• Proceedings of the IEEK Conference
• /
• 2001.09a
• /
• pp.959-962
• /
• 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(3rd Generation 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.

• Journal of computational fluids engineering
• /
• v.12 no.3
• /
• pp.41-46
• /
• 2007

Dynamic behavior of immiscible gas bubble attached to the wall in channel flow plays very important role in many engineering applications. Special attention has been paid to micro direct methanol fuel cell(${\mu}$DMFC) where surface tension becomes dominant factor with minor gravitational effect due to its reduced size. Therefore, displacement of $CO_2$ bubble generating on a cathode side in ${\mu}$DMFC can be very difficult and efficient removal of $CO_2$ bubbles will affect the overall machine performance considerably. We have focused our efforts on studying the dynamic behavior of immiscible bubble attached to the one side of the wall on 2D rectangular channel subject to external shear flow. We used Level Contour Reconstruction Method(LCRM) which is the simplified version of front tracking method to track the bubble interface motion. Effects of Reynolds number, Weber number, advancing/receding contact angle and property ratio on bubble detachment characteristic has been numerically identified.

• Transactions on Electrical and Electronic Materials
• /
• v.14 no.6
• /
• pp.291-294
• /
• 2013

When subjected to a change in dimensions, the device performance decreases. Multi-gate SOI devices, viz. the Double Gate MOSFET (DG-MOSFET), are expected to make inroads into integrated circuit applications previously dominated exclusively by planar MOSFETs. The primary focus of attention is how channel engineering (i.e. Graded Channel (GC)) and gate engineering (i.e. Dual Insulator (DI)) as gate oxide) creates an effect on the device performance, specifically, leakage current ($I_{off}$), on current ($I_{on}$), and DIBL. This study examines the performance of the devices, by virtue of a simulation analysis, in conjunction with N-channel DG-MOSFETs. The important parameters for improvement in circuit speed and power consumption are discussed. From the analysis, DG-DI MOSFET is the most suitable candidate for high speed switching application, simultaneously providing better performance as an amplifier.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.4 no.2
• /
• pp.37-43
• /
• 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
• /
• 2001.06a
• /
• pp.217-220
• /
• 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)
• /
• v.15 no.12
• /
• pp.4439-4455
• /
• 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 the Korea Society of Computer and Information
• /
• v.29 no.7
• /
• pp.11-20
• /
• 2024

In this paper, we propose the BAG (Bit-width Aware Generator) and the Intermediate Layer Knowledge Distillation using Channel-wise Attention to reduce the knowledge gap between a quantized network, a full-precision network, and a generator in GDFQ (Generative Data-Free Quantization). Since the generator in GDFQ is only trained by the feedback from the full-precision network, the gap resulting in decreased capability due to low bit-width of the quantized network has no effect on training the generator. To alleviate this problem, BAG is quantized with same bit-width of the quantized network, and it can generate synthetic images, which are effectively used for training the quantized network. Typically, the knowledge gap between the quantized network and the full-precision network is also important. To resolve this, we compute channel-wise attention of outputs of convolutional layers, and minimize the loss function as the distance of them. As the result, the quantized network can learn which channels to focus on more from mimicking the full-precision network. To prove the efficiency of proposed methods, we quantize the network trained on CIFAR-100 with 3 bit-width weights and activations, and train it and the generator with our method. As the result, we achieve 56.14% Top-1 Accuracy and increase 3.4% higher accuracy compared to our baseline AdaDFQ.

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

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

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