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Titlebook: Microcontrollers in Practice; Marian Mitescu,Ioan Susnea Book 2005 Springer-Verlag Berlin Heidelberg 2005 AVR.EEPROM.EPROM.HC11.MCS51.Micr

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書目名稱Microcontrollers in Practice
編輯Marian Mitescu,Ioan Susnea
視頻videohttp://file.papertrans.cn/634/633168/633168.mp4
概述Most comprehensive and up-to-date book on microcontrollers.Includes supplementary material:
叢書名稱Springer Series in Advanced Microelectronics
圖書封面Titlebook: Microcontrollers in Practice;  Marian Mitescu,Ioan Susnea Book 2005 Springer-Verlag Berlin Heidelberg 2005 AVR.EEPROM.EPROM.HC11.MCS51.Micr
描述.Stressing common characteristics and real applications of the most used microcontrollers, this practical guide provides readers with hands-on knowledge of how to implement three families of microcontrollers (HC11, AVR, and 8051). Unlike the rest of the ocean of literature on individual chips, .Microcontrollers in Practice .supplies side-by-side comparisons and an overview that treats the systems as resources available for implementation. Packed with hundreds of practical examples and exercises to foster mastery of concepts and details, the guide also includes several extended projects. By treating the less expensive 8-bit and RISC microcontrollers, this information-dense manual equips students and home-experimenters with the know-how to put these devices into operation..
出版日期Book 2005
關(guān)鍵詞AVR; EEPROM; EPROM; HC11; MCS51; Microcontroller; PROM; Project; ROM
版次1
doihttps://doi.org/10.1007/3-540-28308-0
isbn_ebook978-3-540-28308-9Series ISSN 1437-0387 Series E-ISSN 2197-6643
issn_series 1437-0387
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

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Book 2005with hundreds of practical examples and exercises to foster mastery of concepts and details, the guide also includes several extended projects. By treating the less expensive 8-bit and RISC microcontrollers, this information-dense manual equips students and home-experimenters with the know-how to put these devices into operation..
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e test the best of such evolved algorithms on a large set of standard benchmarks from the Harwell-Boeing sparse matrix collection against two state-of-the-art algorithms from the literature. Our algorithm outperforms both algorithms by a significant margin, clearly indicating the promise of the appr
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ct remains true for some time after the event; (5) the effect only holds over some time during the progress of the event; (6) the effect becomes true during the progress of the event and remains true until the event completes; (7) the effect becomes true during the progress of the event and remains
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another. Recent approaches in sentiment classification have proposed combining machine learning with background knowledge from sentiment lexicons for improved performance. Thus, we present a simple, yet effective approach for augmenting .3 with background knowledge from SentiWordNet. Evaluation sho
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