• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.756-763
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• 2014

In this paper, we introduce a new sparse signal recovery algorithm referred to as the matching pursuit with greedy tree search (GTMP). The tree search in our proposed method is implemented to minimize the cost function to improve the recovery performance of sparse signals. In addition, a pruning strategy is employed to each node of the tree for efficient implementation. In our performance guarantee analysis, we provide the condition that ensures the exact identification of the nonzero locations. Through empirical simulations, we show that GTMP is effective for sparse signal reconstruction and outperforms conventional sparse recovery algorithms.

• Proceedings of the KSRS Conference
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• v.1
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• pp.282-285
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• 2006

This paper analyzed the characteristics of the Lossy JPEG of the meteorological satellite image, and analyzed the quality of the Lossy JPEG compression, which is proper for the LRIT(Low Rate Information Transmission) to be serviced to the SDUS(Small-scale Data Utilization Station) system of the COMS(Communication, Oceans, Meteorological Satellite). Since COMS is to start running after 2008, we collected the data of the MTSAT-1R(Multi-functional Transport Satellite -1R) for analysis, and after forming the original image to be used to LRIT by each channel and time zone of the satellite image data, we set the different quality with the Lossy JPEG compression, and compressed the original data. For the characteristic analysis of the Lossy JPEG, we measured PSNR(Peak Signal to Noise Rate), compression rate and the time spent in compression following each quality of Lossy JPEG compression. As a result of the analysis of the satellite image data of the MTSAT-1R, the ideal quality of the Lossy JPEG compression was found to be 90% in the VIS Channel, 85% in the IR1 Channel, 80% in the IR2 Channel, 90% in the IR3 Channel and 90% in the IR4 Channel.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.625-628
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• 2007

We present an efficient and robust technique to estimate the velocity of moving targets from a single SAR image. In SAR images, azimuth image shift is a well known phenomenon, which is observed in moving targets having slant-range velocity. Most methods estimated the velocity of moving targets from the distance difference between the road and moving targets or between ship and the ship wake. However, the methods could not be always applied to moving targets because it is difficult to find the road and the ship wake. We adopted a method estimating the velocity of moving targets from azimuth differentials of range-compressed image. This method is based on an assumption that Doppler center frequency shift of moving target causes a phase difference in azimuth differential values. The phase difference is linearly distorted by Doppler rate due to the geometry of SAR image. The linear distortion is eliminated from phase removal procedure, and the constant phase difference is estimated. Finally, range velocity estimates for moving targets are retrieved. This technique is tested using an ENVISAT ASAR image in which several unknown ships are presented. The theoretical accuracy of this technique is discussed by SAR simulation. The advantages and disadvantages of this method over the conventional method are also discussed.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.329-332
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• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1195-1200
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• 2019

This paper proposes a detection algorithm for group testing. Group testing is a problem of finding a very small number of defect samples out of a large number of samples, which is similar to the problem of Compressed Sensing. In this paper, we define a noiseless group testing and propose a probabilistic algorithm for detection of defective samples. The proposed algorithm is constructed such that the extrinsic probabilities between the input and output signals exchange with each other so that the posterior probability of the output signal is maximized. Then, defective samples are found in the group testing problem through a simulation on the detection algorithm. The simulation results for this study are compared with the lower bound in the information theory to see how much difference in failure probability over the input and output signal sizes.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.81-92
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• 2021

The role of neuroimaging in patients with acute ischemic stroke has been gradually increasing. The ultimate goal of stroke imaging is to make a streamlined imaging workflow for safe and efficient treatment based on optimized patient selection. In the era of multimodal comprehensive imaging in strokes, imaging based on computed tomography (CT) has been preferred for use in acute ischemic stroke, because, despite the unique strengths of magnetic resonance imaging (MRI), MRI has a longer scan duration than does CT-based imaging. However, recent improvements, such as multicoil technology and novel MRI acceleration techniques, including parallel imaging, simultaneous multi-section imaging, and compressed sensing, highlight the potential of comprehensive MR-based imaging for strokes. In this review, we discuss the role of stroke imaging in acute ischemic stroke management, as well as the strengths and limitations of MR-based imaging. Given these concepts, we review the current MR acceleration techniques that could be applied to stroke imaging and provide an overview of the previous research on each essential sequence: diffusion-weighted imaging, gradient-echo, fluid-attenuated inversion recovery, contrast-enhanced MR angiography, and MR perfusion imaging.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1343-1358
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• 2020

In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsity estimation is determined via maximum iterations. Different maximum iterations correspond to different thresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variable weak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the residual error value to control the weak threshold. When the residual value decreases, the threshold value continuously increases, so that the atoms contained in the atomic set are closer to the real sparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved the generalized Jaccard coefficient in order to replace the inner product method that is used in the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace the joint expectation for two variables based on the generalized Jaccard coefficient. The improved generalized Jaccard coefficient can be used to generate a more accurate calculation of the correlation between the measurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selecting the wrong atoms. We demonstrate using simulations that the proposed algorithm produces a better reconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.6
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• pp.397-402
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• 2016

To produce adequate electricity in nuclear and thermal power plants, an optimal amount of steam should be supplied to a generator connected to high- and low-pressure steam turbines. A turbine output control device, which is a special steam valve employed to supply or interrupt the steam to the turbine, is operated using a hydraulic servo actuator. In power plants, the performance of servo actuators is degraded by the air generated from the hydraulic system, or causes frequent failures owing to an increase in the wear of the seal. This is due to the seal being burnt as generated heat using the produced compressed air. Some power plants have exhausted air using a fixed orifice, and thus they encounter power loss due to mass flow exhaust. Failures are generated in hydraulic pumps, electric motors, and valves, which are frequently operated. In this study, we perform modeling and analysis of the load-sensing air-exhaust valves, which can be passed through very fine flow under normal use conditions, and exhaust mass flow air at the beginning stage as with existing fixed orifices. Then, we propose a method to prevent failures due to the compressed air, and to ensure the control accuracy of hydraulic servo actuators.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2252-2258
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• 2013

In this paper, parallel orthogonal matching pursuit (POMP) is proposed to supplement the simultaneous orthogonal matching pursuit (S-OMP) which has been widely used as a greedy algorithm for sparse signal recovery for multiple measurement vector (MMV) problem. The process of POMP is simple but effective: (1) multiple indexes maximally correlated with the observation vector are chosen at the first iteration, (2) the conventional S-OMP process is carried out in parallel for each selected index, (3) the index set which yields the minimum residual is selected for reconstructing the original sparse signal. Empirical simulations show that POMP for MMV outperforms than the conventional S-OMP both in terms of exact recovery ratio (ERR) and mean-squared error (MSE).

• Current Optics and Photonics
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• v.4 no.1
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• pp.1-8
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• 2020

We research a system of compressed-sensing computational ghost imaging (CSCGI) based on the intensity fluctuation brought by turbulence. In this system, we used the gamma-gamma intensity-fluctuation model, which is commonly used in transmission systems, to simulate the CSCGI system. By setting proper values of the parameters such as transmission distance, refractive-index structure parameter, and sampling rates, the peak signal-to-noise ratio (PSNR) performance and bit-error rate (BER) performance are obtained to evaluate the imaging quality, which provides a theoretical model to further research the ghost-imaging algorithm.