• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1070-1076
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• 2016

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.628-631
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• 2014

Recently, the proliferation of new applications, e.g., mobile TV, Internet gaming, large file transfer, and the various of user terminals, e.g., smart phones and notebooks, has dramatically increased user traffic and network load. In order to meet this traffic growth, vendors and cellular operators are working on the development of new technologies and cellular standards. Within them, small cell deployment has been heralded as one of most promising way to increase both coverage and capacity of future cellular network. Small cell technology enables to improve capacity of cellular radio network by tight cooperation between small cell and macro cell in multi-tier network where small cells are densely deployed within macro cell coverage. In this paper, we describe the deployment scenarios for cooperation between macro cell and small cells and state-of-the-art technologies related to dense small cell deployment. Then, we also provide design principles and standardization trends for small cell enhancement in 3GPP.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.176-182
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• 2021

This paper evaluates and compares the performance of the Deep Nerual Network (DNN)-based speech enhancement models according to various loss functions. We used a complex network that can consider the phase information of speech as a baseline model. As the loss function, we consider two types of basic loss functions; the Mean Squared Error (MSE) and the Scale-Invariant Source-to-Noise Ratio (SI-SNR), and two types of perceptual-based loss functions, including the Perceptual Metric for Speech Quality Evaluation (PMSQE) and the Log Mel Spectra (LMS). The performance comparison was performed through objective evaluation and listening tests with outputs obtained using various combinations of the loss functions. Test results show that when a perceptual-based loss function was combined with MSE or SI-SNR, the overall performance is improved, and the perceptual-based loss functions, even exhibiting lower objective scores showed better performance in the listening test.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.85-89
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• 2017

In this papers, we apply high performance hardware and machine learning algorithm to build an advanced VAD algorithm for speech enhancement. Since speech is made of series of phoneme, using recurrent neural network (RNN) which consider previous data is proper method to build a speech model. It is impossible to study every noise in real world. So our algorithm is builded by phoneme based study. we detect voice present frames in noisy speech signal and make enhancement of the speech signal. Phoneme based RNN model shows advanced performance in speech signal which has high correlation among each frames. To verify the performance of proposed algorithm, we compare VAD result with label data and speech enhancement result in various noise environments with previous speech enhancement algorithm.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.3
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• pp.75-86
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• 2014

An urban ecosystem is a complex system that includes social, economic and ecosystems. Therefore, it is important to consider its environmental capacity while developing a city plan. Most of the plans, however, consider only the social aspects, which fragments the green spaces and disturbs the movement of species. Sungnam has approximately 100 parks with unexecuted development plans and with great potential to contribute towards urban ecosystem enhancement. Therefore, this study applied network analysis to prioritize the development of city parks and contribute towards improving the green network, with Parus spp. as the target species. To compensate for the drawbacks of binary and possibility-based network analysis, this study included two indices, namely $BC^{PC}_K$, $BC^{IIC}_K$, $dPCconnector_k$ and $dIICconnector_k$. These indices make it possible to find patches that could play an important role in green network enhancement. The urban park with greater value gets a higher priority to be transformed into a park. Thus, our methodology could prove to be very useful in prioritizing the undeveloped parks, thereby supporting decision-making.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.700-706
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• 2013

In this paper, we propose a method for applying random linear network coding in satellite communication to improve reliability. In the proposed protocol, network-coded redundancy (NC-R) packets are transmitted in the PEP (Performance Enhancement Proxy). Therefore, if data packets is lost by wireless channel error, they can be recovered by NC-R packets. We also develop the TCP performance model of the proposed protocol and evaluate the performance of the proposed protocol. In the simulation results, It is shown that the proposed protocol can improve the TCP throughput as compared with that of the conventional TCP because the NC-R packets is sent by the sender-side PEP and the receiver-side PEP use these packets to recover the lost packets, resulting in reducing the packet loss in TCP.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1E
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• pp.16-18
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• 2004

There have been numerous studies on the enhancement of the noisy speech signal. In this paper, I propose a completely new speech enhancement method, that is, a filtering of a dissonant frequency combined with noise reduction algorithm. The simulation results indicate that the proposed method provides a significant gain in audible improvement compared with the conventional method. Therefore if the proposed enhancement scheme is used as a pre-filter, the perceptual quality of speech is greatly enhanced.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.199-202
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• 2003

In this paper, we propose a new 3D coding method for heterogeneous systems over enhanced Access Grid (e-AG) with 3D display using spatio-temporal scalability. The proposed encoder produces four bit-streams: one base layer and enhancement layer l, 2 and 3. The base layer represents a video sequence for left eye with lower spatial resolution. An enhancement layer l provides additional bit-stream needed for reproduction of frames produced in base layer with full resolution. Similarly, the enhancement layer 2 represents a video sequence for right eye with lower spatial resolution and an enhancement layer 3 provides additional bit-stream needed for reproduction of its reference pictures with full resolution. In this system, temporal resolution reduction is obtained by dropping B-frames in the receiver according to network condition. The receiver system can select the spatial and temporal resolution of video sequence with its display condition by properly combining bit-streams.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.135-145
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• 2020

Recently, deep-learning based methods for low-light image enhancement accomplish great success through supervised learning. However, they still suffer from the lack of sufficient training data due to difficulty of obtaining a large amount of low-/normal-light image pairs in real environments. In this paper, we propose an unsupervised learning approach for single low-light image enhancement using the bright channel prior (BCP), which gives the constraint that the brightest pixel in a small patch is likely to be close to 1. With this prior, pseudo ground-truth is first generated to establish an unsupervised loss function. The proposed enhancement network is then trained using the proposed unsupervised loss function. To the best of our knowledge, this is the first attempt that performs a low-light image enhancement through unsupervised learning. In addition, we introduce a self-attention map for preserving image details and naturalness in the enhanced result. We validate the proposed method on various public datasets, demonstrating that our method achieves competitive performance over state-of-the-arts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.632-641
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• 2007

Since the mobile node acts as the router, the Mobile Ad Hoc network requires the security methods that are different from that of network of the wire environment. Also, since the total network can't be included in the transmission area of the mobile node, when one node sends the message to the other node, we need the middle node. But if the middle node is the unreliable malicious node, we can't guarantee the secure message transmission. Also, because all nodes configuring the network are the mobile nodes, they use the restricted battery capacity and the restricted resources. Therefore, because we have trouble performing the encryption that many resources are required when we sending the message, it is vulnerable to the security than the network of the wire environment. Last, because the network topology continues to change by the mobility of nodes configuring the network, we need the security measure that matches the network characteristics. We suggest the routing attack detection for performance enhancement of AODV protocol in Mobile Ad Hoc networks.