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Modeling And Characterization Of Rf And Microwave Power Fets

RRP $68.00

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This is a book about the compact modeling of RF power FETs. In it, you will find descriptions of characterization and measurement techniques, analysis methods, and the simulator implementation, model verification and validation procedures that are needed to produce a transistor model that can be used with confidence by the circuit designer. Written by semiconductor industry professionals with many years' device modeling experience in LDMOS and III-V technologies, this is the first book to address the modeling requirements specific to high-power RF transistors. A technology-independent approach is described, addressing thermal effects, scaling issues, nonlinear modeling, and in-package matching networks. These are illustrated using the current market-leading high-power RF technology, LDMOS, as well as with III-V power devices. This book is a comprehensive exposition of FET modeling, and is a must-have resource for seasoned professionals and new graduates in the RF and microwave power amplifier design and modeling community. All three authors work in the RF Division at Freescale Semiconductor, Inc., in Tempe Arizona. Peter H. Aaen is Modeling Group Manager, Jaime A. Pla is Design Organization Manager, and John Wood is Senior Technical Contributor responsible for RF CAD and Modeling, and a Fellow of the IEEE.


Engineering Modeling And Design

RRP $363.99

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Engineering Modeling and Design is a comprehensive systems engineering text that focuses on systematic principles for designing systems. Concurrent engineering, which requires that from the very start of a project all players (e.g., engineering, maintenance, marketing, customers) are involved as all facets of the system life cycle are considered, is skillfully illustrated through the use of two major case studies. The text describes how a product design proceeds parallel to the process design, explains key duties of systems engineers throughout the product life cycle, and examines the process of system design in terms of life cycle requirements. Projects and problems are presented throughout the text. A homework solutions/instructor's manual is available from the publisher upon request.
Engineering Modeling and Design is an excellent text for engineering design courses in industry and upper division courses on concurrent engineering or total quality management.


Data Modeling Master Class Training Manual

RRP $461.99

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This is the sixth edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com.The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard®. You will know not just how to build a data model, but how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects.Top 10 Objectives 1. Explain data modeling components and identify them on your projects by following a question-driven approach 2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book 3. Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard® 4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing 5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions 6. Practice finding structural soundness issues and standards violations 7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start 8. Use a series of templates for capturing and validating requirements, and for data profiling 9. Evaluate definitions for clarity, completeness, and correctness 10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture.



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