• ETRI Journal
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• v.44 no.6
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• pp.1034-1046
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• 2022

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1535-1554
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• 2016

This paper proposes a novel channel assignment scheme called multi-cluster based dynamic channel assignment (MC-DCA) to improve system performance for the downlink of dense femtocell networks (DFNs) based on orthogonal frequency division multiple access (OFDMA) and frequency division duplexing (FDD). In order to dynamically assign channels for femtocell access points (FAPs), the MC-DCA scheme uses a heuristic method that consists of two steps: one is a multiple cluster assignment step to group FAPs using graph coloring algorithm with some extensions, while the other is a dynamic subchannel assignment step to allocate subchannels for maximizing the system capacity. Through simulations, we first find optimum parameters of the multiple FAP clustering to maximize the system capacity and then evaluate system performance in terms of the mean FAP capacity, unsatisfied femtocell user equipment (FUE) probability, and mean FAP power consumption for data transmission based on a given FUE traffic load. As a result, the MC-DCA scheme outperforms other schemes in two different DFN environments for commercial and office buildings.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.502-512
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• 2016

The wireless carrier sense multiple access with collision detection (WCSMA/CD) and carrier sense multiple access with collision resolution (CSMA/CR) protocols are considered representative distributed collision detection protocols for fully connected dense wireless local area networks. These protocols identify collisions through additional short-sensing within a collision detection (CD) period after the start of data transmission. In this study, we analyze their throughput numerically and show that the throughput has a trade-off that accords with the length of the CD period. Consequently, we obtain the optimal length of the CD period that maximizes the throughput as a closed-form solution. Analysis and simulation results show that the throughput of distributed collision detection protocols is considerably improved when the optimal CD period is allocated according to the number of stations and the length of the transmitted packet.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2259-2266
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• 2016

Recently dense wireless local area networks (WLANs) emerge as the number of WLAN cells and stations increases. In such dense WLAN environment, this paper proposes a new distributed medium access control (MAC) protocol. The proposed MAC protocol extends the previous CSMA with collision resolution (CSMA/CR) that uses a single collision detection (CD) phase and employs multiple CD phases to resolve more collisions. It checks the collision detection in each CD phase and stops the CD phase if consecutive non-detected CD phases occur more than the threshold. Therefore, the proposed protocol can control the number of CD phases adaptively according to the number of accessing stations and increase the probability of collision resolution while decreasing the packet overhead. The simulation results show that the proposed adaptive CSMA/CR protocol employs a variable number of CD phases according to the number of stations and achieves a greater throughput than the previous CSMA/CR protocol using the fixed number of CD phases.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.101-111
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• 2011

A new MAC protocol, MT-MAC(Multi-Tier Medium Access Control) by name, is proposed for dense sensor networks. Depending on the density of nodes in a virtual cluster, the cluster header performs the splitting to several tiers in nodes of virtual cluster. MT-MAC split the tiers to use modfied-SYNC message after receiving the beacon message from the cluster header. Because only the sensor nodes in the same tier communicate each other, less power is consumed and longer network life time is guaranteed. By a simulation method with NS-2, we evaluated our protocol. In dense nodes environments, MT-MAC protocol shows better results than S-MAC in terms of packet delivery rates throughput and energy consumption.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.25-31
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• 2022

In ultra dense networks (UDNs), macro user equipments (MUEs) have significant interference from small-cell access points (SAPs) since a number of SAPs are deployed in the coverage of macro base stations of 5G mobile communication systems. In this paper, we propose a dynamic channel assignment scheme to increase the performance of MUEs for the uplink of UDNs even though the number of SAPs is increased. The target of the proposed dynamic channel assignment scheme is that the signal-to-interference and noise ratio (SINR) of MUEs is above a given SINR threshold assigning different subchannels to SUEs from those of MUEs. Simulation results show that the proposed dynamic channel assignment scheme outperforms others in terms of the mean MUE capacity even though the mean SUE capacity is decreased a little lower.

• The KIPS Transactions:PartC
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• v.17C no.4
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• pp.379-384
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• 2010

Mobile nodes in high mobility ad hoc networks might come together in specific areas. In non-uniformly distributed networks, traffic load can be concentrated to intermediate nodes between dense clusters, and networks performance can be degraded. In this paper, we proposed a cluster based routing protocol that heavy traffic nodes adaptively react according to traffic load. The simulation result shows that the proposed protocol reduce packet loss and end-to-end delay.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.101-104
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• 2003

As optical network technology advances, the Dense-Wavelength Division Multiplexing(DWDM) networks have been widely accepted as a promising approach to the Next Generation Optical Internet (NGOI) backbone networks. Especially. a fault/attack management scheme in NGOI backbone networks is one of the most important issues because a short service disruption in DWDM networks carrying extremely high data rates causes loss of vast traffic volumes. In this paper, we suggest a fault/attack management model for NGOI backbone networks and propose a fault/attack recovery procedure in IP/GMPLS over DWDM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5323-5343
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• 2018

Mobile applications and social networks tend to enhance the need for high-quality content access. To address the rapid growing demand for data services in mobile networks, it is necessary to develop efficient content caching and distribution techniques, aiming at significantly reduction of redundant content transmission and thus improve content delivery efficiency. In this article, we develop optimal cooperative content cache and distribution policy, where a geographical cluster model is designed for content retrieval across the collaborative small cell base stations (SBSs) and replacement of cache framework. Furthermore, we divide the SBS storage space into two equal parts: the first is local, the other is global content cache. We propose an algorithm to minimize the content caching delay, transmission cost and backhaul bottleneck at the edge of networks. Simulation results indicates that the proposed neighbor SBSs cooperative caching scheme brings a substantial improvement regarding content availability and cache storage capacity at the edge of networks in comparison with the current conventional cache placement approaches.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.