Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Estimation for Adaptive Networks Based on Serial-Inspired
  Diffusion   Distributed estimation and processing in networks modeled by graphs have
received a great deal of interest recently, due to the benefits of
decentralised processing in terms of performance and robustness to
communications link failure between nodes of the network. Diffusion-based
algorithms have been demonstrated to be among the most effective for
distributed signal processing problems, through the combination of local node
estimate updates and sharing of information with neighbour nodes through
diffusion. In this work, we develop a serial-inspired approach based on
message-passing strategies that provides a significant improvement in
performance over prior art. The concept of serial processing in the graph has
been successfully applied in sum-product based algorithms and here provides
inspiration for an algorithm which makes use of the most up-to-date information
in the graph in combination with the diffusion approach to offer improved
performance.

26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph