Abstract—Many clinical MRI applications can benefit from combining compressed sensing with multichannel receiver systems because of the potential to achieve reduced scan time and improve image qualities. However, it comes with the increased computational complexity, particular for advanced CS algorithms and with large number of channels. This paper presents a new CS-MRI reconstruction method fo...

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...

The data acquisition speed in photoacoustic computed tomography (PACT) is limited by the laser repetition rate and the number of parallel ultrasound detecting channels. Reconstructing an image with fewer measurements can effectively accelerate the data acquisition and reduce the system cost. We adapt compressed sensing (CS) for the reconstruction in PACT. CS-based PACT is implemented as a nonli...

—The l 1-norm minimization problem plays an important role in the compressed sensing (CS) theory. We present in this letter an algorithm for solving the problem of l 1 Index Terms—L-norm minimization for quaternion signals by converting it to second-order cone programming. An application example of the proposed algorithm is also given for practical guidelines of perfect recovery of quaternion s...

Compressed sensing (CS) algorithm enables sampling rates significantly under classical Nyquist rate without sacrificing reconstructed image quality. It is known that, a great number of images have many similar areas which are composed by the same number of grayscale or color. A new CS scheme, namely clustering compressed sensing (CCS), was proposed for image compression, and it introduces clust...

Orthogonal frequency-division-multiplexing (OFDM) has been actively pursued in underwater acoustic communications. However, OFDM is extremely sensitive to carrier frequency offset (CFO) which cause significant problems. The utilization and performance of traditional channel estimation are poor. Although underwater acoustic communication was recently the focus of research, literatures on joint i...

PURPOSE A typical clinical MR examination includes multiple scans to acquire images with different contrasts for complementary diagnostic information. The multicontrast scheme requires long scanning time. The combination of partially parallel imaging and compressed sensing (CS-PPI) has been used to reconstruct accelerated scans. However, there are several unsolved problems in existing methods. ...

Channel adaptive transmission requires knowledge of channel state information at the transmitter. In temporally correlated MIMO channels, the correlation can be utilized to reduce feedback overhead and improve performance. In this paper, Compressed Sensing (CS) methods and rotative quantization are used to compress and feedback channel state information for MIMO systems as an extension work of ...

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionle...

1 Abstract—Phase modulation is a commonly used modulation mode in digital communication, which usually brings phase sparsity to digital signals. It is naturally to connect the sparsity with the newly emerged theory of compressed sensing (CS), which enables sub-Nyquist sampling of high-bandwidth to sparse signals. For the present, applications of CS theory in communication field mainly focus on ...