• Journal of Broadcast Engineering
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• v.13 no.4
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• pp.452-462
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• 2008

In this paper, we address issues associated with the real-time implementation of the MPEG-1/2 Layer-II (or MUSICAM) and MPEG-4 ER-BSAC decoders for Digital Multimedia Broadcasting (DMB) on TMS320C64x+ that is a fixed-point DSP processor with a clock speed of 330 MHz. To achieve the real-time requirement, they should be optimized in different steps as follows. First of all, a C-code level optimization is performed by sharing the memory, adjusting data types, and unrolling loops. Next, an algorithm level optimization is carried out such as the reconfiguration of bitstream reading, the modification of synthesis filtering, and the rearrangement of the window coefficients for synthesis filtering. In addition, the C-code of a synthesis filtering module of the MPEG-1/2 Layer-II decoder is rewritten by using the linear assembly programming technique. This is because the synthesis filtering module requires the most processing time among all processing modules of the decoder. In order to show how the real-time implementation works, we obtain the percentage of the processing time for decoding and calculate a RMS value between the decoded audio signals by the reference MPEG decoder and its DSP version implemented in this paper. As a result, it is shown that the percentages of the processing time for the MPEG-1/2 Layer-II and MPEG-4 ER-BSAC decoders occupy less than 3% and 11% of the DSP clock cycles, respectively, and the RMS values of the MPEG-1/2 Layer-II and MPEG-4 ER-BSAC decoders implemented in this paper all satisfy the criterion of -77.01 dB which is defined by the MPEG standards.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1517-1524
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• 2022

In this paper, we consider a heterogeneous network (HetNet) consisting of one macro base station and multiple small base stations, and assume the coordinated multi-point transmission between the base stations. In addition, we assume that the channel between the base station and the user consists of path loss and Rayleigh fading. Under these assumptions, we present the energy efficiency (EE) achievable by the user for a given base station and we formulate an optimization problem of dynamic cell selection and transmit power allocation to maximize the total EE of the HetNet. In this paper, we propose an unsupervised deep learning method to solve the optimization problem. The proposed deep learning-based scheme can provide high EE while having low complexity compared to the conventional iterative convergence methods. Through the simulation, we show that the proposed dynamic cell selection scheme provides higher EE performance than the maximum signal-to-interference-plus-noise ratio scheme and the Lagrangian dual decomposition scheme, and the proposed transmit power allocation scheme provides the similar performance to the trust region interior point method which can achieve the maximum EE.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.611-613
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• 2021

Recently, research and development of quantum computers, which exceed the limits of classical computers, have been actively carried out in various fields. Quantum computers, which use quantum mechanics principles in a way different from the electrical signal processing of classical computers, have various quantum mechanical phenomena such as quantum superposition and quantum entanglement. It goes through a very complicated calculation process compared to the calculation of a classical computer for performing an operation using its characteristics. In order to utilize each element efficiently and accurately, it is necessary to visualize the data before driving the actual quantum computer and perform error verification, optimization, reliability, and verification. However, when visualizing all the data of various elements configured inside the quantum computer, it is difficult to intuitively grasp the necessary data, so it is necessary to visualize the data selectively. In this paper, we visualize the data of various elements that make up a quantum computer, and hierarchically visualize the internal circuit components of a quantum computer that are complicatedly configured so that the data can be observed and utilized intuitively.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• Molecules and Cells
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• v.43 no.11
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• pp.909-920
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• 2020

Cytosolic Ca²⁺ levels ([Ca²⁺]_c) change dynamically in response to inducers, repressors, and physiological conditions, and aberrant [Ca²⁺]_c concentration regulation is associated with cancer, heart failure, and diabetes. Therefore, [Ca²⁺]_c is considered as a good indicator of physiological and pathological cellular responses, and is a crucial biomarker for drug discovery. A genetically encoded calcium indicator (GECI) was recently developed to measure [Ca²⁺]_c in single cells and animal models. GECI have some advantages over chemically synthesized indicators, although they also have some drawbacks such as poor signal-to-noise ratio (SNR), low positive signal, delayed response, artifactual responses due to protein overexpression, and expensive detection equipment. Here, we developed an indicator based on interactions between Ca²⁺-loaded calmodulin and target proteins, and generated an innovative GECI sensor using split nano-luciferase (Nluc) fragments to detect changes in [Ca²⁺]_c. Stimulation-dependent luciferase activities were optimized by combining large and small subunits of Nluc binary technology (NanoBiT, LgBiT:SmBiT) fusion proteins and regulating the receptor expression levels. We constructed the binary [Ca²⁺]_c sensors using a multicistronic expression system in a single vector linked via the internal ribosome entry site (IRES), and examined the detection efficiencies. Promoter optimization studies indicated that promoter-dependent protein expression levels were crucial to optimize SNR and sensitivity. This novel [Ca²⁺]_c assay has high SNR and sensitivity, is easy to use, suitable for high-throughput assays, and may be useful to detect [Ca²⁺]_c in single cells and animal models.

• Investigative Magnetic Resonance Imaging
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• v.13 no.2
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• pp.117-126
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• 2009

Purpose : A numerical method of designing a multiple quantum filter (MQF) is presented for the optimum detection of myo-inositol (mI), an important brain metabolite, by using in vivo proton nuclear magnetic resonance spectroscopy ($^1$-HMRS). Materials and Methods : Starting from the characterization of the metabolite, the filter design includes the optimization of the sequence parameters such as the two echo times (TEs), the mixing time (TM), and the flip angle and offset frequency of the 3rd $90^{\circ}$ pulse which converts multiple quantum coherences (MQCs) back into single quantum coherences (SQCs). The optimized filter was then tested both in phantom and in human brains. Results : The results demonstrate that the proposed MQF can improve the signal-to-background ratio of the target metabolite by a factor of more than three by effectively suppressing the signal from the background metabolites. Conclusion : By incorporating a numerical method into the design of MQFs in $^1$-HMRS the spectral integrity of a target metabolite, in particular, with a complicated spin system can be substantially enhanced.

• Investigative Magnetic Resonance Imaging
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• v.21 no.2
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• pp.65-70
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• 2017

Purpose: To optimize the saturation time and maximizing the pH-weighted difference between the normal and ischemic brain regions, on 3-tesla amide proton transfer (APT) imaging using an in vivo rat model. Materials and Methods: Three male Wistar rats underwent middle cerebral artery occlusion, and were examined in a 3-tesla magnetic resonance imaging (MRI) scanner. APT imaging acquisition was performed with 3-dimensional turbo spin-echo imaging, using a 32-channel head coil and 2-channel parallel radiofrequency transmission. An off-resonance radiofrequency pulse was applied with a Sinc-Gauss pulse at a $B_{1,rms}$ amplitude of $1.2{\mu}T$ using a 2-channel parallel transmission. Saturation times of 3, 4, or 5 s were tested. The APT effect was quantified using the magnetization-transfer-ratio asymmetry at 3.5 ppm with respect to the water resonance (APT-weighted signal), and compared with the normal and ischemic regions. The result was then applied to an acute stroke patient to evaluate feasibility. Results: Visual detection of ischemic regions was achieved with the 3-, 4-, and 5-s protocols. Among the different saturation times at $1.2{\mu}T$ power, 4 s showed the maximum difference between the ischemic and normal regions (-0.95%, P = 0.029). The APTw signal difference for 3 and 5 s was -0.9% and -0.7%, respectively. The 4-s saturation time protocol also successfully depicted the pH-weighted differences in an acute stroke patient. Conclusion: For 3-tesla turbo spin-echo APT imaging, the maximal pH-weighted difference achieved when using the $1.2{\mu}T$ power, was with the 4 s saturation time. This protocol will be helpful to depict pH-weighted difference in stroke patients in clinical settings.

• The Korean Journal of Mycology
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• v.47 no.4
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• pp.359-371
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• 2019

To optimize the expression and secretion of ferritin protein associated with ion storage in the mushroom, Pleurotus eryngii, a recombinant secretion vector, harboring the ferritin gene, was constructed using a pPEVPR1b vector under the control of the CaMV 35S promoter and signal sequence of pathogen related protein (PR1b). The ferritin gene was isolated from the T-Fer vector following digestion with EcoRI and HindIII. The gene was then introduced into the pPEVPR1b secretion vector, and it was then named pPEVPR1b-Fer. The recombinant vector was transferred into P. eryngii via Agrobacterium tumefaciens-mediated transformation. The transformants were selected on MCM medium supplemented with kanamycin and its expression was confirmed by SDS-PAGE and western blotting. Expression of ferritin protein was optimized by modifying the culture conditions such as incubation time and temperature in batch and 20 L airlift type fermenter. The optimal conditions for ferritin production were achieved at 25℃ and after incubating for 8 days on MCM medium. The amount of ferritin protein was 2.4 mg/g mycelia, as measured by a quantitative protein assay. However, the signal sequence of PR1b (32 amino acids) seems to be correctly processed by peptidase and ferritin protein may be targeted in the apoplast region of mycelia, and it might not be secreted in the culture medium. The iron binding activity was confirmed by Perls' staining in a 7.5% non-denaturing gel, indicating that the multimeric ferritin (composed of 24 subunits) was formed in P. eryngii mycelia. Mycelium powder containing ferritin was tested as a feed additive in broilers. The addition of ferritin powder stimulated the growth of young broilers and improved their feed efficiency and production index.

• Progress in Medical Physics
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• v.21 no.3
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• pp.281-290
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• 2010

Cone-beam digital tomosynthesis (CBDT) has greatly been paid attention in the image-guided radiation therapy because of its attractive advantages such as low patient dose and less motion artifact. Image quality of tomograms is, however, dependent on the imaging conditions such as the scan angle (${\beta}_{scan}$) and the number of projection views. In this paper, we describe the principle of CBDT based on filtered-backprojection technique and investigate the optimization of imaging conditions. As a system performance, we have defined the figure-of-merit with a combination of signal difference-to-noise ratio, artifact spread function and floating-point operations which determine the computational load of image reconstruction procedures. From the measurements of disc phantom, which mimics an impulse signal and thus their analyses, it is concluded that the image quality of tomograms obtained from CBDT is improved as the scan angle is wider than 60 degrees with a larger step scan angle (${\Delta}{\beta}$). As a rule of thumb, the system performance is dependent on $\sqrt{{\Delta}{\beta}}{\times}{\beta}^{2.5}_{scan}$. If the exact weighting factors could be assigned to each image-quality metric, we would find the better quantitative imaging conditions.

• Journal of Broadcast Engineering
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• v.15 no.3
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• pp.380-390
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• 2010

The most recent standard video codec, H.264/AVC achieves significant coding efficiency by using a rate-distortion optimization(RDO). The RDO is a measurement for selecting the best mode which minimizes the Lagrangian cost among several modes. As a result, the computational complexity is increased drastically in encoder. In this paper, a method for fast intra prediction mode decision is proposed to reduce the RDO complexity. To speed up Intra$4{\times}4$ and Chroma Intra encoding, the proposed method decides the case that MPM (Most Probable Mode) is the best prediction mode. In this case, the RDO process is skipped, and only MPM is used for encoding the block in Intra$4{\times}4$. And the proposed method is also applied to the chroma Intra prediction mode in a similar way to the Intra$4{\times}4$. The experimental results show that the proposed method achieves an average encoding time saving of about 63% with negligible loss of PSNR (Peak Signal-to-Noise Ratio).