Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.
There are several types of problems that can be modeled and solved as minimum spanning tree problems. Sometimes the weighted graph in which we need to determine a minimum spanning tree differs from another weighted graph, in which a minimum spanning tree is already established, only by an edge weight (which is reduced or augmented by a units). We will describe algorithms that determine minimum spanning trees in the new weighted graphs starting from a minimum spanning tree in the original weighted graph.
An image overlapping algorithm, taking into account certain properties
of objects identified in the images (average intensity, movement speed, etc) is
proposed. The algorithm minimizes both memory and time complexity and
it can be used in various applications, especially in medical imaging analysis.
The idea behind the proposed algorithm is surface merging and interpolation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.