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The Complexity of Context-Sensitive Sequences
No full text在此篇論文中，我們使用L系統建模複雜度方法來計算上下文相關文法的複雜度。由於在現有文獻中已被證明出目前並沒有一般性的演算法用以計算上下文相關文法的複雜度，所以我們選擇了幾個常見的上下文相關文法例子來實作，為此我們改進了先前的L系統建模複雜度方法，使其能夠處理任意長度的符號序列。This article discusses how to apply the L-system modeling complexity method to context-sensitive sequences. Since it is proved that there exists no general calculation method to compute the entropy of context-sensitive languages, we choose some common context-sensitive languages and analyze them case by case. For that purpose, we extend the capability of the modeling complexity method in previous work. Our method can deal with arbitrary length sequences.Ch1 Introduction (1)
1.1 The complexity of the L-system (1)
1.2 Preliminary (2)
1.3 Other Applications (12)
Ch2 The Complexity of Context-Sensitive Sequences (14)
Ch3 Extension (31)
Ch4 Summary (34)
A Code (36)
Bibliography (38
Comparisons between serpentine and flat spiral delay lines on transient reflection/transmission waveforms and eye diagrams
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No full textFast methodology for determining eye-diagram characteristics of lossy transmission lines
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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