Compressive sensing (CS) is a newly developed data acquisition and processing technique
that takes advantage of the sparse structure in signals. Normally signals in their primitive …

Structural health monitoring requires collection of large number sample data and sometimes
high frequent vibration data for detecting the damage of structures. The expensive cost for …

Vibration data from mechanical systems carry important information that is useful for
characterization and diagnosis. Standard approaches rely on continually streaming data at a …

One of the key areas that have attracted attention in the construction industry today is
Structural Health Monitoring, more commonly known as SHM. It is a concept developed to …

Wireless sensors produce large amounts of data in long-term online monitoring following the
Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and …

The theory and application of compressive sensing (CS) have received a lot of interest in
recent years. The basic idea in CS is to use a specially-designed sensor to sample signals …

The Shannon/Nyquist sampling theorem specifies that to avoid losing information when
capturing a signal, one must sample at least two times faster than the signal bandwidth. In …

This paper presents a new development in structural health monitoring (SHM) data
management, processing, and mining based on compressive sensing (CS) technique, which …

The safety and reliability are critical for the high-speed rail system, and axle box
accelerations are often utilised for inspections of railways. Due to the Nyquist theorem, there …

Modal analysis classicaly used signals that respect the Shannon/Nyquist theory.
Compressive sampling (or Compressed Sampling, CS) is a recent development in digital …