• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.8-14
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• 2009

In this paper, a convex optimization technique, which is based on the generalized quantization constraint (GQC), is proposed in the compressive sensing reconstruction using quantized measures. The set size of the proposed GQC can be controlled, and through extensive numerical simulations based on the uniform scalar quantizers, the CS reconstruction errors are improved by 3.4-3.6dB compared to the traditional QC method for the CS problems of m/klogn > 2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.108-122
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• 2014

Massive antenna array is an attractive candidate technique for future broadband wireless communications to acquire high spectrum and energy efficiency. However, such benefits can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large for massive antennas, the feedback is burdensome in practice, especially for frequency division duplex (FDD) systems, and needs normally to be reduced. In this paper a novel channel feedback reduction scheme based on the theory of distributed compressive sensing (DCS) is proposed to apply to massive antenna arrays with spatial correlation, which brings substantially reduced feedback load. Simulation results prove that the novel scheme is better than the channel feedback technique based on traditional compressive sensing (CS) in the aspects of mean square error (MSE), cumulative distributed function (CDF) performance and feedback resources saving.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1533-1540
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• 2023

Self-Powered Neutron Detectors(SPNDs) are used to calculate core power distributions, an essential factor in the safe operation of nuclear power plants. Some detectors may fail during normal operation, and signals from failed detectors are isolated from intact signals. The calculated detailed power distribution accuracy depends on the number of available detector signals. Failed detectors decrease the operating margin by enlarging the power distribution measurement error. Therefore, a thorough reconstruction of the failed detector signals is critical. This note suggests a compressive sensing based methodology that rationally reconstructs the readings of failed detectors. The methodology significantly improves reconstruction accuracy by sorting signals and removing high-frequency components from conventional compressive sensing methodology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1961-1966
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• 2016

Compressive sensing has proved that a signal can be restored from less samples than the Nyquist rate. Reducing the required data rate is essential for a variety of fields including compression, transmission, and storage. It has been made lots of attempt to apply the compressive sensing theory into data intensive fields, such as image processing which needs to cover 4K and 8K pictures. In this paper, an image acquisition algorithm based on compressive sensing is proposed. It combines DCT, which can compact the energy of a image into a few coefficients, and the Noiselet transform, which is incoherent with DCT. The DCT coefficients represent the coarse structure of the images while the Noiselet information holds the fine details. Performance experiments with several images show that the proposed image acquisition algorithm not only outperforms the previous results, but also improves the reconstruction quality faster as the number of measurements increases.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.11
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• pp.67-72
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• 2011

In this paper, we investigate the energy detector to detect a primary user. And employ the compressed sensing method to get the lower sampling rate than Nyquist sampling rate. In more wide bandwidth we using the small samples than Nyquist sampling rate samples to recover original signal. we investigate the performance of energy detector with compressive sensing method under suzuki channel. The performance is investigated by simulation and compared to that of conventional energy detector.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.179-190
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• 2020

In this paper, we estimated the Direction of Arrival (DOA) using Conventional BeamForming (CBF), adaptive beamforming and compressive beamforming. Minimum Variance Distortionless Response (MVDR) and Multiple Signal Classification (MUSIC) are used as the adaptive beamforming, and grid-free compressive sensing is applied for the compressive sensing beamforming. Theoretical background and limitations of each technique are introduced, and the performance of each technique is compared through simulation and real experiments. The real experiments are conducted in the presence of reflected signal, transmitting a sound using two speakers and receiving acoustic data through a linear array consisting of eight microphones. Simulation and experimental results show that the adaptive beamforming and the grid-free compressive beamforming have a higher resolution than conventional beamforming when there are uncorrelated signals. On the other hand, the performance of the adaptive beamforming is degraded by the reflected signals whereas the grid-free compressive beamforming still improves the conventional beamforming resolution regardless of reflected signal presence.

• Journal of Satellite, Information and Communications
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• v.9 no.3
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• pp.59-63
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• 2014

This paper describes the basic principles of 3D broadcast system and proposes new 3D broadcast technology that reduces the amount of data by applying CS(Compressed Sensing). Differences between Sampling theory and the CS technology concept were described. Recently proposed CS algorithm AMP(Approximate Message Passing) and CoSaMP(Compressive Sampling Matched Pursuit) were described. This paper compared an accuracy between two algorithms and a calculation time that image data compressed and restored by these algorithms. As result determines a low complexity algorithm for 3D broadcast system.

• Journal of the Optical Society of Korea
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• v.18 no.2
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• pp.146-150
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• 2014

We propose an experimental holographic imaging scheme combining compressive sensing (CS) theory with digital holography in phase-shifting conditions. We use the Mach-Zehnder interferometer for hologram formation, and apply the compressive sensing (CS) approach to the holography acquisition process. Through projecting the hologram pattern into a digital micro-mirror device (DMD), finally we will acquire the compressive sensing measurements using a photodiode. After receiving the data of two holograms via conventional communication channel, we reconstruct the original object using certain signal recovery algorithms of CS theory and hologram reconstruction techniques, which demonstrated the feasibility of the proposed method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.443-456
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• 2013

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 lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of CS. This article gives a brief background on the origins of this idea, reviews the basic mathematical foundation of the theory and then goes on to highlight different areas of its application with a major emphasis on communications and network domain. Finally, the survey concludes by identifying new areas of research where CS could be beneficial.