• Journal of the Korean Society of Radiology
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• v.15 no.3
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• pp.281-291
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• 2021

This study aimed to measure the quantitative changes in images according to the use of compressed sensing in expressing the slow flow rate in TOF MRA test using magnetic resonance imaging. This study set different blood flow rate sections by using auto-injector and flow phantom and compared changes in the SNR, CNR, SSIM, and RMSE measurements by different CS factors between TOF with CS and TOF without CS. One-way ANOVA was performed to test the effect on the image induced by the increase of the CS factor. The results revealed that TOF MRA with CS significantly decreased scan time without significantly affecting SNR and CNR compared to TOF MRA with CS. On the other hand, the differences in SSIM and RMSE between TOF with CS and TOF without CS increased as the CS factor increased. Therefore, it is necessary to efficiently reduce scan time by adapting the CS technique while considering the appropriate range of the CS factor. Additionally, more studies are needed to evaluate CS factors and the similarity precision of images further.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.130-134
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• 2013

This paper describes the basic principles of 3D broadcast system and proposes new 3D broadcast technology that reduce the amount of data by applying CS(Compressed Sensing). Differences between Sampling theory and the CS technology concept was described. Recently proposed CS algorithm AMP(Approximate Message Passing) and CoSaMP(Compressive Sampling Matched Pursuit) was described. Image data that compressed and restored by these algorithm was compared. Calculation time of the algorithm having a low complexity is determined.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.1-7
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• 2009

In recent years, compressed sensing (CS) algorithm has been studied in various research areas including medical imaging. To use the CS algorithm, the signal that is to be reconstructed needs to have the property of sparsity But, most medical images generally don't have this property. One method to overcome this problem is by using sparsifying transform. However, MR imaging, compared to other medical imaging modality, has the unique property that by using appropriate image acquisition pulse sequences, the image contrast can be modified. In this paper, we propose the possibility of applying the CS algorithm with non-sparsifying transform to the pulse sequence modified MR images and improve the reconstruction performance of the CS algorithm by using an appropriate sparsifying transform. We verified the proposed contents by computer simulation using Shepp-Logan phantom and in vivo data.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.308-316
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• 2010

The problem of compressed sensing (CS) based multiple access is studied under the assumption of random data traffic. In many multiple access systems, i.e., wireless sensor networks (WSNs), data arrival is random due to the bursty data traffic for every transmitter. Following the recently developed CS methodology, the technique of compressing the transmitter identities into data transmissions is proposed, such that it is unnecessary for a transmitter to inform the base station its identity and its request to transmit. The proposed compressed multiple access scheme identifies transmitters and recovers data symbols jointly. Numerical simulations demonstrate that, compared with traditional multiple access approaches like carrier sense multiple access (CSMA), the proposed CS based scheme achieves better expectation and variance of packet delays when the traffic load is not too small.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2497-2517
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• 2020

For compressed sensing (CS) applications, it is significant to construct deterministic measurement matrices with good practical features, including good sensing performance, low memory cost, low computational complexity and easy hardware implementation. In this paper, a deterministic construction method of bipolar measurement matrices is presented based on binary sequence family (BSF). This method is of interest to be applied for sparse signal restore and image block CS. Coherence is an important tool to describe and compare the performance of various sensing matrices. Lower coherence implies higher reconstruction accuracy. The coherence of proposed measurement matrices is analyzed and derived to be smaller than the corresponding Gaussian and Bernoulli random matrices. Simulation experiments show that the proposed matrices outperform the corresponding Gaussian, Bernoulli, binary and chaotic bipolar matrices in reconstruction accuracy. Meanwhile, the proposed matrices can reduce the reconstruction time compared with their Gaussian counterpart. Moreover, the proposed matrices are very efficient for sensing performance, memory, complexity and hardware realization, which is beneficial to practical CS.

• 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 Satellite, Information and Communications
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• v.10 no.3
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• pp.57-61
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• 2015

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 was described. CS algorithm SS-CoSaMP(Single-Space Compressive Sampling Matched Pursuit) and AMP(Approximate Message Passing) was described. Image data compressed and restored by these algorithm was compared. Calculation time of the algorithm having a low complexity is determined.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.58-78
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• 2016

Compressed sensing (CS) possesses the potential benefits for spectrum sensing of wideband signal in cognitive radio. The sparsity of signal in frequency domain denotes the number of occupied channels for spectrum sensing. This paper presents a scheme of adaptively adjusting the number of compressed measurements to reduce the unnecessary computational complexity when priori information about the sparsity of signal cannot be acquired. Firstly, a method of sparsity estimation is introduced because the sparsity of signal is not available in some cognitive radio environments, and the relationship between the amount of used data and estimation accuracy is discussed. Then the SNR of the compressed signal is derived in the closed form. Based on the SNR of the compressed signal and estimated sparsity, an adaptive algorithm of adjusting the number of compressed measurements is proposed. Finally, some simulations are performed, and the results illustrate that the simulations agree with theoretical analysis, which prove the effectiveness of the proposed adaptive adjusting of compressed measurements.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.257-267
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• 2017

Compressed Sensing(CS) deals with linear inverse problems. The theoretical results of CS have had an impact on inference problems and presented amazing research achievements in the related fields including signal processing and information theory. However, in order for CS to be applied in practical environments, there are two significant challenges to be solved. One is to guarantee in real time recovery of CS signals, and the other is that the signals have to be sparse. To this end, the latest researches using deep learning technology have emerged. In this paper, we consider CS problems based on deep learning and discuss the latest research results. And the approaches for CS signal reconstruction using deep learning show superior results in terms of recovery time and performance. It is expected that the approaches for CS reconstruction using deep learning shown in recent studies can not only raise the possibility of utilization of CS, but also be highly exploited in the fields of signal processing and communication areas.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.188-190
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• 2012

Compressed Sensing (CS) has been developed for several years. Among many CS algorithms for image, the Block-based Compressed Sensing with Smoothed Projected Landweber (BCS-SPL) demonstrates its excellent performance in low-complexity and near-optimal reconstruction. Several noise filtering algorithms of image reconstruction have been introduced such as the Wiener or the median filters, etc. In general, each filter has its own advantages and disadvantages depending on specific coding scheme. In this paper, we show that reconstruction performance can be varied according to the choice of filter. When a sub-rate value is changed, different filter causes different effect as well. Concerning the sub-rate, an inner filter can be chosen to improve the reconstructed image quality.