• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10A
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• pp.1563-1570
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• 1999

This paper relates to the soft decision method with erasure technique in digital terrestrial television broadcasting system. The proposed decoder use the CSI derived from using the pilots in receiver. The active real(I) and imaginary(Q) data are transferred to the branch metric calculation block that decides the Euclidean distance for the soft decision decoding and also the estimated CSI values are transferred to the same block. After calculating the Euclidean distance for the soft decision decoding, the Euclidean distance of branch metric is multiplied by CSI. To do so, new branch metric values that consider each carrier state information are obtained. We simulated this method in better performance of about 0.15dB to 0.17dB and 2.2dB to 2.9dB in Rayleigh channel than that of the conventional soft decision Viterbi decoding with or without bit interleaver where the constellation is QPSK, 16-QAM and 64-QAM.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.72-80
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• 2017

In this work, an efficient transmit antenna selection approach for the quadrature spatial modulation (QSM) systems is proposed. The conventional Euclidean distance antenna selection (EDAS)-based schemes in QSM have too high computational complexity for practical use. The proposed antenna selection algorithm is based on approximation of the EDAS decision metric employed for QSM. The elimination of imaginary parts in the decision metric enables decoupling of the approximated decision metric, which enormously reduces the complexity. The proposed method is also evaluated via simulations in terms of symbol error rate (SER) performance and compared with the conventional EDAS methods in QSM systems.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.433-444
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• 2024

Our study explores the connection between the Pythagorean theorem and the Fixed-point theorem in metric spaces. Both of which center around the concepts of distance transformations and point relationships. The Pythagorean theorem deals with right triangles in Euclidean space, emphasizing distances between points. In contrast, fixed-point theorems pertain to the points that remain unchanged under specific transformations thereby preserving distances. The article delves into the intrinsic correlation between these concepts and presents a novel study in Euclidean metric spaces, examining the relationship between contraction mapping and Pythagorean Right Triangles. Practical applications are also discussed particularly in the context of image compression. Here, the integration of the Pythagorean right triangle paradigm with contraction mappings results in efficient data representation and the preservation of visual data relation-ships. This illustrates the practical utility of seemingly abstract theories in addressing real-world challenges.

• Journal of the Chungcheong Mathematical Society
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• v.20 no.3
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• pp.203-210
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• 2007

Let X be a hyperbolic region in the complex plane C such that the hyperbolic metrix ${\lambda}_X(w){\mid}dw{\mid}$ exists. Let $R(X)=sup\{{\delta}_X(w):w{\in}X\}$ where ${\delta}_X(w)$ is the euclidean distance from w to ${\partial}X$. Here ${\partial}X$ is the boundary of X. A hyperbolic region X is called a Bloch region if R(X) < ${\infty}$. In this paper, we obtain lower bounds for the hyperbolic metric on Bloch regions in terms of the distance to the boundary.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.95-102
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• 2006

This paper proposes the KB-Fano algorithm which has lower decoding complexity by applying modified Fano-like metric bias to the conventional K-best algorithm. Additionally, an efficient K-best decoding algorithm, named the KR-Fano scheme, is proposed by jointly combining the K-reduction and the KB-Fano schemes. Simulations show that the proposed algerian provides the remarkable improvement from the viewpoints of the BER performance and the decoding complexity as compared to the conventional K-best scheme.

• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.481-491
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• 2003

We present a near-optimal blind decision feedback equalizer (DFE) for Advanced Television Systems Committee digital television (DTV) receivers. By adopting a modified trellis decoder (MTD) with trace back depth of 1 for the decision device In the DFE, we obtain a hardware-efficient near-optimal blind DFE approaching to the optimal DFE which has no error propagation. The MTD uses absolute distance instead of Euclidean distance for computation of a path metric, resulting. In reduced computational complexity. Comparing to the conventional slicer, the MTD shows outstanding performance improvement of decision error probability and is comparable to the original trellis decoder using Euclidean distance. Reducing error propagation in the DFE leads to the improvement of convergence performance in terms of convergence speed and residual error. Simulation results show that the proposed blind DFE performs much better than the blind DFE with the slicer.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.43-55
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• 2016

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

• Phonetics and Speech Sciences
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• v.1 no.3
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• pp.73-85
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• 2009

This paper introduces the technique of synthesizing and evaluating human utterances with exaggerated or atypical prosody. Prosody exaggeration can be implemented by manipulating either the fundamental frequency (F0) contour, the segmental durations, or the intensity contour of an utterance. Of these three prosodic elements, two or more can be exaggerated at the same time. The algorithms of synthesis and evaluation were suggested. Learner utterances exaggerated in each of the three prosodic features were evaluated with respect to their original native versions in terms of the differences in their F0 contours, the segmental durations, and the intensity contours. The measure of differences was the Euclidean distance metric between the matching points in their F0 and intensity contours. The measure was calculated after the exaggerated learner utterances were aligned by the segments and rendered identical to their native version in terms of their segmental durations. For the evaluation of the segmental durations, no prior modifications were made in durations and the same measure was used. The results from the pilot experiment suggest the viability of this measure in the evaluation of learner utterances with atypical prosody with respect to their native versions.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.215-225
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• 2012

In multimedia database, a multidimensional space-based indexing has been used to increase search efficiency. However, this method is inefficient in terms of ubiquity because it uses Euclidean distance as a scale of distance calculation. On the contrary, a metric space-based indexing method, in which metric axiom is prerequisite is widely available because a metric scale other than Euclidean distance could be used. This paper is attempted to propose a way of improving VP-tree, one of the metric space indexing methods. The VP-tree calculates the distance with an object which is ultimately linked to the a leaf node depending on the node fit for the search range from a root node and examines if it is appropriate with the search range. Because search speed decreases as the number of distance calculations at the leaf node increases, however, this paper has proposed a method which uses the latest interface on query object as the base point of trigonometric inequality for improvement after focusing on the trigonometric inequality-based range compression method in a leaf node. This improvement method would be able to narrow the search range and reduce the number of distance calculations. According to a system performance test using 10,000 video data, the new method reduced search time for similar videos by 5-12%, compared to a conventional method.

• The Journal of the Acoustical Society of Korea
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• v.31 no.4
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• pp.266-272
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• 2012

Speaker segmentation is a process of automatically detecting the speaker boundary points in the audio data. Speaker segmentation methods are divided into two categories depending on whether they use a prior knowledge or not: One is the model-based segmentation and the other is the metric-based segmentation. In this paper, we introduce the eigenvoice-based speaker weight distance method and compare it with the representative metric-based methods. Also, we employ and compare the Euclidean and cosine similarity functions to calculate the distance between speaker weight vectors. And we verify that the speaker weight distance method is computationally very efficient compared with the method directly using the distance between the speaker adapted models constructed by the eigenvoice technique.