• 대한화학회지
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• 제33권1호
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• pp.143-146
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• 1989

• International Journal of Control, Automation, and Systems
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• 제6권6호
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• pp.800-808
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• 2008

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

• BMB Reports
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• 제36권5호
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• pp.427-432
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• 2003

Jute is the principal coarse fiber for commercial production and use in Bangladesh. Therefore, the development of a high-yielding and environmental-stress tolerant jute variety would be beneficial for the agro economy of Bangladesh. Two molecular fingerprinting techniques, random-amplified polymorphic DNA (RAPD) and amplified-fragment length polymorphism (AFLP) were applied on six jute samples. Two of them were cold-sensitive varieties and the remaining four were cold-tolerant accessions. RAPD and AFLP fingerprints were employed to generate polymorphism between the cold-sensitive varieties and cold-tolerant accessions because of their simplicity, and also because there is no available sequence information on jute. RAPD data were obtained by using 30 arbitrary oligonucleotide primers. Five primers were found to give polymorphism between the varieties that were tested. AFLP fingerprints were generated using 25 combinations of selective-amplification primers. Eight primer combinations gave the best results with 93 polymorphic fragments, and they were able to discriminate the two cold-sensitive and four cold-tolerant jute populations. A cluster analysis, based on the RAPD and AFLP fingerprint data, showed the population-specific grouping of individuals. This information could be useful later in marker-aided selection between the cold-sensitive varieties and cold-tolerant jute accessions.

• 대한전자공학회논문지SP
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• 제40권4호
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• pp.248-255
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• 2003

본 논문에서는 상호상관함수를 이용한 지문인식 기법을 제시한다. 본 기법은 특징추출, 지문정렬, 지문매칭으로 구성되는데, 이중에서 본 논문은 새로운 지문정렬 기법을 제안한다. 본 지문정렬 기법에서는 특징점의 융선방향 각도에 대해 상호상관함수를 적용하여 두 지문사이의 회전각도를 구하고, 그후 회전된 지문으로부터 두 지문간의 이동변위를 구하여 지문을 정렬한다. 본 정렬기법은 과도한 탐색에 의존하지 않고 두 지문의 회전각도와 이동변위를 구할 수 있다 제시된 기법으로 지문인식 실험을 한 결과 2.086%의 타인수락오류율(FAR)에 대해 17.299%의 본인거부오류율(FRR)을 얻을 수 있었다.

• 정보처리학회논문지C
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• 제12C권7호
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• pp.959-964
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• 2005

최근에 KIM03[9]은 지문과 스마트카드를 이용한 ID기반의 인증 기법을 제안하였다. 하지만 Scott[10]은 KIM03의 인증 기법이 소극적 공격(Passive attack)에 취약함을 보였다. 본 논문에서는 KIM03의 인증 기법에 존재하는 문제점을 해결할 수 있는 강화된 ID기반의 인증 기법을 제안한다. 특히, 본 논문에서 제안한 기법은 기존의 ID 기반의 암호화 시스템이 공유하는 문제점인 ID 복구문제(Repairability)를 해결한다. 본 논문에서 제안한 ID 기반의 인증 프로토콜은 ID기반의 암호화 시스템의 장점을 유지하면서 이 방식의 문제점들을 효율적으로 해결한다.

• 전기학회논문지
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• 제65권6호
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• pp.1060-1069
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• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

• 한국정보과학회논문지:소프트웨어및응용
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• 제34권5호
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• pp.455-464
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• 2007

지문인식 시스템의 성능을 평가하기 위해서는 대규모 지문 DB를 구축하는 것이 필요하다. 지문을 수집하는 것은 매우 고비용의 작업이기 때문에, 지문 평가용 DB의 구축은 많이 이루어지지 않았고 실제로 소수의 평가용 DB만이 공개되어 있다. 뿐만 아니라 이들 DB는 제한된 환경에서 수집되어 있어 실제 다양한 환경에 대한 지문인식 시스템의 성능을 정확히 평가하기가 어렵다. 본 논문에서는 유전자 알고리즘을 이용하여 소수의 학습 샘플로부터 실제 환경에서 발생하는 다양한 영향을 고려한 지문영상을 자동으로 생성하는 방법을 제안한다. 제안하는 방법을 이용하여 생성된 지문은 실제 환경에서 수집된 지문과 유사한 특성을 가지기 때문에, 실제로 다수의 지문 영상을 수집하지 않고도 대상 환경에서의 성능평가가 가능하다. 실제 지문과의 비교를 통하여 제안하는 방법의 유용성을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3414-3434
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• 2016

Classical key generation is complicated to update and key distribution generally requires fixed infrastructures. In order to eliminate these restrictions researchers have focused much attention on physical-layer (PHY-layer) based key generation methods. In this paper, we present a PHY-layer fingerprints based fuzzy key generation scheme, which works to prevent primary user emulation (PUE) attacks and spectrum sensing data falsification (SSDF) attacks, with multi-node collaborative defense strategies. We also propose two algorithms, the EA algorithm and the TA algorithm, to defend against eavesdropping attacks and tampering attacks in mobile cognitive radio networks (CRNs). We give security analyses of these algorithms in both the spatial and temporal domains, and prove the upper bound of the entropy loss in theory. We present a simulation result based on a MIMO-OFDM communication system which shows that the channel response characteristics received by legitimates tend to be consistent and phase characteristics are much more robust for key generation in mobile CRNs. In addition, NIST statistical tests show that the generated key in our proposed approach is secure and reliable.

• 디지털융복합연구
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• 제10권11호
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• pp.419-425
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• 2012

최근 홈네트워크 시스템의 추세는 가정보안기능 위주의 현실적인 시스템을 채택하고 있으며, 긴급사태알림과 방문자 확인 서비스가 제공되고 있다. 본 논문에서는 홈네트워크 서비스를 제공받지 못하는 계층을 대상으로 방문자 확인이 가능한 오픈소스 피지컬 컴퓨팅 플랫폼인 아두이노 기반의 지문인식 서버 알림 시스템을 구축하였다. 본 시스템에서는 홈 내부 및 외부에서 방문확인이 가능하며, 일정 횟수 이상의 접근 시도시 푸쉬 서버를 구축하여 스마트 다바이스로 비인가 접근 상황을 알림으로써 저가형의 홈 접근 보안성을 제공한다.

• 한국응용약물학회:학술대회논문집
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• 한국응용약물학회 2007년도 Proceedings of The Convention
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• pp.19-27
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• 2007

An important potential of metabolomics-based approach is the possibility to develop fingerprints of diseases or cellular responses to classes of compounds with known common biological effect. Such fingerprints have the potential to allow classification of disease states or compounds, to provide mechanistic information on cellular perturbations and pathways and to identify biomarkers specific for disease severity and drug efficacy. Metabolic profiles of biological fluids contain a vast array of endogenous metabolites. Changes in those profiles resulting from perturbations of the system can be observed using analytical techniques, such as NMR and MS. $^1H$ NMR was used to generate a molecular fingerprint of serum or urinary sample, and then pattern recognition technique was applied to identity molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized by NMR spectroscopy. We investigated the metabolic changes in human normal and clinical samples using $^1H$ NMR. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease population. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes.