De-noising and extraction of the weak signature are crucial to fault prognostics in which
case features are often very weak and masked by noise. The wavelet transform has been …

The development and use of low-rank approximate nonnegative matrix factorization (NMF)
algorithms for feature extraction and identification in the fields of text mining and spectral …

Sparsity of representations of signals has been shown to be a key concept of fundamental
importance in fields such as blind source separation, compression, sampling and signal …

A methodology for automatically identifying and clustering semantic features or topics in a
heterogeneous text collection is presented. Textual data is encoded using a low rank …

Accurate definition of the similarity measure is a key component in image registration. Most
commonly used intensity-based similarity measures rely on the assumptions of …

In the era of data deluge,“big data” generated by mechanical equipment creates higher
requirements for the field of mechanical fault diagnosis. Intelligent diagnosis methods have …

Wireless channel characteristics have significant impacts on channel modeling, estimation,
and communication performance. While the channel sparsity is an important characteristic of …

We introduce greedy basis pursuit (GBP), a new algorithm for computing sparse signal
representations using overcomplete dictionaries. GBP is rooted in computational geometry …

Recently, channel sparsity has been considered as a nature of wireless channels in many
researches of intelligent communications, and an increasing number of investigations are …

This paper presents real-time queueing network theory, the extension of real-time queueing
theory introduced by JP Lehoczky (1996) to Jackson queueing networks. This theory …