Tabla de Contenidos: “…3.2 Locations of the Typical Rock Avalanches -- 4 Characteristics of Typical Rock Avalanche Deposits -- 4.1 Luanshibao Rock Avalanche -- 4.1.1 Geological Setting and General Features of the Luanshibao Rock Avalanche -- 4.1.2 Sedimentary Features of the Avalanche Deposit -- 4.2 Nyixoi Chongco Rock Avalanche -- 4.2.1 Geological Setting and General Features of the Nyixoi Chongco Rock Avalanche -- 4.2.2 Sedimentary Features of the Avalanche Deposit -- 4.3 Tagarma Rock Avalanche -- 4.3.1 Geological Setting and General Features of the Tagarma Rock Avalanche -- 4.3.2 Sedimentary Features of the Avalanche Deposit -- 4.4 Iymek Rock Avalanche -- 4.4.1 Geological Setting and General Features of the Iymek Rock Avalanche -- 4.4.2 Sedimentary Features of the Avalanche Deposit -- 5 Discussion -- 6 Conclusion -- References -- Part II: Original Articles -- Landslide Susceptibility Zonation Using GIS-Based Frequency Ratio Approach in the Kulon Progo Mountains Area, Indonesia -- 1 Introduction -- 2 Research Area -- 3 Methodology -- 4 Dataset and Analysis -- 5 Result &amp -- Discussion -- 6 Conclusion -- References -- Physically-Based Regional Landslide Forecasting Modelling: Model Set-up and Validation -- 1 Introduction -- 2 HIRESSS Model and Study Are a -- 2.1 HIRESSS Model -- 2.2 Study Area -- 3 Data Collection and Preparation -- 3.1 Static Data -- 3.2 Dynamic Data -- 4 HIRESSS Simulation and Analysis of the Results -- 4.1 Monte Carlo Simulations -- 4.2 Analysis of the Model Output and Validation -- 5 Conclusion -- References -- Consequence: Frequency Matrix as a Tool to Assess Landslides Risk -- 1 Introduction -- 2 The Main Principle of Matrix Use -- 3 The Conceptual Background -- 4 Example of Risk Matrix -- 5 Issues Linked to the Use of Matrix -- 5.1 Scale and Verbal Terms -- 5.1.1 The Classes of Consequences and Frequency or Probability…”

Tabla de Contenidos: “…7.7 Stock indices and stock index futures -- 7.8 Definition of stock index futures contracts -- 7.9 Advantages of using stock index futures -- 7.10 Cash-and-carry pricing for stock index futures -- 7.11 Stock index futures prices in practice -- 7.12 Turning cash into share portfolios and share portfolios into cash -- 8 Swaps -- 8.1 Definition of interest rate and cross-currency swaps -- 8.2 Development of the swap market -- 8.3 Interest rate swaps -- 8.4 Non-standard interest rate swaps -- 8.5 Overnight indexed swaps -- 8.6 Cross-currency swaps -- 8.7 Basic applications for swaps -- 8.8 Asset swaps -- 8.9 CMS and CMT swaps -- 8.10 Inflation swaps -- 8.11 Equity and dividend swaps -- 8.12 Commodity swaps -- 8.13 Volatility and variance swaps -- 8.14 Exotic swaps -- 8.15 ISDA documentation -- 8.16 Changes in market infrastructure after the credit crisis -- 9 Pricing and valuing swaps -- 9.1 Principles of swap valuation and pricing -- 9.2 Discount factors and the discount function -- 9.3 Calculating discount factors from swap and forward rates -- 9.4 Generating the discount function -- 9.5 Relationship between zero, swap and forward rates -- 9.6 Valuation and pricing of interest rate swaps -- 9.7 Valuation and pricing of currency swaps -- 9.8 Cancelling a swap -- 9.9 Hedging swaps with futures -- 9.10 The convexity correction -- 9.11 Credit risk of swaps -- 9.12 Collateralised vs. non-collateralised swaps -- 9.13 LIBOR-OIS discounting -- 10 Options - basics and pricing -- 10.1 Why options are different -- 10.2 Definitions -- 10.3 Options terminology -- 10.4 Value and profit profiles at maturity -- 10.5 Pricing options -- 10.6 The behaviour of financial prices -- 10.7 The Black-Scholes model -- 10.8 The binomial approach -- 10.9 The Monte Carlo approach -- 10.10 Finite difference methods -- 11 Options - volatility and the Greeks -- 11.1 Volatility…”

Tabla de Contenidos: “…Introduction -- 4.2. Markov chain Monte Carlo methods…”

Tabla de Contenidos: “…-- Preface xxi / /Acknowledgments xxv / /Summary of Notations xxvii / /About the Cover xxix / /About the Companion Website xxxi / /1 Mathematical Background and Analysis Techniques 1 / /1.1 Introduction 1 / /1.2 The Fourier Transform and Fourier Series 5 / /1.3 Pulse Distortion with Ideal Filter Models 16 / /1.4 Correlation Processing 19 / /1.5 Random Variables and Probability 20 / /1.6 Random Processes 41 / /1.7 The Matched Filter 44 / /1.8 The Likelihood and Log-Likelihood Ratios 46 / /1.9 Parameter Estimation 47 / /1.10 Modem Configurations and Automatic Repeat Request 55 / /1.11 Windows 57 / /1.12 Matrices Vectors and Related Operations 66 / /1.13 Often Used Mathematical Procedures 70 / /1.14 Often Used Mathematical Relationships 71 / /2 Digital Signal Processing and Modem Design Considerations 81 / /2.1 Introduction 81 / /2.2 Discrete Amplitude Sampling 81 / /2.3 Discrete-Time Sampling 87 / /2.4 Signal Reconstruction Following Discrete-Time Sampling 91 / /2.5 Baseband Sampling 92 / /2.6 Bandpass Sampling 92 / /2.7 Corrections for Nonideal Modulators and Demodulators 99 / /2.8 Multirate Signal Processing and Interpolation 106 / /Appendix 2A Amplitude Quantization Function Subprogram 121 / /Appendix 2B Hilbert Transform Parameters 122 / /Appendix 2C Derivation of Parabolic Interpolation Error 126 / /3 Digital Communications 133 / /3.1 Introduction 133 / /3.2 Digital Data Modulation and Optimum Demodulation Criteria 135 / /3.3 Information and Channel Capacity 139 / /3.4 Bit-Error Probability Bound on Memoryless Channel 148 / /3.5 Probability Integral and the Error Function 150 / /4 Phase Shift Keying (PSK) Modulation Demodulation and Performance 153 / /4.1 Introduction 153 / /4.2 Constant Envelope Phase-Modulated Waveforms 154 / /4.3 Non-Constant Envelope Phase-Modulated Waveforms 175 / /4.4 Phase-Modulated Waveform Spectrums and Performance 178 / /5 Frequency Shift Keying (FSK) Modulation Demodulation and Performance 207 / /5.1 Introduction 207 / /5.2 Coherent Detection of BFSK - Known Frequency and Phase 207 / /5.3 Noncoherent Detection of BFSK - Known Frequency and Unknown Phase 210 / /5.4 Case Studies: Coherent and Noncoherent BFSK Performance Simulation 211 / /5.5 Noncoherent Detection of BFSK - Unknown Frequency and Phase 214 / /5.6 BFSK Spectral Density with Arbitrary Modulation Index 219 / /6 Amplitude Shift Keying Modulation Demodulation and Performance 227 / /6.1 Introduction 227 / /6.2 Amplitude Shift Keying (ASK) 227 / /6.3 Quadrature Amplitude Modulation (QAM) 234 / /6.4 Alternate QAM Waveform Constellations 236 / /6.5 Case Study: 16-ary QAM Performance Evaluation 236 / /6.6 Partial Response Modulation 237 / /7 M-ary Coded Modulation 251 / /7.1 Introduction 251 / /7.2 Coherent Detection of Orthogonal Coded Waveforms 252 / /7.3 Noncoherent Detection of M-ary Orthogonal Waveforms 253 / /7.4 Coherent Detection of M-ary Biorthogonal Waveforms 256 / /8 Coding for Improved Communications 261 / /8.1 Introduction 261 / /8.2 Pulse Code Modulation 261 / /8.3 Gray Coding 268 / /8.4 Differential Coding 269 / /8.5 Pseudo-Random Noise Sequences 270 / /8.6 Binary Cyclic Codes 273 / /8.7 Cyclic Redundancy Check Codes 274 / /8.8 Data Randomizing Codes 276 / /8.9 Data Interleaving 277 / /8.10 Wagner Coding and Decoding 279 / /8.11 Convolutional Codes 283 / /8.12 Turbo and Turbo-Like Codes 299 / /8.13 LDPC Code and TPC 313 / /8.14 Bose-Chaudhuri-Hocquenghem Codes 315 / /Appendix 8A 328 / /Appendix 8B 329 / /9 Forward Error Correction Coding Without Bandwidth Expansion 339 / /9.1 Introduction 339 / /9.2 Multi-h M-ary CPM 340 / /9.3 Case Study: 2-h 4-ary 1REC CPM 350 / /9.4 Multiphase Shift Keying Trellis-Coded Modulation 362 / /9.5 Case Study: Four-State 8PSK-TCM Performance Over Satellite Repeater 367 / /10 Carrier Acquisition and Tracking 375 / /10.1 Introduction 375 / /10.2 Bandpass Limiter 377 / /10.3 Baseband Phaselock Loop Implementation 378 / /10.4 Phase-Error Generation 378 / /10.5 First-Order Phaselock Loop 380 / /10.6 Second-Order Phaselock Loop 380 / /10.7 Third-Order Phaselock Loop 390 / /10.8 Optimum Phase Tracking Algorithms 396 / /10.9 Squaring Loss Evaluation 406 / /10.10 Case Study: BPSK and QPSK Phaselock Loop Performance 408 / /10.11 Case Study: BPSK Phase Tracking Performance of a Disadvantaged Transmit Terminal 410 / /11 Waveform Acquisition 413 / /11.1 Introduction 413 / /11.2 CW Preamble Segment Signal Processing 416 / /11.3 Symbol Synchronization Preamble Segment 432 / /11.4 Start-of-Message (SOM) Preamble segment 452 / /11.5 Signal-to-Noise Ratio Estimation 452 / /12 Adaptive Systems 463 / /12.1 Introduction 463 / /12.2 Optimum Filtering - Wiener's Solution 464 / /12.3 Finite Impulse Response-Adaptive Filter Estimation 465 / /12.4 Intersymbol Interference and Multipath Equalization 469 / /12.5 Interference and Noise Cancellation 472 / /12.6 Recursive Least Square (RLS) Equalizer 473 / /12.7 Case Study: LMS Linear Feedforward Equalization 474 / /12.8 Case Study: Narrowband Interference Cancellation 474 / /12.9 Case Study: Recursive Least Squares Processing 480 / /13 Spread-Spectrum Communications 485 / /13.1 Introduction 485 / /13.2 Spread-Spectrum Waveforms and Spectrums 487 / /13.3 Jammer and Interceptor Encounters 499 / /13.4 Communication Interceptors 502 / /13.5 Bit-Error Performance of DSSS Waveforms with Jamming 504 / /13.6 Performance of MFSK with Partial-Band Noise Jamming 512 / /13.7 Performance of DCMPSK with Partial-Band Noise Jamming 514 / /13.8 FHSS Waveforms with Multitone Jamming 515 / /13.9 Approximate Performance with Jammer Threats 521 / /13.10 Case Study: Terrestrial Jammer Encounter and Link-Standoff Ratio 522 / /14 Modem Testing Modeling and Simulation 531 / /14.1 Introduction 531 / /14.2 Statistical Sampling 532 / /14.3 Computer Generation of Random Variables 539 / /14.4 Baseband Waveform Description 545 / /14.5 Sampled Waveform Characterization 547 / /14.6 Case Study: BPSK Monte Carlo Simulation 548 / /14.7 System Performance Evaluation Using Quadrature Integration 550 / /14.8 Case Study: BPSK Bit-Error Evaluation with PLL Tracking 551 / /14.9 Case Study: QPSK Bit-Error Evaluation with PLL Tracking 553 / /15 Communication Range Equation and Link Analysis 557 / /15.1 Introduction 557 / /15.2 Receiver and System Noise Figures and Temperatures 560 / /15.3 Antenna Gain and Patterns 568 / /15.4 Rain Loss 571 / /15.5 Electric Field Wave Polarization 573 / /15.6 Phase-Noise Loss 578 / /15.7 Scintillation Loss 583 / /15.8 Multipath Loss 583 / /15.9 Interface Mismatch Loss 584 / /15.10 Miscellaneous System Losses 585 / /15.11 Nonlinear Power Amplifier Analysis and Simulation 585 / /15.12 Computer Modeling of TWTA and SSPA Nonlinearities 588 / /15.13 Establishing Signal Levels for Simulation Modeling 590 / /15.14 Case Study: Performance Simulation of SRRC-QPSK with SSPA Nonlinearity 592 / /15.15 Link Budget Analysis 596 / /16 Satellite Orbits 603 / /16.1 Introduction 603 / /16.2 Satellite Orbits 606 / /16.3 Earth Stations 607 / /16.4 Path Loss Doppler and Doppler-rate 609 / /16.5 Satellite Viewing 609 / /16.6 Satellite Orbit Selection 610 / /16.7 Satellite Orbit Position Estimation From Parameter Measurements 611 / /16.8 Case Study: Example Satellite Encounters 612 / /17 Communications Through Bandlimited Time-Invariant Linear Channels 617 / /17.1 Introduction 617 / /17.2 Inphase and Quadrature Channel Response 618 / /17.3 Inphase and Quadrature Channel Response to Arbitrary Signal 619 / /17.4 Pulse Modulated Carrier Signal Characteristics 621 / /17.5 Channel Response to a Pulsed Modulated Waveform 622 / /17.6 Example Performance Simulations 623 / /17.7 Example of Channel Amplitude and Phase Responses 624 / /17.8 Example Channel Amplitude Phase and Delay Functions 627 / /18 Communications in Fading Environments 633 / /18.1 Introduction 633 / /18.2 Ricean Fading Channels 634 / /18.3 Ricean Cumulative Distribution 635 / /18.4 Application of Ricean Channel Model 635 / /18.5 Performance of Several Binary Modulation…”

Tabla de Contenidos: “…5.2.2 Factors Considered for Cash Flow Computation -- 5.2.3 Process of Computation -- 5.3 Project Evaluation Criteria -- 5.3.1 NPV Rule -- 5.3.2 Profitability Index -- 5.3.3 Profitability Ratio Versus NPV -- 5.3.4 IRR Rule -- 5.3.5 NPV versus IRR -- 5.3.6 Modified IRR (MIRR) -- 5.3.7 Pay-back Period -- 5.3.8 Accounting Rate of Return -- Summary -- Points to Remember -- Descriptive Questions -- Objective-type Questions -- Numerical Problems -- Solved Numerical Problems -- Reference -- Select Further Readings -- Chapter 6: Capital Budgeting in Practice -- 6.1 Capital Rationing -- 6.1.1 Conditions of Capital Rationing -- 6.1.2 Capital Rationing and the Choice for a Proposal -- 6.2 Capital Budgeting Under Inflationary Conditions -- 6.3 Decision Concerning Mutually Exclusive Proposalswith Unequal Lives -- 6.3.1 Annualised NPV Method -- 6.3.2 Replacement Chain Method -- 6.4 The Conditions of Risk -- 6.4.1 Inclusion of Risk Factor in Cash Flow -- 6.4.2 Risk Analysis Based on Portfolio Approach -- 6.4.3 Sensitivity Analysis -- 6.4.4 Scenario Analysis -- 6.4.5 Monte Carlo Simulation -- 6.5 Managerial Options and the Cash Flow -- 6.5.1 The Decision-tree Approach -- 6.6 International Capital Budgeting -- 6.6.1 Parent's Perspective and the Cash flow -- 6.6.2 Parent-Subsidiary Perspective -- Summary -- Points to Remember -- Descriptive Questions -- Objective-type Questions -- Numerical Problems -- Solved Numerical Problems -- References -- Select Further Readings -- Chapter 7: Cost of Capital -- 7.1 Significance of Cost of Capital -- 7.2 Computation of the Cost of Capital -- 7.2.1 Cost of Debt -- 7.2.2 Cost of Preference Share Capital -- 7.2.3 Cost of Equity Shares -- 7.2.4 Cost of Retained Earnings -- 7.3 Weighted Average Cost of Capital -- 7.3.1 The Measurement -- 7.3.2 The Influencing Factors -- 7.4 Marginal Cost of Capital -- Summary -- Points to Remember…”

Tabla de Contenidos: “…7.9.3 Isomap 308 -- 7.9.4 Locally-Linear Embedding 308 -- 7.9.5 Laplacian Eigenmaps 309 -- 7.9.6 Semidefinite Embedding 309 -- 7.10 Ensemble Learning 311 -- 7.11 Markov Chain Monte Carlo 312 -- 7.12 Filtering Technique 313 -- 7.12.1 Kalman Filtering 314 -- 7.12.2 Particle Filtering 318 -- 7.12.3 Collaborative Filtering 319 -- 7.13 Bayesian Network 320 -- 7.14 Summary 321 -- 8 Agile Transmission Techniques (I): Multiple Input Multiple Output 323 -- 8.1 Benefits of MIMO 323 -- 8.1.1 Array Gain 323 -- 8.1.2 Diversity Gain 323 -- 8.1.3 Multiplexing Gain 324 -- 8.2 Space Time Coding 324 -- 8.2.1 Space Time Block Coding 325 -- 8.2.2 Space Time Trellis Coding 326 -- 8.2.3 Layered Space Time Coding 326 -- 8.3 Multi-User MIMO 327 -- 8.3.1 Space-Division Multiple Access 327 -- 8.3.2 MIMO Broadcast Channel 328 -- 8.3.3 MIMO Multiple Access Channel 330 -- 8.3.4 MIMO Interference Channel 331 -- 8.4 MIMO Network 334 -- 8.5 MIMO Cognitive Radio Network 336 -- 8.6 Summary 337 -- 9 Agile Transmission Techniques (II): Orthogonal Frequency Division Multiplexing 339 -- 9.1 OFDM Implementation 339 -- 9.2 Synchronization 341 -- 9.3 Channel Estimation 343 -- 9.4 Peak Power Problem 345 -- 9.5 Adaptive Transmission 345 -- 9.6 Spectrum Shaping 347 -- 9.7 Orthogonal Frequency Division Multiple Access 347 -- 9.8 MIMO OFDM 349 -- 9.9 OFDM Cognitive Radio Network 349 -- 9.10 Summary 350 -- 10 Game Theory 351 -- 10.1 Basic Concepts of Games 351 -- 10.1.1 Elements of Games 351 -- 10.1.2 Nash Equilibrium: Definition and Existence 352 -- 10.1.3 Nash Equilibrium: Computation 354 -- 10.1.4 Nash Equilibrium: Zero-Sum Games 355 -- 10.1.5 Nash Equilibrium: Bayesian Case 355 -- 10.1.6 Nash Equilibrium: Stochastic Games 356 -- 10.2 Primary User Emulation Attack Games 360 -- 10.2.1 PUE Attack 360 -- 10.2.2 Two-Player Case: A Strategic-Form Game 361 -- 10.2.3 Game in Queuing Dynamics: A Stochastic Game 362 -- 10.3 Games in Channel Synchronization 368 -- 10.3.1 Background of the Game 368 -- 10.3.2 System Model 368.…”

Tabla de Contenidos: “…Ryu 309 -Computer simulation on the isochronal annealing of irradiated Fe using the kinetic Monte Carlo method by G.G. Lee, J.H. Kwon 313 -Creep properties in air and helium environments of Alloy 617 For a VHTR by W.G. …”

Tabla de Contenidos: “…5.22.1 A Simple Master-Worker Setup -- 5.22.2 A Multithreaded Master-Worker Setup -- Exercises -- Chapter 6: GPU programming -- 6.1 GPU Programming -- 6.2 CUDA's programming model: threads, blocks, and grids -- 6.3 CUDA's execution model: streaming multiprocessors and warps -- 6.4 CUDA compilation process -- 6.5 Putting together a CUDA project -- 6.6 Memory hierarchy -- 6.6.1 Local Memory/Registers -- 6.6.2 Shared Memory -- 6.6.3 Constant Memory -- 6.6.4 Texture and Surface Memory -- 6.7 Optimization techniques -- 6.7.1 Block and Grid Design -- 6.7.2 Kernel Structure -- 6.7.3 Shared Memory Access -- 6.7.4 Global Memory Access -- 6.7.5 Page-Locked and Zero-Copy Memory -- 6.7.6 Unified Memory -- 6.7.7 Asynchronous Execution and Streams -- 6.7.7.1 Stream Synchronization: Events and Callbacks -- 6.8 Dynamic parallelism -- 6.9 Debugging CUDA programs -- 6.10 Profiling CUDA programs -- 6.11 CUDA and MPI -- 6.12 Case studies -- 6.12.1 Fractal Set Calculation -- 6.12.1.1 Version #1: One thread per pixel -- 6.12.1.2 Version #2: Pinned host and pitched device memory -- 6.12.1.3 Version #3: Multiple pixels per thread -- 6.12.1.4 Evaluation -- 6.12.2 Block Cipher Encryption -- 6.12.2.1 Version #1: The case of a standalone GPU machine -- 6.12.2.2 Version #2: Overlapping GPU communication and computation -- 6.12.2.3 Version #3: Using a cluster of GPU machines -- 6.12.2.4 Evaluation -- Exercises -- Chapter 7: The Thrust template library -- 7.1 Introduction -- 7.2 First steps in Thrust -- 7.3 Working with Thrust datatypes -- 7.4 Thrust algorithms -- 7.4.1 Transformations -- 7.4.2 Sorting and searching -- 7.4.3 Reductions -- 7.4.4 Scans/prefix sums -- 7.4.5 Data management and manipulation -- 7.5 Fancy iterators -- 7.6 Switching device back ends -- 7.7 Case studies -- 7.7.1 Monte carlo integration -- 7.7.2 DNA Sequence alignment -- Exercises…”

“…Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. …”

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“…What You’ll Learn: Set up a business case abstraction in an influence diagram to communicate the essence of the problem to other stakeholders Model the inherent uncertainties in the problem with Monte Carlo simulation using the R language Communicate the results graphically Draw appropriate insights from the results Develop creative decision strategies for thorough opportunity cost analysis Calculate the value of information on critical uncertainties between competing decision strategies to set the budget for deeper data analysis Construct appropriate information to satisfy the parameters for the Monte Carlo simulation when little or no empirical data are available…”

“…On the basis of a structured uncertainty classification, methods based on Monte Carlo simulation can be used for a consistent modelling, propagation and visualisation of the different types of uncertainty. …”

“…Explains how to balance cloud computing functionality with data center efficiency Covers key requirements for power management, cooling, server planning, virtualization, and storage management Describes advanced methods for modeling cloud computing cost including Real Option Theory and Monte Carlo Simulations Blends theoretical and practical discussions with insights for developers, consultants, and analysts considering data center development…”

“…In all, the authors present a host of applications - many of which go beyond standard Excel or VBA usages, for example, how to estimate logit models with maximum likelihood, or how to quickly conduct large-scale Monte Carlo simulations. Clearly written with a multitude of practical examples, the new edition of Credit Risk Modeling using Excel and VBA will prove an indispensible resource for anyone working in, studying or researching this important field. …”

“…Also included in the Third Edition: • Description of scheduling • Definition of schedule model • Uses and benefits of the schedule model • Definitions of key terms and steps for scheduling • Detailed descriptions of scheduling components • Guidance on the principles and concepts of schedule model creation and use • Descriptions of schedule model principles and concepts • Differentiations in schedule model, schedule model instances, and presentations • Detailed descriptions of critical path method, critical chain, program evaluation and review technique (PERT), rolling wave planning, and Monte Carlo simulation • Uses and applications of adaptive project management approaches, such as agile, in scheduling • Guidance and information on generally accepted good practices associated with the planning, development, maintenance, communication, and reporting processes of an effective schedule model…”

“…Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyze complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing…”

“…Among other destinations, best-selling author Mark Tungate visits Swiss watchmakers, the Champagne houses of France, the diamond district of Antwerp, the luxury enclave of Monte Carlo, the discreet ateliers of the last craftsmen and a host of brands in Paris - the self-proclaimed capital of elegance. …”