Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much
more efficient way than the established Nyquist sampling theorem. CS has recently gained a …

In today's digital world, data transmission and storage is becoming a massive problem. This
is because the data produced by various sensors worldwide is outstrip** the ability to …

Compressive sensing allows the reconstruction of original signals from a much smaller
number of samples as compared to the Nyquist sampling rate. The effectiveness of …

The basic theories and techniques in compressive sensing (CS) are established on the
sampling and reconstruction of one-dimensional (1D) signals. When it is applied to two …

Multiple description coding (MDC) is one of the widely used mechanisms to combat packet-
loss in non-feedback systems. However, the number of descriptions in the existing MDC …

In this paper, a novel approach for generating the secure measurement matrix for
compressive sensing (CS) based on linear feedback shift register (LFSR) is presented. The …

According to the latest research, it is very much clear that in future we require a huge amount
of data as modern advancement in communication and signal processing generates a large …

Compressive sensing (CS) framework is very suitable for onboard image compression of
high-resolution remote sensing cameras in the visible/near-infrared range (VI/NI-RSC) …

Compression of medical data remains challenging because of the loss in clarity of
compressed images. In medical field, it is necessary to have high image quality in region of …