The Intel Microprocessor Family: Software and Hardware Principles and Application
Readers will be able to build and program their own 8088 single-board computer by applying the interfacing concepts and techniques presented in this book. Coverage begins with the software architecture of the 80×86 family, including software model, instruction set and flag, and addressing modes. Abundant examples illustrate basic programming concepts such as the use of data structures, numeric conversion, string handling, and arithmetic. Hardware details of the entire 80×86 family are then examined, from pin and signal descriptions to memory and input/output system design. Advanced topics, including protected mode, WIN32 and Linux programming, and MMX technology are also introduced.
FEATURES:
- Robust coverage of hardware and software principles provides readers with good overall exposure to the 80×86 family.
- Features a wide variety of interfacing applications, including a complete microprocessor-based system that can be easily duplicated
- Teaches the relationship between the use of C programming and assembly language for interfacing and control.
- Coverage of 16-bit and 32-bit programming, exposure to the Windows WIN32 and Linux execution environments, and the process of structured programming provides readers with a strong programming foundation.


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Conventional model-based data processing methods are computationally expensive and require experts’ knowledge for the modelling of a system; neural networks provide a model-free, adaptive, parallel-processing solution. Neural Networks in a Softcomputing Framework presents a thorough review of the most popular neural-network methods and their associated techniques. This concise but comprehensive textbook provides a powerful and universal paradigm for information processing. Each chapter provides state-of-the-art descriptions of the important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms, are introduced. These are powerful tools for neural-network learning. Array signal processing problems are discussed in order to illustrate the applications of each neural-network model. Neural Networks in a Softcomputing Framework is an ideal textbook for graduate students and researchers in this field because in addition to grasping the fundamentals, they can discover the most recent advances in each of the popular models. The systematic survey of each neural-network model and the exhaustive list of references will enable researchers and students to find suitable topics for future research. The important algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.









