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Adaptive control of low-level radio frequency signals based on in-phase and quadrature components
Rezaeizadeh, A.

اطلاعات کتابشناختی

Adaptive control of low-level radio frequency signals based on in-phase and quadrature components
پدیدآور اصلی :   Rezaeizadeh, A.
ناشر :   Institute of Electrical and Electronics Engineers Inc,
سال انتشار  :   2017
موضوع ها :   Adaptive control. Amplitude and phase. Free-Electron laser. Linear accelerator. Radio...
شماره راهنما :  

جستجو در محتوا

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فهرست مطالب

  • Cover (2)
  • Half Title (7)
  • Title (9)
  • Copyright (10)
  • Contents (12)
  • Preface (19)
  • The Authors (22)
  • 1 The Material of Multivariate Analysis (24)
    • 1.1 Examples of Multivariate Data (24)
      • 1.1.1 Example 1.1: Storm Survival of Sparrows (24)
      • 1.1.2 Example 1.2: Egyptian Skulls (27)
      • 1.1.3 Example 1.3: Distribution of a Butterfly (28)
      • 1.1.4 Example 1.4: Prehistoric Dogs from Thailand (32)
      • 1.1.5 Example 1.5: Cost of a Healthy Diet in Europe (33)
    • 1.2 Preview of Multivariate Methods (36)
    • 1.3 The Multivariate Normal Distribution (40)
    • 1.4 Computer Programs (41)
    • References (44)
  • 2 Matrix Algebra (45)
    • 2.1 The Need for Matrix Algebra (45)
    • 2.2 Matrices and Vectors (45)
    • 2.3 Operations on Matrices (48)
    • 2.4 Matrix Inversion (50)
    • 2.5 Quadratic Forms (51)
    • 2.6 Eigenvalues and Eigenvectors (51)
    • 2.7 Vectors of Means and Covariance Matrices (52)
    • 2.8 Missing Values in Matrices Used for Multivariate Statistics (54)
    • 2.9 Further Reading (56)
    • References (60)
  • 3 Displaying Multivariate Data (65)
    • 3.1 The Problem of Displaying Many Variables in Two Dimensions (65)
    • 3.2 Plotting Index Variables (67)
    • 3.3 The Draftsman's Plot (68)
    • 3.4 The Representation of Individual Data Points (70)
    • 3.5 Profiles of Variables (72)
    • 3.6 Discussion and Further Reading (74)
    • References (76)
  • 4 Tests of Significance with Multivariate Data (82)
    • 4.1 Simultaneous Tests on Several Variables (82)
    • 4.2 Comparison of Mean Values for Two Samples: The Single-Variable Case (82)
    • 4.3 Comparison of Mean Values for Two Samples: The Multivariate Case (84)
      • 4.3.1 Example 4.1: Testing Mean Values for Bumpus’ Female Sparrows (85)
    • 4.4 Multivariate versus Univariate Tests (87)
    • 4.5 Comparison of Variation for Two Samples: The Single-Variable Case (88)
    • 4.6 Comparison of Variation for Two Samples: The Multivariate Case (88)
      • 4.6.1 Example 4.3: Testing Variation for Female Sparrows (90)
    • 4.7 Comparison of Means for Several Samples (95)
    • 4.8 Comparison of Variation for Several Samples (99)
      • 4.8.1 Example 4.4: Comparison of Samples of Egyptian Skulls (100)
    • 4.9 Computer Programs (102)
    • References (106)
  • 5 Measuring and Testing Multivariate Distances (111)
    • 5.1 Multivariate Distances (111)
    • 5.2 Distances between Individual Observations (112)
      • 5.2.1 Example 5.1: Distances between Dogs and Related Species (114)
    • 5.3 Distances between Populations and Samples (116)
      • 5.3.1 Example 5.2: Distances between Samples of Egyptian Skulls (118)
    • 5.4 Multivariate Similarities (122)
    • 5.5 Presence-Absence Data (123)
      • 5.5.1 Example 5.3 Measuring Similarities between Two Plant Species (124)
    • 5.6 The Mantel Randomization Test (125)
      • 5.6.1 Example 5.4: More on Distances between Samples of Egyptian Skulls (128)
    • 5.7 Computer Programs (129)
    • 5.8 Discussion and Further Reading (130)
    • References (131)
  • 6 Principal Components Analysis (138)
    • 6.1 Definition of Principal Components (138)
    • 6.2 Procedure for a Principal Components Analysis (143)
      • 6.2.1 Example 6.1: Body Measurements of Female Sparrows (147)
      • 6.2.2 Example 6.2: Cost of a Healthy Diet in Europe (151)
    • 6.3 Principal Components Analysis and Missing Data (155)
      • 6.3.1 Example 6.3 Principal Component Analysis of the Costs of a Healthy Diet in Europe with Missing Data (156)
    • 6.4 Computer Programs (159)
    • 6.5 Further Reading (160)
    • References (167)
  • 7 Factor Analysis (174)
    • 7.1 The Factor Analysis Model (174)
    • 7.2 Procedure for a Factor Analysis (177)
    • 7.3 Principal Components Factor Analysis (180)
    • 7.4 Using a Factor Analysis Program to Do Principal Components Analysis (182)
      • 7.4.1 Example 7.1: Cost of Healthy Food in European Countries (182)
    • 7.5 Options in Analyses (187)
    • 7.6 The Value of Factor Analysis (187)
    • 7.7 Discussion and Further Reading (188)
    • References (189)
  • 8 Discriminant Function Analysis (193)
    • 8.1 The Problem of Separating Groups (193)
    • 8.2 Discrimination Using Mahalanobis Distances (194)
    • 8.3 Canonical Discriminant Functions (195)
    • 8.4 Tests of Significance (199)
    • 8.5 Assumptions (200)
      • 8.5.1 Example 8.1: Comparison of Samples of Egyptian Skulls (200)
      • 8.5.2 Example 8.2: Discriminating between Groups of European Countries (203)
    • 8.6 Allowing for Prior Probabilities of Group Membership (211)
    • 8.7 Stepwise Discriminant Function Analysis (211)
    • 8.8 Jackknife Classification of Individuals (212)
    • 8.9 Assigning Ungrouped Individuals to Groups (212)
    • 8.10 Logistic Regression (213)
      • 8.10.1 Example 8.3: Storm Survival of Female Sparrows (Reconsidered) (215)
      • 8.10.2 Example 8.4: Comparison of Two Samples of Egyptian Skulls (216)
    • 8.11 Computer Programs (220)
    • 8.12 Discussion and Further Reading (220)
    • References (221)
  • 9 Cluster Analysis (227)
    • 9.1 Uses of Cluster Analysis (227)
    • 9.2 Types of Cluster Analysis (227)
    • 9.3 Hierarchical Methods (228)
    • 9.4 Problems with Cluster Analysis (231)
    • 9.5 Measures of Distance (233)
    • 9.6 Principal Components Analysis with Cluster Analysis (233)
      • 9.6.1 Example 9.1: Clustering of European Countries (234)
      • 9.6.2 Example 9.2: Relationships between Canine Species (239)
    • 9.7 Computer Programs (241)
    • 9.8 Discussion and Further Reading (242)
    • References (245)
  • 10 Canonical Correlation Analysis (249)
    • 10.1 Generalizing a Multiple Regression Analysis (249)
    • 10.2 Procedure for a Canonical Correlation Analysis (250)
    • 10.3 Tests of Significance (251)
    • 10.4 Interpreting Canonical Variates (252)
      • 10.4.1 Example 10.1: Environmental and Genetic Correlations for Colonies of a Butterfly (253)
      • 10.4.2 Example 10.2: Soil and Vegetation Variables in Belize (255)
    • 10.5 Computer Programs (259)
    • 10.6 Further Reading (259)
    • References (260)
  • 11 Multidimensional Scaling (263)
    • 11.1 Constructing a Map from a Distance Matrix (263)
    • 11.2 Procedure for Multidimensional Scaling (265)
      • 11.2.1 Example 11.1: Road Distances between New Zealand Towns (267)
      • 11.2.2 Example 11.2: The Voting Behavior of Congressmen (273)
    • 11.3 Computer Programs (280)
    • 11.4 Further Reading (281)
    • References (281)
  • 12 Ordination (287)
    • 12.1 The Ordination Problem (287)
    • 12.2 Principal Components Analysis (288)
      • 12.2.1 Example 12.1: Plant Species in the Steneryd Nature Reserve (289)
      • 12.2.2 Example 12.2: Burials in Bannadi (292)
    • 12.3 Principal Coordinates Analysis (296)
      • 12.3.1 Example 12.3: Plant Species in the Steneryd Nature Reserve (Revisited) (300)
      • 12.3.2 Example 12.4: Burials in Bannadi (Revisited) (301)
    • 12.4 Multidimensional Scaling (304)
      • 12.4.1 Example 12.5: Plant Species in the Steneryd Nature Reserve (Again) (305)
      • 12.4.2 Example 12.6: Burials in Bannadi (Again) (307)
    • 12.5 Correspondence Analysis (308)
      • 12.5.1 Example 12.7: Plant Species in the Steneryd Nature Reserve (Yet Again) (312)
    • 12.6 Comparison of Ordination Methods (314)
    • 12.7 Computer Programs (314)
    • 12.8 Further Reading (315)
    • References (316)
  • 13 Epilogue (323)
    • 13.1 The Next Step (323)
    • 13.2 Some General Reminders (324)
    • References (325)
  • Index (351)
  • Index of R Functions and Packages (366)
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