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로라 멘델홀 두란노 2004 목회와 신학 Vol.- No.186
우리는 하나님의 가족으로 입양되고,우리의 죄에 대해 형벌을 요구하는 율법에서 속량 받았으며,하나님나라의 후사들이 되었다.요셉과 그의 온가문과 함께 우리는 하나님의 가족으로 입양되었다.너와 나,수치스런 행위에 연루된 모든 사람들,소외자,무위도식자,계부 계모,우리의 가계에 일원이 되리라곤 결코 상상하지 못할 사람들이 이제 동일하게 하나님의 가족에 입양되었다.예수님의 탄생과 관련된 기적들 중에 하나는 입양이다.그것은 요셉이 예수님을 자기 가문에 입양하고 예수님이 요셉과 그의 모든 가계를 입양한 일이다.우리는 모두 하나님의 가족 안에 입양되었다.우리는 더이상 율법 아래의 노예가 아니라 하나님나라의 상속자들이다.그러므로 우리가 서로 어떻게 대하는가는 매우 중요한 문제이다.우리는 서로 가족으로 대해야 한다.그러나 우리는 한가족이기 때문에 길을 찾아야 하고,서로 한 가족이 될 방안들을 찾기 위해 열심히 노력해야 한다.우리는 모두 예수님의 가족 속에 입양되었기 때문이다.올해 성탄절에 우리가 양자 된 은혜를 기리고자 준비하면서 평화의 시즌이 미움의 시즌으로 폭발하지 않게 하자,왜냐하면 다음 말씀처럼 우리는 한가족이기 때문이다.
Encapsulation of Single Small Gold Nanoparticles by Diblock Copolymers
Chen, Hong Y.,Abraham, Sinoj,Mendenhall, Juana,Delamarre, Soazig C.,Smith, Kahli,Kim, Il,Batt, Carl A. WILEY-VCH Verlag 2008 CHEMPHYSCHEM -WEINHEIM- Vol.9 No.3
<B>Graphic Abstract</B> <P>Hairy micelles are obtained by using amphiphilic diblock copolymers with long hydrophilic blocks which favor single encapsulation of Au nanoparticles (ca. 5 nm, see figure). Antibody molecules can be attached to the hydrogel layers of the encapsulated Au nanoparticles to give nanoparticle/biomolecule conjugates. <img src='wiley_img/14394235-2008-9-3-CPHC200700598-content.gif' alt='wiley_img/14394235-2008-9-3-CPHC200700598-content'> </P>
Application of Wavelet-Based RF Fingerprinting to Enhance Wireless Network Security
Klein, Randall W.,Temple, Michael A.,Mendenhall, Michael J. The Korea Institute of Information and Commucation 2009 Journal of communications and networks Vol.11 No.6
This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.
Application ofWavelet-Based RF Fingerprinting to Enhance Wireless Network Security
Randall W. Klein,Michael A. Temple,Michael J. Mendenhall 한국통신학회 2009 Journal of communications and networks Vol.11 No.6
This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving “air monitor” applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-CWT) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-CWT features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.
Measurement of the Electron-Antineutrino Angular Correlation in Neutron β Decay
Darius, G.,Byron, W. A.,DeAngelis, C. R.,Hassan, M. T.,Wietfeldt, F. E.,Collett, B.,Jones, G. L.,Dewey, M. S.,Mendenhall, M. P.,Nico, J. S.,Park, H.,Komives, A.,Stephenson, E. J. American Physical Society 2017 Physical Review Letters Vol.119 No.4