based on the compressed sensing theory, if a signal is sparse in a suitable space, by using the optimization methods, signal could be accurately reconstructed from measurements that are significantly less than the theoretical shannon requirements. the sparse representation may exist for the signal and it is not available for the noise; this could be used to distinguish these two. on the other h...

Compressed sensing theory breaks through the limit that two times the bandwidth of the signal sampling rate in Nyquist theorem, providing a guideline for new methods for image acquisition and compression. For still images, block compressed sensing (BCS) has been designed to reduce the size of sensing matrix and the complexity of sampling and reconstruction. However, BCS algorithm assigns the sa...

Compressed sensing is a new data compression method which can recover a sparse or compressible signal from a small number of linear and non-adaptive measurements. To solve the problems of high sampling rate and massive data storage faced by traditional collection and compression methods of power quality, compressed sensing algorithm is used in the paper. Gaussian random measurement matrix is us...

Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A pr...

This study presents a sub-Nyquist sampling model using compressed sensing (CS) as a new signal processing framework to acquire and reconstruct sparse signals. High-speed periodic signals were sampled using low frequency sampling circuit and reconstructed via CS recovery algorithm, resulting in a high equivalent sampling frequency. This prototype system is able to capture repetitive waveforms at...

Hariharan, Sathya N. M.S., Purdue University, August 2014. Improved Random Demodulator for Compressed Sensing Applications . Major Professor: Walter D. Leon-Salas. The advances in the field of signal processing have led to the continuous increase in the bandwidth of signals. Sampling these signals becomes harder and harder due to the increased bandwidth. This brings in need for a complex high r...