• Title/Summary/Keyword: Fast identification

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Fast Adaptive Parameter Estimation Algorithm using Unit Vector (단위 벡터를 이용한 고속 적응 계수 예측 알고리즘)

  • Cho, Ju-Phil
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.8 no.3
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    • pp.1-7
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    • 2008
  • This paper proposes a new QRD-LS adaptive algorithm with computational complexity of O(N). The main idea of proposed algorithm(D-QR-RLS) is based on the fact that the computation for the unit vector of is made from the process during Givens Rotation. The performance of the algorithm is evaluated through computer simulation of FIR system identification problem. As verified by simulation results, this algorithm exhibits a good performance. And, we can see the proposed algorithm converges to optimal coefficient vector theoretically.

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Study on Grain Boundaries in Single-layer Graphene Using Ultrahigh Resolution TEM

  • Lee, Zong-Hoon
    • Proceedings of the Korean Vacuum Society Conference
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    • 2012.08a
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    • pp.107-107
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    • 2012
  • Recently, large-area synthesis of high-quality but polycrystalline graphene has been advanced as a scalable route to applications including electronic devices. The presence of grain boundaries (GBs) may be detrimental on some electronic, thermal, and mechanical properties of graphene, including reduced electronic mobility, lower thermal conductivity, and reduced ultimate mechanical strength, yet on the other hand, GBs might be beneficially exploited via controlled GB engineering. The study of graphene grains and their boundary is therefore critical for a complete understanding of this interesting material and for enabling diverse applications. I present that scanning electron diffraction in STEM mode makes possible fast and direct identification of GBs. We also demonstrate that dark field TEM imaging techniques allow facile GB imaging for high-angle tilt GBs in graphene. GB mapping is systematically carried out on large-area graphene samples via these complementary techniques. The study of the detailed atomic structure at a GB in suspended graphene uses aberration-corrected atomic resolution TEM at a low kV.

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Identification of Coffee Fragrances Using Needle Trap Device-Gas Chromatograph/Mass Spectrometry (NTD-GC/MS)

  • Eom, In-Yong;Jung, Min-Ji
    • Bulletin of the Korean Chemical Society
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    • v.34 no.6
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    • pp.1703-1707
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    • 2013
  • A fast and simple sampling and sample preparation device, (NTD) has been developed and applied to sample and analyze volatile components from ground coffee beans. Coffee fragrances and other volatile organic compounds (VOCs) were sampled by the NTD and then analyzed by gas chromatograph-mass spectrometry (GC/MS). Divinylbenzene (DVB) particles (80/100 mesh size) were the sorbent bed of the NTD. More than 150 volatile components were first identified based on the database of the mass library and then finally 30 fragrances including caffeine were further confirmed by comparing experimental retention indices (i.e. Kovat index) with literature retention indices. Total sampling time was 10 minutes and no extra solvent extraction and/or reconstitution step need. Straight n-alkanes (C6-C20) were used as retention index probes for the calculation of experimental retention indices. In addition, this report suggests that an empty needle can be an alternative platform for analyzing polymers by pyrolysis-GC/MS.

Depth Image Based Feature Detection Method Using Hybrid Filter (융합형 필터를 이용한 깊이 영상 기반 특징점 검출 기법)

  • Jeon, Yong-Tae;Lee, Hyun;Choi, Jae-Sung
    • IEMEK Journal of Embedded Systems and Applications
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    • v.12 no.6
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    • pp.395-403
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    • 2017
  • Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.

Sparse Multinomial Kernel Logistic Regression

  • Shim, Joo-Yong;Bae, Jong-Sig;Hwang, Chang-Ha
    • Communications for Statistical Applications and Methods
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    • v.15 no.1
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    • pp.43-50
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    • 2008
  • Multinomial logistic regression is a well known multiclass classification method in the field of statistical learning. More recently, the development of sparse multinomial logistic regression model has found application in microarray classification, where explicit identification of the most informative observations is of value. In this paper, we propose a sparse multinomial kernel logistic regression model, in which the sparsity arises from the use of a Laplacian prior and a fast exact algorithm is derived by employing a bound optimization approach. Experimental results are then presented to indicate the performance of the proposed procedure.

Development of a smart wireless sensing unit using off-the-shelf FPGA hardware and programming products

  • Kapoor, Chetan;Graves-Abe, Troy L.;Pei, Jin-Song
    • Smart Structures and Systems
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    • v.3 no.1
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    • pp.69-88
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    • 2007
  • In this study, Field-Programmable Gate Arrays (FPGAs) are investigated as a practical solution to the challenge of designing an optimal platform for implementing algorithms in a wireless sensing unit for structuralhealth monitoring. Inherent advantages, such as tremendous processing power, coupled with reconfigurable and flexible architecture render FPGAs a prime candidate for the processing core in an optimal wireless sensor unit, especially when handling Digital Signal Processing (DSP) and system identification algorithms. This paper presents an effort to create a proof-of-concept unit, wherein an off-the-shelf FPGA development board, available at a price comparable to a microprocessor development board, was adopted. Data processing functions, including windowing, Fast Fourier Transform (FFT), and peak detection, were implemented in the FPGA using a Matlab Simulink-based high-level abstraction tool rather than hardware descriptive language. Simulations and laboratory tests were carried out to validate the design.

Development of a Rapid Spectrophotometric Method for Detecting Bacterial Mucinase Complex

  • Kim, Yoon-Hee;Cha, Jae-Ho
    • Journal of Microbiology and Biotechnology
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    • v.12 no.2
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    • pp.345-348
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    • 2002
  • A rapid spectrophotometric method for detecting the mucinase complex was developed. Bovine submaxillary mucin is cleaved by commercial mucinase between the oligosaccharide chain and the side chain of peptide linkage, thereby liberating the N-acetyl neuraminic acid (NANA). The release of NANA resulted in an increase of absorbance at 280 nm. The susceptibility to NANA by the new method was found to be at least 10-fold more sensitive than the thiobarbituric acid method. Moreover, the quantification of NANA released from mucin by commercial neuraminidase and partially purified Vibrio parahaemolyticus mucinase showed a good linear correlation in proportion to the concentration of the enzyme used. These results demonstrate that the rapid identification of mucin degradation can be determined by a spectrophotometric assay, thereby providing a new, fast, and sensitive method for assaying the bacterial mucinase complex.

Scaling Reuse Detection in the Web through Two-way Boosting with Signatures and LSH

  • Kim, Jong Wook
    • Journal of Korea Multimedia Society
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    • v.16 no.6
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    • pp.735-745
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    • 2013
  • The emergence of Web 2.0 technologies, such as blogs and wiki, enable even naive users to easily create and share content on the Web using freely available content sharing tools. Wide availability of almost free data and promiscuous sharing of content through social networking platforms created a content borrowing phenomenon, where the same content appears (in many cases in the form of extensive quotations) in different outlets. An immediate side effect of this phenomenon is that identifying which content is re-used by whom is becoming a critical tool in social network analysis, including expert identification and analysis of information flow. Internet-scale reuse detection, however, poses extremely challenging scalability issues: considering the large size of user created data on the web, it is essential that the techniques developed for content-reuse detection should be fast and scalable. Thus, in this paper, we propose a $qSign_{lsh}$ algorithm, a mechanism for identifying multi-sentence content reuse among documents by efficiently combining sentence-level evidences. The experiment results show that $qSign_{lsh}$ significantly improves the reuse detection speed and provides high recall.

Implementation of Personalized IP Streaming System (맞춤형 IP 스트리밍 시스템 구현)

  • Yang, Chang-Mo;Kim, Kyung-Won;Lim, Tae-Beom;Kim, Yoon-Sang;Lee, Seok-Pil
    • Proceedings of the KIEE Conference
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    • 2006.10c
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    • pp.515-517
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    • 2006
  • Recently, there is a rapidly growing demand for efficient real-tine playback and transmission of large amounts of multimedia data. But most users' connections are not fast enough to download large chunks of multimedia data. Therefore a streaming technology is needed in which users enable the real-time playback of multimedia date without having to download the whole of the multimedia date. In this paper, we propose a personalized IP streaming system. The proposed IP streaming system enables users to got an intelligent recommendation of multimedia contents based on the user preference information stored on the streaming server or the home media server. Moreover, users are assured of seamless access of streamed content event if they switch to another client device by implementing streaming system based on user identification and device information. We evaluate our approach with simulation results.

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A Fast Algorithm for Target Detection in High Spatial Resolution Imagery

  • Kim Kwang-Eun
    • Proceedings of the KSRS Conference
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    • 2006.03a
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    • pp.7-14
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    • 2006
  • Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

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