• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.357-362
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• 2005

Iris recognition system is the one of the most reliable biometries recognition system. An algorithm is proposed to determine the localized iris from the iris image received from iris input camera in client. For the first step, the algorithm determines the center of pupil. For the second step, the algorithm determines the outer boundary of the iris and the pupillary boundary. The localized iris area is transformed into polar coordinates. After performing three times Wavelet transformation, normalization was done using a sigmoid function. The converting binary process performs normalized value of pixel from 0 to 255 to be binary value, and then the converting binary process is compared pairs of two adjacent pixels. The binary code of the iris is transmitted to the server by the network. In the server, the comparing process compares the binary value of presented iris to the reference value in the database. The process of recognition or rejection is dependent on the value of Hamming Distance. After matching the binary value of presented iris with the database stored in the server, the result is transmitted to the client.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.9
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• pp.29-37
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• 1998

In weighted random pattern testing it is an important issue to find the optimal weight sets for achieving a high fault coverage using a small number of weighted random patterns. In this paper, a new weight set optimization algorithm is developed, which can generate the optimal weight sets in an efficient way using the sampling probabilities of deterministic tests patterns. In addition, the simulation based method of finding the proper maximum Hamming distance is presented. Experimental results for ISCAS 85 benchmark circuits prove the effectiveness of the new weight set optimization algorithm and the method of finding the proper maximum Hamming distance.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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• pp.18-26
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• 2008

The invariance relation existing in the non-negative matrix factorization (NMF) is used for constructing robust image hashes in this work. The image is first re-scaled to a fixed size. Low-pass filtering is performed on the luminance component of the re-sized image to produce a normalized matrix. Entries in the normalized matrix are pseudo-randomly re-arranged under the control of a secret key to generate a secondary image. Non-negative matrix factorization is then performed on the secondary image. As the relation between most pairs of adjacent entries in the NMF's coefficient matrix is basically invariant to ordinary image processing, a coarse quantization scheme is devised to compress the extracted features contained in the coefficient matrix. The obtained binary elements are used to form the image hash after being scrambled based on another key. Similarity between hashes is measured by the Hamming distance. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as Gaussian filtering, moderate noise contamination, JPEG compression, re-scaling, and watermark embedding. Hashes of different images have very low collision probability. Tampering to local image areas can be detected by comparing the Hamming distance with a predetermined threshold, indicating the usefulness of the technique in digital forensics.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.156-166
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• 1995

This paper presents a bin picking method using a slit beam laser in which a robot recognizes all of the unoccluded objects from the top of jumbled objects, and picks them up one by one. Once those unoccluded objects are removed, newly developed unoccluded objects underneath are recognized and the same process is continued until the bin gets empty. To recognize unoccluded objects, a new algotithm to link edges on slices which are generated by the orthogonally mounted laser on the xy table is proposed. The edges on slices are partitioned and classified using convex and concave function with a distance parameter. The edge types on the neighborhood slices are compared, then the hamming distances among identical kinds of edges are extracted as the features of fuzzy membership function. The sugeno fuzzy integration about features is used to determine linked edges. Finally, the pick-up sequence based on MaxMin theory is determined to cause minimal disturbance to the pile. This proposed method may provide a solution to the automation of part handling in manufacturing environments such as in punch press operation or part assembly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.854-859
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• 2005

We construct the fuzzy entropy for measuring of uncertainty with the help of relation between distance measure and similarity measure. Proposed fuzzy entropy is constructed through distance measure. In this study, the distance measure is used Hamming distance measure. Also for the measure of similarity between fuzzy sets or crisp sets, we construct similarity measure through distance measure, and the proposed 려zzy entropies and similarity measures are proved.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.521-526
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• 2005

The fuzzy entropy is proposed for measuring of uncertainty with the help of relation between distance measure and similarity measure. The proposed fuzzy entropy is constructed through a distance measure. In this study, Hamming distance measure is employed for a distance measure. Also a similarity measure is constructed through a distance measure for the measure of similarity between fuzzy sets or crisp sets and the proposed fuzzy entropies and similarity measures are proved.

• ETRI Journal
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• v.31 no.5
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• pp.585-592
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• 2009

This paper presents a method for decoding high minimal distance ($d_{min}$) short codes, termed Cortex codes. These codes are systematic block codes of rate 1/2 and can have higher$d_{min}$ than turbo codes. Despite this characteristic, these codes have been impossible to decode with good performance because, to reach high $d_{min}$, several encoding stages are connected through interleavers. This generates a large number of hidden variables and increases the complexity of the scheduling and initialization. However, the structure of the encoder is well suited for analog decoding. A proof-of-concept Cortex decoder for the (8, 4, 4) Hamming code is implemented in subthreshold 0.25-${\mu}m$ CMOS. It outperforms an equivalent LDPC-like decoder by 1 dB at BER=$10^{-5}$ and is 44 percent smaller and consumes 28 percent less energy per decoded bit.

• ETRI Journal
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• v.39 no.3
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• pp.417-427
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• 2017

This paper investigates the security of constant weight countermeasures, which aim to produce indistinguishable leakage from sensitive variables and intermediate variables, assuming a constant Hamming distance and/or Hamming weight leakages. To investigate the security of recent countermeasures, contrary to many related studies, we assume that the coefficients of the simulated leakage models follow a normal distribution so that we may construct a model with approximately realistic leakages. First, using our simulated leakage model, we demonstrate security holes in these previous countermeasures. Subsequently, in contrast to the hypotheses presented in previous studies, we confirm the resistance of these countermeasures to a standard correlation power analysis (CPA). However, these countermeasures can allow a bitwise CPA to leak a sensitive variable with only a few thousand traces.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.5-9
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• 2008

Researchers have investigated many upper bound techniques applicable to error probabilities on the maximum likelihood (ML) decoding performance of turbo-like codes and low density parity check (LDPC) codes in recent years for a long codeword block size. This is because it is trivial for a short codeword block size. Previous research efforts, such as the simple bound technique [20] recently proposed, developed upper bounds for LDPC codes and turbo-like codes using ensemble codes or the uniformly interleaved assumption. This assumption bounds the performance averaged over all ensemble codes or all interleavers. Another previous research effort [21] obtained the upper bound of turbo-like code with a particular interleaver using a truncated union bound which requires information of the minimum Hamming distance and the number of codewords with the minimum Hamming distance. However, it gives the reliable bound only in the region of the error floor where the minimum Hamming distance is dominant, i.e., in the region of high signal-to-noise ratios. Therefore, currently an upper bound on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix cannot be calculated because of heavy complexity so that only average bounds for ensemble codes can be obtained using a uniform interleaver assumption. In this paper, we propose a new bound technique on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix using ML estimated weight distributions and we also show that the practical iterative decoding performance is approximately suboptimal in ML sense because the simulation performance of iterative decoding is worse than the proposed upper bound and no wonder, even worse than ML decoding performance. In order to show this point, we compare the simulation results with the proposed upper bound and previous bounds. The proposed bound technique is based on the simple bound with an approximate weight distribution including several exact smallest distance terms, not with the ensemble distribution or the uniform interleaver assumption. This technique also shows a tighter upper bound than any other previous bound techniques for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.271-281
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• 2019

Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the relationship between categorical variables for categorical data cluster analysis. Methods: In this study, the association-based dissimilarity was employed to calculate the distance between two categorical data observations and the distance obtained from the association-based dissimilarity was applied to the PAM cluster algorithms to verify its effectiveness. The strength of association between two different categorical variables can be calculated using a mixture of dissimilarities between the conditional probability distributions of other categorical variables, given these two categorical values. In particular, this method is suitable for datasets whose categorical variables are highly correlated. Results: The simulation results using several real life data showed that the proposed distance which considered relationships among the categorical variables generally yielded better clustering performance than the Hamming distance. In addition, as the number of correlated variables was increasing, the difference in the performance of the two clustering methods based on different distance measures became statistically more significant. Conclusion: This study revealed that the adoption of the relationship between categorical variables using our proposed method positively affected the results of cluster analysis.