To leverage the enormous amount of unlabeled data on distributed edge devices, we
formulate a new problem in federated learning called federated unsupervised …

This paper studies the problem of data-adaptive representations for big, distributed data. It is
assumed that a number of geographically-distributed, interconnected sites have massive …

Over the last decades, the cooperative design of complex networked systems has received
an increasing attention in real-world engineering practices. Traditionally, each node in the …

We propose a distributed algorithm for sensor network localization, which is based upon a
decomposition of the nonlinear nonconvex maximum likelihood (ML) localization problem …

Optical coherence tomography (OCT) is a high-resolution and non-invasive optical imaging
technology, which is widely used in many fields. Nevertheless, OCT images are disturbed by …

Proportionate-type adaptive filtering (PtAF) algorithms have been successfully applied to
sparse system identification. The major drawback of the traditional PtAF algorithms based on …

Recently there has been increasing attention towards analysis dictionary learning. In
analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting …

Recently, hashing based approximate nearest neighbors search has attracted much
attention. Extensive centralized hashing algorithms have been proposed and achieved …

Reconfigurable array radiates multiple patterns with only a single array via designing
multiple excitation vectors with common magnitudes for the same element-index excitation …

With the development of sensor and communication technology, industrial systems have
accumulated a large amount of data. This data has provided new perspectives and methods …