Tabla de Contenidos: “…Cover -- Dedication -- Brief Contents -- Contents -- About the Author -- Preface -- Chapter 1: Coordinate Geometry -- 1.1 Introduction -- 1.1.1 Distance between Two Given Points -- 1.2 Section Formula -- 1.2.1 Coordinates of the Point that Divides the Line Joining Two Given Points in a Given Ratio -- 1.2.2 External Point of Division -- 1.2.3 Centroid of a Triangle Given its Vertices -- 1.2.4 Area of Triangle ABC with Vertices A(x1, y1), B(x2, y2) and C(x3, y3) -- 1.2.5 Area of the Quadrilateral Given its Vertices -- Illustrative Examples -- Exercises -- Chapter 2: The Straight Line -- 2.1 Introduction -- 2.1.1 Determination of the General Equation of a Straight Line -- 2.1.2 Equation of a Straight Line Parallel to y-axis and at a Distance of h units from x-axis -- 2.2 Slope of a Straight Line -- 2.3 Slope-intercept Form of a Straight Line -- 2.4 Intercept Form -- 2.5 Slope-point Form -- 2.6 Two Points Form -- 2.7 Normal Form -- 2.8 Parametric Form and Distance Form -- 2.9 Perpendicular Distance on a Straight Line -- 2.10 Intersection of Two Straight Lines -- 2.11 Concurrent Straight Lines -- 2.12 Angle between Two Straight Lines -- 2.13 Equations of Bisectors of the Angle between Two Lines -- Illustrative Examples -- Exercises -- Chapter 3: Pair of Straight Lines -- 3.1 Introduction -- 3.2 Homogeneous Equation of Second Degree in x and y -- 3.3 Angle between the Lines Represented by ax 2 + 2hxy + by 2 = 0 -- 3.4 Equation for the Bisector of the Angles between the Lines Given by ax2 + 2hxy + by2 = 0 -- 3.5 Condition for General Equation of a Second Degree Equation to Represent a Pair of Straight Lines -- Illustrative Examples -- Exercises -- Chapter 4: Circle -- 4.1 Introduction -- 4.2 Equation of a Circle whose Centre is (h, k) and Radius r -- 4.3 Centre and Radius of a Circle Represented by the Equation x2 + y2 + 2gx + 2fy + c = 0.…”

Tabla de Contenidos: “…Local Basis Functions -- 4.1.4 Experimental Model Validation -- 4.1.4.1 Quantifying Modeling Performance -- 4.1.5 Mutually Orthogonal Basis Functions -- 4.1.6 Multi-Antenna Environments and Mutual Coupling -- 4.2 Oscillator Phase Noise -- 4.2.1 Phase-Noise Power Spectrum and Leeson's Equation -- 4.2.2 Phase-Noise Modeling: Free-Running Oscillator -- 4.2.3 Phase-Noise Modeling: Phase-Locked Loop -- 4.3 Data Converters -- 4.3.1 Modeling of Quantization Noise -- 4.4 Statistical Modeling -- 4.4.1 The Bussgang Theorem and the System Model -- 4.5 Stochastic Modeling of Power Ampli ers -- 4.6 Oscillator Phase Noise -- 4.7 Stochastic Modeling of Data Converters -- 4.8 Model Concatenation and Simulations -- 4.8.1 Signal-to-Interference and Noise Ratio -- 4.8.2 Simulations -- 4.8.3 Simulation Results -- References -- 5 Multicarrier Waveforms -- 5.1 Multicarrier Waveforms -- 5.1.1 The Principle of Orthogonality -- 5.1.2 OFDM-Based Waveforms…”

Tabla de Contenidos: “…16.9.2 Metric for comparing quaternion maps -- 16.9.3 Quaternion rings: orientation freedom spaces -- 16.10 Conclusion -- 5 Spherical geometry and quaternions -- 17 Euclidean center of mass and barycentric coordinates -- 17.1 The 1D simplex: N=1 -- 17.2 Triangular simplex: N=2 -- 17.3 Tetrahedral simplex barycenter: N=3 -- 17.4 The N-simplex barycenter -- 17.5 Variational approach to the Euclidean center of mass -- 18 Quaternion averaging and the barycenter -- 18.1 Introduction -- 18.2 The geometry of inter-quaternion distances -- 18.3 Analysis of the quaternion averaging problem -- 19 The quaternion barycentric coordinate problem -- 19.1 Introduction -- 19.2 Review of spherical trigonometry -- 19.3 Spherical triangles as coordinates -- 19.4 Brown-Worsey no-go theorem -- 19.5 Möbius dual coordinates for points in a spherical simplex -- 19.6 Quaternion barycentric coordinates -- 19.7 Remark on intractability of spherical volume ratios for N&gt -- 2 -- 6 Extending quaternions to 4D and SE(3) -- 20 The 4D quaternion-based coordinate and orientation frame alignment problems -- 20.1 Foundations of quaternions for 4D problems -- 20.2 Double-quaternion approach to the 4D RMSD problem -- 20.2.1 Starting point for the 4D RMSD problem -- 20.2.2 A tentative 4D eigensystem -- 20.2.3 Issues with the naive 4D approach -- 20.2.4 Insights from the singular value decomposition -- 20.3 4D orientation frame alignment -- 20.3.1 The 4D orientation frame alignment problem -- 20.4 Extracting quaternion pairs from 4D rotation matrices -- 21 Dual quaternions and SE(3) -- 21.1 Background -- 21.2 Introduction to dual quaternions -- 21.3 The fundamental idea of quaternion-like translations -- 21.4 Refining the dual quaternion to use dual trigonometry -- 21.5 The motion of a dual quaternion -- 21.6 Brief discussion of applications -- 7 Quaternion applications in physics…”

Tabla de Contenidos: “…11.2 Literature Review -- 11.2.1 Neural Networks -- 11.2.2 Trees -- 11.2.3 K-Nearest Neighbors -- 11.3 Proposed Model -- 11.3.1 Predictive Models in Health -- 11.4 Experimental Results and Discussion -- 11.4.1 Prediction of Diabetes with Artificial Neural Networks Supervised Learning Algorithms -- 11.4.2 Improving the Prediction Ratio of Diabetes Diagnoses Using Fuzzy Logic and Neural Networks -- 11.4.3 ARIC: Type 2 Diabetes Risk Predictive Model -- 11.4.4 Evaluation of Neural Network Algorithms for Prediction Models of Type 2 Diabetes -- 11.4.5 Reliable and Objective Recommendation System for the Diagnosis of Chronic Diseases -- 11.5 Conclusion -- References -- Chapter 12 Diagnosis of Neurological Disease Using Bioinspired Algorithms -- 12.1 Introduction -- 12.1.1 Neurological Diseases -- 12.1.2 Introduction to Bioinspired Algorithms -- 12.1.3 Types of Bioinspired Algorithms Commonly Used in Healthcare -- 12.1.4 Advantages and Limitations of Bioinspired Algorithms -- 12.1.5 Limitations -- 12.1.6 Applications of Bioinspired Algorithms in Healthcare -- 12.1.7 Benefits of Bioinspired Algorithms in Healthcare Over Traditional Approaches -- 12.2 Neurological Disease Diagnosis -- 12.2.1 Bioinspired Algorithms for Neurological Disease Diagnosis -- 12.2.2 Neural Networks in Neurological Disease Diagnosis -- 12.2.2.1 How NNs Can Be Trained Using Bioinspired Optimization Techniques -- 12.2.3 Other Bioinspired Algorithms in Neurological Disease Diagnosis -- 12.3 Challenges and Future Directions -- 12.4 Conclusion -- References -- Chapter 13 Optimizing Artificial Neural-Network Using Genetic Algorithm -- 13.1 Introduction -- 13.1.1 ANN -- 13.1.2 Genetic Algorithm -- 13.2 Methodology -- 13.2.1 Mathematical Working -- 13.3 Brief Study on Existing Implementations -- 13.3.1 Using Different Types of ANNs -- 13.3.2 Using MLPs…”

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“…Esta es la idea que defienden los autores coordinados por Orazio Miglino, Maria Luisa Nigrelli y Luigia Simona Sica tras analizar el uso de las nuevas tecnologías en escuelas, universidades y empresas de España, Reino Unido e Italia. …”
Texto completo en Odilo

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