• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.6
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• pp.1-8
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• 2001

Recently, Ryu et al. proposed a multiple target angle tracking algorithm using the angular innovation extracted from the estimated signal subspace. This algorithm obtains the angles of targets and associates data simultaneously. Therefore, it has a simple structure without data association problem. However it requires the calculation of the inverse of a real matrix with dimension (2N+1)${\times}$(2N+1) to obtain the angular innovations of N targets. In this paper, a new linear equation for angular innovation is proposed using the fact that the projection error is zero when the target steering vector is projected onto the signal subspace. As a result, the proposed algorithm dose not require the matrix inversion and is computationally efficient.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.7
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• pp.78-86
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• 2010

This paper discusses the programming model of SODA-II that is a baseband processor for software defined radio (SDR) systems. Signal processing On-Demand Architecture Ⅱ (SODA-II) is an on-chip multiprocessor architecture consisting of four processor cores and each core has both an wide SIMD datapath and a scalar datapath. This architecture is appropriate for baseband processing that is a mixture of vector computations and scalar computations. The programming model of the SODA-II is based on C library routines. Because the library routines hide the details of complex SIMD datapath control procedures, end users can easily program the SODA-II without deep understanding on its architecture. In this paper, we discuss the details of library routines and how these routines are exploited in the implementation of baseband signal processing algorithms. As application examples, we show the implementation result of W-CDMA multipath searcher and OFDM demodulator on the SODA-II.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.53-60
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• 2011

In order to augment the robustness of OFDM system to Rayleigh multipath fading, there exist two smart antenna algorithms, namely, Pre-FFT smart antenna and Post-FFT smart antenna. After the mathematical modeling of both smart antenna algorithms, computer simulations have been carried to compare and analyze the performance of generalized eigen problem based Pre-FFT algorithm and the performance of Wiener solution based Post-FFT algorithm. It has been shown that the Post-FFT smart antenna far outperforms the Pre-FFT smart antenna due to the computational complexities. Especially it is so when the multipath signal arrives at beyond the guard interval and a rich co-channel interferer is introduced. Performance of a subcarrier clustering method proposed to lessen the computing load has been compared to that of a typical Wiener solution based Post-FFT smart antenna. Performance comparison between MRC(Maximum Ratio Combining) diversity based Post-FFT algorithm and typical Post-FFT algorithm has also been carried.

• Korean Institute of Interior Design Journal
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• v.16 no.5
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• pp.38-46
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• 2007

In the late 1970s, the method of quantitative and scientific space structural analysis based on graph theory was introduced to the process of space design, which arranges design and functional elements, as relying heavily on intuition could produce errors due to unverified experiences and prejudices of the designer. As the method of space analysis is complex and hard to express visually and requires repetitive operations, it was discussed theoretically only. However, with the development of computer performance and graphic in recent years, visualization became possible. But the method of visual structural analysis of space is at the level of two dimensions and it is not easy to get accurate data when it is applied to limited three dimensional space such as an interior space. For the visual structural analysis of space, this study presents 4 indices including visibility volume level, pure visibility connection frequency, effective visibility connection frequency, and path visibility connection frequency. This study also presents space division using three dimensional arrangement rather than the existing vector operation method and raytracing algorithm at the lattice constant. Based on this, an analysis tool for the visible regions of three dimensional space that is capable of evaluating at multiple points by using three dimensional game engine and presentation tool that allows the analyzer to interpret the data effectively is made. It is applied to 2 prototype models by displacing Z axis, and the results are compared with UCL Depthmap to verify the validity of data and evaluate its usefulness as a multidimensional, multi-view space analysis tool.

• Genomics & Informatics
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• v.4 no.4
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• pp.161-166
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• 2006

The establishment of DNA microarray technology has enabled high-throughput analysis and molecular profiling of various types of cancers. By using the gene expression data from microarray analysis we are able to investigate diagnostic applications at the molecular level. The most important step in the application of microarray technology to cancer diagnostics is the selection of specific markers from gene expression profiles. In order to select markers of Immortalization and transformation we used c-myc and $H-ras^{V12}$ oncogene-transfected NIH3T3 cells as our model system. We have identified 8751 differentially expressed genes in the immortalization/transformation model by multivariate permutation F-test (95% confidence, FDR<0.01). Using the support vector machine algorithm, we selected 13 discriminative genes which could be used to predict immortalization and transformation with perfect accuracy. We assayed $H-ras^{V12}$-transfected 'transformed' cells to validate our immortalization/transformation dassification system. The selected molecular markers generated valuable additional information for tumor diagnosis, prognosis and therapy development.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.905-910
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• 2013

The flow analysis has been made by applying the turbulent models in the helically coiled tubes of heat transfer. The k-${\varepsilon}$ and Spalart-Allmaras turbulent models are used in which the structured grid is applied for the simulation. The velocity vector, the pressure contour, the change of residuals along the iteration number and the friction factors are simulated by solving the Navier-Stokes equations to make clear the Reynolds number effect. The helical tube increases the centrifugal forces by which the wall shear stress become larger on the outer side of the tube. The centrifugal force makes the heat transfer rate locally larger due to the increase of the flow energy, which finds out the close relationship between the pressure drop and friction factor in the internal flow. The present numerical results are compared with others, for example, in the value of friction factor for validation.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.166-172
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• 2014

Image classification is an important problem in computer vision. However, it is a very challenging problem due to the variability, ambiguity and scale change that exists in images. In this paper, we propose a method of a hypergraph based modeling can consider the higher-order relationships of semantic attributes of a scene image and apply it to a scene image classification. In order to generate the hypergraph optimized for specific scene category, we propose a novel search method based on a probabilistic subspace method and also propose a method to aggregate the expression values of the member semantic attributes that belongs to the searched subsets based on a linear transformation method via likelihood based estimation. To verify the superiority of the proposed method, we showed that the discrimination power of the feature vector generated by the proposed method is better than existing methods through experiments. And also, in a scene classification experiment, the proposed method shows a competitive classification performance compared with the conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.42-48
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• 2016

Since a variety of sound occur in same place and similar sound occurs in other places, the performance of acoustic scene classification is not guaranteed in case of insufficient training data. A Bag of Words (BOW) based histogram feature is foreseen as a method to overcome the problem. However, since the histogram features is made by using a feature distribution, the ordering of sequence of features is ignored. A temporal information such as periodicity and stationarity are also important for acoustic scene classification. In this paper, temporal features about a periodicity and a stationarity are extracted by using a recurrent quantification analysis. In the experiment, performance of the proposed method is shown better than other baseline methods.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1302-1309
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• 2008

Traditional clustering methods, like k-means or fuzzy clustering, are prototype-based methods which are applicable only to convex clusters. On the other hand, spectral clustering tries to find clusters only using local similarity information. Its ability to handle concave clusters has gained the popularity recent years together with support vector machine (SVM) which is a kernel-based classification method. However, as is in SVM, the kernel width plays an important role and has a great impact on the result. Several methods are proposed to decide it automatically, it is still determined based on heuristics. In this paper, we proposed an adaptive method deciding the kernel width based on distance histogram. The proposed method is motivated by the fact that the affinity matrix should be formed into a block diagonal matrix to generate the best result. We use the tradition Euclidean distance together with the random walk distance, which make it possible to form a more apparent block diagonal affinity matrix. Experimental results show that the proposed method generates more clear block structured affinity matrix than the existing one does.