Name
Abstract | ||
|---|---|---|
A crucial first step for signal processing decentralized sensor networks with node-specific interests is to agree upon a common unique labeling of all observed sources in the network. The knowledge "who observes what" is required, e.g. in node-specific audio or video signal enhancement to form node clusters of common interest. Recently proposed in-network distributed adaptive classification and labeling algorithms assume knowledge on the number of objects (clusters), which is not necessarily available in real-world applications. Thus, we consider the problem of estimating the number of data-clusters in the distributed adaptive network set-up. We propose two distributed adaptive cluster enumeration methods. They combine the diffusion principle, where the nodes share information within their local neighborhood only (without fusion center), with the X-means and the PG-means cluster enumeration. Performance is evaluated via simulations and the applicability of the methods is illustrated using a distributed camera network where moving objects appear and disappear from the Line-of-Sight (LOS) and the number of clusters becomes time-varying. |
Year | Venue | Keywords |
|---|---|---|
2016 | European Signal Processing Conference | Distributed Cluster Enumeration,Distributed Classification,Object Labeling,Camera Network,X-means,PG-means,MDMT,Diffusion |
Field | DocType | ISSN |
Convergence (routing),Signal processing,Cluster (physics),Computer science,Enumeration,Camera network,Theoretical computer science,Fusion center,Cluster analysis,Wireless sensor network | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| freweyni k teklehaymanot | 1 | 8 | 3.51 |
| Michael Muma | 2 | 144 | 19.51 |
| Jun Liu | 3 | 0 | 0.34 |
| Abdelhak M. Zoubir | 4 | 1036 | 148.03 |