Tabla de Contenidos: “…2.6.1 Use of Texture and Context -- 2.6.2 Using Ancillary Multisource Data -- 2.7 Epilogue -- References -- Chapter 3 Dimensionality Reduction: Feature Extraction and Selection -- 3.1 Feature Extraction -- 3.1.1 Principal Component Analysis -- 3.1.2 Minimum/Maximum Autocorrelation Factors -- 3.1.3 Maximum Noise Fraction (MNF) Transformation -- 3.1.4 Independent Component Analysis -- 3.1.5 Projection Pursuit -- 3.2 Feature Selection -- 3.2.1 Greedy Search Methods -- 3.2.2 Simulated Annealing -- 3.2.3 Separability Indices -- 3.2.4 Filter-Based Methods -- 3.2.4.1 Correlation-Based Feature Selection -- 3.2.4.2 Information Gain -- 3.2.4.3 Gini Impurity Index -- 3.2.4.4 Minimum Redundancy-Maximum Relevance -- 3.2.4.5 Chi-Square Test -- 3.2.4.6 Relief-F -- 3.2.4.7 Symmetric Uncertainty -- 3.2.4.8 Fisher's Test -- 3.2.4.9 OneR -- 3.2.5 Wrappers -- 3.2.5.1 Genetic Algorithm -- 3.2.5.2 Particle Swarm Optimization -- 3.2.5.3 Feature Selection with SVMs -- 3.2.6 Embedded Methods -- 3.2.6.1 K-Nearest Neighbor-Based Feature Selection -- 3.2.6.2 Feature Selection with Ensemble Learners -- 3.2.6.3 Hilbert-Schmidt Independence Criterion with Lasso -- 3.3 Concluding Remarks -- References -- Chapter 4 Multisource Image Fusion and Classification -- 4.1 Image Fusion -- 4.1.1 Image Fusion Methods -- 4.1.1.1 PCA-Based Image Fusion -- 4.1.1.2 IHS-Based Image Fusion -- 4.1.1.3 Brovey Transform -- 4.1.1.4 Gram-Schmidt Transform -- 4.1.1.5 Wavelet Transform -- 4.1.1.6 Deep Learning for Image Fusion -- 4.1.2 Assessment of Fused Image Quality -- 4.1.3 Performance Evaluation of Fusion Methods -- 4.2 Multisource Classification Using the Stacked-Vector Method -- 4.3 The Extension of Bayesian Classification Theory -- 4.3.1 An Overview -- 4.3.1.1 Feature Extraction -- 4.3.1.2 Probability or Evidence Generation -- 4.3.1.3 Multisource Consensus…”

Tabla de Contenidos: “…Chapter 17 Translate and Recreate Text in an Image -- 17.1 Introduction -- 17.2 Literature Survey -- 17.3 Existing System -- 17.4 Proposed System -- 17.4.1 Flow Chart -- 17.4.2 Experimental Setup -- 17.4.3 Dataset -- 17.4.4 Text Detection and Extraction -- 17.4.5 Auto Spelling Correction -- 17.4.6 Machine Translation and Inpainting -- 17.5 Implementation -- 17.5.1 Text Detection and Extraction -- 17.5.2 Auto Spelling Correction -- 17.5.2.1 Simple RNN -- 17.5.2.2 Embed RNN -- 17.5.2.3 Bidirectional LSTM -- 17.5.2.4 Encoder Decoder With LSTM -- 17.5.2.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance -- 17.5.3 Machine Translation -- 17.5.4 Inpainting -- 17.6 Result Analysis -- 17.6.1 Simple RNN -- 17.6.2 Embed RNN -- 17.6.3 Bidirectional LSTM -- 17.6.4 Encoder Decoder With LSTM -- 17.6.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance -- 17.6.6 BLEU (Bilingual Evaluation Understudy) -- 17.7 Conclusion -- Acknowledgments -- References -- Chapter 18 Multi-Label Indian Scene Text Language Identification: Benchmark Dataset and Deep Ensemble Baseline -- 18.1 Introduction -- 18.2 Related Works -- 18.3 IIITG-MLRIT2022 -- 18.4 Proposed Methodology -- 18.4.1 Transfer Learning -- 18.4.1.1 ResNet50 [37] -- 18.4.1.2 XceptionNet [39] -- 18.4.1.3 DenseNet [38] -- 18.4.1.4 MobileNetV2 [36] -- 18.4.2 Convolutional Neural Network -- 18.4.3 Multi-Label Deep Ensemble Via Majority Voting -- 18.4.4 Weighted Binary Cross Entropy -- 18.5 Training and Experiment -- 18.6 Results and Discussion -- 18.7 Conclusion -- References -- Chapter 19 AI Based Wearables for Healthcare Applications: A Survey of Smart Watches -- 19.1 Introduction -- 19.2 Systematic Review -- 19.2.1 Criterion to Select Research -- 19.2.2 Source of Information -- 19.2.2.1 Search Plan -- 19.2.2.2 Data Abstraction -- 19.2.3 Outcomes -- 19.2.4 Healthcare Applications…”

Tabla de Contenidos: “…Network Functions - s-Domain Analysis of Circuits -- 10.1 Introduction -- 10.1.1 Terminals and Ports -- 10.1.2 Concept of Complex Frequency -- 10.2 Transformed Impedances in s-Domain -- 10.2.1 Resistance -- 10.2.2 Inductance -- 10.2.3 Capacitance -- 10.3 One-Port Network -- 10.3.1 Driving Point Impedance and Admittance Functions -- 10.4 Two-Port Network -- 10.4.1 Network Functions of a Two-port Network -- 10.5 Transfer Function -- 10.6 Network Function in Generalised Form -- 10.7 Poles and Zeros of Network Functions -- 10.7.1 Poles of a Network Function -- 10.7.2 Zeros of a Network Function -- 10.8 Pole-Zero Diagram -- 10.9 Time-Domain Response from Pole-Zero Plot -- 10.10 More Examples on Network Function -- 10.11 Poles and Zeros of Network Functions and Their Significance -- 10.12 Stability Criterion for an Active Network -- 10.13 Examples Based on Pole-Zero Plot -- Review Questions -- 11. …”

“…For the latter, the OECD regional typology is extended to include an accessibility criterion. This criterion is based on the driving time needed for at least half of the population in a region to reach a populated centre of with 50 000 or more inhabitants. …”

“…Utilitarianism also plays a key role, but it is reconceived as a deontological criterion. This approach obviously requires that these criteria be formulated more rigorously than is normally the case. …”

Tabla de Contenidos: “…-- PREFACE xv -- PART I INTRODUCTION TO COGNITIVE RADIOS 1 -- 1 Introduction 3 -- 1.1 Introduction, 3 -- 1.2 Signal Processing and Cognitive Radios, 4 -- 1.3 Software-Defined Radios, 6 -- 1.3.1 Software-Defined Radio Platforms, 14 -- 1.3.2 Software-Defined Radio Systems, 15 -- 1.4 From Software-Defined Radios to Cognitive Radios, 19 -- 1.4.1 The Spectrum Scarcity Problem, 19 -- 1.4.2 Emergence of CRs, 21 -- 1.5 What this Book is About, 22 -- 1.6 Summary, 26 -- 2 The Cognitive Radio 27 -- 2.1 Introduction, 27 -- 2.2 A Functional Model of a Cognitive Radio, 30 -- 2.2.1 Spectrum Knowledge Acquisition (Spectrum Awareness), 30 -- 2.2.2 Communications Decision-Making, 33 -- 2.2.3 Learning in Cognitive Radios, 33 -- 2.3 The Cognitive Radio Architecture, 35 -- 2.3.1 Spectrum Sensing Region of a Cognitive Engine, 36 -- 2.3.2 Radio Reconfiguration Region of a Cognitive Engine, 36 -- 2.3.3 Learning Region of a Cognitive Engine, 37 -- 2.3.4 Memory Region of a Cognitive Engine, 37 -- 2.4 The Ideal Cognitive Radio, 38 -- 2.5 Signal Processing Challenges in Cognitive Radios, 39 -- 2.6 Summary, 40 -- 3 Cognitive Radios and Dynamic Spectrum Sharing 42 -- 3.1 Introduction, 42 -- 3.2 Interference and Spectrum Opportunities, 46 -- 3.3 Dynamic Spectrum Access, 50 -- 3.4 Dynamic Spectrum Leasing, 54 -- 3.5 Challenges in DSS Cognitive Radios, 55 -- 3.6 Cognitive Radios and Future of Wireless Communications, 60 -- 3.7 Summary, 61 -- PART II THEORETICAL FOUNDATIONS 65 -- 4 Introduction to Detection Theory 67 -- 4.1 Introduction, 67 -- 4.2 Optimality Criteria: Bayesian versus Non-Bayesian, 71 -- 4.2.1 The Bayesian Approach, 72 -- 4.2.2 A Non-Bayesian Approach: Neyman / Pearson Optimality Criterion, 73 -- 4.3 Parametric Signal Detection Theory, 75 -- 4.3.1 Bayesian Optimal Detection, 76 -- 4.3.2 Neyman / Pearson Optimal Detection, 82 -- 4.3.3 Another Non-Bayesian Alternative: The Generalized Likelihood Ratio Test, 99 -- 4.3.4 Parametric Signal Detection in Additive Noise, 103 -- 4.4 Nonparametric Signal Detection Theory, 122.…”

“…We have expanded our coverage of model selection criterion measures and introduce an extension of the QIC measure which is applicable for choosing among working correlation structures (see 5.1.2 in particular). …”
Enlace del recurso

“…The immediate relevance and applicability of the models will strengthen the hand of policy analysts for whom the dynamic efficiency of environmental policy is a new, high-profile evaluation criterion…”

“…This theory is shown to be compatible with the compelling psychological criterion of personal identity. Against the background of the theory of memory contents, Bernecker maintains that a memory content need not be the same as, but only similar to, the content of the representation from which it causally derives. …”

“…When appropriate and possible, a strict decision criterion for practitioners should be reported. Data normally involves the assessment of subjects, therefore they must be processed in accordance with national data protection law (where they were obtained or processed) and authors are urged to review the supplement to the APA ethical list. …”

“…This book describes the transformation in American finance from the old reputational model to the existing laissez faire model and argues that the change came as a result of three factors: (1) the growth of reliance on regulation rather than reputation as the primary mechanism for protecting customers and (2) the increasing complexity of regulation, which made technical expertise rather than reputation the primary criterion on which customers choose who to do business with in today's markets ; and (3) the rise of the 'cult of personality' on Wall Street, which has led to a secular demise in the relevance of companies' reputations and the concomitant rise of individual 'rain-makers' reputation as the basis for premium pricing of financial services. …”

“…Naturally Speaking: The Naturalness Criterion and Physics at the LHC (G F Giudice) Prospects for Higgs Boson Searches at the LHC (K Jakobs & M Schumacher) A Review of Spin Determination at the LHC (L-T Wang & I Yavin) Anticipating a New Golden Age (F Wilczek) Strongly Interacting Electroweak Theories and Their Five-Dimensional Analogs at the LHC (A Pomarol) How to Find a Hidden World at the LHC (J D Wells) B Physics at LHCb (M P Altarelli & F Teubert) The LHC and the Universe at Large (P Binétruy)…”

“…The second part pursues that history from the classical encounter with mimesis in Greek thought to the Romantic and post-Romantic elevation of consciousness as a main criterion of poetic art. Defining a "humanistic aesthetic" as it has been viewed since Aristotle, the author shows how, during and after the eighteenth century, form was opened up under the impact of a Kantian and post-Kantian view, epitomized finally by Coleridge's imagination and its consequences for recent theorists. …”

“…Instead of truth as the ultimate criterion of judgment, the only principle according to which affective becomings can be selected and evaluated is the extent to which they proliferate joy. …”

“…Foreword xi Acknowledgments xiii About the Authors xiv Preface xv Plan for the Book xvii Chapter 1: Making HR Measurement Strategic 1 Chapter 2: Analytical Foundations of HR Measurement 21 Chapter 3: The Hidden Costs of Absenteeism 43 Chapter 4: The High Cost of Employee Separations 67 Chapter 5: Employee Health, Wellness, and Welfare 99 Chapter 6: Employee Attitudes and Engagement 125 Chapter 7: Financial Effects of Work-Life Programs 151 Chapter 8: Staffing Utility: The Concept and Its Measurement 171 Chapter 9: The Economic Value of Job Performance 195 Chapter 10: The Payoff from Enhanced Selection 223 Chapter 11: Costs and Benefits of HR Development Programs 245 Chapter 12: Talent-Investment Analysis: Catalyst for Change 271 Appendix A: The Taylor-Russell Tables 285 Appendix B: The Naylor-Shine Table for Determining the Increase in Mean Criterion Score Obtained by Using a Selection Device 297 Index 309…”

“…In the succession of works in this printed version, the criterion that guided us was to highlight how—starting exclusively from chromatographic techniques (HPLC and GC) with conventional detectors and moving to exclusively spectroscopic techniques (MS, FT-IR and Raman)—it is possible arrive at extremely powerful coupled techniques and procedures (HPLC and FT-IR) able to meet research needs. …”

“…We used a top-box scoring method, calculated by dividing the number of patients fulfilling a criterion by the number of patients for whom the quality measure is appropriate, to calculate the percentage of positive responses. …”