• Journal of the Korean Magnetic Resonance Society
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• v.24 no.2
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• pp.59-65
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• 2020

Nitrogen-Vacancy (NV) center in diamond has been an emerging versatile tool for quantum sensing applications. Amongst various applications, nano-scale nuclear magnetic resonance (NMR) using a single or ensemble NV centers has demonstrated promising results, opening possibility of a single molecule NMR for its chemical structural studies or multi-nuclear spin spectroscopy for quantum information science. However, there is a key challenge, which limited the spectral resolution of NMR detection using NV centers; the interrogation duration for NV-NMR detection technique has been limited by the NV sensor spin lifetime (T₁ ~ 3ms), which is orders of magnitude shorter than the coherence times of nuclear spins in bulk liquid samples (T₂ ~ 1s) or intrinsic ¹³C nuclear spins in diamond. Recent studies have shown that quantum memory technique or synchronized readout detection technique can further narrow down the spectral linewidth of NMR signal. In this short review paper, we overview basic concepts of nanoscale NMR using NV centers, and introduce further developments in high spectral resolution NV NMR studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.46-55
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• 2006

The purpose of this study is a development of an automatic mosaicing for applying to large number of airborne multispectral images, which reduces manual operation by human. 2436 airborne multispectral images were acquired from DuncanTech MS4100 camera with three bands; green, red and near infrared. LIDAR(LIght Detection And Ranging) data and GPS/INS(global positioning system/inertial navigation system) data were collected with the multispectral images. First, the multispectral images were converted to image patterns by unsupervised classification. Their patterns were compared with those of adjacent images to derive relative spatial position between images. Relative spatial positions were derived for 80% of the whole images. Second, it accomplished an automatic mosaicing using GPS/INS data and unsupervised classification. Since the time of GPS/INS data did not synchronized the time of readout images, synchronized GPS/INS data with the time of readout image were selected in consecutive data by comparing unsupervised classified images. This method realized mosaicing automatically for 96% images and RMSE (root mean square error) for the spatial precision of mosaiced images was only 1.44 m by validation with LIDAR data.