Drexel University

William C Regli

Ali Shokoufandeh

Dmitriy Bespalov

Cheuk Yiu Ip

Leonard Sieger

Evan A Sultanik

Vincent A Cicirello

The Location Design and Routing problem asks to find a subset of 'depot' nodes and a spanning forest of a graph such that every connected component in the forest contains at least one depot. This problem arises in a number of both logistical and computer networking problems, for example, in selecting the number and location of distribution centers in vehicle routing networks. This problem is functionally equivalent to that of supernode selection in peer-to-peer networks. A distributed algorithm approximates a solution to this problem that runs in a logarithmic number of communication rounds with respect to the number of nodes (independent of the topology of the network), and, under assumptions on the embedding of the edge weights, whose solutions are within a factor of 2 of optimal.

Efficient distributed algorithm for the location design and routing problem

A scale-Space feature extraction technique is based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results show that this technique can be used to perform matching based on local model structure. Scale-space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these techniques is to support matching and content-based retrieval of solid models. Scale-space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that 3D laser scanning may introduce. A new distance function defined on triangles instead of points is introduced. This technique offers a new way to control the feature decomposition process, which results in extraction of features that are more meaningful from an engineering viewpoint. The technique is computationally practical for use in indexing large models.

Multi-scale segmentation and partial matching 3D models

A method of providing an automated classifier for 3D CAD models wherein the method provides an algorithm for learning new classifications. The method enables existing model comparison algorithms to adapt to different classifications that are relevant in many engineering applications. This ability to adapt to different classifications allows greater flexibility in data searching and data mining of engineering data.

Automated learning of model classifications

A method for determining a similarity of a first solid model to a second solid model is disclosed. The method includes the steps of: selecting a set of features for representing the first solid model; extracting features corresponding to the selected set of features from the first solid model; constructing an undirected model dependency graph of the first solid model based on the selected set of features; extracting features corresponding to the selected set of features from the second solid model; constructing an undirected model dependency graph of the second solid model based on the selected set of features; comparing the undirected model dependency graph of the first solid model with the undirected model dependency graph of the second solid model; and outputting a numerical measure indicative of the similarity of the first solid model to the second solid model.

Growing community of inventors

Philadelphia, PA, United States of America

William C Regli