467326
An efficient parallel algorithm for finding rectangular duals of plane triangular graphs
We present an efficient parallel algorithm for constructing rectangular duals of plane triangular graphs. This problem finds applications in VLSI design and floor-planning problems. No NC algorithm for solving this problem was previously known. The algorithm takesO(log2n) time withO(n) processors on a CRCW PRAM, wheren is the number of vertices of the graph.
40482210
Grid Embedding of 4-Connected Plane Graphs
A straight line grid embedding of a plane graph G is a drawing of G such that the vertices are drawn at grid points and the edges are drawn as non-intersecting straight line segments. In this paper, we show that, if a 4-connected plane graph G has at least 4 vertices on its exterior face, then G can be embedded on a grid of size W×H such that W+H≤n, W≤(n+3)/2 and H≤2(n−1)/3, where n is the number of vertices of G. Such an embedding can be computed in linear time.
701
Common-Face Embeddings of Planar Graphs
Given a planar graph $\Ggg$ and a sequence ${\CC}_1,\ldots,{\CC}_q$, where each ${\CC}_i$ is a family of vertex subsets of $\Ggg$, we wish to find a plane embedding of $\Ggg$, if any exists, such that, for each $i\in\{1,\ldots,q\}$, there is a face Fi in the embedding whose boundary contains at least one vertex from each set in CCi. This problem has applications in the recovery of topological information from geographical data and the design of constrained layouts in VLSI. Let $\inputsize$ be the input size, i.e., the total number of vertices and edges in $\Ggg$ and the families CCi, counting multiplicity. We show that this problem is NP-complete in general. We also show that it is solvable in $O(\inputsize\log \inputsize)$ time for the special case in which, for each input family CCi, each set in CCi induces a connected subgraph of the input graph $\Ggg$. Note that the classical problem of simply finding a planar embedding is a further special case of this case with q=0. Therefore, the processing of the additional constraints CC1, . . .,CCq incurs only a logarithmic factor of overhead.
32419724
Grid Embedding of 4-Connected Plane Graphs
A straight line grid embedding of a plane graph G is a drawing of G such that the vertices are drawn at grid points and the edges are drawn as nonintersecting straight line segments. In this paper we show that if a 4-connected plane graph G has at least four vertices on its external face, then G can be embedded on a grid of size W × H such that W + H ≤n, W ≤ (n + 3)/2 and H ≤ 2(n - 1)/3, where n is the number of vertices of G. Such an embedding can be computed in linear time.
11889495
On Even Triangulations of 2-Connected Embedded Graphs
Recently, Hoffmann and Kriegel proved an important combinatorial theorem [SIAM J. Discrete Math., 9 (1996), pp. 210--224]: Every 2-connected bipartite plane multigraph G without 2-cycle faces has a triangulation in which all vertices have even degree (this is called an even triangulation). Combined with the classical Whitney's theorem, this result implies that every such graph has a 3-colorable plane triangulation. Using this theorem, Hoffmann and Kriegel significantly improved the upper bounds of several art gallery and prison guard problems. A complicated O(n2) time algorithm was obtained in [SIAM J. Discrete Math., 9 (1996), pp. 210--224] for constructing an even triangulation of G. Hoffmann and Kriegel conjectured that there is an O(n3/2) time algorithm for solving this problem. 
In this paper, we develop a simple proof of the above theorem. Our proof reveals and relies on a natural correspondence between even triangulations of G and certain orientations of G. Based on this new proof, we obtain a very simple O(n) time algorithm for finding an even triangulation of G. We also extend our proof to show the existence of even triangulations for similar graphs on high genus surface.
15559790
Improved visibility representation of plane graphs
In a visibility representation (VR for short) of a plane graph G, each vertex of G is represented by a horizontal line segment such that the line segments representing any two adjacent vertices of G are joined by a vertical line segment. Rosenstiehl and Tarjan [Discrete Comput. Geom. 1 (1986) 343-353] and Tamassia and Tollis [Discrete Comput. Geom. 1 (1986) 321-341] independently gave linear time VR algorithms for 2-connected plane graph. Using this approach, the height of the VR is bounded by (n - 1), the width is bounded by (2n - 5). After that, some work has been done to find a more compact VR. Kant and He [Theoret. Comput. Sci. 172 (1997) 175-193] proved that a 4-connected plane graph has a VR with width bounded by (n - 1). Kant [Internat. J. Comput. Geom. Appl. 7 (1997) 197-210] reduced the width bound to ⌊3n-6/2⌋ for general plane graphs. Recently, using a sophisticated greedy algorithm, Lin et al. reduced the width bound to ⌊22n-42/15⌋ [Proc. STACS'03, Lecture Notes in Computer Science, vol. 2607, Springer, Berlin, 2003, pp. 14-25].In this paper, we prove that any plane graph G has a VR with width at most ⌊13n-24/9⌋, which can be constructed by using the simple standard VR algorithm in [P. Rosenstiehl, R.E. Tarjan, Discrete Comput. Geom. 1 (1986) 343-353; R. Tamassia, I.G. Tollis, Discrete Comput. Geom. 1 (1986) 321-341].
12494221
Statistical and computational analysis of locality preserving projection
Recently, several manifold learning algorithms have been proposed, such as ISOMAP (Tenenbaum et al., 2000), Locally Linear Embedding (Roweis & Saul, 2000), Laplacian Eigenmap (Belkin & Niyogi, 2001), Locality Preserving Projection (LPP) (He & Niyogi, 2003), etc. All of them aim at discovering the meaningful low dimensional structure of the data space. In this paper, we present a statistical analysis of the LPP algorithm. Different from Principal Component Analysis (PCA) which obtains a subspace spanned by the largest eigenvectors of the global covariance matrix, we show that LPP obtains a subspace spanned by the smallest eigenvectors of the local covariance matrix. We applied PCA and LPP to real world document clustering task. Experimental results show that the performance can be significantly improved in the subspace, and especially LPP works much better than PCA.
12973064
Integrating predicate transition nets with first order temporal logic in the specification and verification of concurrent systems
This paper presents some results of integrating predicate transition nets with first order temporal logic in the specification and verification of concurrent systems. The intention of this research is to use predicate transition nets as a specification method and to use first order temporal logic as a verification method so that their strengths — the easy comprehension of predicate transition nets and the reasoning power of first order temporal logic can be combined. In this paper, a theoretical relationship between the computation models of these two formalisms is presented; an algorithm for systematically translating a predicate transition net into a corresponding temporal logic system is outlined; and a special temporal refutation proof technique is proposed and illustrated in verifying various concurrent properties of the predicate transition net specification of the five dining philosophers problem.
37910692
Replicated shared object model for parallel edge detection algorithm based on spiral architecture
Abstract Edge detection in computer vision and image processing is a process which detects one kind of significant feature in an image that appears as discontinuities in intensities. A parallel edge detection algorithm based on spiral architecture is designed in this paper using a replicated shared object model (RSOM). We use this approach to meet the requirements of replicated object management, update propagation, underlying communication and consistency maintenance among replicated objects. Multi-scale image theory plays an important role in this research.
40658443
A Parallel Algorithm for Object Contour Extraction Within a Spiral Architecture
10726702
Locality Preserving Projections
Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) – a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving Projections are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold. As a result, LPP shares many of the data representation properties of nonlinear techniques such as Laplacian Eigenmaps or Locally Linear Embedding. Yet LPP is linear and more crucially is defined everywhere in ambient space rather than just on the training data points. This is borne out by illustrative examples on some high dimensional data sets.