• Journal of the Korean Chemical Society
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• v.33 no.1
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• pp.143-146
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• 1989

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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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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• v.36 no.5
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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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.248-255
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• 2003

This paper presents a fingerprint recognition algorithm using cross-correlation function. This algorithm consists of minutiae extraction, minutiae alignment and minutiae matching, where we propose a new minutiae alignment method. In our alignment method, the rotation angle between two fingerprints is obtained by using cross-correlation function of the minutia directions, thereafter the displacement is obtained from the rotated fingerprint. This alignment method is capable of finding rotation angle and displacement of two fingerprints without resorting to exhaustive search. Our fingerprint recognition algorithm has been tested on fingerprint images captured with inkless scanner. The experiment results show that 17.299% false rejection ratio(FRR) at 2.086% false acceptance ratio(FAR).

• The KIPS Transactions:PartC
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• v.12C no.7 s.103
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• pp.959-964
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• 2005

Recently, Kim et al. proposed ID-based authentication schemes using smartcards and fingerprints. However, Scott showed that they were vulnerable to the passive eavesdropping attack. Thereby, this paper proposes an enhanced ID-based authentication scheme to solve the problems in Kin et al. scheme. Especially, the proposed scheme solves the ID repairability problem commonly shared in the previous ID based Cryptosystems. The proposed ID-based authentication scheme supports the advantages in the previous ID-based authentication scheme and solves the problems in them effectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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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.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.455-464
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• 2007

The construction of a fingerprint database is important to evaluate the performance of an automatic fingerprint recognition system. Due to the cost of collecting fingerprints, there are only few benchmark databases available. Since it is hard to evaluate how robust the system is in various environments with the databases, this paper proposes a novel method that generates fingerprint images automatically from only a few training samples by using the genetic algorithm. Fingerprints generated by the proposed method include similar characteristics of those collected from the corresponding real environment. The proposed method has been verified by comparing with real fingerprint images, showing the usefulness of the method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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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.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.419-425
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• 2012

In these days, the trends of home networking system is implementation of easily configured system with home security of emergency alarm and visitor verification services. In this paper, we implemented push server system based on Arduino of open source physical computation platform to verify visitors for the homes without home networking services. In the suggested system, visitor verification is performed in and out of home, and home access security of the system could be constructed with low-cost price by use of windows push server system and smart devices with alarm operation in corresponding to not allowed access trying.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2007.11a
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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.