• Journal of Communications and Networks
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• v.18 no.3
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• pp.397-410
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• 2016

This paper proposes a novel cooperative localization method for distributed wireless networks in 3-dimensional (3D) global positioning system (GPS) denied environments. The proposed method, which is referred to as hybrid ellipsoidal variational algorithm (HEVA), combines the use of non-parametric belief propagation (NBP) and variational Bayes (VB) to benefit from both the use of the rich information in NBP and compact communication size of a parametric form. InHEVA, two novel filters are also employed. The first one mitigates non-line-of-sight (NLoS) time-of-arrival (ToA) messages, permitting it to work well in high noise environments with NLoS bias while the second one decreases the number of calculations. Simulation results illustrate that HEVA significantly outperforms traditional NBP methods in localization while requires only 50% of their complexity. The superiority of VB over other clustering techniques is also shown.

• Journal of Microbiology and Biotechnology
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• v.10 no.5
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• pp.714-720
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• 2000

Antigens of Herpes simplex virus type 1 (HSV-1) strain F were immunoblotted to identify the most immunodominant one, and the localization of this antigen was then studied using immunoelectron microscopy. The 67.8 kDa antigen appeared to be the most immunodominant one in a mouse model, and it showed randomly scattered and partially clustered distribution on the surface of the virion. The localization study was performed using immunogold with polyclonal anti-HSV-1 sera produced from BALB/c mice, and immunofluorescence demonstrated that the viral products in the HSV-2 infected Vero cells were distributed throughout the infected host cell, however, mainly on the surface of the host membrane.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.944-946
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• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. We modified the existing Support vector machine algorithm to fit into the distributed hadoop architecture system for localization of a sensor node. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time.

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.525-534
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• 2005

We present several distributed algorithms for localizing nodes of a wireless sensor network. Our algorithms determine locations of nodes based on the connectivity between nodes. The basic idea behind our algorithms is to estimate distances between nearby nodes by counting their common neighbors. We analyze the performance of our algorithms experimentally. The results of experiments show that our algorithms achieve performance improvements upon the existing algorithms

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.437-442
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• 2018

In this paper, we propose a method for estimating the position of a source in a closed form when a single source has coherently distributed property against a circular array. When a sound source reaches a sensor through multipath environments, it is seen as a distributed source and can be represented by four variables: the nominal azimuth, nominal elevation, azimuth angular spread, elevation angular spread. Therefore, it requires a lot of computation by a search method such as DSPE (Distributed Source Parameter Estimator). In this paper, we propose a method of estimating the nominal azimuth and elevation angle in a closed form using correlation function and least squares method for fast position estimation. In particular, if the source is assumed as Gaussian distribution model, the standard deviation is also estimated in a closed form. In the simulation, the validity of the proposed method is confirmed by comparing with the DSPE.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.318-326
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• 2013

In this paper, a novel WUSB (Wireless USB) over WBAN (Wireless Body Area Networks) MAC protocol is proposed to improve efficiency of sensing the personal information. Furthermore, a localization technique based on that protocol is also proposed for indoor localization in a ship. For this purpose, the proposed localization algorithm minimizes power consumption and estimates location with accuracy. It is executed independently at each sensor node on the basis of WUSB over WBAN protocol. And it minimizes power consumption by estimating locations of sensor nodes without GPS (Global Positioning Systems).

• Bulletin of the Korean Chemical Society
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• v.30 no.9
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• pp.1981-1984
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• 2009

Subcellular localization of protein kinase often plays an important role in determining its activity and specificity. Protein kinase C (PKC), a family of multi-gene protein kinases has long been known to be translocated to the particular cellular compartments in response to DAG or its analog phorbol esters. We used C-terminal green fluorescent protein (GFP) fusion proteins of PKC isoforms to visualize the subcellular distribution of individual PKC isoforms. Intracellular localization of PKC-GFP proteins was monitored by fluorescence microscopy after transient transfection of PKC-GFP expression vectors in the HeLa cells. In unstimulated HeLa cells, all PKC isoforms were found to be distributed throughout the cytoplasm with a few exceptions. PKC$\theta$ was mostly localized to the Golgi, and PKC$\gamma$, PKC$\delta$ and PKC$\eta$ showed cytoplasmic distribution with Golgi localization. DAG analog TPA induced translocation of PKC-GFP to the plasma membrane. PKC$\alpha$, PKC$\eta$ and PKC$\theta$ were also localized to the Golgi in response to TPA. Only PKC$\delta$ was found to be associated with the nuclear membrane after transient TPA treatment. These results suggest that specific PKC isoforms are translocated to different intracellular sites and exhibit distinct biological effects.

• Journal of KIISE:Information Networking
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• v.32 no.5
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• pp.574-582
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• 2005

Localization in wireless sensor networks is essential to important network functions such as event detection, geographic routing, and information tracking. Localization is to determine the locations of nodes when node connectivities are given. In this paper, centroid approach known as a distributed algorithm is extended to a centralized algorithm. The centralized algorithm has the advantage of simplicity. but does not have the disadvantage that each unknown node should be in transmission ranges of three fixed nodes at least. The algorithm shows that localization can be formulated to a linear system of equations. We mathematically show that the linear system have a unique solution. The unique solution indicates the locations of unknown nodes are capable of being uniquely determined.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9B
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• pp.832-844
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• 2008

Distributed localization algorithms are necessary for large-scale wireless sensor network applications. In this paper, we introduce an efficient node distribution based localization algorithm that emphasizes simple refinement and low system load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighbor nodes for sensors, update its position estimate by minimizing a local cost function and then passes this update to the neighbor nodes. The update process considers a distribution of nodes for large-scale networks which have same density in a unit area for optimizing the system performance. Neighbor nodes are selected within a range which provides the smallest received signal strength error based on the real experiments. MATLAB simulation showed that the proposed algorithm is more accurate than trilateration and les complex than multidimensional scaling. The implementation on MicaZ using TinyOS-2.x confirmed the practicality of the proposed algorithm.

• Ocean and Polar Research
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• v.30 no.2
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.