• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.121-127
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• 2009

Wireless devices can communicate between each other without existing infrastructure in mobile Ad-hod network. Ad hoc networks can be used under difficult conditions, where it is difficult to construct infrastructures, such as shadowing areas, disaster areas, war area, and so on. In order to support to considerable and various wireless services, more spectrum resources are needed. However, efficient utilization of the frequency resource is difficult because of spectrum scarcity and the conventional frequency regulation. Ad-hoc networks employing cognitive radio (CR) system that guarantee high spectrum utilization provide effective way to increase the network capacity. In this paper, we simulate and compare the performance of conventional single and cooperative spectrum sensing with CR system using ad-hoc networks. And we demonstrate performance improvement by analyzing the system performance.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.793-794
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• 2003

Forest fire monitoring prototype system was developed by National Development Agency of Japan (NASDA) and the Ministry of Agriculture, Forestry and Fisheries of Japan (MAFF) to verify the usefulness of interoperabile system to study new services of Earth observation satellite data distribution for a practical application. In this system, a standard interface of Web based GIS technology, OpenGIS Consortium (OGC) technology, was adopted. This system is also expected to encourage data sharing activities in Digital Asia Network (DAN) as a demonstration system.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• Journal of Microbiology and Biotechnology
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• v.16 no.11
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• pp.1661-1677
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• 2006

Rhizobium and related genera are soil bacteria with great metabolic plasticity. These microorganisms survive in many different environments and are capable of eliciting the formation of nitrogen-fixing nodules on legumes. The successful establishment of symbiosis is precisely regulated and requires a series of signal exchanges between the two partners. Quorum sensing (QS) is a prevalent form of population density-dependent gene regulation. Recently, increasing evidence indicates that rhizobial quorum sensing provides a pervasive regulatory network, which plays a more generalized role in the physiological activity of free-living rhizobia, as well as during symbiosis. Several rhizobia utilize multiple, overlapping quorum sensing systems to regulate diverse properties, including conjugal transfer and copy number control of plasmids, exopolysaccharide biosynthesis, rhizosphere-related functions, and cell growth. Genomic and proteomic analyses have begun to reveal the wide range of functions under quorum-sensing control.

• Journal of Ecology and Environment
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• v.34 no.2
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• pp.149-156
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• 2011

Phenological variables derived from remote sensing are useful in determining the seasonal cycles of ecosystems in a changing climate. Satellite remote sensing imagery is useful for the spatial continuous monitoring of vegetation phenology across broad regions; however, its applications are substantially constrained by atmospheric disturbances such as clouds, dusts, and aerosols. By way of contrast, a tower-based ground remote sensing approach at the canopy level can provide continuous information on canopy phenology at finer spatial and temporal scales, regardless of atmospheric conditions. In this study, a tower-based ground remote sensing system, called the "Phenological Eyes Network (PEN)", which was installed at the Gwangneung Deciduous KoFlux (GDK) flux tower site in Korea was introduced, and daily phenological progressions at the canopy level were assessed using ratios of red, green, and blue (RGB) spectral reflectances obtained by the PEN system. The PEN system at the GDK site consists of an automatic-capturing digital fisheye camera and a hemi-spherical spectroradiometer, and monitors stand canopy phenology on an hourly basis. RGB data analyses conducted between late March and early December in 2009 revealed that the 2G_RB (i.e., 2G - R - B) index was lower than the G/R (i.e., G divided by R) index during the off-growing season, owing to the effects of surface reflectance, including soil and snow effects. The results of comparisons between the daily PEN-obtained RGB ratios and daily moderate-resolution imaging spectroradiometer (MODIS)-driven vegetation indices demonstrate that ground remote sensing data, including the PEN data, can help to improve cloud-contaminated satellite remote sensing imagery.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.642-650
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• 2010

Collaborative spectrum sensing allows secondary users scattered in location to work together to detect the activity of primary users and has been shown to significantly reduce the performance degradation due to fading phenomenon. Most previous works on collaborative spectrum sensing are based on the assumption that local spectrum sensing decisions of secondary users are statistically independent. However, it may not hold in some practical situations with shadowing effect. In this paper, we consider the case that the secondary users are evenly spaced in the form of a linear array and only adjacent secondary users are statistically correlated, and analyze the effect of the statistical correlation on the performance of collaborative spectrum sensing and the throughput of a cognitive radio network. Here we assumed the AND and OR fusion rules for combining the local decisions of secondary users. The analysis showed that the AND fusion rule achieves higher throughput than the OR fusion rule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1392-1413
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• 2022

In order to meet the rapid development of the unmanned aerial vehicle (UAV) communication needs, cooperative spectrum sensing (CSS) helps to identify unused spectrum for the primary users (PU). However, multi-UAV mode (MUM) requires the large communication resource in a cognitive UAV network, resulting in a severe decline of spectrum efficiency (SE) and energy efficiency (EE) and increase of energy consumption (EC). On this account, we extend the traditional 2D spectrum space to 3D spectrum space for the UAV network scenario and enable UAVs to proceed with spectrum sensing behaviors in this paper, and propose a novel multi-slot mode (MSM), in which the sensing slot is divided into multiple mini-slots within a UAV. Then, the CSS process is developed into a composite hypothesis testing problem. Furthermore, to improve SE and EE and reduce EC, we use the sequential detection to make a global decision about the PU channel status. Based on this, we also consider a truncation scenario of the sequential detection under the sensing delay constraint, and further derive a closed-form performance expression, in terms of the CSS performance and cooperative efficiency. At last, the simulation results verify that the performance and cooperative efficiency of MSM outperforms that of the traditional MUM in a low EC.

• Korean Journal of Remote Sensing
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• v.18 no.3
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• pp.155-162
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• 2002

Neural networks have been focused on as a robust classifier for the remotely sensed imagery due to its statistical independency and teaming ability. Also the artificial neural networks have been reported to be more tolerant to noise and missing data. However, unlike the conventional statistical classifiers which use the statistical parameters for the classification, a neural network classifier uses individual training sample in teaming stage. The training performance of a neural network is know to be very sensitive to the discrepancy of the number of the training samples of each class. In this paper, the effect of the population discrepancy of training samples of each class was analyzed with three layered feed forward network. And a method for reducing the effect was proposed and experimented with Landsat TM image. The results showed that the effect of the training sample size discrepancy should be carefully considered for faster and more accurate training of the network. Also, it was found that the proposed method which makes teaming rate as a function of the number of training samples in each class resulted in faster and more accurate training of the network.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.235-243
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• 2007

Sensor network is used to obtain sensing data in various area. The interval to sense the events depends on the type of target application and the amounts of data generated by sensor nodes are not constant. Many applications exploit long sensing interval to enhance the life time of network but there are specific applications that requires very short interval to obtain fine-grained, high-precision sensing data. If the number of nodes in the network is increased and the interval to sense data is shortened, the amounts of generated data are greatly increased and this leads to increased amount of packets to transfer to the network. To transfer large amount of packets fast, it is necessary that the delay between successive packet transmissions should be minimized as possible. In Sensor network, since the Operating Systems are worked on the event driven, the Timer Event is used to transfer packets successively. However, since the transferring time of packet completely is varies very much, it is very hard to set appropriate interval. The longer the interval, the higher the delay and the shorter the delay, the larger the fail of transfer request. In this paper, we propose ESTEO which reduces the delay between successive packet transmissions by using SendDone Event which informs that a packet transmission has been completed.In ESTEO, the delay between successive packet transmissions is shortened very much since the transmission of next packet starts at the time when the transmission of previous packet has completed, irrespective of the transmission timee. Therefore ESTEO could provide high packet transmission rate given large amount of packets.