• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.10
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• pp.850-857
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• 2013

Turbo equalizer system is a method which can improve performance through a combination of the equalizer and decoder. The turbo equalizer has been mainly used a MAP equalizer. However, this turbo equalizer has a disadvantage that has a high computational complexity. To overcome the disadvantage and to improve efficiency of bandwidth, blind turbo equalization system is proposed. blind turbo equalization system has low equalization performance than conventional turbo equalization system. To circumvent this problem, we adapt the beamforming method based on the MUSIC algorithm. we confirmed that the proposed method improves the equalization performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3823-3840
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• 2020

Recently, auto-encoder has emerged as the most popular method in convolutional neural network (CNN) based image compression and has achieved impressive performance. In the traditional auto-encoder based image compression model, the encoder simply sends the features of last layer to the decoder, which cannot allocate bits over different spatial regions in an efficient way. Besides, these methods do not fully exploit the contextual information under different receptive fields for better reconstruction performance. In this paper, to solve these issues, a novel auto-encoder model is designed for image compression, which can effectively transmit the hierarchical features of the encoder to the decoder. Specifically, we first propose an adaptive bit-allocation strategy, which can adaptively select an importance channel. Then, we conduct the multiply operation on the generated importance mask and the features of the last layer in our proposed encoder to achieve efficient bit allocation. Moreover, we present an additional novel perceptual loss function for more accurate image details. Extensive experiments demonstrated that the proposed model can achieve significant superiority compared with JPEG and JPEG2000 both in both subjective and objective quality. Besides, our model shows better performance than the state-of-the-art convolutional neural network (CNN)-based image compression methods in terms of PSNR.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.309-317
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• 2022

In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1290-1298
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• 2000

Turbo Code decoder is an iterate decoding technology, which extracts extrinsic information from the bit to be decoded by calculating both forward and backward metrics, and uses the information to the next decoding step Turbo Code shows excellent performance, approaching Shannon Limit at the view of BER, when the size of Interleaver is big and iterate decoding is run enough. But it has the problems which are increased complexity and delay and difficulty of real-time processing due to Interleaver and iterate decoding. In this paper, it is analyzed that MAP(maximum a posteriori) algorithm which is used as one of Turbo Code decoding, and the factor which determines its performance. MAP algorithm proceeds iterate decoding by determining soft decision value through the environment and transition probability between all adjacent bits and received symbols. Therefore, to improve the performance of MAP algorithm, the trust between adjacent received symbols must be ensured. However, MAP algorithm itself, can not do any action for ensuring so the conclusion is that it is needed more algorithm, so to decrease iterate decoding. Consequently, MAP algorithm and Turbo Code performance are analyzed in the nongaussian channel applying Robust equalization technique in order to input more trusted information into MAP algorithm for the received symbols.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1093-1103
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• 2009

In this paper, we propose a new depth map based distributed multi-view video coding algorithm through an efficient side information generation. A distributed video coding scheme corrects errors between an original image and side information generated at a decoder by using channel coding techniques. Therefore, the more accurate side information is generated, the better performance of distributed video coding scheme is achieved. In the proposed algorithm, a distributed video coding scheme is applied to multi-view video coding based on depth map. Side information is also generated from images of adjacent views through 3D warping by using a depth map and is also combined with MCTI(motion compensated temporal interpolation) which uses images on a temporal axis, and 3D warping. Experimental results show that side information generated by using the proposed algorithm has 0.97dB better average PSNR compared with using MCTI and 3D warping separated. In addition, 8.01% of average bit-rate has been decreased while the same PSNR in R-D curves is kept.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9A
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• pp.917-923
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• 2008

The D-STTD system obtains the diversity gain through the STTD scheme and the Multiplexing gain through parallel structure of the encoder using the STTD scheme known Alamouti Code. We are difficult to use Combining scheme of the STTD scheme for the D-STTD detection in the decoder because the D-STTD system transmits mutually different data in each other STTD encoder for multiplexing gain. Therefore, in this paper we combine the D-STTD system with Linear algorithm, SIC algorithm and OSIC algorithm known multiplexing detection scheme based on MMSE scheme and compare the performance of each system. And we propose the detection scheme of the D-STTD using MAP Algorithm and analyze the performance of each system. The simulation results show that the detector using iterative algorithm has better performance than Linear MMSE Detector. Especially, we show that the detector using MAP algorithm outperforms conventional detector.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.38-46
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• 2020

LOw Frequency Analysis Recording (LOFAR) and Demodulation of Envelop Modulation On Noise (DEMON) grams are bearing-time-frequency plots of underwater acoustic signals, to visualize features for passive sonar. Those grams are characterized by tonal components, for which conventional data coding methods are not suitable. In this work, a novel LOFAR/DEMON gram compression algorithm based on binary map and prediction methods is proposed. We first generate a binary map, from which prediction for each frequency bin is determined, and then divide a frame into several macro blocks. For each macro block, we apply intra and inter prediction modes and compute residuals. Then, we perform the prediction of available bins in the binary map and quantize residuals for entropy coding. By transmitting the binary map and prediction modes, the decoder can reconstructs grams using the same process. Simulation results show that the proposed algorithm provides significantly better compression performance on LOFAR and DEMON grams than conventional data coding methods.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.2
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• pp.6-14
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• 2007

In this paper, we propose and observe the Adaptive Modulation system with optimal Turbo Coded V-BLAST (Vertical-Bell-lab Layered Space-Time) technique that is applied the extrinsic information from MAP (Maximum A Posteriori) Decoder in decoding Algorithm of V-BLAST: ordering and slicing. The extrinsic information is used by a priori probability and the system decoding process is composed of the Main Iteration and the Sub Iteration. And comparing the proposed system with the Adaptive Modulation system using conventional Turbo Coded V-BLAST technique that is simply combined V-BLAST with Turbo Coding scheme, we observe how much throughput performance has been improved. In addition, we observe the proposed system using STD (Selection Transmit Diversity) scheme. As a result of simulation, Comparing with the conventional Turbo Coded V-BLAST technique with the Adaptive Modulation systems, the optimal Turbo Coded V-BLAST technique with the Adaptive Modulation systems has better throughput gain that is about 350 Kbps in 11 dB SNR range. Especially, comparing with the conventional Turbo Coded V-BLAST technique using 2 transmit and 2 receive antennas, the proposed system with STD (Selection Transmit Diversity) scheme show that the improvement of maximum throughput is about 1.77 Mbps in the same SNR range.