• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.675-680
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• 2007

This paper presents a new method for classification of underwater transient signals, which employs frame-based decision with Mel Frequency Cepstral Coefficients(MFCC). The MFCC feature vector is extracted frame-by-frame basis for an input signal that is detected as a transient signal, and Euclidean distances are calculated between this and all MFCC feature. vectors in the reference database. Then each frame of the detected input signal is mapped to the class having minimum Euclidean distance in the reference database. Finally the input signal is classified as the class that has maximum mapping rate in the reference database. Experimental results demonstrate that the proposed method is very promising for classification of underwater transient signals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3A
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• pp.231-237
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• 2010

Multi-input multi-output (MIMO) systems are used to improve the transmission rate in proportion to the number of antennas. However, their computational complexity is very high for the detection in the receiver. The sphere decoding (SD) is a detection algorithm with reduced complexity. In this paper, an improved Schnorr-Euchner SD (SE SD) is proposed based on the minimum mean square error (MMSE) and the Euclidean distance criteria without additional complexity.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.73-82
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• 1996

In this paper, an efficient algorithm for recognizing and positioning occuluded objects in a two-dimensional plane is presented. Model objects and unknown input image are approximated by polygonal boundaries, which are compactly represented by shape functions of the polygons. The input image is partitioned into measningful segments whose end points are at the locations of possible occlusion - i.e. at concave vertices. Each segment is matched against known model objects by calculating a matching measure, which is defined as the minimum euclidean distance between the shape functions. An O(mm(n+m) algorithm for computing the measure is presentd, where n and m are the number of veritces for a model and an unknown object, respectively. Match results from aprtial segments are combined based on mutual compatibility, then are verified using distance transformation and translation vector to produce the final recognition. The proposed algorithm is invariant under translation and rotation of objects, which has been shown by experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5A
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• pp.331-340
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• 2009

In this paper, we propose a new power loading algorithm for orthogonalized spatial multiplexing(OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. Compared to SVD-based transmission scheme, the OSM scheme exhibits a good system performance with lower complexity and feedback overhead. To further improve the performance in OSM systems with power loading, we introduce a geometric approach on the Euclidean distance between the constellation points in the effective channel. Using this approach, we show that the optimal power loading parameters in terms of the minimum distance can be obtained. Simulation results demonstrate that our algorithm provides a 5dB gain at a bit error rate (BER) of $10^{-4}$ over that of no power loading case with both QPSK and 16-QAM. Consequently, our power loading algorithm allows us to significantly improve the system performance with one additional feedback value.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.197-211
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• 2002

In this paper, we propose a multi-Criteria decision making approach to address the problem of finding the best possible solution in credit unions. Sensitivity analysis on the priority structure of the goals has been performed to obtain all possible solutions. The study uses the Euclidean distance method to measure distances of all possible solutions from the identified ideal solution. The possible optimum solution is determined from the minimum distance between the ideal solution and other possible solutions of the Problem.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.2003-2012
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• 1996

The 16- and 32-state Trellis-coded M-ary 4-dimensional (4-D) orthogonal modulation scheme with a frequency-reuse technique have been investigated. Here, 5 coded bits form a rate 4/5 convolutional encoder provide 32 possible symbols. Then the signals are mapped by a M-ary 4-D orthogonal modulator, where each signal has equal energy and is PSK modulated. In the M-ary 4-D modulator, we have employed the vectors which is derived by the optimization technique of signal waveforms in a 4-D sphere. This technique is usedin maximizing the minimum Euclidean distance between a set of signal poits on a multidimensional sphere. By combinig trellis coding with M-ary 4-D modulation and proper set-partitioning, we have obtained a considerable impeovement in the free minimum distance of the system over an AWGN channel. The 16-state scheme obtains coding gains up to 5.5 dB over the uncoded two-independent QPSK scheme and 2.5 dB over the two-independent 2-D TCM scheme. And, the 32-state scheme obtains coding gains up to 6.4 dB over the uncoded two-independent QPSK schemeand 3.4 dB over the two-independent 2-D TCM scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3434-3439
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• 2015

As a criterion of information theoretic learning, the Euclidean distance (ED) of two error probability distribution functions (minimum ED of error, MEDE) has been adopted in nonlinear (decision feedback, DF) supervised equalizer algorithms and has shown significantly improved performance in severe channel distortion and impulsive noise environments. However, the MEDE-DF algorithm has the problem of heavy computational complexity. In this paper, the recursive ED for MEDE-DF algorithm is derived first, and then the feed-forward and feedback section gradients for weight update are estimated recursively. To prove the effectiveness of the recursive gradient estimation for the MEDE-DF algorithm, the number of multiplications are compared and MSE performance in impulsive noise and underwater communication environments is compared through computer simulation. The ratio of the number of multiplications between the proposed DF and the conventional MEDE-DF algorithm is revealed to be $2(9N+4):2(3N^2+3N)$ for the sample size N with the same MSE learning performance in the impulsive noise and underwater channel environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.2
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• pp.96-103
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• 2010

This paper studies the performance of STTC with minimum Hamming distance (MHD) mapping in order to improve the bit error rate (BER) performance. Unfortunately, the MHD mapping used in trellis coded modulation (TCM) or multiple trellis coded modulation (MTCM) cannot be directly applied to STTC because the trellis structure of STTC is generally different from that of TCM or MTCM. Therefore, we need a simple modification to apply the MHD mapping concept in STTC. The core of the modification assigns information bits with a Hamming distance in proportion to the sum of the Euclidean distance to trellis branch of STTC. To the best knowledge, this combination has not been considered yet. The BER performance is examined with simulations and the performance of MHD mapping is compared to that of well known natural mapping and Gray mapping on both fast Rayleigh as well as fast Rician fading channels. It is shown that the performance of MHD mapping is much better than that of natural mapping or Gray mapping over fast Rician fading channels, especially.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.68-78
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• 1999

A new algorithm for tracking feature points in an image sequence is presented. Most existing feature tracking algorithms often produce false trajectories, because the matching measures do not precisely reflect motion characteristics. In this paper, three attributes including spatial coordinate, motion direction and motion magnitude are used to calculate the feature point correspondence. The trajectories of feature points are determined by calculation the matching measure, which is defined as the minimum weighted Euclidean distance between two feature points. The weights of the attributes are updated reflecting the motion characteristics, so that the robust tracking of feature points is achieved. The proposed algorithm can find the trajectories correctly which has been shown by experimental results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.77-84
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• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.