• Printed Text 1. Fundamentals of Quantitative Design and Analysis 2. Memory Hierarchy Design 3. Instruction-Level Parallelism and Its Exploitation 4. Data-Level Parallelism in Vector, SIMD, and GPU Architectures 5. Multiprocessors and Thread-Level Parallelism 6. The Warehouse-Scale Computer 7. Domain Specific Architectures A. Instruction Set Principles B. Review of Memory Hierarchy C. Pipelining: Basic and Intermediate Concepts Online D. Storage Systems E. Embedded Systems F. Interconnection Networks G. Vector Processors H. Hardware and Software for VLIW and EPIC I. Large-Scale Multiprocessors and Scientific Applications J. Computer Arithmetic K. Survey of Instruction Set Architectures L. Advanced Concepts on Address Translation M. Historical Perspectives and References.
• (source: Nielsen Book Data)9780128119051 20180312

Computer Architecture: A Quantitative Approach, Sixth Edition has been considered essential reading by instructors, students and practitioners of computer design for over 20 years. The sixth edition of this classic textbook is fully revised with the latest developments in processor and system architecture. It now features examples from the RISC-V (RISC Five) instruction set architecture, a modern RISC instruction set developed and designed to be a free and openly adoptable standard. It also includes a new chapter on domain-specific architectures and an updated chapter on warehouse-scale computing that features the first public information on Google's newest WSC. True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design.
(source: Nielsen Book Data)9780128119051 20180312

• Part I. The technology: Blockchains, bitcoin, and decentralized computing platforms
• Characteristics of blockchains
• Part II. Blockchains, finance, and contracts: Digital currencies and decentralized payment systems
• Smart contracts as legal contracts
• Smart securities and derivatives
• Part III. Blockchains and information systems: Tamper-resistant, certified, and authenticated data
• Resilient and tamper-resistant information systems
• Part IV. Organizations and automation: The future of organizations
• Decentralized autonomous organizations
• Blockchain of things
• Part V. Regulating decentralized, blockchain-based systems: Modes of regulation
• Code as law.

How does Bitcoin mine money from 1s and 0s? Through blockchain, a tool for creating secure, decentralized peer-to-peer applications. The technology has been compared to the Internet in impact. But disintermediation blockchain's greatest benefit cuts out oversight along with middlemen. Blockchain and the Law urges the law to catch up.
(source: Nielsen Book Data)9780674976429 20180430

• Introduction.- Modeling and Identification.- Probability and Stochastic Processes.- Optimal Control.- Stochastic Analysis.- Financial Markets and Instruments.- Bonds.- Portfolio Management.- Derivatives and Structured Financial Instruments.
• (source: Nielsen Book Data)9783319644912 20180514

This book includes a review of mathematical tools like modelling, analysis of stochastic processes, calculus of variations and stochastic differential equations which are applied to solve financial problems like modern portfolio theory and option pricing. Every chapter presents exercises which help the reader to deepen his understanding. The target audience comprises research experts in the field of finance engineering, but the book may also be beneficial for graduate students alike.
(source: Nielsen Book Data)9783319644912 20180514

• part I. Fundamentals. Introduction : how the theory evolved and how it impacts practice
• Fundamentals of linear viscoelasticity
• Basic properties of concrete creep, shrinkage, and drying
• Structural effects of creep and age-adjusted effective modulus method
• Numerical analysis of creep problems
• Uncertainty due to parameter randomness via sampling of deterministic solutions
• Paradigms of application, phenomena affecting creep deformations, and comparisons to measurements on structures.
• part II. Advanced topics. Moisture transport in concrete
• Solidification theory for aging effect on stiffness and basic creep
• Microprestress-solidification theory and creep at variable humidity and temperature
• Physical and statistical justifications of Models 3 and B4 and comparisons to other models
• Effect of cracking and fracture mechanics aspects of creep and shrinkage analysis
• Temperature effect on water diffusion, hydration rate, creep and shrinkage. appendix A. Viscoelastic rheologic models
• appendix B. Historical note on old creep models
• appendix C. Estimates of parameters used by RILEM Model B3
• appendix D. Estimates of parameters used by RILEM Model B4
• appendix E. Creep models recommended by design codes
• appendix F. Continuous retardation spectrum
• appendix G. Free-energy potentials for aging linear viscoelasticity
• appendix H. Updating long-time shrinkage predictions from short-time measurements
• appendix I. Moisture transport characteristics
• appendix J. Moisture transport in porous materials
• appendix K. Nonstandard statistics used in support of some creep and shrinkage models
• appendix L. Method of measurement of creep and shrinkage.

Description Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind photo tagging, self-driving cars, virtual assistants and other previously impossible applications. Deep Learning with R is for developers and data scientists with some R experience who want to use deep learning to solve real-world problems. The book is structured around a series of practical examples that introduce each new concept and demonstrate best practices. You'll begin by learning what deep learning is, how it connects with AI and Machine Learning, and why it's rapidly gaining in importance right now. You'll then dive into practical applications of computer vision, natural language processing, and more. Key features * Understand key machine learning concepts * Set up a computer environment for deep learning * Visualize neural networks * Use recurrent neural networks for text and sequence Classification Audience You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is required. About the technology Although deep learning can be a challenging subject, new technologies make it much easier to get started than ever before. The Keras deep learning library featured in this book puts ease of use and accessibility front and center, making it a great fit for new practitioners.
(source: Nielsen Book Data)9781617295546 20180514

• Introduction
• Principles of integrated design
• Site selection and facility layout
• Modeling for design and operation of biological water resource recovery processes
• Facility hydraulics and pumping
• Odor and air emissions management
• Support systems
• Materials of construction and corrosion control
• Preliminary treatment
• Primary treatment
• Biofilm reactor technology and design
• Suspended-growth treatment processes
• Integrated biological treatment
• Physical and chemical processes for advanced wastewater treatment
• Sidestream treatment
• Natural systems
• Disinfection
• Introduction to solids management
• Storage and transport
• Chemical conditioning
• Solids thickening
• Dewatering
• Stabilization
• Thermal processing
• Use and disposal of residuals and biosolids.

• A Note from the Series Editor xiii About the Authors xv Foreword xvii Preface xxiii Acknowledgments xxvii INTRODUCTION 1 1 Pressing Issues for Engineering Education and the Engineering Profession 3 1.1 A Mismatched Curriculum 3 1.2 Responsibility that Emerges from the Transformative Power of Engineering 7 1.3 Inquiring into the Framing of Benefits and Constraints 9 1.4 Transitioning from Weak to Robust Sustainability 9 1.5 Fostering Inclusive Excellence 10 1.6 Engaging Emerging Interest Groups 11 2 Research Methods 12 3 Theoretical Frameworks 13 4 Engineering for Social Justice 14 4.1 Emerging Organizations Provide New Opportunities 15 4.2 Calls from Engineering Education Leaders 16 4.3 Emerging Scholarship on Engineering and Social Justice 18 5 Engineering for Social Justice Criteria 19 5.1 Listening Contextually to Develop Trust and Empathy 21 5.2 Identifying Structural Conditions 23 5.3 Acknowledging Political Agency and Mobilizing Power 24 5.4 Increasing Opportunities and Resources 26 5.5 Reducing Imposed Risks and Harms 27 5.6 Enhancing Human Capabilities 28 5.7 Engineering and Social Justice Criteria Combined 30 6 Guidelines for Engineering for Social Justice Implementation 31 6.1 Cradle-to-Grave Analysis 31 6.2 Transcending Temporal Delimitations 33 6.3 Culling Multiple Perspectives 33 7 Further Chapters 34 7.1 Ideologies and Mindsets that Render Social Justice Invisible or Irrelevant 34 7.2 Engineering Design 35 7.3 Engineering Sciences 36 7.4 Humanities/Social Science Courses for Engineering Students 36 7.5 E4SJ as Catalyst for Inclusive Excellence in Engineering 37 7.6 Conclusion 37 8 Benefits of E4SJ Approach 37 References 38 1 SOCIAL JUSTICE IS OFTEN INVISIBLE IN ENGINEERING EDUCATION AND PRACTICE 45 1.1 Generic Barriers to Rendering Social Justice Visible 46 1.1.1 Normalcy 46 1.1.2 Superiority 47 1.1.3 Unconscious Biases 47 1.1.4 Personal and Broader Societal Framing 48 1.2 Engineering-Specific Barriers to Rendering Social Justice Visible: Ideologies 49 1.2.1 Technical Social Dualism 50 1.2.2 Depoliticization 52 1.2.3 Meritocracy 55 1.3 Engineering-Specific Barriers to Rendering Social Justice Visible: Mindsets 56 1.3.1 Centrality of Military and Corporate Organizations 57 1.3.2 Uncritical Acceptance of Authority 58 1.3.3 Technical Narrowness 59 1.3.4 Positivism and the Myth of Objectivity 59 1.3.5 Willingness to Help and Persistence 60 References 63 2 ENGINEERING DESIGN FOR SOCIAL JUSTICE 67 2.1 Why Engineering Design Matters 69 2.1.1 Why Design Resembles Actual Engineering Practice Yet Has Limitations 70 2.1.2 Why Design is an Important Yet Undervalued Component of Engineering Education 71 2.2 Engineering for Social Justice: Criteria for Engineering Design Initiatives 71 2.2.1 Listening Contextually 74 2.2.2 Identifying Structural Conditions 78 2.2.3 Acknowledging Political Agency and Mobilizing Power 79 2.2.4 Increasing Opportunities and Resources 82 2.2.5 Reducing Imposed Risks and Harms 85 2.2.6 Enhancing Human Capabilities 86 2.3 Social Justice Criteria Combined 88 2.4 Benefits of Integrating SJ in Design 89 2.5 Limitations of Social Justice Criteria 95 Appendix 2.A Engineering for Social Justice Self-Assessment Checklist 98 Appendix 2.B Design for Social Justice Charrette 100 Acknowledgments 102 References 102 3 SOCIAL JUSTICE IN THE ENGINEERING SCIENCES 107 3.1 Why are the Engineering Sciences the Sacred Cow of the Engineering Curriculum? 108 3.1.1 Engineering Sciences as Shapers of Engineering Identity 108 3.1.2 Pedagogical Tradition in the Engineering Sciences 112 3.2 Why Social Justice is Inherent in Engineering Sciences Course Content 114 3.3 Making Social Justice Visible without Compromising Technical Excellence 116 3.3.1 Social Justice Definition 116 3.3.2 E4SJ Criteria 119 3.4 Examples of Making SJ Visible in the Engineering Sciences 120 3.4.1 E4SJ Criteria Engaged in Introduction to Feedback Control Systems 120 3.4.2 E4SJ Criteria Engaged in Continuous-Time Signals and Systems 127 3.4.3 E4SJ Criteria Engaged in Mass and Energy Balances 128 3.5 Challenges of Integrating Social Justice into the Engineering Sciences 132 3.5.1 Accreditation 132 3.5.2 Student Attitude 133 3.5.3 Faculty Attitude 133 3.6 Opportunities Associated with Integrating Social Justice 135 3.6.1 Student Perspectives on Opportunities 136 3.6.2 Teaching and Scholarship Opportunities for Faculty 139 3.7 Author Narratives on Challenges and Opportunities 141 3.7.1 IFCS Reflection by Dr. Johnson 141 3.7.2 CTSS Reflection by Dr. Huff 142 3.7.2.1 CTSS Follow-Up Reflection by Dr. Huff 143 3.7.3 Mass and Energy Balances Reflection by Dr. Riley 144 3.8 Conclusion 145 Appendix 3.A IFCS Case Study Matrix. The Case Study Options are Mapped to Technical and Social Justice Learning Objectives 146 Appendix 3.B SJ Integration Issues. For Future IFCS Course Iterations, the Key SJ Integration Issues and Their Potential Solutions are Explored 147 Acknowledgments 149 References 149 4 HUMANITIES AND SOCIAL SCIENCES IN ENGINEERING EDUCATION: FROM IRRELEVANCE TO SOCIAL JUSTICE 155 4.1 Humanities and Social Sciences, the Engineering Curriculum, and the Distancing of Engineering Education from Pressing Social Problems 157 4.2 The Cold War, the Anti-Technology Movement, and a Marginalized HSS 160 4.2.1 Humanities and Social Sciences in 1960s and 1970s Engineering Education 161 4.2.2 The Emergence and Evolution of STS 162 4.3 It Is Time: Integration of Engineering and Social Justice Through the HSS The Historical Convergence of ABET 2000 and More 163 4.3.1 Changes in the Institutional Landscape 165 4.3.2 Changes in the Scholarly Landscape 166 4.4 Emerging Curricular Innovations 168 4.5 Engineering and Social Justice at Colorado School of Mines 170 4.5.1 Background 170 4.5.2 Description of the Course Engineering and Social Justice 171 4.5.3 Course Learning Outcomes 172 4.6 Intercultural Communication at Colorado School of Mines 173 4.6.1 Course Background 174 4.6.2 Course Description 174 4.6.3 Learning Outcomes 177 4.7 Document Design and Graphics at Utah State 177 4.7.1 Course Background 178 4.7.2 Course Description 178 4.7.3 Learning Outcomes 179 4.8 Benefits and Limitations 182 4.8.1 Benefits 182 4.8.2 Limitations 183 Appendix 4.A Privilege Walk Questions 184 Appendix 4.B Privilege by Numbers Activity 187 Appendix 4.C Intercultural Communication Foundational Questions 188 Acknowledgments 189 References 190 5 TRANSFORMING ENGINEERING EDUCATION AND PRACTICE 197 5.1 Practical Guidelines: From Problem Space to Program Space 199 5.1.1 E4SJ in the Problem Space 199 5.1.2 E4SJ in the Course Space 202 5.1.3 E4SJ in Boundary Spaces 206 5.1.4 E4SJ in the Program Space 207 5.2 Broader Implications of E4SJ-Infused Transformations 208 5.2.1 Changing Who Becomes an Engineer 208 5.2.2 Changing the Culture of Engineering 211 5.2.3 From a Culture of Disengagement to One of Greater Public Engagement 215 5.3 Identity Challenges and Inspirations 217 5.3.1 Engineering Student Identity Issues 217 5.3.2 Engineering Faculty Identity Issues 223 Appendix 5.A Assignment and Examples of Problem Rewrites 228 References 237 6 CONCLUSION: MAKING SOCIAL JUSTICE VISIBLE AND VALUED 243 6.1 Engineering Justice into Your Career 244 6.1.1 Recognizing Barriers and Opportunities to Making E4SJ Visible 245 6.1.2 Developing Creative Framing on the Road to Tenure and Promotion 246 6.1.3 Engaging Other Stakeholders and Building a Community of Practice 250 6.1.4 Supporting Students interested in E4SJ Beyond the Classroom 250 6.1.5 Enacting E4SJ Outside the Home Institution 252 6.2 Future E4SJ Research Directions 253 6.2.1 Longitudinal Studies 253 6.2.2 Vehicles for Giving Voice to Marginalized Groups 255 References 255 Index 259.
• (source: Nielsen Book Data)9781118757307 20180226

Shows how the engineering curriculum can be a site for rendering social justice visible in engineering, for exploring complex socio-technical interplays inherent in engineering practice, and for enhancing teaching and learning Using social justice as a catalyst for curricular transformation, Engineering Justice presents an examination of how politics, culture, and other social issues are inherent in the practice of engineering. It aims to align engineering curricula with socially just outcomes, increase enrollment among underrepresented groups, and lessen lingering gender, class, and ethnicity gaps by showing how the power of engineering knowledge can be explicitly harnessed to serve the underserved and address social inequalities. This book is meant to transform the way educators think about engineering curricula through creating or transforming existing courses to attract, retain, and motivate engineering students to become professionals who enact engineering for social justice. Engineering Justice offers thought-provoking chapters on: why social justice is inherent yet often invisible in engineering education and practice; engineering design for social justice; social justice in the engineering sciences; social justice in humanities and social science courses for engineers; and transforming engineering education and practice. In addition, this book: Provides a transformative framework for engineering educators in service learning, professional communication, humanitarian engineering, community service, social entrepreneurship, and social responsibilityIncludes strategies that engineers on the job can use to advocate for social justice issues and explain their importance to employers, clients, and supervisorsDiscusses diversity in engineering educational contexts and how it affects the way students learn and develop Engineering Justice is an important book for today s professors, administrators, and curriculum specialists who seek to produce the best engineers of today and tomorrow.
(source: Nielsen Book Data)9781118757307 20180226

• I. Developing Perspective 1. A Modern View of the Universe 2. Discovering the Universe for Yourself 3. The Science of Astronomy II. Key Concepts for Astronomy 4. Making Sense of the Universe: Understanding Motion, Energy, and Gravity 5. Light: The Cosmic Messenger III. Learning from Other Worlds 6. Formation of the Solar System 7. Earth and the Terrestrial Worlds 8. Jovian Planet Systems 9. Asteroids, Comets, and Dwarf Planets: Their Nature, Orbits, and Impacts 10. Other Planetary Systems: The New Science of Distant Worlds IV. Stars 11. Our Star 12. Surveying the Stars 13. Star Stuff 14. The Bizarre Stellar Graveyard V. Galaxies and Beyond 15. Our Galaxy 16. A Universe of Galaxies 17. The Birth of the Universe 18. Dark Matter, Dark Energy, and the Fate of the Universe VI. Life on Earth and Beyond 19. Life in the Universe.
• (source: Nielsen Book Data)9780134446431 20171227

For one-semester courses in astronomy. A practical introduction to Astronomy with an emphasis on critical thinking about our place in the universe This 8th Edition of Essential Cosmic Perspective provides readers without science backgrounds with a streamlined, cutting-edge introduction to astronomy. Built on a strong tradition of effective pedagogy and coverage, the text focuses on skill-building and includes group work exercises that require active participation. Dedicated to bringing an understanding of the universe, its scientific basis and its relevance to our lives, each chapter is written to specific learning goals that build an ideal learning path for readers. Aiming to foster a lifelong learning experience, the authors focus on key concepts, providing big picture context, promoting conceptual understanding, and preferring plain language to jargon. The 8th Edition incorporates the latest scientific updates in the field of astronomy and includes new features that reinforce critical thinking and excite readers' curiosity. New features such as Extraordinary Claims engage readers by presenting extraordinary claims about the universe and how they were either supported or debunked as scientists collected more evidence, reinforcing the process of science and how scientists think critically to evaluate them. My Cosmic Perspective establishes a personal connection between readers and the cosmos as they learn to think critically about the meaning of what they learn in their astronomy studies and beyond. Designed and written for a one semester course, this text shares many of the strengths of its more comprehensive best-selling sibling, The Cosmic Perspective. Also available with Mastering Astronomy Mastering (TM) Astronomy is the leading online homework, tutorial, and assessment system, designed to improve results by engaging students with vetted, interactive content. Instructors ensure students arrive ready to learn by assigning new Interactive Prelecture videos that give students exposure to key concepts before class and open classroom time for active learning or deeper discussions of topics. With Learning Catalytics (TM) instructors can expand on key concepts and encourage student engagement during lecture through questions answered individually or in pairs and groups. Students further master concepts through book-specific Mastering Astronomy assignments, which provide hints and answer-specific feedback that build problem-solving skills. Mastering Astronomy now features Virtual Astronomy Labs, providing assignable online laboratory activities that use Stellarium and Interactive Figures. Note: You are purchasing a standalone product; Mastering (TM) Astronomy does not come packaged with this content. Students, if interested in purchasing this title with Mastering Astronomy, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and Mastering Astronomy, search for: 0134516338 / 9780134516332 Essential Cosmic Perspective Plus Mastering Astronomy with eText, The -- Access Card Package Package consists of: 0134509293 / 9780134509297 Mastering Astronomy with Pearson eText -- ValuePack Access Card -- for Essential Cosmic Perspective, The0134446437 / 9780134446431 Essential Cosmic Perspective, The.
(source: Nielsen Book Data)9780134446431 20171227

• Preface. Chapter 1. The ethics gap in contemporary engineering: Two vignettes ; A gap between education and experience ; Evidence ; Importance ; Unfruitful approaches to bridging the gap ; Preferred approach.
• Chapter 2. Sociological and ethical preliminaries: Sociology of engineering ; Professional engineering society codes of ethics.
• Chapter 3. The fundamental ethical responsibilities of engineers: An ethical responsibilities approach ; Ethical issues and harm ; The fundamental ethical responsibilities of engineers.

• Chapter 4. Case studies of ethical issues in engineering: Case 1. The Cadillac DeVille/Seville engine-control chip (1990-1995) ; Case 2. SDI battlefield management software (1983-1985) ; Case 3. Collaborative research practices at Bell Labs (1997-2002) ; Case 4. The Apple Newton MessagePad (1990-1993) ; Case 5. An employee database management system ; Case 6. The Citicorp Center Tower (1970-1978) ; Case 7. The Union Carbide MIC plant in Bhopal (1970-1984) ; Case 8. The space shuttle Challenger (1983-1986) ; Case 9. A composite-material bicycle project (1989-1992) ; Case 10. Nanotechnology R&D (1985-present) ; Case 11. The Ford Pinto (1969-1972) ; Case 12. Topf & Sons : crematorium ovens for the Nazi SS (1939-1945) ; Case 13. TRW and the US ballistic missile defense system (1995-2003) ; Case 14. The Kansas City Hyatt Regency Hotel walkways (1979-1981) ; Case 15. The Manhattan Westway Project (1974-1985) ; Case 16. Innovations for rural Kenyan farmers (1991-present) ; Case 17. Google street view
• Case 18. Opiod biosynthesis and neural enhancement.

• Chapter 5. Key case ideas and lessons: The leading precept of most current codes of engineering ethics ; The FEREs ; Ethics and the sociology of contemporary engineering ; An ethically problematic pattern of engineering practice ; Whistle-blowing and ethical responsibility ; Risk and idealization of technology in society ; Ethical responsibility and the culture of the engineering workplace ; An overlooked responsibility of engineers ; An engineering professional ; Engineering design, paradigm departure, and the ethics of precaution ; Normalization of risk and routinization of the experimental ; Technology transfer and ethical responsibility ; "Two cultures" and ethical responsibility ; Decontextualization ; The politicization and economization of engineering decision-making ; Negligence ; Workplace culture and the ethically responsible engineer ; Conflicts of interest ; "Design for ..." ; Ethical issues in engineering research ; Factors conducive to engineering misconduct.

• Chapter 6. Resources and options for ethically responsible engineers: Organizational resources ; Legal resources and options ; Employment-related options
• Chapter 7. Conclusion: Bucciarelli's critique of U.S. engineering-ethics education ; A foundational-contextual ethical responsibilities approach ; Two quotations.
• Bibliography
• Index.

An exploration of the ethics of practical engineering through analyses of eighteen case studies. The Ethical Engineer explores ethical issues that arise in engineering practice, from technology transfer to privacy protection to whistle-blowing. Presenting key ethics concepts and real-life examples of engineering work, Robert McGinn illuminates the ethical dimension of engineering practice and helps students and professionals determine engineers' context-specific ethical responsibilities. McGinn highlights the "ethics gap" in contemporary engineering-- the disconnect between the meager exposure to ethical issues in engineering education and the ethical challenges frequently faced by engineers. He elaborates four "fundamental ethical responsibilities of engineers" (FEREs) and uses them to shed light on the ethical dimensions of diverse case studies, including ones from emerging engineering fields. After examining the extent to which the actions of engineers in the cases align with the FEREs, McGinn recapitulates key ideas used in analyzing the cases and spells out the main lessons they suggest. He identifies technical, social, and personal factors that induce or press engineers to engage in misconduct and discusses organizational, legal, and individual resources available to those interested in ethically responsible engineering practice.

• PREFACE Chapter 1 - Introduction to experimental designPART I - Statistical principles on design of experiments Chapter 2 - One-factor designs and the analysis of variance Chapter 3 - Some further considerations on one-factor design and ANOVA Chapter 4 - Multiple-comparison testingChapter 5 - Orthogonality, orthogonal decomposition, and their role in modern experimental designPART II - Identifying active factors Chapter 6 - Two-factor cross-classification designs Chapter 7 - Nested, or hierarchical, designs Chapter 8 - Designs with three or more factors: Latin-square and related designsPART III - Studying factors' effects (suggestion) Chapter 9 - Two-level factorial designs Chapter 10 - Confounding/blocking in 2k designsChapter 11 - Two-level fractional-factorial designs Chapter 12 - Designs with factors at three levels Chapter 13 - Introduction to Taguchi methodsPART IV - Regression analysis, surface designs, and other topicsChapter 14 - Simple regression Chapter 15 - Multiple and step-wise regression Chapter 16 - Introduction to Response-Surface Methodology Chapter 17 - Introduction to mixture design and triangular surfacesChapter 18 - Literature on experimental design and discussion of some topics not covered in the text.
• (source: Nielsen Book Data)9783319645827 20180416

This text introduces and provides instruction on the design and analysis of experiments for a broad audience. Formed by decades of teaching, consulting, and industrial experience in the Design of Experiments field, this new edition contains updated examples, exercises, and situations covering the science and engineering practice. This text minimizes the amount of mathematical detail, while still doing full justice to the mathematical rigor of the presentation and the precision of statements, making the text accessible for those who have little experience with design of experiments and who need some practical advice on using such designs to solve day-to-day problems. Additionally, an intuitive understanding of the principles is always emphasized, with helpful hints throughout.
(source: Nielsen Book Data)9783319645827 20180416

Building on the success of the best-selling Fundamental Accounting Principles text, authors John Wild, Ken W. Shaw, and Barbara Chiappetta created Financial and Managerial Accounting: Information for Decisions to provide a corporate perspective and balanced coverage in this growing course area. With its step-by-step approach, FinMan streamlines complex accounting processes and helps students build confidence by mastering key concepts and procedures. Chapter opening vignettes using dynamic entrepreneurs appeal to all students and show the relevance of accounting. Students are encouraged to think like a businessperson and apply what they learn. A wide variety of assignments provide instructors with materials to teach, assess, and challenge students on several levels. Join your colleagues and the students that have used this best-selling learning system to advance their education and careers.
(source: Nielsen Book Data)9781259726705 20180702

• Preface.- 1 Introduction.- 2 Optimizing Image Quality.- 3 Computational Color Imaging.- 4 Optimization Methods for SAR.- 5 Computational Spectral Ultrafast Imaging.- 6 Discriminative Sparse Representation.- 7 Sparsity-based Nonlocal Image Restoration.- 8 Sparsity Constrained Estimation.- 9 Optimization Problems Associated with Manifolds.
• (source: Nielsen Book Data)9783319616087 20180604

This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization with the goal of designing tractable algorithms. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems. The first part includes a review of the mathematical methods and foundations required, and covers topics in image quality optimization and assessment. The second part of the book discusses concepts in image formation and capture from color imaging to radar and multispectral imaging. The third part focuses on sparsity constrained optimization in image processing and vision and includes inverse problems such as image restoration and de-noising, image classification and recognition and learning-based problems pertinent to image understanding. Throughout, convex optimization techniques are shown to be a critically important mathematical tool for imaging science problems and applied extensively. Convex Optimization Methods in Imaging Science is the first book of its kind and will appeal to undergraduate and graduate students, industrial researchers and engineers and those generally interested in computational aspects of modern, real-world imaging and image processing problems.
(source: Nielsen Book Data)9783319616087 20180604

• Preface xv About the Author xix 1 Electrons, Photons, and Phonons 1.1 Selected Concepts of Quantum Mechanics 1.1.1 The dual nature of the photon 1.1.2 The dual nature of the electron 1.1.3 Electrons in confined environments 1.2 Selected Concepts of Statistical Mechanics 1.2.1 Thermal motion and thermal energy 1.2.2 Thermal equilibrium 1.2.3 Electron statistics 1.3 Selected Concepts of Solid-State Physics 1.3.1 Bonds and bands 1.3.2 Metals, insulators, and semiconductors 1.3.3 Density of states 1.3.4 Lattice vibrations: phonons 1.4 Summary 1.5 Further reading Problems 2 Carrier Statistics in Equilibrium 2.1 Conduction and Valence Bands-- Bandgap-- Holes 2.2 Intrinsic Semiconductor 2.3 Extrinsic Semiconductor 2.3.1 Donors and acceptors 2.3.2 Charge neutrality 2.3.3 Equilibrium carrier concentration in a doped semiconductor 2.4 Carrier Statistics in Equilibrium 2.4.1 Conduction and valence band density of states 2.4.2 Equilibrium electron concentration 2.4.3 Equilibrium hole concentration 2.4.4 np product in equilibrium 2.4.5 Location of Fermi level 2.5 Summary 2.6 Further Reading Problems 3 Carrier Generation and Recombination 3.1 Generation and Recombination Mechanisms 3.2 Thermal Equilibrium: Principle of Detailed Balance 3.3 Generation and Recombination Rates in Thermal Equilibrium 3.3.1 Band-to-band optical generation and recombination 3.3.2 Auger generation and recombination 3.3.3 Trap-assisted thermal generation and recombination 3.4 Generation and Recombination Rates Outside Equilibrium 3.4.1 Quasi-neutral low-level injection-- recombination lifetime 3.4.2 Extraction-- generation lifetime 3.5 Dynamics of Excess Carriers in Uniform Situations 3.5.1 Example 1: Turn-on transient 3.5.2 Example 2: Turn-off transient 3.5.3 Example 3: A pulse of light 3.6 Surface Generation and Recombination 3.7 Summary 3.8 Further Reading Problems 4 Carrier Drift and Diffusion 4.1 Thermal Motion 4.1.1 Thermal velocity 4.1.2 Scattering 4.2 Drift 4.2.1 Drift velocity 4.2.2 Velocity saturation 4.2.3 Drift current 4.2.4 Energy band diagram under electric field 4.3 Diffusion 4.3.1 Fick's first law 4.3.2 The Einstein relation 4.3.3 Diffusion current 4.4 Transit Time 4.5 Nonuniformly Doped Semiconductor in Thermal Equilibrium 4.5.1 Gauss' law 4.5.2 The Boltzmann relations 4.5.3 Equilibrium carrier concentration 4.6 Quasi-Fermi Levels and Quasi-Equilibrium 4.7 Summary 4.8 Further Reading Problems 5 Carrier Flow 5.1 Continuity Equations 5.2 Surface Continuity Equations 5.2.1 Free surface 5.2.2 Ohmic contact 5.3 Shockley Equations 5.4 Simplifications of Shockley Equations to One-Dimensional Quasi-Neutral Situations 5.5 Majority-Carrier Situations 5.5.1 Example 1: Semiconductor bar under voltage 5.5.2 Example 2: Integrated resistor 5.6 Minority-Carrier Situations 5.6.1 Example 3: Diffusion and bulk recombination in a "long" bar 5.6.2 Example 4: Diffusion and surface recombination in a "short" bar 5.6.3 Length scales of minority carrier situations 5.7 Dynamics of Majority-Carrier Situations 5.8 Dynamics of Minority-Carrier Situations 5.8.1 Example 5: Transient in a bar with S = â 5.9 Transport in Space-Charge and High-Resistivity Regions 5.9.1 Example 6: Drift in a high-resistivity region under external electric field 5.9.2 Comparison between SCR and QNR transport 5.10 Carrier Multiplication and Avalanche Breakdown 5.10.1 Example 7: Carrier multiplication in a high-resistivity region with uniform electric field 5.11 Summary 5.12 Further Reading Problems 6 PN Junction Diode 6.1 The Ideal PN Junction Diode 6.2 Ideal PN Junction in Thermal Equilibrium 6.3 Current-Voltage Characteristics of The Ideal PN Diode 6.3.1 Electrostatics under bias 6.3.2 I-V characteristics: qualitative discussion 6.3.3 I-V characteristics: quantitative models 6.4 Charge-Voltage Characteristics of Ideal PN Diode 6.4.1 Depletion charge 6.4.2 Minority carrier charge 6.5 Equivalent Circuit Models of The Ideal PN Diode 6.6 Nonideal and Second-Order Effects 6.6.1 Short diode 6.6.2 Space-charge generation and recombination 6.6.3 Series resistance 6.6.4 Breakdown voltage 6.6.5 Nonuniform doping distributions 6.6.6 High-injection effects 6.7 Integrated PN Diode 6.7.1 Isolation 6.7.2 Series resistance 6.7.3 High-low junction 6.8 Summary 6.9 Further Reading Problem 7 Schottky Diode and Ohmic Contact 7.1 The Ideal Schottky Diode 7.2 Ideal Schottky Diode in Thermal Equilibrium 7.2.1 A simpler system: a metal-metal junction 7.2.2 Energy band lineup of metal-semiconductor junction 7.2.3 Electrostatics of metal-semiconductor junction in equilibrium 7.3 Current-Voltage Characteristics of Ideal Schottky Diode 7.3.1 Electrostatics under bias 7.3.2 I-V characteristics: qualitative discussion 7.3.3 I-V characteristics: thermionic emission model 7.4 Charge-Voltage Characteristics of Ideal Schottky Diode 7.5 Equivalent Circuit Models for The Ideal Schottky Diode 7.6 Nonideal and Second-Order Effects 7.6.1 Series resistance 7.6.2 Breakdown voltage 7.7 Integrated Schottky Diode 7.8 Ohmic Contacts 7.8.1 Lateral ohmic contact: transmission-line model 7.8.2 Boundary conditions imposed by ohmic contacts 7.9 Summary 7.10 Further Reading Problems 8 The Si Surface and the Metal-OxideSemiconductor Structure 8.1 The Semiconductor Surface 8.2 The Ideal Metal-Oxide-Semiconductor Structure 8.3 The Ideal Metal-Oxide-Semiconductor Structure at Zero Bias 8.3.1 General relations for the electrostatics of the ideal MOS structure 8.3.2 Electrostatic of the MOS structure under zero bias 8.4 The Ideal Metal-Oxide Semiconductor Structure Under Bias 8.4.1 Depletion 8.4.2 Flatband 8.4.3 Accumulation 8.4.4 Threshold 8.4.5 Inversion 8.4.6 Summary of charge-voltage characteristics 8.5 Dynamics of The MOS Structure 8.5.1 Quasi-static C-V characteristics 8.5.2 High-frequency C-V characteristics 8.5.3 Deep depletion 8.6 Weak Inversion and The Subthreshold Regime 8.7 Three-Terminal MOS Structure 8.8 Summary 8.9 Further Reading Problems 9 The "Long" Metal-Oxide-Semiconductor Field-Effect Transistor 9.1 The Ideal MOSFET 9.2 Qualitative Operation of The Ideal MOSFET 9.3 Inversion Layer Transport in The Ideal MOSFET 9.4 Current-Voltage Characteristics of The Ideal MOSFET 9.4.1 The cut-off regime 9.4.2 The linear regime 9.4.3 The saturation regime 9.4.4 DC large-signal equivalent-circuit model of ideal MOSFET 9.4.5 Energy band diagrams 9.5 Charge-Voltage Characteristics of The Ideal MOSFET 9.5.1 Depletion charge 9.5.2 Inversion charge 9.6 Small-Signal Behavior of Ideal MOSFET 9.6.1 Small-signal equivalent circuit model of ideal MOSFET 9.6.2 Short-circuit current-gain cut-off frequency, fT, of ideal MOSFET in saturation 9.7 Nonideal Effects in MOSFET 9.7.1 Body effect 9.7.2 Effect of back bias 9.7.3 Channel-length modulation 9.7.4 The subthreshold regime 9.7.5 Source and drain resistance 9.8 Summary 9.9 Further Reading Problems 10 The "Short" Metal-Oxide-Semiconductor Field-Effect Transistor 10.1 MOSFET Short-Channel Effects: Transport 10.1.1 Mobility degradation 10.1.2 Velocity saturation 10.2 MOSFET Short-Channel Effects: Electrostatics 10.2.1 Threshold voltage dependence on gate length: VT rolloff 10.2.2 Threshold voltage dependence on VDS: drain-induced barrier lowering (DIBL) 10.2.3 Subthreshold swing dependence on gate length and VDS 10.3 MOSFET Short-Channel Effects: Gate Stack Scaling 10.3.1 Gate capacitance 10.3.2 Gate leakage current 10.4 MOSFET High-Field Effects 10.4.1 Electrostatics of velocity saturation region 10.4.2 Impact ionization and substrate current 10.4.3 Output conductance 10.4.4 Gate-induced drain leakage 10.5 MOSFET Scaling 10.5.1 The MOSFET as a switch 10.5.2 Constant field scaling of the ideal MOSFET 10.5.3 Constant voltage scaling of the ideal MOSFET 10.5.4 Generalized scaling of short MOSFETs 10.5.5 MOSFET scaling: a historical perspective 10.5.6 Evolution of MOSFET design 10.6 Summary 10.7 Further Reading Problems 11 The Bipolar Junction Transistor 11.1 The Ideal BJT 11.2 Current-Voltage Characteristics of The Ideal BJT 11.2.1 The forward-active regime 11.2.2 The reverse regime 11.2.3 The cut-off regime 11.2.4 The saturation regime 11.2.5 Output I-V characteristics 11.3 Charge-Voltage Characteristics of Ideal BJT 11.3.1 Depletion charge 11.3.2 Minority carrier charge.
• (source: Nielsen Book Data)9780134670904 20171218

A modern take on microelectronic device engineering Microelectronics is a 50-year-old engineering discipline still undergoing rapid evolution and societal adoption. Integrated Microelectronic Devices: Physics and Modeling fills the need for a rigorous description of semiconductor device physics that is relevant to modern nanoelectronics. The central goal is to present the fundamentals of semiconductor device operation with relevance to modern integrated microelectronics. Emphasis is devoted to frequency response, layout, geometrical effects, parasitic issues and modeling in integrated microelectronics devices (transistors and diodes). In addition to this focus, the concepts learned here are highly applicable in other device contexts. This text is suitable for a one-semester junior or senior-level course by selecting the front sections of selected chapters (e.g. 1-9). It can also be used in a two-semester senior-level or a graduate-level course by taking advantage of the more advanced sections.
(source: Nielsen Book Data)9780134670904 20171218

• What is a model?
• Supervised and unsupervised machine learning
• Sampling statistics and model training in R
• Regression in a nutshell
• Neural networks in a nutshell
• Tree-based methods
• Other advanced methods
• Machine learning with the caret package
• Encyclopedia of machine learning models in caret.

Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you'll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you'll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training Understand machine learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear regression models used in business and science Use single-layer and multilayer neural networks for calculating outcomes Look at how tree-based models work, including popular decision trees Get a comprehensive view of the machine learning ecosystem in R Explore the powerhouse of tools available in R's caret package.
(source: Nielsen Book Data)9781491976449 20180604

Give students a rigorous, complete, and integrated treatment of the mechanics of materials -- an essential subject in mechanical, civil, and structural engineering. This leading text, Goodno/Gere's MECHANICS OF MATERIALS, 9E, examines the analysis and design of structural members subjected to tension, compression, torsion, and bending -- laying the foundation for further study.
(source: Nielsen Book Data)9781337093347 20170227

This open access book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which "big-data" generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. "Materials informatics" is the central paradigm in the new trend. "Nanoinformatics" is its essential subset, which focuses on nanostructures of materials such as surfaces, interfaces, dopants, and point defects, playing a critical role in determining materials properties. There have been significant advances in experimental and computational techniques to characterize individual atoms in nanostructures and to gain quantitative information. The collaboration of researchers in materials science and information science is growing actively and is creating a new trend in materials science and engineering. This book is open access under a CC BY license.
(source: Nielsen Book Data)9789811076169 20180530

• Cheap changes everything
• The magic of prediction
• Why it's called intelligence
• Data is the new oil
• The new division of labor
• Unpacking decisions
• The value of judgment
• Taming complexity
• What machines can learn
• Fully automated decision-making
• Deconstructing workflows
• Decomposing decisions
• Job redesign
• AI in the C-suite
• When AI transforms your business
• Managing AI risk
• Beyond business.

"What does AI mean for your business? Read this book to find out." -- Hal Varian, Chief Economist, Google Artificial intelligence does the seemingly impossible, magically bringing machines to life--driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future. But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs. When AI is framed as cheap prediction, its extraordinary potential becomes clear. Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.
(source: Nielsen Book Data)9781633695672 20180423