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Medical Coding Certification Exam Preparation

RRP $313.99

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"Medical Coding Certification Exam Preparation" is designed to aid in the review for the Certified Professional Coder (CPC) exam. While not intended to be an introduction to medical coding, it provides an extensive review of the topics students need to know for the CPC exam, including coverage of anatomy, medical terminology, pathophysiology, as well as the concepts, guidelines and rules of medical coding. Based on their significant teaching experience and affiliations with AAPC, authors Cynthia Stewart and Cynthia Ward bring a fresh approach to preparing for the exam. The book is organized around the principle that students need the tools to understand how to break cases down and how to translate services, procedures, and diagnosis into codes. The importance of understanding and knowing how to locate and use the guidelines inherent to the ICD, CPT and HCPCS coding manuals is emphasized throughout the book with the use of Spotlights and Mentor Coding Tips. Each chapter contains questions similar to those found on the CPC exam. In addition, each of the five units of the book ends with a Unit Exam, meant to be a cumulative review. Finally, the book concludes with a comprehensive practice exam using ICD-9. (An ICD-10 exam is also available to help prepare students for the ICD-10 proficiency exam in development by AAPC.) Additional practice is available through Connect Plus, McGraw-Hill's online assignment and assessment tool.


Complexity-aware High Efficiency Video Coding 2016

RRP $354.99

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This book discusses computational complexity of High Efficiency Video Coding (HEVC) encoders with coverage extending from the analysis of HEVC compression efficiency and computational complexity to the reduction and scaling of its encoding complexity. After an introduction to the topic and a review of the state-of-the-art research in the field, the authors provide a detailed analysis of the HEVC encoding tools compression efficiency and computational complexity. Readers will benefit from a set of algorithms for scaling the computational complexity of HEVC encoders, all of which take advantage from the flexibility of the frame partitioning structures allowed by the standard. The authors also provide a set of early termination methods based on data mining and machine learning techniques, which are able to reduce the computational complexity required to find the best frame partitioning structures. The applicability of the proposed methods is finally exemplified with an encoding time control system that employs the best complexity reduction and scaling methods presented throughout the book. The methods presented in this book are especially useful in power-constrained, portable multimedia devices to reduce energy consumption and to extend battery life. They can also be applied to portable and non-portable multimedia devices operating in real time with limited computational resources.


Trellis Decoding Of Block Codes

RRP $546.99

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It is a great pleasure to be asked to write the Preface for this book on trellis decoding of error correcting block codes. The subject is extremely significant both theoretically and practically, and is very timely because of recent devel- opments in the microelectronic implementation and range of application of error-control coding systems based on block codes. The authors have been notably active in signal processing and coding research and development for several years, and therefore very well placed to contribute to the state of the art on the subject of trellis decoding. In particular, the book represents a unique approach to many practical aspects of the topic. As the authors point out, there are two main classes of error control codes: block codes and convolutinal codes. Block codes came first historically and have a well-developed mathematical structure. Convolutional codes come later, and have developed heuristically, though a more formal treatment has emerged via recent developments in the theory of symbolic dynamics. Max- imum likelihood (ML) decoding of powerful codes in both these classes is computationally complex in the general case; that is, ML decoding fails into the class of NP-hard computational problems. This arieses because the de- coding complexity is an exponential function of key parameters of the code.



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