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A CCB bc1.Symbology = Neodynamic.SDK.Barcode.Symbology.UpcACCB . set the value to encode eg Primary .Related: ISBN Generation Java , Excel Intelligent Mail Generation , UPC-E Generator VB.NET Complexity In comparison to contention aware list scheduling, the time complexity under the involvement contention model does not increase The additional scheduling of the edge on the processors increases the effort to implement the algorithm, but it does not modify its time complexity For example, the complexity of the second part of simple list scheduling (Algorithm 9) with start/ nish time minimization and the end technique is O(P(V + EO(routing))) (see Section 751) Also, the complexity does not increase for the insertion technique (Section 61), even though the individual scheduling of a node and an edge on a processor is more complex In the involvement contention model, there are more objects on the processors, namely, nodes and edges, which must be searched to nd an idle time interval On all processors there are at most O(V + E) nodes and edges; hence, the total complexity for the scheduling of the nodes increases from O(V2 ), under the contention model, to O(V(V + E)) The scheduling of the edges on the processors is O(E(V + E)), compared to O(PE) with the end technique, and on the links it remains O(PE2 O(routing)) Hence, the total complexity of the second part of simple list scheduling with start/ nish time minimization and the insertion technique is O 2 + VE + PE2 O(routing)), that is, O(V2 + PE2 O(routing)), as under the contention model (Section 751) 842 Two-Phase Heuristics The generic form of a two-phase scheduling algorithm was already outlined in Algorithm 13 The rst phase establishes the processor allocation and the second phase constructs the actual schedule with a simple list scheduling heuristic, as described in Section 52 The next paragraphs discuss how such an algorithm is applied under the involvement contention model Phase 1 Processor Allocation The processor allocation can originate from any heuristic or can be extracted from a given schedule For example, a schedule produced under the classic or contention model might serve as the input (see also Section 52) Using the rst two steps of a clustering based heuristic, that is, the clustering itself and the cluster-to-processor mapping (Algorithm 14, Section 53), is very promising, because clustering tries to minimize communication costs, which is even more important under the involvement contention model The clustering itself can be performed under the classic or, as described in Section 753, under the contention model Even the involvement contention model can be used with a straightforward extension of the considerations made in Section 753 Genetic Algorithm In Sinnen [172] and Sinnen et al [180], a genetic algorithm, called GICS (genetic involvement contention scheduling), is proposed for the determination of the processor allocation The simple idea is that the genetic algorithm searches for an ef cient processor allocation, while the actual scheduling is performed with a list scheduling heuristic as discussed later for phase 2. telugu ocr software onlineFree online OCR service that allows to convert scanned images, faxes, screenshots, PDF documents and ebooks to text, can process 122 languages and ... ocr software online Free Online OCR PDF - Best PDF OCR Scanner & Converter Online
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