examples of Conditional Expectation and Mean Square Error Optimization -- Useful Distributions -- Information Entropy -- Moment Generating Functions -- Monte Carlo Sampling Methods -- Useful Inequalities -- Part 3 Statistics -- Python Modules for Statistics -- Types of Convergence -- Estimation Using Maximum Likelihood -- Hypothesis Testing and P-Values -- Confidence Intervals -- Linear Regression -- Maximum A-Posteriori -- Robust Statistics -- Bootstrapping -- Gauss Markov -- Nonparametric Methods -- Survival Analysis -- Part 4 Machine Learning -- Introduction -- Python Machine Learning Modules -- Theory of Learning -- Decision Trees -- Boosting Trees -- Logistic Regression -- Generalized Linear Models -- Regularization -- Support Vector Machines -- Dimensionality Reduction -- Clustering -- Ensemble Methods -- Deep Learning -- Notation -- References -- Index.Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming. · Features a novel combination of modern Python implementations and underlying mathematics to illustrate and visualize the foundational ideas of probability, statistics, and machine learning; · Includes meticulously worked-out numerical examples, all reproducible using the Python code provided in the text, that compute and visualize statistical and machine learning models thus enabling the reader to not only implement these models but understand their inherent trade-offs; · Utilizes modern Python modules such as Statsmodels, Tensorflow, Keras, Sympy, and Scikit-learn, along with embedded "Programming Tips" to encourage readers to develop quality Python codes that implement and illustrate practical concepts.TelecommunicationComputer scienceMathematicsMathematical statisticsEngineering mathematicsEngineeringData processingStatisticsData miningCommunications Engineering, NetworksProbability and Statistics in Computer ScienceMathematical and Computational Engineering ApplicationsStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesData Mining and Knowledge DiscoveryPython (Llenguatge de programació)thubAprenentatge automàticthubProbabilitatsthubProcessament de dadesthubLlibres electrònicsthubTelecommunication.Computer scienceMathematics.Mathematical statistics.Engineering mathematics.EngineeringData processing.Statistics.Data mining.Communications Engineering, Networks.Probability and Statistics in Computer Science.Mathematical and Computational Engineering Applications.Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.Data Mining and Knowledge Discovery.Python (Llenguatge de programació)Aprenentatge automàticProbabilitatsProcessament de dades006.31005.133Unpingco José1969-1075978MiAaPQMiAaPQMiAaPQ9910629298103321Python for probability, statistics, and machine learning3057893UNINA