Human behaviour analysis has important applications in many emergency management problems as Intelligent Video Surveillance (IVS) for crowding situations. In many VS systems, supervision from a human operator is needed; for example, in overcrowding situations, the experience of a security operator is crucial in order to redirect people flow for the maintenance of an acceptable safety level. An ...

This paper discusses sample allocation problem (SAP) in frequency-domain Compressive Sampling (CS) of timedomain signals. An analysis that is relied on two fundamental CS principles; the Uniform Random Sampling (URS) and the Uncertainty Principle (UP), is presented. We show that CS on a singleand multi-band signals performs better if the URS is done only within the band and suppress the out-ban...

A sparsifying transform for use in Compressed Sensing (CS) is a vital piece of image reconstruction for Magnetic Resonance Imaging (MRI). Previously, Translation Invariant Wavelet Transforms (TIWT) have been shown to perform exceedingly well in CS by reducing repetitive line pattern image artifacts that may be observed when using orthogonal wavelets. To further establish its validity as a good ...

The mathematical theory of Compressed Sensing has been applied to various engineering areas ranging from one-pixel cameras, to range imaging and medical ultrasound imaging, to name a few. The theory developed within CS suggests that one can achieve perfect reconstruction of a signal x P R from a small number of random measurements y P R , far below the typical Shannon-Nyquist sampling limit. Re...

Compressive sampling (CS) offers bandwidth, power, and memory size reduction compared to conventional (Nyquist) sampling. These are very attractive features for the design of modern complementary metal-oxide semiconductor (CMOS) image sensors, cameras, and camera systems. However, very few integrated circuit (IC) designs based on CS exist because of the missing link between the well-established...

Reduction of MRI data acquisition time is an important goal in the MRI field. Undersampling k-t space is a solution to reduce acquisition time. MR images may have sparse or compressible presentations in appropriate transform domains, such as wavelets. According to the Compressive Sensing (CS) theory, they can be recovered from randomly undersampled k-t space data that guarantees incoherency bet...

Compressed sensing (CS) methods can be used to reconstruct MRI signals from highly undersampled data [1]. Recent studies have shown that selecting gridded samples using a Poisson disk sampling pattern yields improved reconstructions over the traditional uniform random sampling of a Cartesian grid [2]. We extend this work by evaluating the effects of Poisson disk sampling of a continuous k-space...

Introduction: Compressed Sensing (CS) is an emerging field that suggests that data compression can be implicitly incorporated into the data acquisition process [1,2,3]. CS is regarded by some as an alternative to Shannon’s Nyquist sampling theory. The Nyquist sampling theory states that the number of samples required to perfectly reconstruct a signal is determined by its bandwidth. In contrast,...

the focus of this paper is to consider the compressed sensing problem. it is stated that the compressed sensing theory, under certain conditions, helps relax the nyquist sampling theory and takes smaller samples. one of the important tasks in this theory is to carefully design measurement matrix (sampling operator). most existing methods in the literature attempt to optimize a randomly initiali...