• Journal of Magnetics
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• v.21 no.3
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• pp.330-339
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• 2016

A magnetic field sensing system with a single primary sensor and multiple reference sensors deployed locally and orthogonally, was proposed for downlink signal reception and interference cancelling for Through-the-Earth Communication (TEC). This paper mathematically analyzes a design optimization process for a search coil magnetometer (SCM), and applies that process to minimize the bandwidth of the primary SCM for TEC signal reception and the volume of reference SCMs for multiple distributions. The primary SCM achieves a 3-dB bandwidth of 7 Hz, a sensitivity threshold of 120 fT/${\surd}$Hz, and a volume of $2.32{\times}10^{-4}m^3$. The entire sensing system volume is as small as $10^{-2}m^3$. Experiments with interference from industrial frequency harmonics demonstrated an average of 36 dB and 18 dB improvements in signal-to-interference ratio and signal-to-interference plus noise ratio, respectively, using multichannel recursive-least-squares algorithm. Thus, the proposed sensing system can reduce the interference effectively and allows reliable downlink signal reception.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1301-1318
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• 2017

In order to utilize the licensed channel of cognitive radio (CR) when the primary user (PU) is detected busy, a benefit-exchange access mode based on cooperative communication is proposed to allow secondary user (SU) to access the busy channel through giving assistance to PU's communication in exchange for some transmission bandwidth. A holistic joint optimization problem is formulated to maximize the total throughput of CR system through jointly optimizing the parameters of cooperative spectrum sensing (CSS), including the local sensing time, the pre-configured sensing decision threshold, the forward power of cooperative communication, and the bandwidth and transmission power allocated to SUs in benefit-exchange access mode and traditional access mode, respectively. To solve this complex problem, a combination of bi-level optimization, interior-point optimization and exhaustive optimization is proposed. Simulation results show that, compared with the tradition throughput maximizing model (TTMM), the proposed holistic joint optimization model (HJOM) can make use of the channel effectively even if PU is busy, and the total throughput of CR obtains a considerable improvement by HJOM.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.947-959
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• 2021

Aiming at the problem of low accuracy in the water boundary automatic extraction of islands from GF-2 remote sensing image with high resolution in three bands, new water edges automatic extraction method in island based on GF-2 remote sensing images, genetic algorithm (GA) method, is proposed in this paper. Firstly, the GA-OTSU threshold segmentation algorithm based on the combination of GA and the maximal inter-class variance method (OTSU) was used to segment the island in GF-2 remote sensing image after pre-processing. Then, the morphological closed operation was used to fill in the holes in the segmented binary image, and the boundary was extracted by the Sobel edge detection operator to obtain the water edge. The experimental results showed that the proposed method was better than the contrast methods in both the segmentation performance and the accuracy of water boundary extraction in island from GF-2 remote sensing images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.119-122
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• 2010

Cognitive Radioo(CR)기술은 주파수 자원을 효율적으로 이용하기 위해 Joseph Mitora가 개념을 적립한 것으로 유비쿼터스 시대의 부족한 주파수 자원을 극복 할 수 있는 기술로 각광받고 있다. 이와 같은 무선인지 기술(CR)을 도입하기 위한 핵심적인 요소는 부사용자(Secondary User)가 주사용자(Primary User)의 통신을 방해하지 않도록 주파수 점유 여부를 정확하게 판단하는 Spectrum Sensing기술이다. 이 스펙트럼 센싱 기술에 두 가지 종류의 에러가 발생하는데 하나는 미검출 오류(miss error)이고 또 하나는 오경보 오류(false alarm error)이다. 따라서 본 논문은 이 두 가지를 합친 검출 오류(detection error)를 최소화 하기위해 협력 스펙트럼 센싱(Cooperative spectrum sensing)에서 몇 명 이상의 부사용자가 주사용자가 사용중이라 검출하였을 때 최종 결정이 주사용자가 사용한다고 결론 짓는 것이 최적인지를 수학적으로 분석하고 그 최적의 K값을 사용하여 전체 시스템의 성능을 향상 시키는 방법을 제시 한다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1252-1254
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• 2003

Debris flows associated with landslides occur as one of the most devastating natural disasters that threat Taiwan. Typically, three essential factors are needed simultaneously to trigger debris flow, namely sufficient soils and rocks, favorable slope, and abundant water. Among the three essentials, the slope is natural and static without external forcing, while the landslide is generally induced by earthquake or rainfall events, and the water is produced by heavy rainfall events. In this study, we analyzed the landslides triggered by the typhoons Herb (1996) and typhoon Troaji (2001). It is concluded that the statistical data are useful to quantify the threshold of the potential landslide area. Then, the possibility to prevent the debris flow occurrence may be increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5382-5403
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• 2018

In this paper, for a spectrum sensing purpose, we heuristically established a test statistic (TS) from a sample covariance matrix (SCM) for multiple antennas based cognitive radio. The TS is formulated as a scaled-energy which is calculated as a sum of scaled diagonal entries of a SCM; each of the diagonal entries of a SCM scaled by corresponding row's Euclidean norm. On the top of that, by combining theoretical results together with simulation observations, we have approximated a decision threshold of the TS which does not need prior knowledge of noise power and primary user signal. Furthermore, simulation results - which are obtained in a fading environment and in a spatially correlating channel model - show that the proposed method stands effect of noise power mismatch (non-uniform noise power) and has significant performance improvement compared with state-of-the-art test statistics.

• 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.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.339-351
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• 2016

For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel ($STD_{IR}$) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.975-988
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• 2020

Change detection is the process of identifying changes by observing the multi-temporal images at different times, and it is an important technique in remote sensing using satellite images. Among the change detection methods, the unsupervised change detection technique has the advantage of extracting rapidly the change area as a binary image. However, it is difficult to understand the changing pattern of land cover in binary images. This study used grid points generated from seamless digital map to evaluate the satellite image change detection results. The land cover change results were extracted using multi-temporal KOMPSAT-3A (K3A) data taken by Gimje Free Trade Zone and change detection algorithm used Spectral Angle Mapper (SAM). Change detection results were presented as binary images using the methods Otsu, Kittler, Kapur, and Tsai among the automated threshold selection algorithms. To consider the seasonal change of vegetation in the change detection process, we used the threshold of Differenced Normalized Difference Vegetation Index (dNDVI) through the probability density function. The experimental results showed the accuracy of the Otsu and Kapur was the highest at 58.16%, and the accuracy improved to 85.47% when the seasonal effects were removed through dNDVI. The algorithm generated based on this research is considered to be an effective method for accuracy assessment and identifying changes pattern when applied to unsupervised change detection.