• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.4 no.2
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• pp.37-43
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• 2005

A simple scheduler satisfying the proportional fairness (PF) was introduced in wireless access networks and revealed that it can achieve a good compromise between total throughput and user fairness. Though it has received much attention for some time, its application was mainly restricted to the single channel systems. In this paper, we study how to implement the PF scheduler in the multi-channel environments such as OFDMA/TDMA. Besides the traditional PF-SC scheme, we propose a new PF-OPT scheme that is the genuine PF scheduler in a sense of maximizing the total log-utility of users. The simulation results show that PF-OPT gives large throughput under the heterogeneous subchannel statistics.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.217-220
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• 2001

Turbo codes of long block sizes have been known to show very good performance in an AWGN channel and the turbo code has been strongly recommended as error correction code for IMT-2000 in 3GPP(3$^{rd}$ Ceneration Partnership Project). Recently, turbo codes of short block sizes suitable for real time communication systems have attracted a lot of attention. Thus in this paper we consider the turbo code of 1/3 code rate and short frame size of 192 bits in ITU-R channel model. We analyzed the performance of W-CDMA systems of 10MHz bandwidths employing RAKE receiver with not only MRC diversity but also turbo code..

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4439-4455
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• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.483-497
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• 2023

Depth estimation is one of the most complicated and difficult problems to deal with in the light field. In this paper, a compound attention convolutional neural network (CAttNet) is proposed to extract depth maps from light field images. To make more effective use of the sub-aperture images (SAIs) of light field and reduce the redundancy in SAIs, we use a compound attention mechanism to weigh the channel and space of the feature map after extracting the primary features, so it can more efficiently select the required view and the important area within the view. We modified various layers of feature extraction to make it more efficient and useful to extract features without adding parameters. By exploring the characteristics of light field, we increased the network depth and optimized the network structure to reduce the adverse impact of this change. CAttNet can efficiently utilize different SAIs correlations and features to generate a high-quality light field depth map. The experimental results show that CAttNet has advantages in both accuracy and time.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.434.2-434.2
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• 2014

We observe enhanced pH response of solution-gated field-effect transistors (SG-FET) having 1D-2D hybrid channel of vertical grown ZnO nanorods grown on CVD graphene (Gr). In recent years, SG-FET based on Gr has received a lot of attention for biochemical sensing applications, because Gr has outstanding properties such as high sensitivity, low detection limit, label-free electrical detection, and so on. However, low-defect CVD Gr has hardly pH responsive due to lack of hydroxyl group on Gr surface. On the other hand, ZnO, consists of stable wurtzite structure, has attracted much interest due to its unique properties and wide range of applications in optoelectronics, biosensors, medical sciences, etc. Especially, ZnO were easily grown as vertical nanorods by hydrothermal method and ZnO nanostructures have higher sensitivity to environments than planar structures due to plentiful hydroxyl group on their surface. We prepared for ZnO nanorods vertically grown on CVD Gr (ZnO nanorods/Gr hybrid channel) and to fabricate SG-FET subsequently. We have analyzed hybrid channel FETs showing transfer characteristics similar to that of pristine Gr FETs and charge neutrality point (CNP) shifts along proton concentration in solution, which can determine pH level of solution. Hybrid channel SG-FET sensors led to increase in pH sensitivity up to 500%, compared to pristine Gr SG-FET sensors. We confirmed plentiful hydroxyl groups on ZnO nanorod surface interact with protons in solution, which causes shifts of CNP. The morphology and electrical characteristics of hybrid channel SG-FET were characterized by FE-SEM and semiconductor parameter analyzer, respectively. Sensitivity and sensing mechanism of ZnO nanorods/Gr hybrid channel FET will be discussed in detail.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2131-2153
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• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1518-1527
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• 1994

An algorithm for BPSK demodulation of which channel is an electric distribution line is developed, and realized in this paper. To realize the BPSK demodulation by microprocessor, BPSK signal that is received through the distribution line must be converted to digital signal. A hardware which converts BPSK signal to digital one has been designed in this paper, and an algorithm for BPSK demoduation of which channel is distribution line has been also developed in algorithm for BPSK demoduation of which channel is distribution line has been also developed in this paper by paying the attention to the fact that a modulated point appears up and down according to the rising edge and falling edge of the modulated binary signal if the carrier frequency is even times to the modulated binary signal, and by paying the attention to the fact that the signal duration or modulated point is twice of the other point. The microprocessor demodulation system with the algorithm has been realized. The system proved to have 0.02%(or less) bit error rate in real BPSK demodulation.

• Smart Media Journal
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• v.12 no.5
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• pp.28-35
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• 2023

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.

• The KSFM Journal of Fluid Machinery
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• v.17 no.5
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• pp.19-26
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• 2014

The cross flow turbine attracts more and more attention for its relatively wide operating range and simple structure. In this study, a novel type of micro cross flow turbine is developed for application to a step in an irrigational channel. The head of the turbine is only H=4.3m and the turbine inlet channel is open ducted type, which has barely been studied. The efficiency of the turbine with inlet open duct channel is relatively low. Therefore, a guide nozzle on the turbine inlet is attached to improve the performance of the turbine. The guide nozzle shapes are investigated to find the best shape for the turbine. The guide nozzle plays an important role on directing flow at the runner entry, and it also decreases the negative torque loss by reducing the pressure difference in Region 1. There is 12.5% of efficiency improvement by attaching a well shaped guide nozzle on the turbine inlet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3409-3422
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• 2014

The frequency spectrum available for the wireless communication is extremely crowded. In order to improve the spectral efficiency, the two-way relay networks have aroused great attention. A simple $N^{th}$ best-relay selection criterion for the opportunistic two-way relay networks is proposed, which can be implemented easily by extending the distributed timer technique in practice, since the proposed criterion is mainly based on the channel gains. The outage performance of the proposed relay selection scheme is analyzed under the outdated channel state information (CSI), and a tight closed-form lower bound and asymptotic value of the outage probability over Rayleigh fading channels are obtained. Simulation results demonstrate that the tight closed-form lower bound of the outage probability very closely matches with simulated ones in the whole SNR region, and the asymptotic results provide good tight approximations to the simulation ones, especially in the high SNR region.