• Journal of applied mathematics & informatics
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• v.5 no.1
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• pp.13-22
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• 1998

Fractals which represent many of the sets in various scien-tific fields as well as in nature is geometrically too complicate. Then we usually use Hausdorff dimension to estimate their geometrical proper-ties. But to explain the fractals from the hausdorff dimension induced by the Euclidan metric are not too sufficient. For example in digi-tal communication while encoding or decoding the fractal images we must consider not only their geometric sizes but also many other fac-tors such as colours densities and energies etc. So in this paper we define the dimension matrix of the sets by redefining the new metric.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.3
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• pp.57-70
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• 2008

This paper introduces a new paradigm of physical layer security through channel coding for wireless networks. The well known spread spectrum based physical layer security in wireless network is applicable when code division multiple access (CDMA) is used as wireless air link interface. In our proposal, we incorporate the proposed security protocol within channel coding as channel coding is an essential part of all kind of wireless communications. Channel coding has a built-in security in the sense of encoding and decoding algorithm. Decoding of a particular codeword is possible only when the encoding procedure is exactly known. This point is the key of our proposed security protocol. The common parameter that required for both encoder and decoder is generally a generator matrix. We proposed a random selection of generators according to a security key to ensure the secrecy of the networks against unauthorized access. Therefore, the conventional channel coding technique is used as a security controller of the network along with its error correcting purpose.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.11a
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• pp.121-124
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• 2008

최근에는 인터넷 환경에서 여러 형태의 압축된 파일이 이용되고 있으며 통신량의 감소, 통신시간의 절약 등 많은 장점을 가지고 있다. 그래서 많은 압축 기법 그리고 압축 기법에서 동작하는 영상처리기법들이 개발되어 지고 있다. 정보 은닉에서도 JPEG과 같은 압축파일에서 동작하는 알고리즘이 개발되어 지고 있다. 이와 같은 알고리즘들은 주파수변환이나 양자화의 기본적인 룰을 이해하고 있으며 그들의 프로그램에 그러한 룰들을 적용하여 개발에 이용하고 있다. 하지만 정보은닉 알고리즘에 있어, 많은 경우에 데이터 변경 후에 정보은닉의 영향을 평가하였다. 우리는 이 논문에서 정보은닉 처리과정에서 생겨나는 데이터 변경의 영향을 예측하기 위한 방법을 제안하였다. JPEG과 같은 압축 환경에서 정보 은닉 시 적용 가능한 몇 가지 중요한 사실을 여러 경우의 실험을 통하여 얻어냈다. 이러한 사실들은 현재 존재하는(Matrix Encoding, Modified Matrix Encoding 등을 포함한 F3, F4 and F5 알고리즘 등 [1],[5],[6]) 정보은닉 프로그램의 성능향상, 알고리즘 처리시간의 감소와 같은 긍정적인 효과를 거둘 수 있다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.27-32
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• 2014

Two-dimensional hybrid codewords are generated by using each row of identity matrix for spatial encoding and nonideal symmetric balance incomplete block design(BIBD) code for spectral encoding. This spatial/spectral optical code-division multiple-access (OCDMA) network uses single-balanced detectors to abstract the desired information bits and to eliminate the multiple-access interference(MAI). Analytical results show that the number of simultaneous users increases significantly by using the proposed hybrid codes.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.761-765
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• 2010

This paper presents the design of optimal fuzzy classifier for human detection by using genetic algorithms, one of the best-known meta-heuristic search methods. For this purpose, encoding scheme to search the optimal sequential intersection points between adjacent fuzzy membership functions is originally presented for the fuzzy classifier design for HOG (Histograms of Oriented Gradient) descriptors. The intersection points are sequentially encoded in the proposed encoding scheme to reduce the redundancy of search space occurred in the combinational problem. Furthermore, the fitness function is modified with the true-positive and true-negative of the confusion matrix instead of the total success rate. Experimental results show that the two proposed approaches give superior performance in HOG datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2134-2153
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• 2013

A network-coding-based scheme is proposed to improve the energy efficiency of distributed storage systems in WSNs (Wireless Sensor Networks). We mainly focus on two problems: firstly, consideration is given to effective distributed storage technology; secondly, we address how to effectively repair the data in failed storage nodes. For the first problem, we propose a method to obtain a sparse generator matrix to construct network codes, and this sparse generator matrix is proven to be the sparsest. Benefiting from this matrix, the energy consumption required to implement distributed storage is reduced. For the second problem, we designed a network-coding-based iterative repair method, which adequately utilizes the idea of re-encoding at intermediate nodes from network coding theory. Benefiting from the re-encoding, the energy consumption required by data repair is significantly reduced. Moreover, we provide an explicit lower bound of field size required by this scheme, which implies that it can work over a small field and the required computation overhead is very low. The simulation result verifies that the proposed scheme not only reduces the total energy consumption required to implement distributed storage system in WSNs, but also balances energy consumption of the networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.123-129
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• 2006

In this paper, we concentrate on reducing the complexity for efficient encoder. We design structural LDPC code using circulant matrix and permutation matrix and eIRA code. It is possible to design low complex encoder by using shift register and differential encoder and interleaver than general LDPC encoder that use matrix multiplication operation. The code designed by this structure shows similar performance as random code. And the proposed codes can considerably reduce a number of XOR gates.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.503-511
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• 2013

This paper presents a speech enhancement method using non-negative matrix factorization. In training phase, we can obtain each basis matrix from speech and specific noise database. After training phase, the noisy signal is separated from the speech and noise estimate using basis matrix in enhancement phase. In order to improve the performance, we model the change of encoding matrix from training phase to enhancement phase using independent Gaussian distribution models, and then use the constraint of the objective function almost same as that of the above Gaussian models. Also, we perform a smoothing operation to the encoding matrix by taking into account previous value. Last, we apply the Log-Spectral Amplitude type algorithm as gain function.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.601-603
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• 1998

In this paper, Hadamard transform imaging spectrometers utilizing Grill spectrometers are proposed. General Hadamard Transform Spectrometers (HTS) carry out one-encoding through input masks, but Grill spectrometers carry out double-encoding through entrance and exit masks. Thus Grill spectrometers increase the signal-to-noise ratio by double-encoding. we reconfigure the system by using the Grill spectrometers which use a left cyclic S-matrix instead of the conventional right cyclic one. Then, we model the system and apply the mask characteristics method, i.e. $T^{I}$ method, to complete fast algorithm. Through computer simulations, we want to prove the superiority of the proposed system by comparing with the conventional HTS. From Observations concerning the average mean square error(AMSE) associated with estimates from the $T^{I}$ spectrum-recovery method, the relative performances of the two systems are compared.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.