Computer science Books

Book SynopsisChapter 1: Climate Change.- Chapter 2: Current carbon market ecosystem.- Chapter 3: Scandals and scams.- Chapter 4: Critical need for regulation: Integrity of voluntary carbon markets.- Chapter 5: Regulation, Governance and Compliance.- Chapter 6: Regulation, Governance and Compliance.- Chapter 7: Benchmarks and Pricing.- Chapter 8: Additionality.

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Book SynopsisThis book addresses the large gap between the fragmented research ESD (Electrostatic Discharge) and design practices used in VLSI design. It brings a simplified design perspective to IO (Input/Output) and ESD design, and presents a common understanding for the IO designer about ESD constraints and for the ESD Process designer about IO requirements.Table of ContentsESD Protection Methodology. Additional ESD Considerations. Circuits. Layout Issues. ESD and I/O Interactions. Mixed-Voltage ESD. ESD Reliability Measurement and Failure Analysis Basics. Conclusion. Index.

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Book SynopsisThis book is designed for professionals and students in software engineering or information technology who are interested in understanding the dynamics of software development in order to assess and optimize their own process strategies.Trade Review"By taking both a technical and a social approach Raymond Madachy, the author, stimulates the readers interest and makes his book of over 600 pages a very worthwhile title." (Kybernetes, 2008) "When Ed Yourdon says that this is possible the 'best software engineering book' of the year, and possible the decade, one can hardly argue." (Ubiquity, June 10-16, 2008)Table of ContentsForeword xiiiBarry Boehm Preface xvii Part 1 Fundamentals Chapter 1 Introduction and Background 3 1.1 Systems, Processes, Models, and Simulation 6 1.2 Systems Thinking 8 1.2.1 The Fifth Discipline and Common Models 9 1.2.2 Systems Thinking Compared to System Dynamics 9 1.2.3 Weinberg’s Systems Thinking 10 1.3 Basic Feedback Systems Concepts Applied to the Software Process 10 1.3.1 Using Simulation Models for Project Feedback 13 1.3.2 System Dynamics Introductory Example 14 1.4 Brooks’s Law Example 16 1.4.1 Brooks’s Law Model Behavior 19 1.5 Software Process Technology Overview 22 1.5.1 Software Process Modeling 22 1.5.2 Process Lifecycle Models 29 1.5.3 Process Improvement 40 1.6 Challenges for the Software Industry 45 1.7 Major References 47 1.8 Chapter 1 Summary 48 1.9 Exercises 49 Chapter 2 The Modeling Process with System Dynamics 53 2.1 System Dynamics Background 54 2.1.1 Conserved Flows Versus Nonconserved Information 55 2.1.2 The Continuous View Versus Discrete Event Modeling 55 2.1.3 Model Elements and Notations 56 2.1.4 Mathematical Formulation of System Dynamics 56 2.1.5 Using Heuristics 60 2.1.6 Potential Pitfalls 60 2.2 General System Behaviors 61 2.2.1 Goal-Seeking Behavior 61 2.2.2 Information Smoothing 63 2.2.3 Example: Basic Structures for General Behaviors 63 2.3 Modeling Overview 64 2.3.1 An Iterative Process 68 2.3.2 Applying the WinWin Spiral Model 70 2.4 Problem Definition 73 2.4.1 Defining the Purpose 73 2.4.2 Reference Behavior 74 2.4.3 Example: Model Purpose and Reference Behavior 75 2.5 Model Conceptualization 75 2.5.1 Identification of System Boundary 78 2.5.2 Causal Loop Diagrams 79 2.6 Model Formulation and Construction 83 2.6.1 Top-Level Formulation 84 2.6.2 Basic Patterns and Rate Equations 90 2.6.3 Graph and Table Functions 96 2.6.4 Assigning Parameter Values 99 2.6.5 Model Building Principles 101 2.6.6 Model Integration 103 2.6.7 Example: Construction Iterations 104 2.7 Simulation 110 2.7.1 Steady-state Conditions 112 2.7.2 Test Functions 113 2.7.3 Reference Behavior 115 2.8 Model Assessment 116 2.8.1 Model Validation 117 2.8.2 Model Sensitivity Analysis 121 2.8.3 Monte Carlo Analysis 125 2.9 Policy Analysis 126 2.9.1 Policy Parameter Changes 127 2.9.2 Policy Structural Changes 128 2.9.3 Policy Validity and Robustness 129 2.9.4 Policy Suitability and Feasibility 130 2.9.5 Example: Policy Analysis 130 2.10 Continuous Model Improvement 131 2.10.1 Disaggregation 132 2.10.2 Feedback Loops 132 2.10.3 Hypotheses 132 2.10.4 When to Stop? 133 2.10.5 Example: Model Improvement Next Steps 133 2.11 Software Metrics Considerations 134 2.11.1 Data Collection 134 2.11.2 Goal–Question–Metric Framework 135 2.11.3 Integrated Measurement and Simulation 136 2.12 Project Management Considerations 138 2.12.1 Modeling Communication and Team Issues 139 2.12.2 Risk Management of Modeling Projects 140 2.12.3 Modeling Documentation and Presentation 141 2.12.4 Modeling Work Breakdown Structure 142 2.13 Modeling Tools 142 2.14 Major References 145 2.15 Chapter 2 Summary 146 2.15.1 Summary of Modeling Heuristics 148 2.16 Exercises 149 Chapter 3 Model Structures and Behaviors for Software Processes 155 3.1 Introduction 155 3.2 Model Elements 157 3.2.1 Levels (Stocks) 157 3.2.2 Rates (Flows) 159 3.2.3 Auxiliaries 159 3.2.4 Connectors and Feedback Loops 160 3.3 Generic Flow Processes 160 3.3.1 Rate and Level System 160 3.3.2 Flow Chain with Multiple Rates and Levels 161 3.3.3 Compounding Process 162 3.3.4 Draining Process 163 3.3.5 Production Process 163 3.3.6 Adjustment Process 163 3.3.7 Coflow Process 164 3.3.8 Split Flow Process 165 3.3.9 Cyclic Loop 165 3.4 Infrastructures and Behaviors 166 3.4.1 Exponential Growth 166 3.4.2 S-Shaped Growth and S-Curves 167 3.4.3 Delays 169 3.4.4 Balancing Feedback 175 3.4.5 Oscillation 177 3.4.6 Smoothing 180 3.4.7 Production and Rework 182 3.4.8 Integrated Production Structure 183 3.4.9 Personnel Learning Curve 183 3.4.10 Rayleigh Curve Generator 185 3.4.11 Attribute Tracking 186 3.4.12 Attribute Averaging 187 3.4.13 Effort Expenditure Instrumentation 187 3.4.14 Decision Structures 188 3.5 Software Process Chain Infrastructures 192 3.5.1 Software Products 193 3.5.2 Defects 196 3.5.3 People 200 3.6 Major References 203 3.7 Chapter 3 Summary 204 3.8 Exercises 204 Part 2 Applications And Future Directions Introduction to Applications Chapters 211 Chapter 4 People Applications 217 4.1 Introduction 217 4.2 Overview of Applications 221 4.3 Project Workforce Modeling 222 4.3.1 Example: Personnel Sector Model 222 4.4 Exhaustion and Burnout 224 4.4.1 Example: Exhaustion Model 224 4.5 Learning 227 4.5.1 Example: Learning Curve Models 231 4.6 Team Composition 234 4.6.1 Example: Assessing Agile Team Size for a Hybrid Process 235 4.7 Other Application Areas 252 4.7.1 Motivation 252 4.7.2 Personnel Hiring and Retention 256 4.7.3 Skills and Capabilities 260 4.7.4 Team Communication 260 4.7.5 Negotiation and Collaboration 261 4.7.6 Simulation for Personnel Training 263 4.8 Major References 265 4.9 Chapter 4 Summary 265 4.10 Exercises 267 Chapter 5 Process and Product Applications 269 5.1 Introduction 269 5.2 Overview of Applications 273 5.3 Peer Reviews 274 5.3.1 Example: Modeling an Inspection-Based Process 275 5.3.2 Example: Inspection Process Data Calibration 289 5.4 Global Process Feedback (Software Evolution) 291 5.4.1 Example: Software Evolution Progressive and 293 Antiregressive Work 5.5 Software Reuse 299 5.5.1 Example: Reuse and Fourth-Generation Languages 301 5.6 Commercial Off-the-Shelf Software (COTS)-Based Systems 309 5.6.1 Example: COTS Glue Code Development and COTS 310 Integration 5.6.2 Example: COTS-Lifespan Model 317 5.7 Software Architecting 319 5.7.1 Example: Architecture Development During Inception and 319 Elaboration 5.8 Quality and Defects 327 5.8.1 Example: Defect Dynamics 328 5.8.2 Example: Defect Removal Techniques and Orthogonal 330 Defect Classification 5.9 Requirements Volatility 333 5.9.1 Example: Software Project Management Simulator 337 5.10 Software Process Improvement 343 5.10.1 Example: Software Process Improvement Model 346 5.10.2 Example: Xerox Adaptation 354 5.11 Major References 362 5.12 Provided Models 363 5.13 Chapter 5 Summary 363 5.14 Exercises 364 Chapter 6 Project and Organization Applications 369 6.1 Introduction 369 6.1.1 Organizational Opportunities for Feedback 371 6.2 Overview of Applications 372 6.3 Integrated Project Modeling 373 6.3.1 Example: Integrated Project Dynamics Model 373 6.4 Software Business Case Analysis 395 6.4.1 Example: Value-Based Product Modeling 396 6.5 Personnel Resource Allocation 411 6.5.1 Example: Resource Allocation Policy and Contention Models 411 6.6 Staffing 416 6.6.1 Example: Rayleigh Manpower Distribution Model 418 6.6.2 Example: Process Concurrence Modeling 423 6.6.3 Integrating Rayleigh Curves, Process Concurrence, and 441 Brooks’s Interpretations 6.7 Earned Value 442 6.7.2 Example: Earned Value Model 450 6.8 Major References 460 6.9 Provided Models 460 6.10 Chapter 6 Summary 460 6.11 Exercises 462 Chapter 7 Current and Future Directions 469 7.1 Introduction 469 7.2 Simulation Environments and Tools 472 7.2.1 Usability 473 7.2.2 Model Analysis 473 7.2.3 Artificial Intelligence and Knowledge-Based Simulation 474 7.2.4 Networked Simulations 475 7.2.5 Training and Game Playing 475 7.3 Model Structures and Component-Based Model Development 476 7.3.1 Object-Oriented Methods 478 7.3.2 Metamodels 478 7.4 New and Emerging Trends for Applications 479 7.4.1 Distributed Global Development 480 7.4.2 User and People-Oriented Focus 482 7.4.3 Agile and Hybrid Processes 482 7.4.4 Commercial Off-the-Shelf Software 484 7.4.5 Open Source Software Development 486 7.4.6 Personnel Talent Supply and Demand 488 7.5 Model Integration 489 7.5.1 Common Unified Models 489 7.5.2 Related Disciplines and Business Processes 490 7.5.3 Meta-Model Integration 491 7.6 Empirical Research and Theory Building 492 7.6.1 Empirical Data Collection for Simulation Models 493 7.7 Process Mission Control Centers, Analysis, and Training Facilities 494 7.8 Chapter 7 Summary 496 7.9 Exercises 498 Appendix A: Introduction to Statistics of Simulation 501 A.1 Risk Analysis and Probability 502 A.2 Probability Distributions 503 A.2.1 Interpreting Probability Distributions 505 A.2.2 Measures of Location, Variability and Symmetry 506 A.2.3 Useful Probability Distributions 508 A.3 Monte Carlo Analysis 515 A.3.1 Inverse Transform 515 A.3.2 Example: Monte Carlo Analysis 516 A.4 Analysis of Simulation Input 521 A.4.1 Goodness-of-Fit Tests 521 A.5 Experimental Design 523 A.5.1 Example: Experimental Design and Model Response Surface 524 A.6 Analysis of Simulation Output 525 A.6.1 Confidence Intervals, Sample Size, and Hypothesis Testing 525 A.7 Major References 527 A.8 Appendix A Summary 527 A.9 Exercises 529 Appendix B: Annotated System Dynamics Bibliography 531 Appendix C: Provided Models 565 References 571 Index 593

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Book SynopsisThe book describes the fundamental principles of computer arithmetic. Algorithms for performing operations like addition, subtraction, multiplication and division in digit computer systems are presented, with the goal of explaining the concepts behind the algorithms, rather than addressing any direct applications.Trade Review"...the perfect concise reference for computer arithmetic, and I highly recommended it to anyone involved in the study or implementation of such systems." (Computing Reviews.com, June 7, 2005) "This comprehensive treatment of computer arithmetic is ideally suited for upper-level undergraduate or graduate students." (Computing Reviews.com, May 12, 2004) "Lu has prepared one of the best books this reviewer has read….An Excellent book for graduate and senior undergraduate engineering and computer science students.” (Choice, July 2004)Table of ContentsPreface. List of Figures. List of Tables. About the Author. 1. Computer Number Systems. 1.1 Conventional Radix Number System. 1.2 Conversion of Radix Numbers. 1.3 Representation of Signed Numbers. 1.3.1 Sign-Magnitude. 1.3.2 Diminished Radix Complement. 1.3.3 Radix Complement. 1.4. Signed-Digit Number System. 1.5 Floating-Point Number Representation. 1.5.1 Normalization. 1.5.2 Bias. 1.6 Residue Number System. 1.7 Logarithmic Number System. References. Problems. 2. Addition and Subtraction. 2.1 Single-Bit Adders. 2.1.1 Logical Devices. 2.1.2 Single-Bit Half-Adder and Full-Adders. 2.2 Negation. 2.2.1 Negation in One's Complement System. 2.2.2 Negation in Two's Complement System. 2.3 Subtraction through Addition. 2.4 Overflow. 2.5 Ripple Carry Adders. 2.5.1 Two's Complement Addition. 2.5.2 One's Complement Addition. 2.5.3 Sign-Magnitude Addition. References. Problems. 3. High-Speed Adder. 3.1 Conditional-Sum Addition. 3.2 Carry-Completion Sensing Addition. 3.3 Carry-Lookahead Addition (CLA). 3.3.1 Carry-Lookahead Adder. 3.3.2 Block Carry Lookahead Adder. 3.4 Carry-Save Adders (CSA). 3.5 Bit-Partitioned Multiple Addition. References. Problems. 4. Sequential Multiplication. 4.1 Add-and-Shift Approach. 4.2 Indirect Multiplication Schemes. 4.2.1 Unsigned Number Multiplication. 4.2.2 Sign-Magnitude Number Multiplication. 4.2.3 One's Complement Number Multiplication. 4.2.4 Two's Complement Number Multiplication. 4.3 Robertson's Signed Number Multiplication. 4.4 Recoding Technique. 4.4.1 Non-overlapped Multiple Bit Scanning. 4.4.2 Overlapped Multiple Bit Scanning. 4.4.3 Booth's Algorithm. 4.4.4 Canonical Multiplier Recoding. References. Problems. 5. Parallel Multiplication. 5.1 Wallace Trees. 5.2 Unsigned Array Multiplier. 5.3 Two's Complement Array Multiplier. 5.3.1 Baugh-Wooley Two's Complement Multiplier. 5.3.2 Pezaris Two's Complement Multipliers. 5.4 Modular Structure of Large Multiplier. 5.4.1 Modular Structure. 5.4.2 Additive Multiply Modules. 5.4.3 Programmable Multiply Modules. References. Problems. 6. Sequential Division. 6.1 Subtract-and-Shift Approach. 6.2 Binary Restoring Division. 6.3 Binary Non-Restoring Division. 6.4 High-Radix Division. 6.4.1 High-Radix Non-Restoring Division. 6.4.2 SRT Division. 6.4.3 Modified SRT Division. 6.4.4 Robertson's High-Radix Division. 6.5 Convergence Division. 6.5.1 Convergence Division Methodologies. 6.5.2 Divider Implementing Convergence Division Algorithm. 6.6 Division by Divisor Reciprocation. References. Problems. 7. Fast Array Dividers. 7.1 Restoring Cellular Array Divider. 7.2 Non-Restoring Cellular Array Divider. 7.3 Carry-Lookahead Cellular Array Divider. References. Problems. 8. Floating Point Operations. 8.1 Floating Point Addition/Subtraction. 8.2 Floating Point Multiplication. 8.3 Floating Point Division. 8.4 Rounding. 8.5 Extra Bits. References. Problems. 9. Residue Number Operations. 9.1 RNS Addition, Subtraction and Multiplication. 9.2 Number Comparison and Overflow Detection. 9.2.1 Unsigned Number Comparison. 9.2.2 Overflow Detection. 9.2.3 Signed Numbers and Their Properties. 9.2.4 Multiplicative Inverse and the Parity Table. 9.3 Division Algorithm. 9.3.1 Unsigned Number Division. 9.3.2 Signed Number Division. 9.3.3 Multiplicative Division Algorithm. References. Problems. 10. Operations through Logarithms. 10.1 Multiplication and Addition in Logarithmic Systems. 10.2 Addition and Subtraction in Logarithmic Systems. 10.3 Realizing the Approximation. References. Problems. 11. Signed-Digit Number Operations. 11.1 Characteristics of SD Numbers. 11.2 Totally Parallel Addition/Subtraction. 11.3 Required and Allowed Values. 11.4 Multiplication and Division. References. Problems. Index.

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Book SynopsisExplores both counter-terrorism and enabling policy dimensions of emerging information technologies in national security After the September 11th attacks, "connecting the dots" has become the watchword for using information and intelligence to protect the United States from future terrorist attacks.Table of ContentsForeword. Preface. Contributors. Chapter 1: Utilizing Information and Social Science Technology to Understand and Counter the Twenty-First Century Strategic Threat 1 (Robert L. Popp, David Allen, and Claudio Cioffi-Revilla). Chapter 2: Hidden Markov Models and Bayesian Networks for Counter-Terrorism (Krishna Pattipati, Peter Willett, Jeffrey Allanach, Haiying Tu, and Satnam Singh). Chapter 3: Anticipatory Models for Counter-Terrorism (Mark Lazaroff and David Snowden). Chapter 4: Information Processing at Very High Speed Data Ingestion Rates (J. Brian Sharkey, Doyle Weishar, John W. Lockwood, Ron Loui, Richard Rohwer, John Byrnes, Krishna Pattipati, Stephen Eick, David Cousins, and Michael Nicoletti). Chapter 5: Analysis of Heterogeneous Data in Ultrahigh Dimensions (R. A. Ammar, S. A. Demurjian , Sr., I. R. Greenshields, Krishna Pattipati, and S. Rajasekaran). Chapter 6: Semantic Web Technologies for Terrorist Network Analysis (Jennifer Golbeck, Aaron Mannes, and James Hendler). Chapter 7: Improving National and Homeland Security Through Context Knowledge Representation and Reasoning Technologies (Nazli Choucri, Stuart E. Madnick, and Michael D. Siegel). Chapter 8: Anonymized Semantic Directories and a Privacy-Enhancing Architecture for Enterprise Discovery (Jeff Jonas and John Karat). Chapter 9: Facilitating Information Sharing Across Intelligence Community Boundaries Using Knowledge Management and Semantic Web Technologies (Brian Kettler, Gary Edwards, and Mark Hoffman). Chapter 10: Applying Semantic Web Reasoning to Counter-Terrorism (Paul Kogut, Yui Leung, Kathleen M. Ryan, Linda Gohari, Mieczyslaw M. Kotar, and Jerzy J. Letkowski). Chapter 11: Schemer: Consensus-Based Knowledge Validation and Collaboration Services for Virtual Teams of Intelligence Experts (Clifford Behrens, Hyong-Sop Shim, and Devaisis Bassu). Chapter 12: Sharing Intelligence Using Information Supply Chains (Shuang Sun, Xiaocong Fan, and John Yen). Chapter 13: Supporting Knowledge Management In Emergency Crisis Management Domains: Envisioned Designs for Collaborative Work (Michael D. McNeese, Isaac Brewer, Rashaad E. T. Jones, and Erik S. Connors). Chapter 14: Agent-Based Simulations for Disaster Rescue Using the DEFACTO Coordination System (Janusz Marecki, Nathan Schurr, and Milind Tambe). Chapter 15: Transcending the Tower of Babel: Supporting Access to Multilingual Information with Cross-Language Information Retrieval (Douglas W. Oard). Chapter 16: Journey from Analysis to Inquiry: Technology and Transformation of Counter-Terrorism Analysis (Aaron B. Frank and Desmond Saunders-Newton). Chapter 17: Behavioral Network Analysis for Terrorist Detection (Seth A. Greenblatt, Thayne Coffman, and Sherry E. Marcus). Chapter 18: Detecting Terrorist Activities in the Twenty-First Century: A Theory of Detection for Transactional Networks (Tom Mifflin, Chris Boner, Greg Godfrey, and Michael Greenblatt). Chapter 19: Social Network Analysis Via Matrix Decompositions (D. B. Skillicorn). Chapter 20: Legal Standards for Data Mining (Fred H. Cate). Chapter 21: Privacy and Consequences: Legal and Policy Structures for Implementing New Counter-Terrorism Technologies and Protecting Civil Liberty (Paul Rosenzweig). Chapter 22: Designing Technical Systems to Support Policy: Enterprise Architecture, Policy Appliances, and Civil Liberties (K. A. Taipale). Index. About the Editors.

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Book SynopsisCovers topics such as commonsense reasoning, knowledge representation, nonmonotonic reasoning, logic for causation and actions, planning and problem solving, cognitive robotics, logic for agents and actions, inductive reasoning, possibilistic logic, logic and beliefs, logic and language, and computational logic.Table of ContentsContributing Authors. Preface. Part I: Introduction to Logic-Based Artificial Intelligence. 1. Introduction to Logic-Based Artificial Intelligence; J. Minker. Part II: Commonsense Reasoning. 2. Concepts of Logical AI; J. McCarthy. Part III: Knowledge Representation. 3. Two Approaches to Efficient Open-World Reasoning; G. De Giacomo, H. Levesque. 4. Declarative Problem-Solving in DLV; T. Eder, et al. Part IV: Nonmonotonic Reasoning. 5. The Role of Default Logic in Knowledge Representation; J.P. Delgrande, Torsten Schaub. 6. Approximations, stable operators, well-founded fixpoints, applications in nonmonotonic reasoning; M. Denecker, et al. Part V: Logic for Causation, Actions. 7. Getting to the Airport: The Oldest Planning Problem in AI; V. Lifschitz, et al. Part VI: Planning, Problem Solving. 8. Encoding Domain Knowledge for Propositional Planning; H. Kautz, B. Selman. 9. Functional Strips; H. Geffner. Part VII: Logic, Planning, High Level Robotics. 10. Planning with Natural Actions in the Situation Calculus; F. Pirri, R. Reiter. 11. Reinventing Shakey; M. Shanahan. Part VIII: Logic for Agents, Actions. 12. Reasoning Agents in Dynamic Domains; C. Baral, M. Gelfond. 13. Dynamic Logic for Reasoning about Actions, Agents; J.-J.Ch. Meyer. Part IX: Inductive Reasoning. 14. Logic-Based Machine Learning; S. Muggleton, F. Marginean. Part X: Possibilistic Logic. 15. Decision, Nonmonotonic Reasoning, Possibilistic Logic; S. Benferhat, etal. Part Xl: Logic, Beliefs. 16. The Role(s) of Belief in AI; D. Perlis. 17. Modeling the Beliefs of Other Agents; R.H. Thomason. Part XII: Logic, Language. 18. The Situations We Talk about; L.K. Schuberi. Part XIII: Computational Logic. 19. Linear Time Datalog, Branching.Time Logic; G. Goitlob, et al. 20. On the Expressive Power of Planning Formalisms; B. Nebel. Part XIV: Knowledge Base System Implementations. 21. Extending the Smodels System with Cardinality, Weight Constraints; I. Niemelä, P. Simons. 22. Nonmonotonic Reasoning in LDL++; H. Wang, C. Zaniolo. Part XV: Applications of Theorem Proving, Logic Programming. 23. Towards a Mechanically Checked Theory of Computation; J. Strother Moore. 24. Logic-Based Techniques in Data Integration; A.Y. Levy. Index.

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Book SynopsisPython for Accounting and Finance: A Mind-Mapping Approach is an innovative textbook written for accounting and finance students and professionals with no prior coding experience. It introduces Python programming through a mind mapping approach that presents complex concepts in a clear and structured way to support understanding and retention.The book is organised into four parts: Python Basics, Data Analysis and Visualisation, Automation, and Machine Learning. It places programming within practical accounting and finance contexts so that learners can see the direct application of coding in their field. The textbook uses mind maps as a core instructional method. These visual diagrams show programming concepts and their connections, helping learners to understand, organise and recall information, particularly those who learn through visual representation.Enhanced with Google Colab notebooks, this book creates a highly supportive learning experience, equipping students of accounting and finance with essential programming skills for their speciality.

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Book SynopsisThe next enterprise computing era will rely on the synergy between both technologies: semantic web and model-driven software development (MDSD). The semantic web organizes system knowledge in conceptual domains according to its meaning. It addresses various enterprise computing needs by identifying, abstracting and rationalizing commonalities, and checking for inconsistencies across system specifications. On the other side, model-driven software development is closing the gap among business requirements, designs and executables by using domain-specific languages with custom-built syntax and semantics. It focuses on using modeling languages as programming languages. Among many areas of application, we highlight the area of configuration management. Consider the example of a telecommunication company, where managing the multiple configurations of network devices (routers, hubs, modems, etc.) is crucial. Enterprise systems identify and document the functional and physicaTable of ContentsList of Figures xv List of Tables xix Foreword xxi Preface xxiii Acronyms xxvii Part I Fundamentals 1 Introduction 3 1.1 Motivation 3 1.2 Research Questions 5 2 Model-Driven Engineering Foundations 9 2.1 Introduction 9 2.2 Model-Driven Engineering Structure 9 2.2.1 Models 11 2.2.2 Metamodels 11 2.2.3 Modeling Languages 13 2.2.4 Model Transformations 17 2.2.5 Query Languages 17 2.3 Technical Spaces 19 2.4 Conclusion 20 3 Ontology Foundations 21 3.1 Introduction 21 3.2 Ontology 22 3.2.1 Ontology Modeling 22 3.3 The Ontology Web Language 24 3.3.1 OWL 2 Syntax 24 3.3.2 OWL 2 Semantics 27 3.3.3 World Assumption and Name Assumption 27 3.4 Ontology Services 31 3.4.1 Reasoning Services 31 3.4.2 Querying 31 3.5 Ontology Engineering Services 33 3.5.1 Explanation 33 3.5.2 Ontology Matching 34 3.6 Rules 34 3.7 Metamodels for Ontology Technologies 35 3.7.1 OWL Metamodels 35 3.7.2 SPARQL Metamodel 40 3.8 Ontological Technical Spaces 41 3.9 Conclusion 43 4 Marrying Ontology and Model-Driven Engineering 44 4.1 Introduction 44 4.2 Similarities between OWL Modeling and UML Class-Based Modeling 45 4.3 Commonalities and Variations 46 4.3.1 Language 47 4.3.2 Formalism 49 4.3.3 Data Model 49 4.3.4 Reasoning 50 4.3.5 Querying 51 4.3.6 Rules 51 4.3.7 Transformation 52 4.3.8 Mediation 52 4.3.9 Modeling Level 53 4.4 The State of the Art of Integrated Approaches 54 4.4.1 Model Validation 54 4.4.2 Model Enrichment 56 4.4.3 Ontology Modeling 58 4.5 Existing Work on Classifying Integrated Approaches 58 4.6 Conclusion 59 Conclusion of Part I Part II The TwoUse Approach 5 The TwoUse Conceptual Architecture 65 5.1 Introduction 65 5.2 Requirements for Integrating Ontology Technologies and Model-Driven Engineering 66 5.2.1 Usage of Ontology Services in MDE 66 5.2.2 Usage of MDE Techniques in OWL Ontology Engineering 67 5.3 Addressing the Requirements with the TwoUse Approach 68 5.4 Metamodeling Architecture 70 5.4.1 The TwoUse Metamodel 70 5.5 Syntax 72 5.5.1 UML Profile for OWL 72 5.5.2 Pure UML Class Diagrams 75 5.5.3 Textual Notation 75 5.6 Conclusion 77 6 Query Languages for Integrated Models 78 6.1 Introduction 78 6.2 Combining Existing Approaches 78 6.3 Querying Ontologies Using OWL Syntax: SPARQLAS 80 6.3.1 SPARQLAS Concrete Syntax 80 6.3.2 SPARQLAS Metamodel 81 6.3.3 Transformation from SPARQLAS to SPARQL 81 6.4 Querying Integrated Models: SPARQLAS4TwoUse 82 6.5 Conclusion 84 7 The TwoUse Toolkit 86 7.1 Introduction 86 7.2 Use Case Descriptions 87 7.3 A Generic Architecture for MDE and Ontology Engineering 87 7.3.1 Core Services 88 7.3.2 Engineering Services 89 7.3.3 Front-End 90 7.4 Instantiating the Generic Model-Driven Architecture: The TwoUse Toolkit 90 7.5 Conclusion 93 Conclusion of Part II Part III Applications in Model-Driven Engineering 8 Improving Software Design Patterns with Owl 99 8.1 Introduction 99 8.2 Case Study 100 8.2.1 Applying the Strategy Pattern 100 8.2.2 Extending to the Abstract Factory 101 8.2.3 Drawbacks 103 8.3 Application of the TwoUse Approach 104 8.3.1 OWL for Conceptual Modeling 104 8.3.2 TwoUse for Software Design Patterns: The Selector Pattern 105 8.4 Validation 109 8.4.1 Participants and Collaborations 109 8.4.2 Applicability 110 8.4.3 Drawbacks 110 8.4.4 Advantages 110 8.5 Related Work 111 8.6 Conclusion 111 9 Modeling Ontology-Based Information Systems 112 9.1 Introduction 112 9.2 Case Study 113 9.2.1 UML Class-Based Software Development 113 9.2.2 Ontology-Based Software Development 116 9.3 Application of the TwoUse Approach 117 9.3.1 Concrete Syntax 118 9.3.2 Abstract Syntax 119 9.3.3 Querying 121 9.4 Validation 121 9.4.1 Limitations 123 9.5 Conclusion 123 10 Enabling Linked Data Capabilities to MOF Compliant Models 124 10.1 Introduction 124 10.2 Case Study 125 10.2.1 Requirements 127 10.3 Application of the TwoUse Approach 128 10.3.1 Model Extension 128 10.3.2 Model Transformation 130 10.3.3 Matching 131 10.3.4 Querying with SPARQLAS 131 10.4 Validation 132 10.4.1 Limitations 134 10.5 Related Work 134 10.6 Conclusion 135 Conclusion of Part III Part IV Applications in the Semantic Web 11 Model-Driven Specification of Ontology Translations 141 11.1 Introduction 141 11.2 Case Study 142 11.3 Application of the TwoUse Approach 145 11.3.1 Concrete Syntax 145 11.3.2 Metamodels 146 11.3.3 Model Libraries 148 11.3.4 Semantics 148 11.3.5 Ontology Translation Process 148 11.3.6 Implementation 149 11.4 Examples 150 11.5 Analysis 153 11.6 Related Work 154 11.7 Conclusion 155 12 Automatic Generation of OntologyAPIs156 12.1 Introduction 156 12.2 Case Study 158 12.3 Application of the TwoUse Approach 161 12.3.1 Key Domain Concepts 161 12.3.2 agogo Concrete Syntax by Example 163 12.3.3 Implementation 166 12.4 Analysis 167 12.5 Related Work 169 12.6 Conclusion 170 13 Using Templates in Owl Ontologies 171 13.1 Introduction 171 13.2 Case Study 172 13.3 Application of the TwoUse Approach 174 13.3.1 Extending the OWL Metamodel with Templates 174 13.3.2 Semantics of Templates 177 13.3.3 Notations for Templates in OWL 179 13.3.4 Query Templates 180 13.4 Analysis 181 13.4.1 Limitations 182 13.5 Related Work 182 13.6 Conclusion 183 Conclusion of Part IV 14 Conclusion 187 14.1 Contributions 187 14.2 Outlook 189 14.2.1 Ongoing Research 189 Appendix A 191 A.1 EBNF Defi nition of the Concrete Textual Syntax for TwoUse 191 A.2 EBNF Grammar of SPARQLAS Functional Syntax 192 A.3 EBNF Grammar of SPARQLAS Manchester Syntax 197 A.4 SPARQLAS Metamodel 202 A.5 Ecore to OWL: Translation Rules 204 Appendix B206 B.1 Use Cases 206 B.1.1 Design Integrated Models 206 B.1.2 Design Integrated UML Class Diagram 206 B.1.3 Design Integrated Ecore Model 207 B.1.4 Specify SPARQLAS4TwoUse Query Operations 207 B.1.5 Transform to OWL 207 B.1.6 Compute Alignments 208 B.1.7 Browse 208 B.1.8 Explain Axioms 209 B.1.9 Query UML Class-Based Models 209 B.1.10 Query OWL Ontologies 209 B.1.11 Design Ontology Engineering Services 209 B.1.12 Design Ontology API 210 B.1.13 Design Ontology Translation 210 B.1.14 Design Ontology Template 210 B.1.15 Generate Service 211 B.2 Connecting Use Cases with Requirements 211 References 212 Index 226

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Book SynopsisWith the EHR (Electronic Health Record) being central to most health informatics applications, several countries have initiated programs for implementing national EHR infrastructures.Table of ContentsPreface XVII Acronyms XXVII PART ONE Introduction 1 Introduction to EHR 3 1.1 Introduction, 3 1.2 Definition of EHR, 4 1.3 Functions of EHR, 5 1.4 Significance of EHR, 6 1.5 Factors Affecting Implementation of EHR, 7 1.6 Role of Standards, 8 1.7 Role of Clinical Coding Systems, 9 1.8 Role of Standard Frameworks, 9 1.9 Case Studies of National EHR Implementations, 10 PART TWO EHR Standards 2 Standard for EHR Architecture Requirements 15 2.1 Introduction, 15 2.2 ISO/TS 18308 Requirement Specification, 16 2.2.1 Content Structure Model, 16 2.2.2 Inclusion of Clinical and Record Processes, 17 2.2.3 Content Exchange, 17 2.2.4 Privacy and Security, 17 2.2.5 Legal Considerations, 17 2.2.6 Ethical, Consumer/Cultural Aspects, 18 2.2.7 Future-proof Framework, 18 2.3 Discussion, 18 2.4 Conclusion, 20 3 Standard for Healthcare Concepts 23 3.1 Introduction, 23 3.2 CEN/TC EN 13940-1, 24 3.2.1 Actors in Continuity of Care, 24 3.2.2 Health Issues and Their Management, 25 3.2.3 Concepts Related to Responsibility, 25 3.2.4 Time-Related Concepts, 26 3.2.5 Concepts Related to Knowledge, Activities, and Decision Support, 27 3.2.6 Health Data Management, 28 3.3 CEN/TC prEN 13940-2, 28 3.3.1 Healthcare Process, 29 3.4 Discussion, 30 3.5 Conclusion, 32 4 Standard for EHR Functional Specifications 33 4.1 Introduction, 33 4.2 HL7 EHR-S Functional Model, 34 4.2.1 Functional Profiles, 34 4.2.2 Exchange, 36 4.2.3 Security/Privacy, 37 4.3 Comparison of HL7 EHR-S FM and ISO/TS 18308, 38 4.4 Discussion, 39 4.5 Conclusion, 40 5 Standard for EHR Communication 43 5.1 Introduction, 43 5.2 CEN/ISO EN 13606 Requirement Specification, 45 5.2.1 Part 1: Reference Model, 45 5.2.2 Part 2: Archetypes Interchange Specification, 46 5.2.3 Part 3: Reference Archetypes and Term Lists, 49 5.2.4 Part 4: Security, 49 5.2.5 Part 5: Exchange Models, 50 5.3 Discussion, 51 5.4 Conclusion, 53 6 Messaging Standard for Healthcare Data 57 6.1 Introduction, 57 6.2 HL7 v2.x, 58 6.2.1 Message Structure, 60 6.2.2 Auxiliary Messaging Protocols, 61 6.2.3 Usage Scenario, 62 6.2.4 Example of HL7 v2.x Message, 62 6.3 Discussion, 64 6.4 Conclusion, 67 7 Model-Based Messaging Standard for Healthcare Data 69 7.1 Introduction, 69 7.2 HL7 v3, 69 7.2.1 Message Structure, 70 7.2.2 Interaction Model, 74 7.2.3 Role-Based Access Control, 74 7.2.4 HL7 v3 and SNOMED CT, 75 7.2.5 HL7 v3 and Service-Oriented Architecture (SOA), 75 7.3 HL7 v2.x and v3 Comparison, 75 7.4 Discussion, 75 7.5 Conclusion, 77 8 Clinical Document Standards 81 8.1 Introduction, 81 8.2 Clinical Document Architecture (CDA), 82 8.2.1 Document Structures, 82 8.2.2 Example of CDA Component, 83 8.3 Continuity of Care Document (CCD), 85 8.3.1 Example of CCD Component, 86 8.4 Clinical Document Exchange, 87 8.5 Discussion, 87 8.6 Conclusion, 89 9 Standard for Medical Imaging and Communication 93 9.1 Introduction, 93 9.2 DICOM, 94 9.2.1 Information Model, 94 9.2.2 Message Exchange Model, 96 9.3 Improvements in DICOM Standard, 96 9.4 Discussion, 96 9.5 Conclusion, 100 10 Standard for Patient Health Summary 103 10.1 Introduction, 103 10.2 Continuity of Care Record (CCR), 103 10.2.1 Structural Model, 104 10.2.2 Exchange, 106 10.3 Discussion, 107 10.4 Conclusion, 108 PART THREE Coding Systems 11 Coding System for Classification of Diseases and Related Health Problems 113 11.1 Introduction, 113 11.2 ICD, 114 11.2.1 Chapters, 114 11.2.2 Blocks, 114 11.3 Improvements in ICD-10, 114 11.4 Discussion, 116 11.5 Conclusion, 116 12 Coding System for Laboratory Tests and Observations 119 12.1 Introduction, 119 12.2 LOINC, 120 12.2.1 Code Classification, 120 12.2.2 Code Structure, 120 12.2.3 Regenstrief LOINC Mapping Assistant (RELMA), 122 12.3 Discussion, 122 12.4 Conclusion, 123 13 Coding System for Patient Care Procedures 125 13.1 Introduction, 125 13.2 CPT, 126 13.2.1 Data Model, 127 13.2.2 CPT Sections, 128 13.2.3 CPT Index, 128 13.2.4 CPT Symbols, 128 13.2.5 CPT Modifiers, 128 13.2.6 Descriptive Qualifiers, 129 13.3 Discussion, 129 13.4 Conclusion, 130 14 Extended Coding System for Patient Care Procedures 131 14.1 Introduction, 131 14.2 HCPCS, 132 14.2.1 Level I Codes, 132 14.2.2 Level II Codes, 132 14.3 Discussion, 134 14.4 Conclusion, 134 15 Comprehensive Coding System for Clinical Terms 137 15.1 Introduction, 137 15.2 SNOMED CT, 138 15.2.1 Concepts, 138 15.2.2 Structure of SNOMED CT Code, 140 15.3 SNOMED CT Database Browsers, 141 15.4 Discussion, 141 15.5 Conclusion, 142 16 Unified Medical Language System 145 16.1 Introduction, 145 16.2 UMLS-Supported Coding Systems, 146 16.3 UMLS Architecture, 146 16.3.1 Metathesaurus, 146 16.3.2 Semantic Network, 148 16.3.3 Specialist Lexicon and Lexical Tools, 149 16.4 UMLS Licensing, 150 16.5 Discussion, 150 16.6 Conclusion, 151 17 Other Coding Systems 153 17.1 Introduction, 153 17.2 AHFS Drug Information (AHFS DI), 154 17.3 Current Dental Terminology (CDT), 154 17.4 International Classification of Diseases for Oncology (ICD-O), 155 17.5 International Classification of Functioning, Disability and Health (ICF), 155 17.6 Coding Systems for Nursing Practices, 156 17.6.1 North American Nursing Diagnosis Association (NANDA), 156 17.6.2 Nursing Interventions Classification (NIC), 156 17.6.3 Nursing Outcomes Classification (NOC), 156 17.7 Radiology Lexicon (RADLEX), 157 17.8 RxNorm, 157 17.9 Discussion, 157 17.10 Conclusion, 158 PART FOUR Standard Frameworks 18 openEHR 163 18.1 Introduction, 163 18.2 openEHR Process Model, 164 18.3 openEHR Architecture, 166 18.3.1 EHR Information Model, 168 18.3.2 Exchange, 170 18.4 Discussion, 170 18.5 Conclusion, 172 19 Integrating the Healthcare Enterprise (IHE) 175 19.1 Introduction, 175 19.2 IHE Domains, 176 19.2.1 Integration Profiles, 176 19.2.2 Integration Statements, 179 19.2.3 Technical Frameworks, 179 19.3 IHE Initiatives on Electronic Health Record, 180 19.4 Exchange, 180 19.4.1 Cross-Enterprise Document Sharing (XDS), 180 19.5 Security, 181 19.5.1 Audit Trail & Node Authentication (ATNA), 181 19.6 Discussion, 182 19.7 Conclusion, 183 PART FIVE Case Studies: National EHR Efforts 20 Australia's HealthConnect 189 20.1 Introduction, 189 20.2 Overview, 190 20.3 Architecture, 190 20.3.1 EHR Concept, 190 20.3.2 EHR Design, 191 20.3.3 e-Health Services, 193 20.3.4 National Privacy Principles (NPP), 194 20.3.5 Exchange, 194 20.4 Discussion, 195 20.5 Conclusion, 196 21 Austria's ELGA 199 21.1 Introduction, 199 21.2 Overview, 200 21.3 Architecture, 200 21.3.1 Master Patient Index, 201 21.3.2 HSP Index, 201 21.3.3 Authorization System, 202 21.3.4 HSP System, 202 21.3.5 Storage (Document Registry), 202 21.3.6 Network, 202 21.3.7 ELGA Portal, 203 21.4 Functional Implementation, 203 21.4.1 Healthcare Services, 205 21.5 Exchange, 205 21.6 Discussion, 205 21.7 Conclusion, 207 22 Canada's EHRS Blueprint 211 22.1 Introduction, 211 22.2 Overview, 211 22.3 Architecture, 212 22.3.1 Electronic Health Record Solution (EHRS), 212 22.3.2 Electronic Health Record Infostructure (EHRi), 213 22.3.3 Exchange, 216 22.3.4 Legal Framework, 217 22.4 Discussion, 217 22.5 Conclusion, 219 Bibliography, 219 23 Denmark’s MedCom 221 23.1 Introduction, 221 23.2 Overview, 221 23.3 Architecture, 222 23.3.1 EHR Concept, 222 23.3.2 EHR Design, 223 23.3.3 Danish Health Data Network, 223 23.3.4 Security Infrastructure, 224 23.3.5 National Health Portal (Sundheds.dk), 224 23.3.6 Exchange, 225 23.4 Discussion, 225 23.5 Conclusion, 226 24 Hong Kong's eHR Sharing System 229 24.1 Introduction, 229 24.2 Overview, 229 24.3 Architecture, 230 24.3.1 E-Health Engagement Initiative (EEI), 230 24.3.2 eHR Sharing System, 231 24.3.3 Exchange, 233 24.3.4 Security/Privacy Guidelines, 233 24.4 Discussion, 234 24.5 Conclusion, 235 25 India's Health IT Initiatives 239 25.1 Introduction, 239 25.2 Overview, 240 25.3 ITIH Framework, 240 25.4 Recommendations on Guidelines, Standards, and Practices for Telemedicine in India, 242 25.5 iHIND, 244 25.5.1 Architecture, 245 25.5.2 Confidentiality, Access, and Security, 246 25.5.3 Standards, 246 25.6 Other Initiatives, 247 25.6.1 Integrated Disease Surveillance Project, 247 25.6.2 National Rural Telemedicine Network (NRTN), 247 25.6.3 National Medical College Network, 248 25.6.4 Standardization of EHR, 248 25.7 Discussion, 249 25.8 Conclusion, 251 26 Netherlands' AORTA 253 26.1 Introduction, 253 26.2 Overview, 253 26.3 Architecture, 254 26.3.1 Dutch Electronic Patient Dossier (EPD) System, 254 26.3.2 NICTIZ Healthcare IT Infrastructure Workflow, 255 26.3.3 Exchange, 256 26.3.4 Security/Privacy Guidelines, 256 26.4 Discussion, 256 26.5 Conclusion, 257 27 Singapore's NEHR 259 27.1 Introduction, 259 27.2 Overview, 259 27.3 Architecture, 260 27.3.1 Health Clusters, 260 27.3.2 EMR Exchange (EMRX), 260 27.3.3 National Electronic Health Record (NEHR), 261 27.4 Discussion, 263 27.5 Conclusion, 264 28 Sweden's NPO 267 28.1 Introduction, 267 28.2 Overview, 268 28.3 Architecture, 269 28.3.1 Clinical Process Model, 269 28.3.2 Information Model, 270 28.3.3 Sjunet, 271 28.3.4 Electronic Catalog For Health and Social Care, 271 28.3.5 Secure it in Health Services, 271 28.3.6 Basic Services for Information, 271 28.3.7 Regulatory Framework for Information Interoperability in Healthcare, 272 28.3.8 National Patient Summary, 273 28.4 Discussion, 273 28.5 Conclusion, 274 29 Taiwan's Health Information Network 277 29.1 Introduction, 277 29.2 Overview, 278 29.3 Architecture, 278 29.3.1 National Health Information Network (HIN) 2.0, 279 29.4 Exchange, 280 29.4.1 TMT Standard, 280 29.5 Discussion, 282 29.6 Conclusion, 284 30 United Kingdom's Spine 287 30.1 Introduction, 287 30.2 Overview, 287 30.3 Architecture, 288 30.3.1 Spine Infrastructure, 288 30.3.2 Structure of Summary Care Record, 290 30.3.3 Content of Summary Care Record, 290 30.3.4 Security Infrastructure, 291 30.3.5 Exchange, 292 30.4 Discussion, 292 30.5 Conclusion, 294 31 USA's EHR Meaningful Use 295 31.1 Introduction, 295 31.2 Overview, 296 31.3 EHR Meaningful Use, 296 31.3.1 Requirement Specifications, 297 31.4 National Health Information Network (NHIN), 301 31.4.1 NHIN Architecture, 301 31.4.2 Exchange Services, 303 31.4.3 Transaction Profiles, 303 31.4.4 Authorization Framework, 304 31.4.5 NHIN Trial Project, 304 31.4.6 NHIN Direct Project, 304 31.5 Discussion, 304 31.6 Conclusion, 307 PART SIX Findings and Conclusion 32 Findings and Conclusion 313 32.1 EHR Standards, 313 32.2 Coding Systems, 317 32.3 Standard Frameworks, 318 32.4 Case Studies: National EHR Efforts, 319 32.4.1 Quantitative Assessment, 320 32.4.2 Qualitative Assessment, 324 32.5 Recommended Phases for Implementing A National EHR System, 325 Glossary 327 Index 339

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Book SynopsisEXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite imagTable of ContentsPreface xiii Acknowledgements xv 1 A Comprehensive Review of Various Machine Learning Techniques 1Pooja Pathak and Parul Choudhary 1.1 Introduction 1 1.1.1 Random Forest 2 1.1.2 Decision Tree 3 1.1.3 Support Vector Machine 4 1.1.4 Naive Bayes 5 1.1.5 K-Means Clustering 6 1.1.6 Principal Component Analysis 6 1.1.7 Linear Regression 6 1.1.8 Logistic Regression 7 1.1.9 Semi-Supervised Learning 8 1.1.10 Transductive SVM 9 1.1.11 Generative Models 9 1.1.12 Self-Training 9 1.1.13 Relearning 9 1.2 Conclusions 9 2 Artificial Intelligence and Image Recognition Algorithms 11Siddharth, Anuranjana and Sanmukh Kaur 2.1 Introduction 12 2.2 Traditional Image Recognition Algorithms 13 2.2.1 Harris Corner Detector (1988) 13 2.2.2 SIFT (2004) 15 2.2.3 ASIFT 16 2.2.4 SURF (2006) 17 2.3 Neural Network-Based Algorithms 21 2.4 Convolutional Neural Network Architecture 22 2.5 Various CNN Architectures 23 2.5.1 LeNet-5 (1998) 23 2.5.2 AlexNet (2012) 24 2.5.3 VGGNet (2014) 24 2.5.4 GoogleNet (2015) 24 3 Efficient Architectures and Trade-Offs for FPGA-Based Real-Time Systems 31L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem 3.1 Overview of FPGA-Based Real-Time System 31 3.1.1 Key Elements of Real-Time System 32 3.1.2 Real-Time System and its Computation 32 3.1.3 FPGA Functionality and Applications 33 3.1.4 FPGA Applications 33 3.1.5 FPGA Architecture 34 3.1.6 Reconfigurable Architectures 35 3.2 Hybrid FPGA Configurations and its Algorithms 38 3.2.1 Hybrid FPGA 38 3.2.2 Hybrid FPGA Architecture 39 3.2.3 Hybrid FPGA Configuration 40 3.3 Hybrid FPGA Algorithms 42 3.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation 44 3.4 CNN Hardware Accelerator Architecture Overview 46 3.5 Summary 47 4 A Low-Power Audio Processing Using Machine Learning Module on FPGA and Applications 49Suman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud 4.1 Introduction 49 4.2 Existing Machine Learning Modules and Audio Classifiers 50 4.3 Audio Processing Module Using Machine Learning 56 4.4 Application of Proposed FPGA-Based ML Models 57 4.5 Implementation of a Microphone on FPGA 59 4.6 Conclusion 60 4.7 Future Scope 60 5 Synthesis and Time Analysis of FPGA-Based DIT-FFT Module for Efficient VLSI Signal Processing Applications 65Siba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda 5.1 Introduction 66 5.2 Implementation of DIT-FFT Algorithm 67 5.2.1 A Quick Overview of DIT-FFT 67 5.2.2 Algorithmic Representation with Example 69 5.2.3 Simulated Output Waveform 69 5.3 Synthesis of Designed Circuit 71 5.4 Static Timing Analysis of Designed Circuit 73 5.5 Result and Discussion 77 5.6 Conclusion 77 6 Artificial Intelligence-Based Active Virtual Voice Assistant 81Swathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash 6.1 Introduction 82 6.2 Literature Survey 83 6.3 System Functions 87 6.4 Model Training 88 6.5 Discussion 90 6.5.1 Furnishing Movie Recommendations 91 6.5.2 KNN Algorithm Book Recommendation 92 6.6 Results 93 6.7 Conclusion 102 7 Image Forgery Detection: An Approach with Machine Learning 105Madhusmita Mishra, Silvia Tittotto and Santos Kumar Das 7.1 Introduction 105 7.2 Historical Background 107 7.3 CNN Architecture 109 7.4 Analysis of Error Level of Image 113 7.5 Proposed Model of Image Forgery Detection, Results and Discussion 115 7.6 Conclusion 118 7.7 Future Research Directions 119 8 Applications of Artificial Neural Networks in Optical Performance Monitoring 123Isra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam 8.1 Introduction 123 8.2 Algorithms Employed for Performance Monitoring 129 8.2.1 Artificial Neural Networks 129 8.2.2 Deep Neural Networks 130 8.2.3 Convolutional Neural Networks 131 8.2.3.1 Convolutional Layer 131 8.2.3.2 Non-Linear Layer 132 8.2.3.3 Pooling Layer 132 8.2.3.4 Fully Connected Layer 132 8.2.4 Support Vector Regression (SVR) 133 8.2.5 Support Vector Machine (SVM) 133 8.2.6 Kernel Ridge Regression (KRR) 133 8.2.7 Long Short-Term Memory (LSTM) 133 8.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks 134 8.3.1 Performance Monitoring 134 8.3.2 Applications of AI in Optical Networking 135 8.4 Optical Impairments and Fault Management 135 8.4.1 Noise 135 8.4.2 Distortion 135 8.4.3 Timing 136 8.4.4 Component Faults 136 8.4.5 Transmission Impairments 137 8.4.6 Fault Management in Optical Network 137 8.5 Conclusion 138 9 Website Development with Django Web Framework 141Sanmukh Kaur, Anuranjana and Yashasvi Roy 9.1 Introduction 141 9.2 Salient Features of Django 142 9.2.1 Complete 142 9.2.2 Versatile 142 9.2.3 Secure 142 9.2.4 Scalable 143 9.2.5 Maintainable 143 9.2.6 Portable 143 9.3 UI Design 143 9.3.1 HTML 143 9.3.2 CSS 144 9.3.3 Bootstrap 144 9.4 Methodology 144 9.5 UI Design 144 9.6 Backend Development 148 9.6.1 Login Page 148 9.6.2 Registration Page 149 9.6.3 User Tracking 149 9.7 Ouputs 150 9.8 Conclusion 152 10 Revenue Forecasting Using Machine Learning Models 155Yashasvi Roy and Sanmukh Kaur 10.1 Introduction 155 10.2 Types of Forecasting 156 10.2.1 Qualitative Forecasting 156 10.2.1.1 Industries That Use Qualitative Forecasting 157 10.2.1.2 Qualitative Forecasting Methods 158 10.2.2 Quantitative Forecasting 158 10.2.2.1 Quantitative Forecasting Methods 159 10.2.3 Artificial Intelligence Forecasting 160 10.2.3.1 Artificial Neural Network (ANN) 160 10.2.3.2 Support Vector Machine (SVM) 161 10.3 Types of ML Models Used in Finance 162 10.3.1 Linear Regression 162 10.3.1.1 Simple Linear Regression 162 10.3.1.2 Multiple Linear Regression 162 10.3.2 Ridge Regression 163 10.3.3 Decision Tree 164 10.3.3.1 Prediction of Continuous Variables 164 10.3.3.2 Prediction of Categorical Variables 165 10.3.4 Random Forest Regressor 165 10.3.5 Gradient Boosting Regression 166 10.3.5.1 Advantages of Gradient Boosting 167 10.4 Model Performance 167 10.4.1 R-Squared Method 167 10.4.2 Mean Squared Error (MSE) 167 10.4.3 Root Mean Square Error (RMSE) 168 10.5 Conclusion 168 11 Application of Machine Learning Optimization Techniques in Wind Resource Assessment 171Udhayakumar K. and Krishnamoorthy R. 11.1 Introduction 172 11.2 Wind Data Analysis Methods 173 11.2.1 Wind Characteristics Parameters 173 11.2.2 Wind Speed Distribution Methods 173 11.2.3 Weibull Method 174 11.2.4 Goodness of Fit 175 11.3 Wind Site and Measurement Details 175 11.3.1 Seasonal Wind Periods 176 11.3.2 Machine Learning and Optimization Techniques 176 11.3.2.1 Moth Flame Optimization (MFO) Method 176 11.4 Results and Discussions 180 11.4.1 Wind Characteristics 182 11.4.1.1 Kayathar Station (Onshore) 182 11.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station 187 11.4.1.3 Jafrabad (Gujarat-Nearshore) 192 11.4.2 Wind Distribution Fitting 195 11.4.2.1 Kayathar Station (Onshore) 196 11.4.2.2 Bimodal Behaviour 196 11.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution 202 11.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting 203 11.4.3 Optimization Methods for Parameter Estimation 212 11.4.3.1 Optimization Parameters Comparison 212 11.4.4 Wind Power Density Analysis (WPD) 214 11.4.4.1 Comparison of Wind Power Density 215 11.5 Research Summary 221 11.6 Conclusions 222 12 IoT to Scale-Up Smart Infrastructure in Indian Cities: A New Paradigm 227Indu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala 12.1 Introduction 228 12.2 Technological Progress: A Brief History 229 12.3 What is the Internet of Things (IoT)? 230 12.4 Economic Effects of Internet of Things 230 12.5 Infrastructure and Smart Infrastructure: The Difference 232 12.5.1 What is Smart Infrastructure? 233 12.5.2 What are the Principles of Smart Infrastructure? 234 12.5.3 Components of IoT-Based Smart City Project 235 12.6 Architecture for Smart Cities 236 12.6.1 Networking Technologies 237 12.6.2 Network Topologies 237 12.6.3 Network Architectures 238 12.6.3.1 Home Area Networks (HANs) 238 12.6.3.2 Field/Neighborhood Area Networks (FANs/NANs) 238 12.6.3.3 Wide Area Networks (WANs) 238 12.6.3.4 Network Protocols 238 12.7 IoT Technology in India’s Smart Cities: The Current Scenario 239 12.8 Challenges in IoT-Based Smart City Projects 243 12.8.1 Technological Challenges 243 12.8.1.1 Privacy and Security 243 12.8.1.2 Smart Sensors and Infrastructure Essentials 243 12.8.1.3 Networking in IoT Systems 244 12.8.1.4 Big Data Analytics 244 12.8.2 Financial - Economic Challenges 244 12.9 Role of Explainable AI 245 12.10 Conclusion and Future Scope 246 References 246 Index 251

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Book SynopsisTopics in Artificial Intelligence Applied to Industry 4.0 Forward thinking resource discussing emerging AI and IoT technologies and how they are applied to Industry 4.0 Topics in Artificial Intelligence Applied to Industry 4.0 discusses the design principles, technologies, and applications of emerging AI and IoT solutions on Industry 4.0, explaining how to make improvements in infrastructure through emerging technologies. Providing a clear connection with different technologies such as IoT, Big Data, AR and VR, and Blockchain, this book presents security, privacy, trust, and other issues whilst delving into real-world problems and case studies. The text takes a highly practical approach, with a clear insight on how readers can increase productivity by drastically shortening the time period between the development of a new product and its delivery to customers in the market by 50%. This book also discusses how to save energy across systems to ensure competitiveness in a global market

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Book SynopsisAUTOMATIC SPEECH RECOGNITION and TRANSLATION for LOW-RESOURCE LANGUAGES This book is a comprehensive exploration into the cutting-edge research, methodologies, and advancements in addressing the unique challenges associated with ASR and translation for low-resource languages. Automatic Speech Recognition and Translation for Low Resource Languages contains groundbreaking research from experts and researchers sharing innovative solutions that address language challenges in low-resource environments. The book begins by delving into the fundamental concepts of ASR and translation, providing readers with a solid foundation for understanding the subsequent chapters. It then explores the intricacies of low-resource languages, analyzing the factors that contribute to their challenges and the significance of developing tailored solutions to overcome them. The chapters encompass a wide range of topics, ranging from both the theoretical and practical aspects of ASR and translation for low-resour

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Book SynopsisAugment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more.Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors.Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the bTable of Contents1 Einleitung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Aufbau der Kapitel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Grundgerüst des PyCharm-Projekts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Grundgerüst für die Unit Tests mit PyTest . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Anmerkung zum Programmierstil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5 Anmerkung zu den Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.6 Ausprobieren der Beispiele und Lösungen . . . . . . . . . . . . . . . . . . . . . . . 9I Grundlagen 112 Mathematische Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1 Einführung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.1 Römische Zahlen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.1.2 Zahlenspielereien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.2.1 Aufgabe 1: Grundrechenarten (★✩✩✩✩) . . . . . . . . . . . . . . . . . 212.2.2 Aufgabe 2: Zahl als Text (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 222.2.3 Aufgabe 3: Vollkommene Zahlen (★★✩✩✩) . . . . . . . . . . . . . . . 222.2.4 Aufgabe 4: Primzahlen (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 232.2.5 Aufgabe 5: Primzahlpaare (★★✩✩✩). . . . . . . . . . . . . . . . . . . . . 232.2.6 Aufgabe 6: Prüfsumme (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 232.2.7 Aufgabe 7: Römische Zahlen (★★★★✩) . . . . . . . . . . . . . . . . . . 242.2.8 Aufgabe 8: Kombinatorik (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 242.2.9 Aufgabe 9: Armstrong-Zahlen (★★✩✩✩) . . . . . . . . . . . . . . . . . 252.2.10 Aufgabe 10: Max Change Calculator (★★★★✩) . . . . . . . . . . . 252.2.11 Aufgabe 11: Befreundete Zahlen (★★✩✩✩) . . . . . . . . . . . . . . . 262.2.12 Aufgabe 12: Primfaktorzerlegung (★★★✩✩) . . . . . . . . . . . . . . 262.3 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.3.1 Lösung 1: Grundrechenarten (★✩✩✩✩) . . . . . . . . . . . . . . . . . . 272.3.2 Lösung 2: Zahl als Text (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 292.3.3 Lösung 3: Vollkommene Zahlen (★★✩✩✩) . . . . . . . . . . . . . . . . 312.3.4 Lösung 4: Primzahlen (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 33viii Inhaltsverzeichnis2.3.5 Lösung 5: Primzahlpaare (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 352.3.6 Lösung 6: Prüfsumme (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 392.3.7 Lösung 7: Römische Zahlen (★★★★✩) . . . . . . . . . . . . . . . . . . . 402.3.8 Lösung 8: Kombinatorik (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 432.3.9 Lösung 9: Armstrong-Zahlen (★★✩✩✩) . . . . . . . . . . . . . . . . . . 462.3.10 Lösung 10: Max Change Calculator (★★★★✩) . . . . . . . . . . . . 492.3.11 Lösung 11: Befreundete Zahlen (★★✩✩✩) . . . . . . . . . . . . . . . . 502.3.12 Lösung 12: Primfaktorzerlegung (★★★✩✩) . . . . . . . . . . . . . . . 523 Rekursion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.1 Einführung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.1.1 Mathematische Beispiele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.1.2 Algorithmische Beispiele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593.1.3 Typische Probleme: Endlose Aufrufe und RecursionError 643.2 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663.2.1 Aufgabe 1: Fibonacci (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 663.2.2 Aufgabe 2: Ziffern verarbeiten (★★✩✩✩) . . . . . . . . . . . . . . . . . 663.2.3 Aufgabe 3: ggT / GCD (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 673.2.4 Aufgabe 4: Reverse String (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . 683.2.5 Aufgabe 5: Array Sum (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 683.2.6 Aufgabe 6: Array Min (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 683.2.7 Aufgabe 7: Konvertierungen (★★✩✩✩) . . . . . . . . . . . . . . . . . . . 693.2.8 Aufgabe 8: Exponentialfunktion (★★✩✩✩) . . . . . . . . . . . . . . . . 703.2.9 Aufgabe 9: Pascal’sches Dreieck (★★✩✩✩) . . . . . . . . . . . . . . . 713.2.10 Aufgabe 10: Zahlenpalindrome (★★★★✩) . . . . . . . . . . . . . . . . 713.2.11 Aufgabe 11: Permutationen (★★★✩✩) . . . . . . . . . . . . . . . . . . . 723.2.12 Aufgabe 12: Count Substrings (★★✩✩✩) . . . . . . . . . . . . . . . . . 723.2.13 Aufgabe 13: Lineal (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . . 733.3 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.3.1 Lösung 1: Fibonacci (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . 743.3.2 Lösung 2: Ziffern verarbeiten (★★✩✩✩) . . . . . . . . . . . . . . . . . . 763.3.3 Lösung 3: ggT / GCD (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 783.3.4 Lösung 4: Reverse String (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 803.3.5 Lösung 5: Array Sum (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 813.3.6 Lösung 6: Array Min (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . 833.3.7 Lösung 7: Konvertierungen (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . 843.3.8 Lösung 8: Exponentialfunktion (★★✩✩✩) . . . . . . . . . . . . . . . . . 873.3.9 Lösung 9: Pascal’sches Dreieck (★★✩✩✩) . . . . . . . . . . . . . . . 903.3.10 Lösung 10: Zahlenpalindrome (★★★★✩) . . . . . . . . . . . . . . . . . 933.3.11 Lösung 11: Permutationen (★★★✩✩) . . . . . . . . . . . . . . . . . . . . 963.3.12 Lösung 12: Count Substrings (★★✩✩✩) . . . . . . . . . . . . . . . . . . 993.3.13 Lösung 13: Lineal (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Inhaltsverzeichnis ix4 Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054.1 Einführung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054.2 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1114.2.1 Aufgabe 1: Zahlenumwandlungen (★★✩✩✩) . . . . . . . . . . . . . . 1114.2.2 Aufgabe 2: Joiner (★✩✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1114.2.3 Aufgabe 3: Reverse String (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . 1124.2.4 Aufgabe 4: Palindrom (★★★✩✩). . . . . . . . . . . . . . . . . . . . . . . . . 1124.2.5 Aufgabe 5: No Duplicate Chars (★★★✩✩) . . . . . . . . . . . . . . . . 1134.2.6 Aufgabe 6: Doppelte Buchstaben entfernen (★★★✩✩) . . . . . 1134.2.7 Aufgabe 7: Capitalize (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 1144.2.8 Aufgabe 8: Rotation (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . 1154.2.9 Aufgabe 9: Wohlgeformte Klammern (★★✩✩✩) . . . . . . . . . . . 1154.2.10 Aufgabe 10: Anagramm (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 1164.2.11 Aufgabe 11: Morse Code (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 1164.2.12 Aufgabe 12: Pattern Checker (★★✩✩✩) . . . . . . . . . . . . . . . . . . 1174.2.13 Aufgabe 13: Tennis-Punktestand (★★★✩✩) . . . . . . . . . . . . . . . 1174.2.14 Aufgabe 14: Versionsnummern (★★✩✩✩) . . . . . . . . . . . . . . . . 1184.2.15 Aufgabe 15: Konvertierung str_to_number (★★✩✩✩) . . . . 1184.2.16 Aufgabe 16: Print Tower (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . 1194.2.17 Aufgabe 17: Gefüllter Rahmen (★★✩✩✩) . . . . . . . . . . . . . . . . . 1194.2.18 Aufgabe 18: Vokale raten (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 1194.3 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1204.3.1 Lösung 1: Zahlenumwandlungen (★★✩✩✩) . . . . . . . . . . . . . . . 1204.3.2 Lösung 2: Joiner (★✩✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1234.3.3 Lösung 3: Reverse String (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 1244.3.4 Lösung 4: Palindrom (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 1264.3.5 Lösung 5: No Duplicate Chars (★★★✩✩) . . . . . . . . . . . . . . . . . 1294.3.6 Lösung 6: Doppelte Buchstaben entfernen (★★★✩✩) . . . . . . 1314.3.7 Lösung 7: Capitalize (★★✩✩✩). . . . . . . . . . . . . . . . . . . . . . . . . . 1324.3.8 Lösung 8: Rotation (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . . 1364.3.9 Lösung 9: Wohlgeformte Klammern (★★✩✩✩) . . . . . . . . . . . . 1374.3.10 Lösung 10: Anagramm (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 1394.3.11 Lösung 11: Morse Code (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 1404.3.12 Lösung 12: Pattern Checker (★★✩✩✩) . . . . . . . . . . . . . . . . . . . 1424.3.13 Lösung 13: Tennis-Punktestand (★★★✩✩) . . . . . . . . . . . . . . . . 1444.3.14 Lösung 14: Versionsnummern (★★✩✩✩) . . . . . . . . . . . . . . . . . 1474.3.15 Lösung 15: Konvertierung str_to_number (★★✩✩✩) . . . . . 1484.3.16 Lösung 16: Print Tower (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . 1514.3.17 Lösung 17: Gefüllter Rahmen (★★✩✩✩) . . . . . . . . . . . . . . . . . 1534.3.18 Lösung 18: Vokale raten (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 154x Inhaltsverzeichnis5 Basisdatenstrukturen: Listen, Sets und Dictionaries . . . . . . . . . . . 1575.1 Einführung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1575.1.1 Sequenzielle Datentypen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1575.1.2 Listen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1595.1.3 Mengen (Sets) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1635.1.4 Schlüssel-Wert-Abbildungen (Dictionaries) . . . . . . . . . . . . . . . . 1645.1.5 Der Stack als LIFO-Datenstruktur . . . . . . . . . . . . . . . . . . . . . . . . 1665.1.6 Die Queue als FIFO-Datenstruktur . . . . . . . . . . . . . . . . . . . . . . . 1675.2 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1715.2.1 Aufgabe 1: Gemeinsame Elemente (★★✩✩✩). . . . . . . . . . . . . 1715.2.2 Aufgabe 2: Eigener Stack (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 1715.2.3 Aufgabe 3: List Reverse (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 1715.2.4 Aufgabe 4: Duplikate entfernen (★★✩✩✩) . . . . . . . . . . . . . . . . 1725.2.5 Aufgabe 5: Maximaler Gewinn (★★★✩✩) . . . . . . . . . . . . . . . . . 1725.2.6 Aufgabe 6: Längstes Teilstück (★★★✩✩) . . . . . . . . . . . . . . . . . 1735.2.7 Aufgabe 7: Wohlgeformte Klammern (★★✩✩✩) . . . . . . . . . . . 1735.2.8 Aufgabe 8: Pascal’sches Dreieck (★★★✩✩) . . . . . . . . . . . . . . . 1745.2.9 Aufgabe 9: Check Magic Triangle (★★★✩✩) . . . . . . . . . . . . . . 1745.2.10 Aufgabe 10: Häufigste Elemente (★★✩✩✩) . . . . . . . . . . . . . . . 1755.2.11 Aufgabe 11: Addition von Ziffern (★★★✩✩) . . . . . . . . . . . . . . . 1755.2.12 Aufgabe 12: List Merge (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 1765.2.13 Aufgabe 13: Excel Magic Select (★★✩✩✩) . . . . . . . . . . . . . . . 1765.2.14 Aufgabe 14: Stack Based Queue (★★✩✩✩) . . . . . . . . . . . . . . . 1775.3 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1785.3.1 Lösung 1: Gemeinsame Elemente (★★✩✩✩) . . . . . . . . . . . . . 1785.3.2 Lösung 2: Eigener Stack (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 1805.3.3 Lösung 3: List Reverse (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 1815.3.4 Lösung 4: Duplikate entfernen (★★✩✩✩) . . . . . . . . . . . . . . . . . 1845.3.5 Lösung 5: Maximaler Gewinn (★★★✩✩) . . . . . . . . . . . . . . . . . . 1865.3.6 Lösung 6: Längstes Teilstück (★★★✩✩) . . . . . . . . . . . . . . . . . . 1885.3.7 Lösung 7: Wohlgeformte Klammern (★★✩✩✩) . . . . . . . . . . . . 1905.3.8 Lösung 8: Pascal’sches Dreieck (★★★✩✩). . . . . . . . . . . . . . . . 1945.3.9 Lösung 9: Check Magic Triangle (★★★✩✩) . . . . . . . . . . . . . . . 1965.3.10 Lösung 10: Häufigste Elemente (★★✩✩✩) . . . . . . . . . . . . . . . . 1995.3.11 Lösung 11: Addition von Ziffern (★★★✩✩) . . . . . . . . . . . . . . . . 2005.3.12 Lösung 12: List Merge (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 2045.3.13 Lösung 13: Excel Magic Select (★★✩✩✩) . . . . . . . . . . . . . . . . 2085.3.14 Lösung 14: Stack Based Queue (★★✩✩✩) . . . . . . . . . . . . . . . 210Inhaltsverzeichnis xi6 Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2136.1 Einführung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2136.1.1 Eindimensionale Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2146.1.2 Mehrdimensionale Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2226.1.3 Typische Fehler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2276.1.4 Besonderheiten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2286.1.5 Rekapitulation: NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2296.2 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2356.2.1 Aufgabe 1: Gerade vor ungeraden Zahlen (★★✩✩✩) . . . . . . . 2356.2.2 Aufgabe 2: Flip (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2356.2.3 Aufgabe 3: Palindrom (★★✩✩✩). . . . . . . . . . . . . . . . . . . . . . . . . 2356.2.4 Aufgabe 4: Inplace Rotate (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . 2366.2.5 Aufgabe 5: Jewels Board Init (★★★✩✩) . . . . . . . . . . . . . . . . . . 2366.2.6 Aufgabe 6: Jewels Board Erase Diamonds (★★★★✩) . . . . . . 2386.2.7 Aufgabe 7: Spiral-Traversal (★★★★✩) . . . . . . . . . . . . . . . . . . . . 2396.2.8 Aufgabe 8: Add One to Array As Number (★★✩✩✩). . . . . . . . 2396.2.9 Aufgabe 9: Sudoku-Checker (★★★✩✩) . . . . . . . . . . . . . . . . . . . 2406.2.10 Aufgabe 10: Flood-Fill (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 2416.2.11 Aufgabe 11: Array Min und Max (★★✩✩✩) . . . . . . . . . . . . . . . . 2426.2.12 Aufgabe 12: Array Split (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . 2436.2.13 Aufgabe 13: Minesweeper Board (★★★✩✩) . . . . . . . . . . . . . . . 2446.3 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2466.3.1 Lösung 1: Gerade vor ungeraden Zahlen (★★✩✩✩) . . . . . . . . 2466.3.2 Lösung 2: Flip (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2506.3.3 Lösung 3: Palindrom (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 2536.3.4 Lösung 4: Inplace Rotate (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . 2556.3.5 Lösung 5: Jewels Board Init (★★★✩✩) . . . . . . . . . . . . . . . . . . . 2596.3.6 Lösung 6: Jewels Board Erase Diamonds (★★★★✩) . . . . . . . 2656.3.7 Lösung 7: Spiral-Traversal (★★★★✩) . . . . . . . . . . . . . . . . . . . . . 2736.3.8 Lösung 8: Add One to Array As Number (★★✩✩✩) . . . . . . . . 2776.3.9 Lösung 9: Sudoku-Checker (★★★✩✩) . . . . . . . . . . . . . . . . . . . 2786.3.10 Lösung 10: Flood-Fill (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 2836.3.11 Lösung 11: Array Min und Max (★★✩✩✩) . . . . . . . . . . . . . . . . 2876.3.12 Lösung 12: Array Split (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 2906.3.13 Lösung 13: Minesweeper Board (★★★✩✩) . . . . . . . . . . . . . . . 294xii InhaltsverzeichnisII Fortgeschrittenere und kniffligere Themen 3017 Rekursion Advanced . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3037.1 Memoization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3037.1.1 Memoization für Fibonacci-Zahlen . . . . . . . . . . . . . . . . . . . . . . . . 3037.1.2 Memoization für Pascal’sches Dreieck . . . . . . . . . . . . . . . . . . . . 3057.1.3 Memoization mit Python-Bordmitteln . . . . . . . . . . . . . . . . . . . . . . 3077.2 Backtracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3117.2.1 n-Damen-Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3117.3 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3157.3.1 Aufgabe 1: Türme von Hanoi (★★★✩✩) . . . . . . . . . . . . . . . . . . 3157.3.2 Aufgabe 2: Edit Distance (★★★★✩) . . . . . . . . . . . . . . . . . . . . . . 3167.3.3 Aufgabe 3: Longest Common Subsequence (★★★✩✩) . . . . . 3167.3.4 Aufgabe 4: Weg aus Labyrinth (★★★✩✩) . . . . . . . . . . . . . . . . . 3177.3.5 Aufgabe 5: Sudoku-Solver (★★★★✩) . . . . . . . . . . . . . . . . . . . . 3187.3.6 Aufgabe 6: Math Operator Checker (★★★★✩) . . . . . . . . . . . . . 3197.3.7 Aufgabe 7: Wassereimer-Problem (★★★✩✩) . . . . . . . . . . . . . . 3207.3.8 Aufgabe 8: Alle Palindrom-Teilstrings (★★★★✩) . . . . . . . . . . . 3217.3.9 Aufgabe 9: n-Damen-Problem (★★★✩✩) . . . . . . . . . . . . . . . . . 3217.4 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3227.4.1 Lösung 1: Türme von Hanoi (★★★✩✩) . . . . . . . . . . . . . . . . . . . 3227.4.2 Lösung 2: Edit Distance (★★★★✩). . . . . . . . . . . . . . . . . . . . . . . 3277.4.3 Lösung 3: Longest Common Subsequence (★★★✩✩) . . . . . . 3307.4.4 Lösung 4: Weg aus Labyrinth (★★★✩✩) . . . . . . . . . . . . . . . . . . 3347.4.5 Lösung 5: Sudoku-Solver (★★★★✩) . . . . . . . . . . . . . . . . . . . . . 3377.4.6 Lösung 6: Math Operator Checker (★★★★✩). . . . . . . . . . . . . . 3447.4.7 Lösung 7: Wassereimer-Problem (★★★★✩). . . . . . . . . . . . . . . 3477.4.8 Lösung 8: Alle Palindrom-Teilstrings (★★★★✩) . . . . . . . . . . . . 3507.4.9 Lösung 9: n-Damen-Problem (★★★✩✩) . . . . . . . . . . . . . . . . . . 3548 Binärbäume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3618.1 Einführung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3618.1.1 Aufbau, Begrifflichkeiten und Anwendungsbeispiele . . . . . . . . 3618.1.2 Binärbäume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3628.1.3 Binärbäume mit Ordnung: binäre Suchbäume . . . . . . . . . . . . . 3638.1.4 Traversierungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3658.1.5 Balancierte Bäume und weitere Eigenschaften . . . . . . . . . . . . . 3678.1.6 Bäume für die Beispiele und Übungsaufgaben . . . . . . . . . . . . . 3698.2 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3718.2.1 Aufgabe 1: Tree Traversal (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 3718.2.2 Aufgabe 2: In-, Pre- und Postorder iterativ (★★★★✩) . . . . . . . 3718.2.3 Aufgabe 3: Tree-Höhe berechnen (★★✩✩✩) . . . . . . . . . . . . . . 3718.2.4 Aufgabe 4: Kleinster gemeinsamer Vorfahre (★★★✩✩) . . . . . 372Inhaltsverzeichnis xiii8.2.5 Aufgabe 5: Breadth-First (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . 3728.2.6 Aufgabe 6: Level Sum (★★★★✩) . . . . . . . . . . . . . . . . . . . . . . . . 3738.2.7 Aufgabe 7: Tree Rotate (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . 3738.2.8 Aufgabe 8: Rekonstruktion (★★★✩✩) . . . . . . . . . . . . . . . . . . . . 3748.2.9 Aufgabe 9: Math Evaluation (★★✩✩✩) . . . . . . . . . . . . . . . . . . . 3748.2.10 Aufgabe 10: Symmetrie (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 3758.2.11 Aufgabe 11: Check Binary Search Tree (★★✩✩✩) . . . . . . . . . 3768.2.12 Aufgabe 12: Vollständigkeit (★★★★★) . . . . . . . . . . . . . . . . . . . . 3768.2.13 Aufgabe 13: Tree Printer (★★★★★) . . . . . . . . . . . . . . . . . . . . . . 3788.3 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3818.3.1 Lösung 1: Tree Traversal (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 3818.3.2 Lösung 2: In-, Pre- und Postorder iterativ (★★★★✩) . . . . . . . . 3838.3.3 Lösung 3: Tree-Höhe berechnen (★★✩✩✩) . . . . . . . . . . . . . . . 3908.3.4 Lösung 4: Kleinster gemeinsamer Vorfahre (★★★✩✩) . . . . . . 3918.3.5 Lösung 5: Breadth-First (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . 3948.3.6 Lösung 6: Level Sum (★★★★✩) . . . . . . . . . . . . . . . . . . . . . . . . . 3968.3.7 Lösung 7: Tree Rotate (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 3998.3.8 Lösung 8: Rekonstruktion (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . 4028.3.9 Lösung 9: Math Evaluation (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . 4078.3.10 Lösung 10: Symmetrie (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 4088.3.11 Lösung 11: Check Binary Search Tree (★★✩✩✩) . . . . . . . . . . 4138.3.12 Lösung 12: Vollständigkeit (★★★★★). . . . . . . . . . . . . . . . . . . . . 4158.3.13 Lösung 13: Tree Printer (★★★★★) . . . . . . . . . . . . . . . . . . . . . . . 4239 Suchen und Sortieren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4339.1 Einführung Suchen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4339.1.1 Binärsuche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4359.2 Einführung Sortieren. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4369.2.1 Insertion Sort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4369.2.2 Selection Sort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4389.2.3 Merge Sort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4409.2.4 Quick Sort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4419.2.5 Bucket Sort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4439.2.6 Schlussgedanken . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4449.3 Aufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4459.3.1 Aufgabe 1: Contains All (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 4459.3.2 Aufgabe 2: Partitionierung (★★★★✩). . . . . . . . . . . . . . . . . . . . . 4459.3.3 Aufgabe 3: Binärsuche (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 4469.3.4 Aufgabe 4: Insertion Sort (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 4469.3.5 Aufgabe 5: Selection Sort (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . 4479.3.6 Aufgabe 6: Quick Sort (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 4479.3.7 Aufgabe 7: Bucket Sort (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . 4489.3.8 Aufgabe 8: Suche in rotierten Daten (★★★★✩) . . . . . . . . . . . . 448xiv Inhaltsverzeichnis9.4 Lösungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4509.4.1 Lösung 1: Contains All (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 4509.4.2 Lösung 2: Partitionierung (★★★★✩) . . . . . . . . . . . . . . . . . . . . . 4519.4.3 Lösung 3: Binärsuche (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 4539.4.4 Lösung 4: Insertion Sort (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 4569.4.5 Lösung 5: Selection Sort (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . 4579.4.6 Lösung 6: Quick Sort (★★★✩✩) . . . . . . . . . . . . . . . . . . . . . . . . . 4589.4.7 Lösung 7: Bucket Sort (★★✩✩✩) . . . . . . . . . . . . . . . . . . . . . . . . 4609.4.8 Lösung 8: Suche in rotierten Daten (★★★★✩) . . . . . . . . . . . . . 46110 Schlusswort und ergänzende Literatur . . . . . . . . . . . . . . . . . . . . . . . . 46710.1 Schlusswort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46710.1.1 Gelerntes pro Kapitel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46710.1.2 Bedenkenswertes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46910.2 Knobelaufgaben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47110.2.1 Goldsäcke – Fälschung entdecken . . . . . . . . . . . . . . . . . . . . . . . 47110.2.2 Pferderennen – schnellste drei Pferde ermitteln . . . . . . . . . . . . 47210.3 Ergänzende Literatur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475III Anhang 479A Kurzeinführung Pytest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481A.1 Schreiben und Ausführen von Tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481A.1.1 Installation von Pytest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481A.1.2 Beispiel: Ein erster Unit Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482A.1.3 Ausführen von Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482A.1.4 Behandlung erwarteter Exceptions . . . . . . . . . . . . . . . . . . . . . . . 484A.1.5 Parametrisierte Tests mit Pytest . . . . . . . . . . . . . . . . . . . . . . . . . . 485A.2 Weiterführende Literatur zu Pytest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486B Kurzeinführung Dekoratoren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487C Schnelleinstieg O-Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493C.1 Abschätzungen mit der O-Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493C.1.1 Komplexitätsklassen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494C.1.2 Komplexität und Programmlaufzeit . . . . . . . . . . . . . . . . . . . . . . . 496Literaturverzeichnis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

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