• Title/Summary/Keyword: Initialization vector

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Iterative LBG Clustering for SIMO Channel Identification

  • Daneshgaran, Fred;Laddomada, Massimiliano
    • Journal of Communications and Networks
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    • v.5 no.2
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    • pp.157-166
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    • 2003
  • This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.

Efficient Iris Recognition through Improvement of Feature Vector and Classifier

  • Lim, Shin-Young;Lee, Kwan-Yong;Byeon, Ok-Hwan;Kim, Tai-Yun
    • ETRI Journal
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    • v.23 no.2
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    • pp.61-70
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    • 2001
  • In this paper, we propose an efficient method for personal identification by analyzing iris patterns that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform, and two straightforward but efficient mechanisms for a competitive learning method such as a weight vector initialization and the winner selection. With all of these novel mechanisms, the experimental results showed that the proposed system could be used for personal identification in an efficient and effective manner.

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A FPGA Implementation of Stream Cipher Algorithm Dragon (Dragon스트림 암호 알고리즘의 하드웨어 구현)

  • Kim, Hun-Wook;Hyun, Hwang-Gi;Lee, Hoon-Jae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.11 no.9
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    • pp.1702-1708
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    • 2007
  • Dragon Stream Cipher is proposed for software base implementation in the eSTREAM project. Now this stream cipher is selected as a phase 3 focus candidate. Dragon is a new stream cipher contructed using a single word based NIFSR(non-linear feed back shift register) and 128/256 key/IV(Initialization Vector). Dragon is the keystream generator that produce 64bits of keystream. In this paper, we present an implementation of Drag(m stream cipher algorithm in hardware. Finally, the implementation is on Altera FPGA device, EP3C35F672I and the timing simulation is done on Altera's Quartus II. A result of 111MHz maximum clock rate and 7.1Gbps is throughput is obtained from the implementation.

Digital Endoscopic Image Segmentation using Deformable Models

  • Yoon, Sung-Won;Kim, Jeong-Hoon;Lee, Myoung-Ho
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.57.4-57
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    • 2002
  • $\textbullet$ Image segmentation is an essential technique of image analysis. In spite of the traditional issues in contour initialization and boundary concavities, active contour models(snakes) are popular and known as successful methods for segmentation. $\textbullet$ We could find in experiment that snake using Gaussian External Force is fast in time but low in accuracy and snake using Gradient Vector Flow by Chenyang Xu and Jerry L. Prince is high in accuracy but slow in time. $\textbullet$ In this paper, we presented a new active contour model, GGF snake, for segmentation of endoscopic image. Proposed GGF snake made up for the defects of the traditional snakes in contour initialization and boundary...

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Improved Single Feistel Circuit Supporter by A Chaotic Genetic Operator

  • JarJar, Abdellatif
    • Journal of Multimedia Information System
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    • v.7 no.2
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    • pp.165-174
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    • 2020
  • This document outlines a new color image encryption technology development. After splitting the original image into 240-bit blocks and modifying the first block by an initialization vector, an improved Feistel circuit is applied, sponsored by a genetic crossover operator and then strong chaining between the encrypted block and the next clear block is attached to set up the confusion-diffusion and heighten the avalanche effect, which protects the system from any known attack. Simulations carried out on a large database of color images of different sizes and formats prove the robustness of such a system.

Analysis On Encryption Process In Data For Satellite

  • Bae, Hee-Jin
    • Proceedings of the KSRS Conference
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    • 2008.10a
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    • pp.216-219
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    • 2008
  • It is necessary to study encryption for protection and safe transmission of the important information. Specially, the security in satellite data is also getting more and more important. This paper introduces DES and TDES algorithm, studies how to apply to satellite data with those algorithms and process of encryption and decryption for satellite data. Proposed encryption process in this paper will be utilized in satellite data for encryption in many satellites.

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IV attack protection through IV having (IV 해쉬를 통한 IV 공격 방어)

  • 이영지;김태윤
    • Proceedings of the Korean Information Science Society Conference
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    • 2001.04a
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    • pp.421-423
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    • 2001
  • IPSec(IP Security)은 데이터가 공개적으로 전송되는 네트워크 데이터에 암호화와 인증, 무결성을 제공하기 위해 사용되는 프로토콜이다. IPSec 안에는 여러 프로토콜이 있는데, 그 중에 실제 패킷에 암호화와 인증, 무결성을 추가해 전달하기 위해서는 ESP(Encapsulation Security Payload)라는 프로토콜이 사용된다. 이 ESP(Encapsulation Security Payload)라는 프로토콜이 사용된다. 이 ESP는 패킷을 암호화하기 위해 DES-CBC 모드를 사용하는데, 여기에서 IV(Initialization Vector) 값이 쓰인다. 이 값은 패킷 복호화를 하기 위해 공개적으로 전달이 되기 때문에 중간에 공격자에 의해 공격 당할 위험이 많다. 본 논문에서는 IV 공격을 방지하기 위해 IV의 값을 해쉬 함수를 통해 한번 해슁을 한 다음에, IV 값을 안전하게 전달하는 방법을 제시하고자 한다.

Study on the direct approach to reinitialization in using level set method for simulating incompressible two-phase flows (비압축성 2 상유동의 모사를 위한 level set 방법에서의 reinitialization 직접 접근법에 관한 연구)

  • Cho, Myung-H.;Choi, Hyoung-G.;Yoo, Jung-Y.
    • 한국전산유체공학회:학술대회논문집
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    • 2008.03b
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    • pp.568-571
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    • 2008
  • The computation of moving interface by the level set method typically requires reinitializations of level set function. An inaccurate estimation of level set function ${\phi}$ results in incorrect free-surface capturing and thus errors such as mass gain/loss. Therefore, accurate and robust reinitialization process is essential to the free-surface flows. In the present paper, we pursue further development of the reinitialization process, which evaluates directly level set function ${\phi}$ using a normal vector in the interface without solving the re-distancing equation of hyperbolic type. The Taylor-Galerkin approximation and P1P1splitting FEM are adopted to discretize advection equation of the level set function and the Navier-Stokes equation, respectively. Advection equation of free surface and re-initialization process are validated with benchmark problems, i.e., a broken dam flow and time-reversed single vortex flow. The simulation results are in good agreement with the existing results.

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Study on the Solution of Reinitialization Equation for Level Set Method in the Simulation of Incompressible Two-Phase Flows (비압축성 2 상유동의 모사를 위한 Level Set 방법의 Reinitialization 방정식의 해법에 관한 연구)

  • Cho, Myung-Hwan;Choi, Hyoung-Gwon;Yoo, Jung-Yul
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.32 no.10
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    • pp.754-760
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    • 2008
  • Computation of moving interface by the level set method typically requires the reinitialization of level set function. An inaccurate estimation of level set function $\phi$ results in incorrect free-surface capturing and thus errors such as mass gain/loss. Therefore, an accurate and robust reinitialization process is essential to the simulation of free-surface flows. In the present paper, we pursue further development of the reinitialization process, which evaluates level set function directly using a normal vector on the interface without solving there-distancing equation of hyperbolic type. The Taylor-Galerkin approximation and P1P1 splitting/SUPG (Streamline Upwind Petrov-Galerkin) FEM are adopted to discretize advection equation of the level set function and the incompressible Navier-Stokes equation, respectively. Advection equation and re-initialization process of free surface capturing are validated with benchmark problems, i.e., a broken dam flow and timereversed single vortex flow. The simulation results are in good agreement with the existing results.

Face Recognition using Fisherface Method with Fuzzy Membership Degree (퍼지 소속도를 갖는 Fisherface 방법을 이용한 얼굴인식)

  • 곽근창;고현주;전명근
    • Journal of KIISE:Software and Applications
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    • v.31 no.6
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    • pp.784-791
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    • 2004
  • In this study, we deal with face recognition using fuzzy-based Fisherface method. The well-known Fisherface method is more insensitive to large variation in light direction, face pose, and facial expression than Principal Component Analysis method. Usually, the various methods of face recognition including Fisherface method give equal importance in determining the face to be recognized, regardless of typicalness. The main point here is that the proposed method assigns a feature vector transformed by PCA to fuzzy membership rather than assigning the vector to particular class. In this method, fuzzy membership degrees are obtained from FKNN(Fuzzy K-Nearest Neighbor) initialization. Experimental results show better recognition performance than other methods for ORL and Yale face databases.