Plumb the depths of core motion design fundamentals and harness the essential techniques of this diverse and innovative medium. Combine basic art and design principles with creative storytelling to create compelling style frames, design boards, and motion design projects. Here, in one volume, Austin Shaw covers all the principles any serious motion designer needs to know in order to make their artistic visions a reality and confidently produce compositions for clients, including: Illustration techniques Typography Compositing Cinematography Incorporating 3D elements Matte painting Concept development, and much more Lessons are augmented by illustrious full color imagery and practical exercises, allowing you to put the techniques covered into immediate practical context. Industry leaders and pioneers, including Karin Fong, Bradley G Munkowitz (GMUNK), Will Hyde, Erin Sarofsky, Danny Yount, and many more, contribute their professional perspectives, share personal stories, and provide visual examples of their work. Additionally, a robust companion website (www.focalpress.com/cw/shaw) features project files, video tutorials, bonus PDFs, and rolling updates to keep you informed on the latest developments in the field.

Delivering a solid introduction to assembly language and embedded systems, ARM Assembly Language: Fundamentals and Techniques, Second Edition continues to support the popular ARM7TDMI, but also addresses the latest architectures from ARM, including Cortex-A, Cortex-R, and Cortex-M processors-all of which have slightly different instruction sets, p

Urban and regional planning programs aspire to prepare practitioners to write and implement plans, primarily at the local level of government. These programs are very much "professional" in their aspirations, as opposed to research oriented. Yet, academic planning programs often place greater emphasis on theory than practice. For decades, the planning academy has acknowledged a major disconnect between what the planning academy teaches students and the techniques and skills needed to be a successful professional practitioner. Fundamentals of Plan Making will give planning students an understanding of research design as it applies to planning, develop familiarity with various data sources, and help them acquire knowledge and the ability to conduct basic planning analyses such as population projections, housing needs assessments, development impact analyses, and land use plans. Students will also learn how to implement the various citizen participation methods used by planners and develop an appreciation of the values and roles of practicing planners. In Fundamentals of Plan Making, Edward Jepson and Jerry Weitz bring their extensive experience as practicing academics and give planning students the practical, hands on tools they need to implement the various methods used to create and implement real plans and policies. Its chapters on transportation, housing, environment, economic development and other core development topics also make it a handy reference for planning practitioners.

Fundamentals and Techniques

This book covers the theory of derivatives pricing and hedging as well as techniques used in mathematical finance. The authors use a top-down approach, starting with fundamentals before moving to applications, and present theoretical developments alongside various exercises, providing many examples of practical interest.A large spectrum of concepts and mathematical tools that are usually found in separate monographs are presented here. In addition to the no-arbitrage theory in full generality, this book also explores models and practical hedging and pricing issues. Fundamentals and Advanced Techniques in Derivatives Hedging further introduces advanced methods in probability and analysis, including Malliavin calculus and the theory of viscosity solutions, as well as the recent theory of stochastic targets and its use in risk management, making it the first textbook covering this topic. Graduate students in applied mathematics with an understanding of probability theory and stochastic calculus will find this book useful to gain a deeper understanding of fundamental concepts and methods in mathematical finance.

A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.

“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning