Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cloud-aided collaborative estimation by ADMM-RLS algorithms for
  connected vehicle prognostics   As the connectivity of consumer devices is rapidly growing and cloud
computing technologies are becoming more widespread, cloud-aided techniques for
parameter estimation can be designed to exploit the theoretically unlimited
storage memory and computational power of the cloud, while relying on
information provided by multiple sources. With the ultimate goal of developing
monitoring and diagnostic strategies, this report focuses on the design of a
Recursive Least-Squares (RLS) based estimator for identification over a group
of devices connected to the cloud. The proposed approach, that relies on
Node-to-Cloud-to-Node (N2C2N) transmissions, is designed so that: (i) estimates
of the unknown parameters are computed locally and (ii) the local estimates are
refined on the cloud. The proposed approach requires minimal changes to local
(pre-existing) RLS estimators.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol