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Portrait of a city: London : Engl.-Dtsch.-Französ. (2012. 550 S. m. zahlr. z. Tl. farb. Fotos. 34,6 cm): Golden, Reuel/ Captions written by Barry Miles: BOOKS KINOKUNIYA
Book Details
Portrait of a city: London : Engl.-Dtsch.-Französ. (2012. 550 S. m.  zahlr. z. Tl. farb. Fotos. 34,6 cm)
Portrait of a city: London : Engl.-Dtsch.-Französ. (2012. 550 S. m. zahlr. z. Tl. farb. Fotos. 34,6 cm)
Published Date :
Binding : Hardcover
ISBN : 9783836528771

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Book Description
Academic Descriptors: KNO5870

Samuel Johnson famously said thattired of life." London's remarkable history, architecture, landmarks, streets, style, cool, swagger, and stalwart residents are pictured in hundreds of compelling photographs sourced from a wide array of archives around the world. London is a vast sprawling metropolis, constantly evolving and growing, yet throughout its complex past and shifting present, the humor, unique character, and bulldog spirit of the people have stayed constant. This book salutes all those Londoners, their city, and its history. In addition to the wealth of images included in this book, many previously unpublished, London's history is told through hundreds of quotations, lively essays, and references from key movies, books, and records.From Victorian London to the Swinging 60s; from the Battle of Britain to Punk; from the Festival of Britain to the 2012 Olympics; from the foggy cobbled streets to the architectural masterpieces of the millennium; from rough pubs to private drinking clubs; from Royal Weddings to raves, from the charm of the East End to the wonders of the Westminster; from Chelsea girls to Hoxton hipsters; from the power to glory: in page after page of stunning photographs, reproduced big and bold like the city itself, London at last gets the photographic tribute it deserves.1 Starting SPSS Lesson 2 The SPSS Main Menus and Toolbar Lesson 3 Using SPSS Help Lesson 4 A Brief SPSS Tour Unit 2 Creating and Working with Data Files Lesson 5 Defining Variables Lesson 6 Entering and Editing Data Lesson 7 Inserting and Deleting Cases and Variables Lesson 8 Selecting, Copying, Cutting, and Pasting Data Lesson 9 Printing and Exiting an SPSS Data File Lesson 10 Exporting and Importing SPSS Data Lesson 11 Validating SPSS Data Unit 3 Working with Data Lesson 12 Finding Values, Variables, and Cases Lesson 13 Recoding Data and Computing Values Lesson 14 Sorting, Transposing, and Ranking Data Lesson 15 Splitting and Merging Files Unit 4A Working with SPSS Charts and Output for Windows Lesson 16A Creating an SPSS Chart Lesson 17A Enhancing SPSS Charts Lesson 18A Using the Viewer and Pivot Tables Unit 4B Working with SPSS Charts and Output for Macintosh Lesson 16B Creating an SPSS Chart Lesson 17B Enhancing SPSS Charts Lesson 18B Using the Viewer and Pivot Tables Part II Working with SPSS Procedures Unit 5 Creating Variables and Computing Descriptive Statistics Lesson 19 Creating Variables Lesson 20 Univariate Descriptive Statistics for Qualitative Variables Lesson 21 Univariate Descriptive Statistics for Quantitative Variables Unit 6 t Test Procedures Lesson 22 One-Sample t Test Lesson 23 Paired-Samples t Test Lesson 24 Independent-Samples t Test Unit 7 Univariate and Multivariate Analysis-of-Variance Techniques Lesson 25 One-Way Analysis of Variance Lesson 26 Two-Way Analysis of Variance Lesson 27 One-Way Analysis of Covariance Lesson 28 One-Way Multivariate Analysis of Variance Lesson 29 One-Way Repeated-Measures Analysis of Variance Lesson 30 Two-Way Repeated-Measures Analysis of Variance Unit 8 Correlation, Regression, and Discriminant Analysis Procedures Lesson 31 Pearson Product-Moment Correlation Coefficient Lesson 32 Partial Correlations Lesson 33 Bivariate Linear Regression Lesson 34 Multiple Linear Regression Lesson 35 Discriminant Analysis Unit 9 Scaling Procedures Lesson 36 Factor Analysis Lesson 37 Internal Consistency Estimates of Reliability Lesson 38 Item Analysis Using the Reliability Procedure Unit 10 Nonparametric Procedures Lesson 39 Binomial Test Lesson 40 One-Sample Chi-Square Test Lesson 41 Two-Way Contingency Table Analysis Using Crosstabs Lesson 42 Two Independent-Samples Test: The Mann-Whitney U Test Lesson 43 K Independent-Samples Tests: The Kruskal-Wallis and the Median Tests Lesson 44 Two Related-Samples Tests: The McNemar, the Sign, and the Wilcoxon Tests Lesson 45 K Related-Samples Tests: The Friedman and the Cochran TestsBF BG BH BI BJ BM BN BO BR BS BT BW BY BZ CC CD CF CG CH CI CK CL CM CN CO CR CS CU CV CX CY CZ DE DJ DK DM DO DZ EC EE EG EH ER ES ET FI FJ FK FM FO FR GA GD GE GF GH GI GL GM GN GP GQ GR GS GT GU GW GY HK HM HN HR HT HU ID IL IN IO IQ IS IT JM JO JP KE KG KH KI KM KN KP KR KW KY KZ LA LB LC LI LK LR LS LT LU LV LY MA MC MD MG MH MK ML MM MN MO MP MQ MR MS MT MU MV MW MX MY MZ NA NC NE NF NG NI NL NO NP NR NU OM PA PE PF PG PH PK PL PM PN PR PS PT PW PY QA RE RO RU RW SA SB SC SD SE SG SH SI SJ SK SL SM SN SO SR ST SV SY SZ TC TD TF TG TH TJ TK TL TM TN TO TR TT TV TW TZ UA UG UM UY UZ VA VC VE VG VI VN VU WF WS YE YT ZM ZWAlgorithms for CDSs Subtraction-based Localized Algorithms for CDSs Distributed Algorithms for CDSs Related Literature about PNM model Remarks Problem Statement Assumptions Network Model Problem Definition Remarks RMCDS-GA Algorithm GA Overview Representation of Chromosomes Population Initialization Fitness Function Selection (Reproduction) Scheme Genetic Operations Crossover Mutation Replacement Policy Genetic Algorithms with Immigrants Schemes Performance Evaluation Simulation Environment Simulation Results Conclusion Constructing Load-balanced Virtual Backbones in Probabilistic Wireless Sensor Networks via Multi-Objective Genetic Algorithm Introduction Related Work CDS-based VBs under DNM Related Literature about PNM model Literature Review of MOGAs Remarks Network Model and Problem Definition Assumptions Network Model Preliminary Problem Definition LBVBP-MOGA Algorithm Overview of MOGAs Multi-objective Problem (MOP) Definitions and Overview GA Overview MOGA Overview Design of LBVBP-MOGA Representation of Chromosomes Population Initialization Fitness Function Selection Scheme and Replacement Policy Genetic Operations . Convergence Analysis Performance Evaluation Simulation Environment Simulation Results Conclusion Constructing Load-balanced Data Aggregation Trees in Probabilistic Wireless Sensor Network Introduction Related Work Energy-efficient Aggregation Scheduling Minimum Latency Aggregation Scheduling Maximum Lifetime Aggregation Scheduling Remarks Network Model and Problem Definition Assumptions Network Model Problem Definition Remarks Connected Maximal Independent Set INP Formulation of LBMIS Approximation Algorithm Connecting LBMIS LBPNA for Non-leaf Nodes Load-Balanced Data Aggregation Tree ILP Formulation of LBPNA for Leaf Nodes Randomized Approximation Algorithm Performance Evaluation Simulation Environment Scenario1: Change side length of square area Scenario 2: Change node transmission range Scenario 3: Change total number of nodes Conclusion APPLICATIONS Reliable and Energy Efficient Target Coverage for Wireless Sensor Networks Introduction Related Work Target Coverage Other Coverage Remarks Network Model and Related Definitions Network Model Related Definitions Problem Formulation Our Proposed Algorithm alpha-RMSC Heuristic Algorithm Overview Contribution Function Relation between MSC and alpha-RMSC Performance Evaluation Simulation 1: Control Failure Probability Simulation 2: Comparison between alpha -RMSC and MSC Conclusion CDS-based Multi-regional Query Processing in Wireless Sensor Networks Introduction RelatedWork Periodic Query Scheduling Dynamic Query Scheduling Remarks Problem Formulation Network Model Multi-regional Query Problem Definition Multi-regional Query Scheduling Construction of MRQF MRQSA Scheduling Initialization Scheduling Algorithm Performance Analysis Performance Evaluation Simulation Environment Simulation Results Conclusion CDS-based Snapshot and Continuous Data Collection in Dual-radio Multi-channel Wireless Sensor Networks Introduction Related Work Capacity for Single-radio Single-channel Wireless Networks Capacity for Multi-channel Wireless Networks Remarks Network Model and Preliminaries Network Model Routing Tree Vertex Coloring Problem Capacity of SDC Scheduling Algorithm for SDC Capacity Analysis Discussion Capacity of CDC Compressive Data Gathering (CDG) Pipeline Scheduling Capacity Analysis Simulations and Results Analysis Performance of MPS Performance of PS Impacts of N and M Conclusion CDS-based Distributed Data Collection in Wireless Sensor Networks Introduction Related Works Data Collection Capacity Multicast Capacity Uni/Broadcast Capacity Uni/Broadcast Capacity for Random Wireless Networks Uni/Broadcast Capacity for Arbitrary Wireless Networks Unicast Capacity for Mobile Wireless Networks Remarks Network Model Carrier-sensing Range Distributed Data Collection and Capacity Distributed Data Collection Capacity Analysis R0-PCR-based Distributed Data Aggregation Data Collection and Aggregation under Poisson Distribution Model Simulation Results DDC Capacity versus R0 and alpha Scalability of DDC PerformanceofDDA Conclusion CDS-based Broadcast Scheduling in Cognitive Radio Networks Introduction Related Work Broadcast Scheduling in Traditional Wireless Networks Broadcast Scheduling in CRNs Remarks System Model and Problem Definition Network Model Interference Model Problem Definition Broadcasting Tree and Coloring CDS-based Broadcasting Tree Tessellation and Coloring Broadcast Scheduling under UDG Model MLBS under UDG Model Analysis of MBS-UDG Broadcast Latency of MBS-UDG Broadcast Redundancy of MBS-UDG Broadcast Scheduling under PrIM Redundancy of MBS-PrIM Simulation and Analysis Broadcast Latency of MBS Broadcast Redundancy of MBS Conclusion References Index