• Ocean and Polar Research
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• v.27 no.2
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• pp.189-195
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• 2005

Several models predict large and potentially abrupt ocean circulation changes due to anthropogenic greenhouse-gas emissions. These circulation changes drive-in the models-considerable oceanic oxygen trend. A sound estimate of the observed oxygen trends can hence be a powerful tool to constrain predictions of future changes in oceanic deepwater formation, heat and carbon dioxide uptake. Estimating decadal scale oxygen trends is, however, a nontrivial task and previous studies have come to contradicting conclusions. One key potential problem is that changes in the historical observation network might introduce considerable errors. Here we estimate the likely magnitude of these errors for a subset of the available observations in the Southern Ocean. We test three common data analysis methods south of Australia and focus on the decadal-scale trends between the 1970's and the 1990's. Specifically, we estimate errors due to sparsely sampled observations using a known signal (the time invariant, temporally averaged, World Ocean Atlas 2001) as a negative control. The crossover analysis and the objective analysis methods are for less prone to spatial sampling location biases than the area averaging method. Subject to numerous caveats, we find that errors due to sparse sampling for the area averaging method are on the order of several micro-moles $kg^{-1}$. for the crossover and the objective analysis method, these errors are much smaller. For the analyzed example, the biases due to changes in the spatial design of the historical observation network are relatively small compared to the tends predicted by many model simulations. This raises the possibility to use historic oxygen trends to constrain model simulations, even in sparsely sampled ocean basins.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.7
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• pp.797-804
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• 2012

In this paper, we propose a QAPM(Quadrature Amplitude Position Modulation) modulation using compressive sensing for the purpose of power efficiency improvement. QAPM modulation is a combination technique of QAM (quadrature amplitude modulation) and PPM(Pulse Position Modulation). Therefore it can decrease the transmission power and improve BER performance. Moreover, even if the band width is widened when the number of positions is increased, high sparsity characteristic caused by position number can be applied to compressive sensing technique. Compressive sensing has recently studied as a method that can be successfully reconstructed from the small number of measurements for sparse signal. Therefore, the proposed system can lower price of receiver by reducing sampling rate and has performance improved by using QAPM modulation. And the results are confirmed through simulations.

• Investigative Magnetic Resonance Imaging
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• v.14 no.1
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• pp.10-20
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• 2010

Purpose : Recently, the Recon Challenge at the 2009 ISMRM workshop on Data Sampling and Image Reconstruction at Sedona, Arizona was held to evaluate feasibility of highly accelerated acquisition of time resolved contrast enhanced MR angiography. This paper provides the step-by-step description of the winning results of k-t FOCUSS in this competition. Materials and Methods : In previous works, we proved that k-t FOCUSS algorithm successfully solves the compressed sensing problem even for less sparse cardiac cine applications. Therefore, using k-t FOCUSS, very accurate time resolved contrast enhanced MR angiography can be reconstructed. Accelerated radial trajectory data were synthetized from X-ray cerebral angiography images and provided by the organizing committee, and radiologists double blindly evaluated each reconstruction result with respect to the ground-truth data. Results : The reconstructed results at various acceleration factors demonstrate that each components of compressed sensing, such as sparsifying transform and incoherent sampling patterns, etc can have profound effects on the final reconstruction results. Conclusion : From reconstructed results, we see that the compressed sensing dynamic MR imaging algorithm, k-t FOCUSS enables high resolution time resolved contrast enhanced MR angiography.