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Titlebook: Microcontroller Programming and Interfacing TI MSP 430 PART II; Steven F. Barrett,Daniel J. Pack Book 2011 Springer Nature Switzerland AG

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书目名称Microcontroller Programming and Interfacing TI MSP 430 PART II
编辑Steven F. Barrett,Daniel J. Pack
视频video
丛书名称Synthesis Lectures on Digital Circuits & Systems
图书封面Titlebook: Microcontroller Programming and Interfacing TI MSP 430 PART II;  Steven F. Barrett,Daniel J. Pack Book 2011 Springer Nature Switzerland AG
描述This book provides a thorough introduction to the Texas Instruments MSP430 microcontroller. The MSP430 is a 16-bit reduced instruction set (RISC) processor that features ultra low power consumption and integrated digital and analog hardware. Variants of the MSP430 microcontroller have been in production since 1993. This provides for a host of MSP430 products including evaluation boards, compilers, and documentation. A thorough introduction to the MSP430 line of microcontrollers, programming techniques, and interface concepts are provided along with considerable tutorial information with many illustrated examples. Each chapter provides laboratory exercises to apply what has been presented in the chapter. The book is intended for an upper level undergraduate course in microcontrollers or mechatronics but may also be used as a reference for capstone design projects. Also, practicing engineers already familiar with another microcontroller, who require a quick tutorial on the microcontroller, will find this book very useful.
出版日期Book 2011
版次1
doihttps://doi.org/10.1007/978-3-031-79831-3
isbn_ebook978-3-031-79831-3Series ISSN 1932-3166 Series E-ISSN 1932-3174
issn_series 1932-3166
copyrightSpringer Nature Switzerland AG 2011
The information of publication is updating

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Communication Systems, After reading this chapter, the reader should be able to
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Microcontroller Programming and Interfacing TI MSP 430 PART II978-3-031-79831-3Series ISSN 1932-3166 Series E-ISSN 1932-3174
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Steven F. Barrett,Daniel J. Packdata stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This pape
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Steven F. Barrett,Daniel J. Packime required by these three algorithms follows a lognormal distribution. In low complexity problems, the lognormal distribution looses some accuracy to describe the algorithm run-times. The lognormality of the run-times opens the use of powerful parametric statistics to compare execution times, whic
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