introduction:in this study, ecg signals have been embedded into medical images to create a novel blind watermarking method. the embedding is done when the original image is compressed using the ezw algorithm. the extraction process is performed at the decompression time of the watermarked image. materials and methods: the multi-resolution watermarking with a secret key algorithm developed in th...

In this paper we present embeddings of complete binary trees into butter y networks with or without wrap-around connections. Let m be an even integer and q = m+ blogmc 1. We show how to embed a 2q+1 1-node complete binary tree T (q) into a (m + 1)2m+1node wrap-around butter y Bw(m+1) with a dilation of 4; and how to embed T (q) into a (m+2)2m+2-node wrap-around butter y Bw(m+2) with an optimal ...

Quadtree and pyramid structures have attracted considerable attention in recent years. They are increasingly being applied to the fields of digital image and signal processing. As a result, the efficient embedding of these structures in VLSI arrays has become an important research topic. In this paper, we propose three schemes to embed either quadtrees or pyramids in a rectangular, hexagonal, o...

In this paper we present embeddings of complete binary trees into butterfly networks with or without wrap-around connections. Let m be an even integer and q = m+ LlogmJ-l. We show how to embed a 2+I -I-node complete binary tree T(q) into a (m + 1)2111+1_ node wrap-around butterfly B1l1(m +1) with a dilation of 4, and how to embed T(q) into a (m + 2)2+_node wrap-around butterfly B 1l1 (m +2) wit...

Representation learning has attracted significant interest in the community and has been shown to be successful in tasks involving one graph, such as link prediction and node classification. In this paper, we conduct an empirical study of two leading deep learning based node embedding methods, node2vec and SDNE, to examine their suitability for problems that involvemultiple graphs. Although the...

Laplacian embedding provides a lowdimensional representation for the nodes of a graph where the edge weights denote pairwise similarity among the node objects. It is commonly assumed that the Laplacian embedding results preserve the local topology of the original data on the low-dimensional projected subspaces, i.e., for any pair of graph nodes with large similarity, they should be embedded clo...

Abstract Graph embedding is a transformation of nodes network into set vectors. A good should capture the underlying graph topology and structure, node-to-node relationship, other relevant information about graph, its subgraphs, themselves. If these objectives are achieved, an meaningful, understandable, often compressed representation network. Unfortunately, selecting best challenging task ver...

What is the best way to describe a user in a social network with just a few numbers? Mathematically, this is equivalent to assigning a vector representation to each node in a graph, a process called graph embedding. We propose a novel framework, GEM-D that unifies most of the past algorithms such as LapEigs, DeepWalk and node2vec. GEM-D achieves its goal by decomposing any graph embedding algor...

numerical analyses have shown that successful flow simulations and the accuracy of solution noticeably depend on the number of nodes used in computational meshing. a suitable meshing should have the capability of adapting with main flow parameters. because the number of total nodes that can be used in numerical simulation is limited, making such grid for complex flows is almost difficult, if it...

In this paper we show that any embedding of a 2m + I-node complete binary tree T into an m-node complete binary tree H requires a dilation of at least 3 when every node of H is assigned one interior and one leaf node of T, except one node which is assigned one interior and two leaf nodes of T. aThis work was supported by the Office of Naval Research under Contracts NOOOl484-K-DS02 and NOOOl4-86...