• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.83-93
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• 2013

By comparing Wiener's MMSE on stochastic signal transmission with Shannon's mutual information first proved by C.E. Shannon in terms of information theory, connections between two approaches were investigated. What Wiener wanted to see in signal transmission in noisy channel is to try to capture fundamental limits for signal quality in signal estimation. On the other hands, Shannon was interested in finding fundamental limits of signal quantity that maximize the uncertainty in mutual information using the entropy concept in noisy channel. First concern of this paper is to show that in deriving limits of Shannon's point to point fundamental channel capacity, Shannon's mutual information obtained by exploiting MMSE combiner and Wiener filter's MMSE are interelated by integro-differential equantion. Then, At the meeting point of Wiener's MMSE and Shannon's mutual information the upper bound of spectral efficiency and the lower bound of energy efficiency were computed. Choosing a proper lattice-type code of a mod-${\Lambda}$AWGN channel model and MMSE estimation of ${\alpha}$ confirmed to lead to the fundamental Shannon capacity limits.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.2
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• pp.41-47
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• 2021

Purpose There are many cyclotrons compared to the land area of the Republic of Korea. Because GMP certification is required and the nuclear medicine test does not apply for insurance, the number of examinations for nuclear medicine is decreasing. Therefore, there is a high probability of early decommissioning of the cyclotron. However, we do not unusually perform the radioactivation evaluation on concrete that can be classified as radioactive waste during the decommissioning of the cyclotron. In this study, we aim to confirm the radioactivation in the concrete surface using Handheld Radionuclide Identification Devices (RIDs). Materials and Methods Because there is no cyclotron being decommissioning in the Republic of Korea, it was impossible to perform the coring of concrete for radioactivation analysis. In this study, we used the KIRAMS-13 and analyzed the concrete surface in the target direction in the cyclotron room. After setting the target direction as the center, radionuclides were measured for about five months at thirty points with vertical and horizontal intervals of 30 cm. We used the RIIDEye(Detector: NaI(Tl) detector, manufacturer: Thermo) in this study and set the measurement time per point to one day (24 hours). Results Co-60 and Cs-137 were detected in some measurement points, and we confirmed the radioactivity of Co-60 detected at the most points. As a result, we found that the radioactivity of Co-60 was high in the diagonal direction (from the lower-left direction to the upper right direction) based on the center of the target. However, we think it is impossible to apply the corresponding results to all cyclotrons because we performed the study using only one cyclotron. Conclusion In thirty measurement points, we could confirm the radioactive nuclides and the relative radioactivity using the results of portable nuclides analyzer. Therefore, we expect that we can use the portable nuclides analyzer to select the coring position of concrete during the decommissioning of the cyclotron. Also, if we secure the radioactivation data for several years, we expect to make a more accurate estimate of radioactive waste during the preparation period of decommissioning of the cyclotron.