標題: Titlebook: Microcontrollers in Practice; Marian Mitescu,Ioan Susnea Book 2005 Springer-Verlag Berlin Heidelberg 2005 AVR.EEPROM.EPROM.HC11.MCS51.Micr [打印本頁] 作者: Jurisdiction 時間: 2025-3-21 16:26
書目名稱Microcontrollers in Practice影響因子(影響力)
書目名稱Microcontrollers in Practice影響因子(影響力)學科排名
書目名稱Microcontrollers in Practice網絡公開度
書目名稱Microcontrollers in Practice網絡公開度學科排名
書目名稱Microcontrollers in Practice被引頻次
書目名稱Microcontrollers in Practice被引頻次學科排名
書目名稱Microcontrollers in Practice年度引用
書目名稱Microcontrollers in Practice年度引用學科排名
書目名稱Microcontrollers in Practice讀者反饋
書目名稱Microcontrollers in Practice讀者反饋學科排名
作者: 催眠藥 時間: 2025-3-21 23:02 作者: photopsia 時間: 2025-3-22 01:50 作者: 雄辯 時間: 2025-3-22 06:05
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..作者: agglomerate 時間: 2025-3-22 10:59 作者: 擔心 時間: 2025-3-22 16:23 作者: 離開 時間: 2025-3-22 19:40
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作者: filial 時間: 2025-3-22 23:21 作者: 欺騙世家 時間: 2025-3-23 01:22
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 作者: bibliophile 時間: 2025-3-23 08:02
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作者: 陰郁 時間: 2025-3-23 10:38
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作者: acheon 時間: 2025-3-23 17:32
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 作者: 飛行員 時間: 2025-3-23 19:11 作者: 產生 時間: 2025-3-23 23:09 作者: bourgeois 時間: 2025-3-24 02:59 作者: 擔心 時間: 2025-3-24 09:08
e an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up 作者: 青少年 時間: 2025-3-24 14:40
th the actions of agents and factors intrinsic to the environment which agents have no control over. The effects of bounding agents’ visual input on learning and performance in various scenarios where the complexity of Tileworld is altered is analysed using computer simulations. Our results show tha作者: 護航艦 時間: 2025-3-24 17:13
ty to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Nei作者: MUT 時間: 2025-3-24 20:50
ty to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Nei作者: 變化無常 時間: 2025-3-25 01:37
ptimal solution, turn out to be very similar to how probabilities are handled within a Bayesian Network. The paper presents a branch-and-bound algorithm for solving this new class of problems, analyzes its computational complexity and reports some encouraging experimental results.作者: bourgeois 時間: 2025-3-25 04:38
Springer-Verlag Berlin Heidelberg 2005作者: 場所 時間: 2025-3-25 09:16
Microcontrollers in Practice978-3-540-28308-9Series ISSN 1437-0387 Series E-ISSN 2197-6643 作者: 安慰 時間: 2025-3-25 15:20 作者: 保全 時間: 2025-3-25 18:58
Springer Series in Advanced Microelectronicshttp://image.papertrans.cn/m/image/633168.jpg作者: Rejuvenate 時間: 2025-3-25 22:37 作者: Graves’-disease 時間: 2025-3-26 01:25 作者: WAIL 時間: 2025-3-26 05:11
in such a way as to minimize the maximum absolute difference between the labels of adjacent vertices. The problem is isomorphic to the important problem of reordering the rows and columns of a symmetric matrix so that its non-zero entries are maximally close to the main diagonal — a problem which pr作者: Cumbersome 時間: 2025-3-26 11:51 作者: 贊成你 時間: 2025-3-26 15:27
tion of the so-called (most) General Temporal Constraint (GTC) is formulated, which guarantees the common-sense assertion that “the beginning of the effect cannot precede the beginning of its causal event”. It is shown that there are actually in total 8 possible temporal causal relationships which s作者: sphincter 時間: 2025-3-26 20:50
-Of-Words (BOW) model, where individual terms are used as features. However, the BOW model is unable to sufficiently model the variation inherent in natural language text. Term-relatedness metrics are commonly used to overcome this limitation by capturing latent semantic concepts or topics in docume作者: 背書 時間: 2025-3-26 23:55
-Of-Words (BOW) model, where individual terms are used as features. However, the BOW model is unable to sufficiently model the variation inherent in natural language text. Term-relatedness metrics are commonly used to overcome this limitation by capturing latent semantic concepts or topics in docume作者: Biguanides 時間: 2025-3-27 03:55 作者: 突變 時間: 2025-3-27 08:45 作者: 國家明智 時間: 2025-3-27 10:17 作者: 冥想后 時間: 2025-3-27 14:01
ing approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current 作者: 頂點 時間: 2025-3-27 20:17
