Tabla de Contenidos: “…Front Cover -- Software Quality Assurance -- Copyright Page -- Contents -- List of Contributors -- Biography -- Deployability -- Release Plan -- Moving Through the Tool Chain -- Trade-offs -- General Scenarios and Tactics -- Microservices -- Continuous Deployment -- Roll Back -- The Number of Quality Attributes Is Growing -- References -- Foreword -- References -- Preface -- Introduction -- Why a New Book on Software Quality -- Book Outline -- 1 Quality concerns in large-scale and complex software-intensive systems -- 1.1 Introduction -- 1.2 Software Quality Management -- 1.3 Software Quality Models -- 1.4 Addressing System Qualities -- 1.5 Assessing System Qualities -- 1.5.1 Assessment Processes -- 1.5.2 Metrics and Measurements -- 1.6 Current Challenges and Future Directions of Software Quality -- 1.7 Conclusion -- References -- 2 An introduction to modern software quality assurance -- 2.1 Introduction -- 2.2 Requirement Conformance Versus Customer Satisfaction -- 2.3 Measurement -- 2.4 Quality Perspectives -- 2.5 Quality Models -- 2.6 Non-Functional Requirements -- 2.7 Cost of Quality -- 2.8 Verification and Validation -- 2.9 Role of Formal Methods -- 2.10 Role of Testing and Automated Testing -- 2.11 Reliability -- 2.12 Security -- 2.13 Safety -- 2.14 Reviews and Usability -- 2.15 Reviews and Postmortems -- 2.16 User Experience -- 2.17 Social Media, Cloud Computing, and Crowdsourcing -- 2.18 Maintenance and Change Management -- 2.19 Defect Analysis and Process Improvement -- 2.20 Role of Product and Process Metrics -- 2.21 Statistical SQA -- 2.22 Change Management -- 2.23 Agile Development Processes -- 2.24 Conclusions/Best Practices -- References -- 3 Defining software quality characteristics to facilitate software quality control and software process improvement -- 3.1 Overview -- 3.2 Process Based Approaches to Software Quality…”

Tabla de Contenidos: “…Chapter 9 Generation-Z Student Video-Based Learning Pedagogy Preference and Teaching Challenges -- 9.1 Introduction -- 9.1.1 Purpose of the Chapter -- 9.2 Generation Z Behavioral Feature -- 9.3 Video-Based Learning Motives (VBLM) -- 9.4 Video-Based Learning Platform -- 9.5 Teachers Role Transformation -- 9.6 Conclusion -- 9.7 Limitation and Further Research Scope -- References -- Chapter 10 Quantitative Monitoring and Analysis of Rare Symptoms of COVID-19 Infection: Application of a Text and Citation Management Software as a Tool -- 10.1 Introduction: The COVID Pandemic -- 10.2 Materials and Methods -- 10.2.1 REVMAN Software -- 10.2.2 Data Input Pane -- 10.2.3 Systematic Application of REVMAN on COVID 19 Rare Symptoms -- 10.2.4 Database Scrutiny -- 10.2.5 Combining BOOLEAN and MeSH Terms for Optimizing the Application Software -- 10.2.6 Secondary Data on Rare Symptoms of COVID-19 -- 10.2.7 Data Extraction -- 10.2.8 Systematic Analysis -- 10.2.9 Statistical Analysis -- 10.3 Results and Discussion -- 10.4 Conclusion -- References -- Chapter 11 Role and Impact of ICT on Rapidly Advancing New Age Teaching Pedagogy in Higher Educational Institutions in Oman -- 11.1 Introduction -- 11.1.1 Evolution of ICT in the Field of Higher Education in Oman -- 11.1.2 ICT Tools Used for Teaching -- 11.2 ICT Methodologies Adopted for Teaching -- 11.3 Gaps Between Deliverables and Delivered -- 11.3.1 Types of Gaps -- 11.3.2 Product/Market Gap -- 11.3.3 Performance Gap -- 11.3.4 Manpower Gap -- 11.4 Causes of the Technological Gaps -- 11.5 Ways to Fill in the Gaps -- 11.5.1 SWOT Analysis -- 11.5.2 Fishbone - Cause and Effect Analysis of ICT in Education -- 11.5.2.1 Language Barriers -- 11.5.2.2 Ease of Access -- 11.5.2.3 Privacy -- 11.5.2.4 Technology -- 11.5.3 McKinsey 7S Model -- 11.6 ICT Training -- 11.7 Importance of ICT Training in the Field of Education…”

Tabla de Contenidos: “…Fault seeding -- 3.4.2. Statistics -- 3.4.3. A posteriori -- 3.4.4. Avoiding introduction of defects -- 3.5. …”

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: “…3.9.2 Domains of real-world applications -- 3.9.3 Financial applications -- 3.9.4 Medical and data science -- 3.9.5 Internet of Things -- 3.10 Conclusion -- References -- Part II: Machine intelligence in network technologies -- Chapter 4: Deformation prediction and monitoring using real-time WSN and machine learning algorithms: A review -- 4.1 Introduction -- 4.2 Causes of landslides -- 4.2.1 Climate changes -- 4.2.2 Earthquake -- 4.2.3 Deforestation -- 4.3 Early warning system -- 4.3.1 Risk Knowledge -- 4.3.2 Monitoring and warning services -- 4.3.3 Dissemination and communication -- 4.3.4 Response capability -- 4.3.5 Classification of early warning system -- 4.4 Landslide monitoring techniques -- 4.4.1 Multi-antenna GPS deformation monitoring systems -- 4.4.2 Monitoring landslide deformation using InSAR Technique -- 4.4.3 Electro-Mechanical System (MEMS) tilt sensor -- 4.4.4 Low-cost vibration sensor network -- 4.4.5 Extensometer -- 4.4.6 Rain gauge -- 4.5 Landside prediction modeling and forecasting using machine learning and statistical analysis -- 4.6 Conclusion -- Acknowledgments -- References -- Chapter 5: Unmanned aerial vehicle: Integration in healthcare sector for transforming interplay among smart cities -- 5.1 Introduction -- 5.1.1 Objectives of the chapter -- 5.1.2 Significance of study -- 5.2 UAVs in healthcare: Applications and benefits -- 5.2.1 Specific applications of UAVs in healthcare sector -- 5.2.1.1 Transportation -- 5.2.1.2 Livestock monitoring -- 5.2.1.3 Disaster relief -- 5.2.1.4 Public health surveillance and medical research -- 5.2.2 Benefits of UAVs in healthcare sector -- 5.3 Communication protocols for UAVs in healthcare -- 5.3.1 Diverse communication protocols suitable for UAVs in healthcare settings -- 5.3.2 Addressing challenges and requirements of real-time data transmission -- 5.4 Deployment strategies and logistics…”

Tabla de Contenidos: “…5.5.4 Barriers to Adoption and Uneven Progress -- 5.6 Industry 5.0: Toward a Human-Centric Future -- 5.6.1 Introduction to Industry 5.0 -- 5.6.2 Concept of Human-Centric Manufacturing -- 5.6.3 Technologies Empowering Workers and Fostering Innovation -- 5.6.4 Value Creation for Businesses and Society -- 5.7 Limitations of Industry 5.0 -- 5.8 Future Research of Industry 5.0 -- 5.9 Conclusion -- References -- Chapter 6 The Industrial Revolution: From Mechanisation (1.0) to Smart Automation (5.0) -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Stages of Transformation (Industry 1.0-1.4) -- 6.3.1 Industry 1.0 -- 6.3.2 Industry 2.0 -- 6.3.3 Industry 3.0 -- 6.3.4 Industry 4.0 -- 6.4 Latest Phase of Industrialisation -- 6.4.1 Industry 5.0 -- 6.4.2 Advancements in Industry 5.0 -- 6.4.3 Advantages of Industry 5.0 -- 6.4.4 Challenges -- 6.5 Conclusion -- References -- Chapter 7 Enhancing the Digital Economy in the Context of the Fourth Industrial Revolution: The Case of Vietnam -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Data Selection and Methodology -- 7.4 Results and Discussion -- 7.4.1 Frequency Analysis -- 7.4.2 Descriptive Statistics -- 7.4.3 The Test of Cronbach's Alpha and EFA -- 7.4.4 Correlation Matrix -- 7.4.5 Regression Results -- 7.5 Conclusions and Future Scope -- References -- Chapter 8 Machinery to Mind: Navigating the Transformation From Industry 1.0 to Industry 5.0 -- 8.1 Introduction -- 8.2 Industry 1.0: The Era of Mechanization -- 8.2.1 Limitations of Industry 1.0 -- 8.3 Industry 2.0: The Age of Mass Production -- 8.3.1 Limitations of Industry 2.0 -- 8.4 Industry 3.0: The Rise of Computers -- 8.4.1 Limitations of Industry 3.0 -- 8.5 Industry 4.0: The Era of Smart Manufacturing -- 8.5.1 Limitations of Industry 4.0 -- 8.6 The Emergence of Industry 5.0 -- 8.6.1 Innovations and Modernizations -- 8.6.2 Applications of Industry 5.0.…”

Tabla de Contenidos: “…Core pandas -- 1 Introducing pandas -- 1.1 Data in the 21st century -- 1.2 Introducing pandas -- 1.2.1 Pandas vs. graphical spreadsheet applications -- 1.2.2 Pandas vs. its competitors -- 1.3 A tour of pandas -- 1.3.1 Importing a data set -- 1.3.2 Manipulating a DataFrame -- 1.3.3 Counting values in a Series -- 1.3.4 Filtering a column by one or more criteria -- 1.3.5 Grouping data -- Summary -- 2 The Series object -- 2.1 Overview of a Series -- 2.1.1 Classes and instances -- 2.1.2 Populating the Series with values -- 2.1.3 Customizing the Series index -- 2.1.4 Creating a Series with missing values -- 2.2 Creating a Series from Python objects -- 2.3 Series attributes -- 2.4 Retrieving the first and last rows -- 2.5 Mathematical operations -- 2.5.1 Statistical operations -- 2.5.2 Arithmetic operations -- 2.5.3 Broadcasting -- 2.6 Passing the Series to Python's built-in functions -- 2.7 Coding challenge -- 2.7.1 Problems -- 2.7.2 Solutions -- Summary -- 3 Series methods -- 3.1 Importing a data set with the read_csv function -- 3.2 Sorting a Series -- 3.2.1 Sorting by values with the sort_values method -- 3.2.2 Sorting by index with the sort_index method -- 3.2.3 Retrieving the smallest and largest values with the nsmallest and nlargest methods -- 3.3 Overwriting a Series with the inplace parameter -- 3.4 Counting values with the value_counts method -- 3.5 Invoking a function on every Series value with the apply method -- 3.6 Coding challenge -- 3.6.1 Problems -- 3.6.2 Solutions -- Summary -- 4 The DataFrame object…”

Tabla de Contenidos: “…3.5.2 Forecast the daily closing price of GOOGL -- 3.5.3 Forecast the daily closing price of a stock of your choice -- Summary -- Part 2. Forecasting with statistical models -- 4 Modeling a moving average process -- 4.1 Defining a moving average process -- 4.1.1 Identifying the order of a moving average process -- 4.2 Forecasting a moving average process -- 4.3 Next steps -- 4.4 Exercises -- 4.4.1 Simulate an MA(2) process and make forecasts -- 4.4.2 Simulate an MA(q) process and make forecasts -- Summary -- 5 Modeling an autoregressive process -- 5.1 Predicting the average weekly foot traffic in a retail store -- 5.2 Defining the autoregressive process -- 5.3 Finding the order of a stationary autoregressive process -- 5.3.1 The partial autocorrelation function (PACF) -- 5.4 Forecasting an autoregressive process -- 5.5 Next steps -- 5.6 Exercises -- 5.6.1 Simulate an AR(2) process and make forecasts -- 5.6.2 Simulate an AR(p) process and make forecasts -- Summary -- 6 Modeling complex time series -- 6.1 Forecasting bandwidth usage for data centers -- 6.2 Examining the autoregressive moving average process -- 6.3 Identifying a stationary ARMA process -- 6.4 Devising a general modeling procedure -- 6.4.1 Understanding the Akaike information criterion (AIC) -- 6.4.2 Selecting a model using the AIC -- 6.4.3 Understanding residual analysis -- 6.4.4 Performing residual analysis -- 6.5 Applying the general modeling procedure -- 6.6 Forecasting bandwidth usage -- 6.7 Next steps -- 6.8 Exercises -- 6.8.1 Make predictions on the simulated ARMA(1,1) process -- 6.8.2 Simulate an ARMA(2,2) process and make forecasts -- Summary -- 7 Forecasting non-stationary time series -- 7.1 Defining the autoregressive integrated moving average model -- 7.2 Modifying the general modeling procedure to account for non-stationary series…”

Tabla de Contenidos: “…2.2.4 Big Data Analytics -- 2.2.5 Cloud Computing -- 2.2.6 Cyber Security -- 2.2.6.1 Cyber-Security Challenges in Industry 4.0 -- 2.2.7 Augmented Reality and Virtual Reality -- 2.2.8 Simulation -- 2.2.8.1 Need of Simulation in Smart Manufacturing -- 2.2.8.2 Advantages of Simulation -- 2.2.8.3 Simulation and Digital Twin -- 2.2.9 Digital Twins -- 2.2.9.1 Integration of Horizontal and Vertical Systems -- 2.2.10 IoT and IIoT in Industry 4.0 -- 2.2.11 Artificial Intelligence in Industry 4.0 -- 2.2.12 Implications of the Study for Academicians and Practitioners -- 2.3 Summary and Conclusions -- 2.3.1 Benefits of Industry 4.0 -- 2.3.2 Challenges in Industry 4.0 -- 2.3.3 Future Directions -- Acknowledgement -- References -- Chapter 3 IoT-Based Intelligent Manufacturing System: A Review -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Research Procedure -- 3.3.1 The Beginning and Advancement of SM/IM -- 3.3.2 Beginning of SM/IM -- 3.3.3 Defining SM/IM -- 3.3.4 Potential of SM/IM -- 3.3.5 Statistical Analysis of SM/IM -- 3.3.6 Future Endeavour of SM/IM -- 3.3.7 Necessary Components of IoT Framework -- 3.3.8 Proposed System Based on IoT -- 3.3.9 Development of IoT in Industry 4.0 -- 3.4 Smart Manufacturing -- 3.4.1 Re-Configurability Manufacturing System -- 3.4.2 RMS Framework Based Upon IoT -- 3.4.3 Machine Control -- 3.4.4 Machine Intelligence -- 3.4.5 Innovation and the IIoT -- 3.4.6 Wireless Technology -- 3.4.7 IP Mobility -- 3.4.8 Network Functionality Virtualization (NFV) -- 3.5 Academia Industry Collaboration -- 3.6 Conclusions -- References -- Chapter 4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process -- Abbreviations -- 4.1 Introduction and Literature Reviews -- 4.1.1 Motivation Behind the Study -- 4.1.2 Objective of the Chapter -- 4.2 Network in Smart Manufacturing System…”

Tabla de Contenidos: “…Quantitative -- 6.4.2 Qualitative and Quantitative at Different Stages of the Product Innovation Process -- 6.4.3 Target Market Representation -- 6.4.4 Sampling Methods -- 6.4.5 Sample Size and the Statistical Basis of Probability Sampling -- 6.5 Market Research Methods -- 6.5.1 Focus Groups -- 6.5.2 In-depth Interviews -- 6.5.3 Ethnography -- 6.5.4 Customer Site Visits -- 6.5.5 Social Media -- 6.5.6 Surveys -- 6.5.7 Consumer Panels -- 6.5.8 Sensory Testing -- 6.5.9 Trained Panels -- 6.5.10 Concept Tests and Concept Sorts…”

Tabla de Contenidos: “…3.7.8 Cut-Off Examination -- 3.7.9 Physical Examination -- 3.7.10 Statistical Sampling -- 3.7.11 Surprise Checking -- 3.7.12 Audit Flow Chart -- 3.7.13 Test of Control -- 3.7.14 Internal Control Questionnaires -- 3.7.15 Audit Tests -- 3.8 Representation by Management -- 3.8.1 Representation by Management as Audit Evidence -- 3.8.2 Documentation of Representation by Management -- 3.8.3 Letter of Representation -- 3.9 Delegation, Supervision and Review of Audit Work -- 3.9.1 Delegation -- 3.9.2 Supervision -- 3.9.3 Review -- 3.9.4 Control of Quality of Audit Work -- 3.10 Professional Scepticism -- 3.11 Audit Risk and Materiality -- 3.11.1 Concept of Materiality -- 3.11.2 Audit Risk -- 3.11.3 Relationship Between Materiality and Audit Risk -- 3.12 Final Review -- 3.13 Case Studies -- Chapter 4: Internal Control, Internal Check and Internal Audit -- 4.1 Introduction -- 4.2 Internal Control -- 4.2.1 Definition -- 4.2.2 Basic Elements of Internal Control -- 4.2.3 Objectives of Internal Control -- 4.2.4 Evaluation of Internal Control -- 4.2.5 Internal Control and the Auditor -- 4.2.6 Internal Control Checklist -- 4.2.7 Internal Control Questionnaire -- 4.2.8 Internal Control and Computerised Environment -- 4.2.9 Internal Control and Corporate Governance -- 4.2.10 Internal Control in Specific Areas of Business -- 4.3 Internal Check -- 4.3.1 Definition -- 4.3.2 General Considerations in Framing a System of Internal Check -- 4.3.3 Objectives of Internal Check -- 4.3.4 Internal Check and the Auditor -- 4.3.5 General Principles of Internal Check for a Few Transactions -- 4.4 Internal Audit -- 4.4.1 Definition -- 4.4.2 Basic Principles of Establishing Internal Auditing in a Business Concern -- 4.4.3 Scope and Objectives of Internal Audit -- 4.4.4 Essential Elements of Internal Audit -- 4.4.5 Area of Internal Audit…”

Tabla de Contenidos: “…Cover -- Preface -- Acknowledgements -- Contents -- Chapter 1: Concept of Acids and Bases -- 1.1 Introduction -- 1.1.1 What Is an Acid or a Base -- 1.1.2 Properties of Acids -- 1.1.3 Properties of Bases -- 1.2 Acidity and Basicity of Molecules -- 1.2.1 Acidity -- 1.2.2 Carbon Acids -- 1.2.3 Nitrogen Acids -- 1.2.4 Organosulphur Oxyacids -- 1.2.5 Basicity -- 1.2.6 Effects Decreasing Electron Density on Nitrogen -- 1.3 Definition of pka -- 1.4 pH Box -- 1.4.1 pka Box -- 1.4.2 pH of Strong Acids and Bases -- 1.4.3 Strong Acids -- 1.4.4 Weak Acids -- 1.4.5 Strong Bases -- 1.4.6 Weak Bases -- 1.4.7 pH of Weak Acids and Bases -- 1.5 Hard and Soft Acids and Bases -- 1.5.1 Lewis Acids and Bases -- 1.5.2 Hard and soft Acids -- 1.5.3 Hard and Soft Bases -- 1.5.4 Hard and Soft Acid-Base Classification -- 1.6 Effect of Structure on Strength of Acids and Bases -- 1.6.1 Field Effect -- 1.6.2 Resonance Effect -- 1.6.3 Periodic Table correlation -- 1.6.4 Statistical Effect -- 1.6.5 Hydrogen-bonding -- 1.6.6 Steric Effect -- 1.6.7 Hybridization -- 1.7 Effects of Medium on Acid and Base Strength -- 1.8 Levelling Effect -- 1.9 Summary -- Problems -- Objective Type Questions -- Chapter 2: Delocalized Chemical Bonding and Electronic Effects -- 2.1 Introduction -- 2.2 Resonance -- 2.3 Resonance Energy -- 2.4 Resonance Effect -- 2.5 Hyperconjugation (Baker-N athan Effect) -- 2.5.1 Negative hyperconjugation -- 2.6 Tautomerism -- 2.6.1 Mechanism of Keto-Enol Interconversion -- 2.6.2 Differences between Tautomerism and Resonance -- 2.7 Nitro-acinitro System -- 2.8 Inductive Effect -- 2.9 Electromeric Effect -- 2.10 Steric Effect -- 2.11 Hydrogen Bonding -- 2.12 Summary -- Problems -- Objective Type Questions -- Chapter 3: Aliphatic Nucleophilic Substitution Reactions -- 3.1 Introduction -- 3.2 Mechanism of SN2 Reaction -- 3.3 Nucleophile in SN2 Reaction…”

Tabla de Contenidos: “…Preface xv -- Acknowledgments xxi -- Contributors xxiii -- PART I CDN AND MEDIA STREAMING BASICS 1 -- 1 CLOUD-BASED CONTENT DELIVERY AND STREAMING 3 /Mukaddim Pathan -- 1.1 Introduction 3 -- 1.2 CDN Overview 5 -- 1.3 Workings of a CDN 10 -- 1.4 CDN Trends 21 -- 1.5 Research Issues 28 -- 1.6 Conclusion 29 -- References 29 -- 2 LIVE STREAMING ECOSYSTEMS 33 /Dom Robinson -- 2.1 Introduction 33 -- 2.2 Live Streaming Pre-Evolution 34 -- 2.3 Live, Linear, Nonlinear 35 -- 2.4 Media Streaming 37 -- 2.5 Related Network Models 38 -- 2.6 Streaming Protocol Success 43 -- 2.7 Platform Divergence and Codec Convergence 44 -- 2.8 Adaptive Bitrate (ABR) Streaming 45 -- 2.9 Internet Radio and HTTP 48 -- 2.10 Conclusion 48 -- References 49 -- 3 PRACTICAL SYSTEMS FOR LIVE STREAMING 51 /Dom Robinson -- 3.1 Introduction 51 -- 3.2 Common Concepts in Live Streaming 52 -- 3.3 The Practicals 56 -- 3.4 Conclusion 69 -- References 70 -- 4 EFFICIENCY OF CACHING AND CONTENT DELIVERY IN BROADBAND ACCESS NETWORKS 71 /Gerhard Haslinger -- 4.1 Introduction 71 -- 4.2 Options and Properties for Web Caching 73 -- 4.3 Zipf Laws for Requests to Popular Content 75 -- 4.4 Efficiency and Performance Modeling for Caches 76 -- 4.5 Effect of Replacement Strategies on Cache Hit Rates 78 -- 4.6 Replacement Methods Based on Request Statistics 81 -- 4.7 Global CDN and P2P Overlays for Content Delivery 84 -- 4.8 Summary and Conclusion 86 -- Acknowledgments 87 -- References 87 -- 5 ANYCAST REQUEST ROUTING FOR CONTENT DELIVERY NETWORKS 91 /Hussein A. …”

Tabla de Contenidos: “…3.5 Inclusion of electromagnetic interactions via the gauge principle: the Dirac prediction of g = 2 for the electron -- Problems -- 4 Lorentz Transformations and Discrete Symmetries -- 4.1 Lorentz transformations -- 4.1.1 The KG equation -- 4.1.2 The Dirac equation -- 4.2 Discrete transformations: P, C and T -- 4.2.1 Parity -- 4.2.2 Charge conjugation -- 4.2.3 CP -- 4.2.4 Time reversal -- 4.2.5 CPT -- Problems -- II Introduction to Quantum Field Theory -- 5 Quantum Field Theory I: The Free Scalar Field -- 5.1 The quantum field: (i) descriptive -- 5.2 The quantum field: (ii) Lagrange-Hamilton formulation -- 5.2.1 The action principle: Lagrangian particle mechanics -- 5.2.2 Quantum particle mechanics à la Heisenberg-Lagrange-Hamilton -- 5.2.3 Interlude: the quantum oscillator -- 5.2.4 Lagrange-Hamilton classical field mechanics -- 5.2.5 Heisenberg-Lagrange-Hamilton quantum field mechanics -- 5.3 Generalizations: four dimensions, relativity and mass -- Problems -- 6 Quantum Field Theory II: Interacting Scalar Fields -- 6.1 Interactions in quantum field theory: qualitative introduction -- 6.2 Perturbation theory for interacting fields: the Dyson expansion of the S-matrix -- 6.2.1 The interaction picture -- 6.2.2 The S-matrix and the Dyson expansion -- 6.3 Applications to the 'ABC' theory -- 6.3.1 The decay C A + B -- 6.3.2 A + B A + B scattering: the amplitudes -- 6.3.3 A + B A + B scattering: the Yukawa exchange mechanism, s and u channel processes -- 6.3.4 A + B A + B scattering: the differential cross section -- 6.3.5 A + B A + B scattering: loose ends -- Problems -- 7 Quantum Field Theory III: Complex Scalar Fields, Dirac and Maxwell Fields -- Introduction of Electromagnetic Interactions -- 7.1 The complex scalar field: global U(1) phase invariance, particles and antiparticles -- 7.2 The Dirac field and the spin-statistics connection…”

Tabla de Contenidos: “…9.2.2 Evaluation Process -- 9.3 Question and Answer Model -- 9.3.1 Most Widely-used Question Types -- 9.4 A Short Introduction to AI and Machine Learning -- 9.5 Selection of Machine Learning Algorithms to address our Problem -- 9.5.1 Reinforced Learning (RL) -- 9.6 Evaluation Process -- 9.6.1 Question Delivery -- 9.6.2 Question Attributes -- 9.7 Evaluator States and Actions -- 9.8 Implementation -- 9.8.1 Listing 1 -- 9.8.2 Listing 2 -- 9.8.3 Implementation Details -- 9.8.4 Testing the Evaluator -- 9.8.5 TestCase Output -- 9.9 Conclusion -- References -- 10 Investigating Artificial Intelligence Usage for Revolution in E-Learning during COVID-19 -- 10.1 Introduction -- 10.2 Review of Existing Literature -- 10.3 Objective of the Study -- 10.4 Research Methodology -- 10.5 Data Analysis and Discussion -- 10.6 Implications and Conclusion -- 10.7 Limitation and Future Scope -- Acknowledgement -- References -- 11 Employee Churn Management Using AI -- 11.1 Introduction -- 11.2 Proposed Methodology -- 11.2.1 Dataset Review -- 11.3 Model Building -- 11.3.1 Train Test Split -- 11.3.2 Model Building -- 11.3.3 Random Forest Classifier -- 11.3.4 XGBoost -- 11.4 Comparison -- 11.4.1 AUC-ROC Curve -- 11.5 Conclusion -- References -- 12 Machine Learning: Beginning of a New Era in the Dominance of Statistical Methods of Forecasting -- 12.1 Introduction -- 12.2 Analyzing Prominent Studies -- 12.3 Tabulation of prominent studies forecasting Time Series Data using Machine Learnings Techniques -- 12.4 Conclusion -- References -- 13 Recurrent Neural Network-Based Long Short-Term Memory Deep Neural Network Model for Forex Prediction -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Working Principle of LSTM -- 13.4 Results and Simulations Study -- 13.4.1 Data Preparation -- 13.4.2 Performance Measure -- 13.5 Results and Discussion -- 13.6 Conclusion -- References…”

Tabla de Contenidos: “…2 Compound Interest -- 3 A Word on Continuous Compounding -- Self‐Test 19.2 -- 4 Doubling Time: The Rule of 70 -- Self‐Test 19.3 -- 5 Discounting -- Self‐Test 19.4 -- Answers to Self‐Test 19.1 -- Answers to Self‐Test 19.2 -- Answers to Self‐Test 19.3 -- Answers to Self‐Test 19.4 -- Chapter 20 Rate, Time, and Distance -- 1 The Magic Formula -- 2 The Terms -- 3 Finding Distance -- Self‐Test 20.1 -- 4 Finding Rate -- Self‐Test 20.2 -- 5 Finding Time -- Self‐Test 20.3 -- 6 Rate, Time, and Distance Problems -- Self‐Test 20.4 -- 7 Speed Limit Problems -- Self‐Test 20.5 -- Answers to Self‐Test 20.1 -- Answers to Self‐Test 20.2 -- Answers to Self‐Test 20.3 -- Answers to Self‐Test 20.4 -- Answers to Self‐Test 20.5 -- Chapter 21 Personal Finance -- 1 Mark‐Down Problems -- Self‐Test 21.1 -- 2 Sales Tax Problems -- Self‐Test 21.2 -- 3 Credit Cards -- SELF‐TEST 21.3 -- 4 Federal Income Tax -- Self‐Test 21.4 -- 5 Mortgage Interest and Taxes -- Self‐Test 21.5 -- Answers to Self‐Test 21.1 -- Answers to Self‐Test 21.2 -- Answers to Self‐Test 21.3 -- Answers to Self‐Test 21.4 -- Answers to Self‐Test 21.5 -- Chapter 22 Business Math -- 1 Commissions -- Self‐Test 22.1 -- 2 Mark‐Ups -- Self‐Test 22.2 -- 3 Discounting from List Price -- Self‐Test 22.3 -- 4 Quantity Discounts -- Self‐Test 22.4 -- 5 2/10 n/30 -- Self‐Test 22.5 -- 6 Chain Discounts -- Self‐Test 22.6 -- 7 Profit -- Self‐Test 22.7 -- Answers to Self‐Test 22.1 -- Answers to Self‐Test 22.2 -- Answers to Self‐Test 22.3 -- Answers to Self‐Test 22.4 -- Answers to Self‐Test 22.5 -- Answers to Self‐Test 22.6 -- Answers to Self‐Test 22.7 -- Chapter 23 A Taste of Statistics -- 1 Distributions of Data -- Self‐Test 23.1 -- 2 Measures of Center -- Self‐Test 23.2 -- 3 Measures of Spread -- Median and Quartiles -- 5‐Number Summary -- Range and Interquartile Range -- Mean and Standard Deviation -- Self‐Test 23.3.…”

Tabla de Contenidos: “…4.7.1 Examples of the Application of Fuzzy Methods in Infrastructure Management -- 4.8 Summary and Conclusion -- 4.9 Questions and Exercises -- Further Reading -- Chapter 5 Automatic Detection and Its Applications in Infrastructure -- 5.1 Introduction -- 5.1.1 Photometric Hypotheses (PH) -- 5.1.2 Geometric and Photometric Hypotheses (GPH) -- 5.1.3 Geometric Hypotheses (GH) -- 5.1.4 Transform Hypotheses (TH) -- 5.2 The Framework for Automatic Detection of Abnormalities in Infrastructure Images -- 5.2.1 Wavelet Method -- 5.2.2 High Amplitude Wavelet Coefficient Percentage (HAWCP) -- 5.2.3 High-Frequency Wavelet Energy Percentage (HFWEP) -- 5.2.4 Wavelet Standard Deviation (WSTD) -- 5.2.5 Moments of Wavelet -- 5.2.6 High Amplitude Shearlet Coefficient Percentage (HASHCP) -- 5.2.7 High-Frequency Shearlet Energy Percentage (HFSHEP) -- 5.2.8 Fractal Index -- 5.2.9 Moments of Complex Shearlet -- 5.2.10 Central Moments q -- 5.2.11 Hu Moments -- 5.2.12 Bamieh Moments -- 5.2.13 Zernike Moments -- 5.2.14 Statistic of Complex Shearlet -- 5.2.15 Contrast of Complex Shearlet -- 5.2.16 Correlation of Complex Shearlet -- 5.2.17 Uniformity of Complex Shearlet -- 5.2.18 Homogeneity of Complex Shearlet -- 5.2.19 Entropy of Complex Shearlet -- 5.2.20 Local Standard Deviation of Complex Shearlet Index (F_Local_STD) -- 5.3 Summary and Conclusion -- 5.4 Questions and Exercises -- Further Reading -- Chapter 6 Feature Extraction and Fragmentation Methods -- 6.1 Introduction -- 6.2 Low-Level Feature Extraction Methods -- 6.3 Shape-Based Feature (SBF) -- 6.3.1 Center of Gravity (COG) or Center of Area (COA) -- 6.3.2 Axis of Least Inertia (ALI) -- 6.3.3 Average Bending Energy -- 6.3.4 Eccentricity Index (ECI) -- 6.3.5 Circularity Ratio (CIR) -- 6.3.6 Ellipse Variance Feature (EVF) -- 6.3.7 Rectangularity Feature (REF) -- 6.3.8 Convexity Feature (COF)…”

Tabla de Contenidos: “…5.3.4 The Need for Post-Quantum Cryptography -- 5.4 Algorithms Proposed for Post-Quantum Cryptography -- 5.4.1 Code-Based Cryptography -- 5.4.2 Lattice-Based Cryptography -- 5.4.3 Multivariate Cryptography -- 5.4.4 Hash-Based Cryptography -- 5.4.5 Supersingular Elliptic Curve Isogeny Cryptography -- 5.4.6 Quantum-Resistant Symmetric Key Cryptography -- 5.5 Launching of the Project Called "Open Quantum Safe" -- 5.6 Algorithms Proposed During the NIST Standardization Procedure for Post-Quantum Cryptography -- 5.7 Hardware Requirements of Post-Quantum Cryptographic Algorithms -- 5.7.1 NTRUEncrypt -- 5.7.1.1 Polynomial Multiplication -- 5.7.1.2 Hardware to Accelerate NTRUEncrypt -- 5.7.2 Hardware-Software Design to Implement PCQ Algorithms -- 5.7.3 Implementation of Cryptographic Algorithms Using HLS -- 5.8 Challenges on the Way of Post-Quantum Cryptography -- 5.9 Post-Quantum Cryptography Versus Quantum Cryptography -- 5.10 Future Prospects of Post-Quantum Cryptography -- References -- Chapter 6 A Statistical Characterization of MCX Crude Oil Price with Regard to Persistence Behavior and Seasonal Anomaly -- 6.1 Introduction -- 6.2 Related Literature -- 6.3 Data Description and Methodology -- 6.3.1 Data -- 6.3.2 Methodology -- 6.3.2.1 Characterizing Persistence Behavior of Crude Oil Return Time Series Using Hurst Exponent -- 6.3.2.2 Zipf Plot -- 6.3.2.3 Seasonal Anomaly in Oil Returns -- 6.4 Analysis and Findings -- 6.4.1 Persistence Behavior of Daily Oil Stock Price -- 6.4.2 Detecting Seasonal Pattern in Oil Prices -- 6.5 Conclusion and Implications -- References -- Appendix -- Chapter 7 Some Fixed Point and Coincidence Point Results Involving Gα-Type Weakly Commuting Mappings -- 7.1 Introduction -- 7.2 Definitions and Mathematical Preliminaries -- 7.2.1 Definition: G-metric Space (G-ms) -- 7.2.2 Definition: t-norm…”

Tabla de Contenidos: “…Integrating data sets through statistical matching -- 8.4. Construct household groupings for each data source separately on the basis of a common variable -- 8.5. …”

Tabla de Contenidos: “…2.2.13 Leave-One-Out Encoding -- 2.2.14 James-Stein Encoding -- 2.2.15 M-Estimator Encoding -- 2.2.16 Using HephAIstos to Encode Categorical Data -- 2.3 Time-Related Features Engineering -- 2.3.1 Date-Related Features -- 2.3.2 Lag Variables -- 2.3.3 Rolling Window Feature -- 2.3.4 Expending Window Feature -- 2.3.5 Understanding Time Series Data in Context -- 2.4 Handling Missing Values in Machine Learning -- 2.4.1 Row or Column Removal -- 2.4.2 Statistical Imputation: Mean, Median, and Mode -- 2.4.3 Linear Interpolation -- 2.4.4 Multivariate Imputation by Chained Equation Imputation -- 2.4.5 KNN Imputation -- 2.5 Feature Extraction and Selection -- 2.5.1 Feature Extraction -- 2.5.1.1 Principal Component Analysis -- 2.5.1.2 Independent Component Analysis -- 2.5.1.3 Linear Discriminant Analysis -- 2.5.1.4 Locally Linear Embedding -- 2.5.1.5 The t-Distributed Stochastic Neighbor Embedding Technique -- 2.5.1.6 More Manifold Learning Techniques -- 2.5.1.7 Feature Extraction with HephAIstos -- 2.5.2 Feature Selection -- 2.5.2.1 Filter Methods -- 2.5.2.2 Wrapper Methods -- 2.5.2.3 Embedded Methods -- 2.5.2.4 Feature Importance Using Graphics Processing Units (GPUs) -- 2.5.2.5 Feature Selection Using HephAIstos -- Further Reading -- Chapter 3 Machine Learning Algorithms -- 3.1 Linear Regression -- 3.1.1 The Math -- 3.1.2 Gradient Descent to Optimize the Cost Function -- 3.1.3 Implementation of Linear Regression -- 3.1.3.1 Univariate Linear Regression -- 3.1.3.2 Multiple Linear Regression: Predicting Water Temperature -- 3.2 Logistic Regression -- 3.2.1 Binary Logistic Regression -- 3.2.1.1 Cost Function -- 3.2.1.2 Gradient Descent -- 3.2.2 Multinomial Logistic Regression -- 3.2.3 Multinomial Logistic Regression Applied to Fashion MNIST -- 3.2.3.1 Logistic Regression with scikit-learn -- 3.2.3.2 Logistic Regression with Keras on TensorFlow…”