Tabla de Contenidos: “…Nombre moyen d'années d'études de la population d'âge actif dans les pays de l'OCDE, 1970 et 1998 -- Graphique IV.2. Dépenses de R-D dans les pays de l'OCDE, dans les années 80 et 90 -- Graphique IV.3. …”

Tabla de Contenidos: “…Recettes publiques fiscales, 2008 -- Figure 0.3 La politique budgétaire contribue peu à la réduction des inégalités en Amérique latine -- Figure 0.4 Les dépenses publiques sont moins importantes en Amérique latine que dans l'OCDE -- Figure 0.5 Les jeunes latino-américains de 15 ans obtiennent de moins bons résultats en lecture que les jeunes de l'OCDE, et les différences socio-économiques pèsent plus lourd sur leurs performances -- Figure 0.6 La fracture du haut débit est importante et s'aggrave en Amérique latine -- Figure 0.7 Le défi de l'Amérique latine : mobiliser les investissements des entreprises en faveur de la R&amp -- D -- Figure 1.1 Marge budgétaire avant et après la crise financière, 2000-07 et 2007-09 -- Figure 1.2 Amérique latine (13 pays) : variation trimestrielle des composantes des dépenses, 2009-10 -- Figure 1.3 Amérique latine : part de la Chine dans les exportations et les importations totales, par pays, 2000 et 2009 -- Figure 1.4 Amérique du Sud et Amérique centrale : indices des termes de l'échange, 2000-10 -- Figure 1.5 Évolution mensuelle des prix internationaux des produits de base, entre janvier 2003 et août 2011.…”

Tabla de Contenidos: “…Relative Sample Size -- 7.10 Python: Random Sampling Strategies -- 7.10.1 Implement Simple Random Sample (SRS) -- 7.10.2 Determining Confidence Intervals -- 7.10.3 Bootstrap Sampling to Determine Confidence Intervals for a Mean -- 7.10.4 Advanced Sampling Techniques -- 7.10.4.1 Stratified Sampling for Categorical Variables -- 7.10.4.2 Stratified Sampling of Continuous Variables -- Exercises -- Chapter 8 More than Two Samples or Categories -- 8.1 Count Data-R × C Tables -- 8.2 The Role of Experiments (Many Are Costly) -- 8.2.1 Example: Marriage Therapy -- 8.3 Chi‐Square Test -- 8.3.1 Alternate Option -- 8.3.2 Testing for the Role of Chance -- 8.3.3 Standardization to the Chi‐Square Statistic -- 8.3.4 Chi‐Square Example on the Computer -- 8.4 Single Sample-Goodness‐of‐Fit…”

Tabla de Contenidos: “…Debug Tool and debugging PL/I -- 15.1 Preparing a PL/I program for debugging -- 15.1.1 Notes on preparing a PL/I V2R3 program for Debug Tool -- 15.1.2 Notes on all other PL/I compilers -- 15.2 A quick run through the program -- 15.3 A quick test using the MFI debugger -- 15.4 A debug session with the remote debugger -- Chapter 16. …”

Tabla de Contenidos: “…5.7 Exercises -- References -- Chapter 6 System Reliability Analysis -- 6.1 Important Notes and Assumptions -- 6.2 Reliability Block Diagram Method -- 6.2.1 Series System -- 6.2.2 Parallel Systems -- 6.2.3 k-Out-of-n Redundant Systems -- 6.2.4 Standby Systems -- 6.2.5 Load-Sharing Systems -- 6.3 Complex System Evaluation Methods -- 6.3.1 Decomposition Method -- 6.3.2 Path Tracing Methods (Path Sets, Cut Sets) -- 6.4 Fault Tree and Success Tree Methods -- 6.4.1 Fault Tree Method -- 6.4.2 Evaluation of Fault Trees -- 6.4.2.1 Analysis of Logic Trees Using Boolean Algebra -- 6.4.2.2 Combinatorial (Truth Table) Technique for Evaluation of Logic Trees -- 6.4.2.3 Binary Decision Diagrams -- 6.4.3 Success Tree Method -- 6.5 Event Tree Method -- 6.5.1 Construction of Event Trees -- 6.5.2 Evaluation of Event Trees -- 6.6 Event Sequence Diagram Method -- 6.7 Failure Modes and Effects Analysis -- 6.7.1 Objectives of FMEA -- 6.7.2 FMEA/FMECA Procedure -- 6.7.3 FMEA Implementation -- 6.7.3.1 FMEA Using MIL-STD-1629A -- 6.7.3.2 FMEA Using SAE J1739 -- 6.7.4 FMECA Procedure: Criticality Analysis -- 6.7.4.1 Failure Probability Failure Rate Data Source -- 6.7.4.2 Failure Effect Probability β -- 6.7.4.3 Failure Rate λ[sub(p)] -- 6.7.4.4 Failure Mode Ratio α -- 6.7.4.5 Operating Time T -- 6.7.4.6 Failure Mode Criticality Number C[sub(m)] -- 6.7.4.7 Item Criticality Number C[sub(r)] -- 6.8 Exercises -- References -- Chapter 7 Reliability and Availability of Repairable Components and Systems -- 7.1 Definition of Repairable System and Types of Repairs -- 7.2 Variables of Interest: Availability, ROCOF, MTBF -- 7.3 Repairable System Data -- 7.4 Stochastic Point Processes (Counting Processes) -- 7.4.1 Homogeneous Poisson Process -- 7.4.2 Renewal Process -- 7.4.3 Nonhomogeneous Poisson Process -- 7.4.4 Generalized Renewal Process -- 7.5 Data Analysis for Point Processes…”

Tabla de Contenidos: “…Teeth of Cis-Baikal Middle Holocene hunter-gatherers selected for this study. L = Left -- R = Right -- M1, 2 or 3 = Upper 1st, 2nd or 3rd molar -- M1, 2 or 3 = Lower 1st, 2nd or 3rd molar. -- Table 4. …”

Tabla de Contenidos: “…4.2.4.9 The Need For Prescriptive Analytics in Maintenance: A Case Study -- 4.3 Big Data Analytics Methods -- 4.3.1 Defining Big Data Analytics -- 4.3.2 Defining Big Data Via the Three Vs -- 4.3.2.1 Data Volume as a Defining Attribute of Big Data -- 4.3.2.2 Data Type Variety as a Defining Attribute of Big Data -- 4.3.2.3 Data Feed Velocity as a Defining Attribute of Big Data -- 4.3.3 Text Analytics -- 4.3.4 Audio Analytics -- 4.3.5 Video Analytics -- 4.3.6 Social Media Analytics -- 4.4 Maintenance Strategies with Big Data Analytics -- 4.5 Data-Driven and Model-Driven Approaches -- 4.5.1 Data Mining and Knowledge Discovery -- 4.6 Maintenance Descriptive Analytics -- 4.7 Maintenance Diagnostic Analytics -- 4.8 Maintenance Predictive Analytics -- 4.9 Maintenance Prescriptive Analytics -- 4.10 Big Data Analytics Methods -- 4.10.1 Text Analytics -- 4.10.2 Audio Analytics -- 4.10.3 Video Analytics -- 4.10.4 Social Media Analytics -- 4.11 Big Data Management and Governance -- 4.12 Big Data Access and Analysis -- 4.13 Big Data Visualisation -- 4.14 Big Data Ingestion -- 4.15 Big Data Cluster Management -- 4.16 Big Data Distributions -- 4.17 Data Governance -- 4.18 Data Access -- 4.19 Data Analysis -- 4.20 Bid Data File System -- 4.20.1 Quantcast File System -- 4.20.2 Hadoop Distributed File System -- 4.20.3 Cassandra File System (CFS) -- 4.20.4 GlusterFS -- 4.20.5 Lustre -- 4.20.6 Parallel Virtual File System -- 4.20.7 Orange File System (OrangeFS) -- 4.20.8 BeeGFS -- 4.20.9 MapR-FS -- 4.20.9.1 Kudu -- References -- Chapter 5 Data-Driven Decision-Making -- 5.1 Data for Decision-Making -- 5.1.1 Data-Driven Decision-Making -- 5.1.2 The Process of Data-Driven Decision-Making -- 5.1.3 The Context of Data-Driven Decision-Making -- 5.1.4 The Importance of Data-Driven Decision-Making -- 5.1.5 Common Challenges of Data-Driven Decision-Making…”

Tabla de Contenidos: “…7.9 Math Library Functions -- 7.10 Recursion -- Summary -- Key Words -- Questions -- Chapter 8: Arrays and Structures in C++ -- 8.1 Introduction -- 8.2 Initializing Arrays -- 8.3 Accessing Values of an Array -- 8.4 Multi-Dimensional Arrays -- 8.4.1 Initialization -- 8.4.2 Accessing a Multi-dimensional Array -- 8.5 Arrays as Parameters -- 8.6 Character Sequences -- 8.7 Structures -- 8.7.1 Features of Structures -- 8.7.2 Union -- Summary -- Key Words -- Questions -- Chapter 9: Classes and Objects in C++ -- 9.1 Introduction -- 9.2 A Simple Class -- 9.2.1 An Example Program -- 9.2.2 Explanation -- 9.2.3 Creating Objects -- 9.2.4 Accessing Member Functions -- 9.3 Constructors and Destructors -- 9.3.1 Constructors -- 9.3.2 Parameterized Constructors -- 9.3.3 Constructor Overloading -- 9.3.4 Default Constructor -- 9.3.5 Destructors -- 9.4 Operator Overloading -- 9.4.1 Overloading Binary Operator '+' -- 9.4.2 Overloading a Unary Operator '++' -- 9.4.3 friend Functions -- 9.5 Inheritance -- 9.5.1 Derived Classes -- 9.5.2 Access Types -- 9.5.3 Forms of Inheritance with Examples -- 9.6 Polymorphism -- 9.6.1 Virtual Function -- 9.6.2 Abstract Class -- Summary -- Key Words -- Questions -- Unit IV: Database Management -- Chapter 10: Database Management System -- 10.1 Introduction -- 10.2 Database and Database System -- 10.2.1 Defintions -- 10.2.2 Components of Database System -- 10.3 File-oriented Approach -- 10.4 Database Approach -- 10.5 Data Models -- 10.5.1 High-level or Conceptual Data Model -- 10.5.1.1 Entity -- 10.5.1.2 Attribute -- 10.5.1.3 Relationship -- 10.5.1.4 Entity-Relationship (E-R) Model -- 10.5.2 Representation or Implementation Data Model -- 10.5.2.1 Relational Database Model -- 10.5.2.2 Hierarchical Database Model -- 10.5.2.3 Network Database Model -- 10.6 Architecture of Database System -- 10.7 Data Independence -- 10.8 Data Dictionary…”

Tabla de Contenidos: “….) -- 3.3 TIPOS DE ARTICULACIONES -- 3.4 CADENA CINEMÁTICA -- 3.5 CINEMÁTICA DIRECTA -- 3.5.1 Coordenadas generalizadas -- 3.5.2 Convención Denavit-Hartenberg (DH) -- 3.5.3 Convención Denavit-Hartenberg (...) -- 3.5.4 Robot 3R (...) -- 3.5.5 La tabla de parámetros DH -- 3.5.6 Cálculo de la cinemática directa a partir de la tabla DH -- 3.5.7 Convención DH en Matlab© -- 3.5.8 Construcción de un sistema robótico en Matlab© -- 3.5.9 Gráfica de un sistema robótico en Matlab© -- 3.5.10 Cálculo de la cinemática directa en Matlab© -- 3.5.11 Modificación de cinemática directa en Matlab© -- 3.6 EJEMPLOS DEL CÁLCULO DE LA CINEMÁTICA (...) -- 3.6.1 Robot cilíndrico -- 3.6.2 Robot antropomórfico -- 3.6.3 Eslabón esférico -- 3.6.4 Robot TQ MA2000 -- 3.6.5 Robot manipulador Stanford -- 3.6.6 Robot manipulador SCARA -- 3.6.7 Robot humanoide Dany Walker -- 3.7 RESUMEN DEL CAPÍTULO -- 3.8 EJERCICIOS RECOMENDADOS -- 3.9 LECTURAS RECOMENDADAS -- 4 CINEMÁTICA INVERSA -- 4.1 EL PROBLEMA DE LA CINEMÁTICA INVERSA -- 4.1.1 Solución al problema de cinemática inversa -- 4.1.2 Método geométrico -- 4.1.3 Distancias de ajuste -- 4.1.4 Desacoplamiento cinemático -- 4.1.5 Solución para el robot antropomórfico de 6-DOF -- 4.1.6 Solución del robot SCARA -- 4.1.7 Solución del robot SCARA en Matlab© -- 4.2 MÉTODOS ANALÍTICOS E ITERATIVOS -- 4.3 SOLUCIÓN ITERATIVA DEL PROBLEMA DE CINEMÁTICA INVERSA -- 4.3.1 Convergencia de la solución iterativa -- 4.3.2 Cálculo de la cinemática inversa en Matlab© -- 4.4 RESUMEN DEL CAPÍTULO -- 4.5 EJERCICIOS PROPUESTOS -- 4.6 BIBLIOGRAFÍA RECOMENDADA -- 5 CINEMÁTICA DIFERENCIAL -- 5.1 VELOCIDAD LINEAL Y ROTACIONAL -- 5.1.1 Velocidad lineal -- 5.1.2 Velocidad rotacional -- 5.2 EL VECTOR DE VELOCIDAD -- 5.2.1 Simulación del vector de velocidad en Matlab©…”

Tabla de Contenidos: “…Why ARM? -- Cortex-M -- Cortex-R -- Cortex-A -- 3.3.3. Cortex-M Processor Comparison -- 3.3.4. …”

“…Anhand einfacher Schritt-für-Schritt-Anleitungen zeigt er Ihnen, wie Sie Ihr NAS als Private Cloud und Home Server optimal einrichten: Dateien sichern, verwalten und mit anderen teilen, Benutzer verwalten, Fernzugriff einrichten, automatische Backups erstellen sowie Office-Dokumente und Multimedia-Dateien freigeben und mit dem SmartTV und anderen Ger̃ten wiedergeben. F|r alle, die noch mehr aus ihrem Synology NAS herausholen m̲chten, geht der Autor auf weiterführende Themen wie Datensicherheit und die Überwachung und Optimierung des Betriebs ein und zeigt Ihnen die Konfiguration abseits der grafischen Benutzeroberfl̃che für die Einrichtung eines eigenen Webservers und der beliebten Cloud-Lösung Nextcloud…”

Tabla de Contenidos: “…IBM TotalStorage SAN b-type family -- 8.1 Product description -- 8.1.1 IBM TotalStorage SAN16B-2 fabric switch -- 8.1.2 IBM TotalStorage SAN32B-2 fabric switch -- 8.1.3 IBM TotalStorage SAN Switch M14 -- 8.1.4 IBM TotalStorage SAN256B director -- 8.1.5 IBM TotalStorage SAN 16B-R -- 8.2 Switch features -- 8.2.1 Advanced WEB TOOLS -- 8.2.2 Advanced Performance Monitoring -- 8.2.3 Advanced Security -- 8.2.4 Advanced Zoning -- 8.2.5 Extended Fabric -- 8.2.6 Fabric Manager -- 8.2.7 Fabric Watch -- 8.2.8 ISL Trunking -- 8.2.9 Dynamic Path Selection -- 8.2.10 Remote Switch -- 8.3 Advanced Security -- 8.3.1 Host-to-Switch Domain -- 8.3.2 Administrator-to-Security Management Domain -- 8.3.3 Security Management-to-Fabric Domain -- 8.3.4 Switch-to-Switch Domain -- 8.3.5 Fabric configuration servers -- 8.3.6 Management access controls -- 8.3.7 Device connection controls -- 8.3.8 Switch connection controls -- 8.3.9 Fibre Channel Authentication Protocol -- 8.4 ISL -- 8.4.1 ISLs without trunking or dynamic path selection -- 8.4.2 ISLs with trunking -- 8.4.3 Dynamic Path Selection -- 8.4.4 Switch count -- 8.4.5 Distributed fabrics -- 8.5 FICON -- 8.5.1 FICON servers…”

Tabla de Contenidos: “…References 67 -- PART II COMPUTATIONAL ATTENTION MODELS 73 -- 3 Computational Models in the Spatial Domain 75 -- 3.1 Baseline Saliency Model for Images 75 -- 3.1.1 Image Feature Pyramids 76 -- 3.1.2 Centre-Surround Differences 79 -- 3.1.3 Across-scale and Across-feature Combination 80 -- 3.2 Modelling for Videos 81 -- 3.2.1 Extension of BS Model for Video 81 -- 3.2.2 Motion Feature Detection 81 -- 3.2.3 Integration for Various Features 83 -- 3.3 Variations and More Details of BS Model 84 -- 3.3.1 Review of the Models with Variations 85 -- 3.3.2 WTA and IoR Processing 87 -- 3.3.3 Further Discussion 90 -- 3.4 Graph-based Visual Saliency 91 -- 3.4.1 Computation of the Activation Map 92 -- 3.4.2 Normalization of the Activation Map 94 -- 3.5 Attention Modelling Based on Information Maximizing 95 -- 3.5.1 The Core of the AIM Model 96 -- 3.5.2 Computation and Illustration of Model 97 -- 3.6 Discriminant Saliency Based on Centre-Surround 101 -- 3.6.1 Discriminant Criterion Defined on Centre-Surround 102 -- 3.6.2 Mutual Information Estimation 103 -- 3.6.3 Algorithm and Block Diagram of Bottom-up DISC Model 106 -- 3.7 Saliency Using More Comprehensive Statistics 107 -- 3.7.1 The Saliency in Bayesian Framework 108 -- 3.7.2 Algorithm of SUN Model 110 -- 3.8 Saliency Based on Bayesian Surprise 113 -- 3.8.1 Bayesian Surprise 113 -- 3.8.2 Saliency Computation Based on Surprise Theory 114 -- 3.9 Summary 116 -- References 117 -- 4 Fast Bottom-up Computational Models in the Spectral Domain 119 -- 4.1 Frequency Spectrum of Images 120 -- 4.1.1 Fourier Transform of Images 120 -- 4.1.2 Properties of Amplitude Spectrum 121 -- 4.1.3 Properties of the Phase Spectrum 123 -- 4.2 Spectral Residual Approach 123 -- 4.2.1 Idea of the Spectral Residual Model 124 -- 4.2.2 Realization of Spectral Residual Model 125 -- 4.2.3 Performance of SR Approach 126 -- 4.3 Phase Fourier Transform Approach 127 -- 4.3.1 Introduction to the Phase Fourier Transform 127 -- 4.3.2 Phase Fourier Transform Approach 128.…”