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In this paper we give an algorithm to generate all distributions of distinguishable objects to bins without repetition. Our algorithm generates each distribution in constant time. To the best of our knowledge, our algorithm is the first algorithm which generates each solution in O(1) time in the ordinary sense. As a byproduct of our algorithm, we obtain a new algorithm to enumerate all multiset partitions when the number of partitions is fixed and the partitions are numbered. In this case, the algorithm generates each multiset partitions in constant time (in the ordinary sense). Finally, we extend the algorithm to the case when the bins have priorities associated with them. Overall space complexity of the algorithm is O(mklgn), where there are m bins and the objects fall into k different classes. In a companion paper, the generation of all distributions of identical objects to bins is also considered. [ABSTRACT FROM AUTHOR]

This paper deals with the issue of developing efficient algorithms for reconfiguring two-dimensional VLSI arrays linked by four-port switches in the presence of faulty processing elements (PEs). The proposed algorithm reroutes the arrays with faults in both row and column directions at the same time. Unlike previous work, the compensation technique to replace the faulty PE is not restricted to the adjacent rows of the excluded row. Instead, we consider the neighbor rows of any faulty PE for compensation purposes. The nonfaulty PEs lying in the excluded rows are also effectively utilized to form the maximal target arrays, making the proposed algorithm more efficient in terms of both the percentages of harvest and the degradation of VLSI arrays for random and clustered faults. Empirical study shows that the improvement in harvest increases with increasing fault size and is more notable for maximal square target arrays than for maximal target arrays. Our investigations show that the improvement can be up to 8 percent and 23 percent for a 256 × 256 VLSI array with random faults of size 25 percent for maximal target arrays and for maximal square target arrays, respectively. [ABSTRACT FROM AUTHOR]

Abstract: For many scientists working with digital topographic data, extracting lineaments or linear features is an important step in structuring and analyzing raw data. A ridge axis, which represents the top a mountain ridge, is one of the most important topographic features used in a wide variety of applications. Algorithms and software for automating the extraction of ridges or ridge axes from DEMs are, however, still not easily available or not widely acceptable. In this paper, we present a user-friendly Visual Basic program that automates the extraction of the ridge axis system from DEM data, based on the profile-recognition and polygon-breaking algorithm (PPA). An important feature of PPA is that it takes a global approach, as opposed to the local neighborhood operators used in many other algorithms. Each segment detected by PPA considers not only relations with contiguous neighboring grid points, but also strives to preserve the continuity of the global trend. This is an attempt to simulate human operators, who always factor in the overall trend of the lineament before delineating its local parts. PPA starts by connecting all points in a neighborhood that can possibly lie on the ridge axis, thus forming a belt of polygons in the first step. Next, a polygon breaking process eliminates unwanted segments according to the assumption that a ridge segment cannot be the side of any closed polygon, and that the result should be a purely dendritic line pattern. Finally, a branch-reduction process is executed to eliminate all parallel false ridges that remained due to the conservative approach taken in the first step. Results indicate that PPA is reasonably successful in picking out ridges that would have been identified manually by experts. In addition to providing a detailed user interface for executing PPA, several modifications were made to significantly improve the computational efficiency of PPA, as compared to the original version published in 1998. The source codes are provided for free download on the website listed above. [Copyright &y& Elsevier]