• Title/Summary/Keyword: Vector Sum

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Enhanced Cross Search algorithm using Predicted Motion Vector for Fast Block Motion Estimation

  • Ko, Byung-Kwan;Kwak, Tong-Ill;Hwang, Bo-Hyun;Yun, Jong-Ho;Choi, Myung-Ryul
    • 한국정보디스플레이학회:학술대회논문집
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    • 2008.10a
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    • pp.749-752
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    • 2008
  • Various Motion Estimation (ME) algorithms have been proposed since ME requires large computational complexity. The proposed algorithm employs Enhanced Cross Search Pattern (ECSP) using motion vector of neighbor-blocks to search the motion vector. The experimental results show that proposed algorithm reduces the search point up to 35% compared to conventional methods.

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STABILITY OF AN n-DIMENSIONAL QUADRATIC FUNCTIONAL EQUATION

  • Jin, Sun-Sook;Lee, Yang-Hi
    • Journal of the Chungcheong Mathematical Society
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    • v.31 no.4
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    • pp.397-409
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    • 2018
  • In this paper, we investigate the generalized Hyers-Ulam stability of the functional equation $$f\({\sum\limits_{i=1}^{n}}x_i\)+{\sum\limits_{1{\leq}i<j{\leq}n}}f(x_i-x_j)-n{\sum\limits_{i=1}^{n}f(x_i)=0$$ for integer values of n such that $n{\geq}2$, where f is a mapping from a vector space V to a Banach space Y.

GENERALIZED CUBIC MAPPINGS OF r-TYPE IN SEVERAL VARIABLES

  • Kang, Dong Seung
    • Journal of the Chungcheong Mathematical Society
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    • v.20 no.1
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    • pp.37-45
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    • 2007
  • Let X, Y be vector spaces. In this paper, we investigate the generalized Hyers-Ulam-Rassias stability problem for a cubic function $f:X{\rightarrow}Y$ satisfies $$r^3f(\frac{\Sigma_{j=1}^{n-1}x_j+2x_n}{r})+r^3f(\frac{\Sigma_{j=1}^{n-1}x_j-2x_n}{r})+8\sum_{j=1}^{n-1}f(x_j)=2f{\sum_{j=1}^{n-1}}x_j)+4{\sum_{j=1}^{n-1}}(f(x_j+x_n)+f(x_j-x_n))$$ for all $x_1,{\cdots},x_n{\in}X$.

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Nontrivial Complex Equivariant Vector Bundles over $S^1$ (원 위에서의 Nontrivial Complex Equivariant Vector Bundle)

  • Kim, Sung-Sook
    • The Journal of Natural Sciences
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    • v.10 no.1
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    • pp.13-16
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    • 1998
  • Every complex vector bundle over $S^1$ splits sum of line bundle and the first Chern class classify complex line bundle. This implies every complex vector bundle over $S^1$ is trivial. In this paper, we show the existence of some nontrivial complex vector bundle over $S^1$ in the equivariant case.

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Composite Differential Evolution Aided Channel Allocation in OFDMA Systems with Proportional Rate Constraints

  • Sharma, Nitin;Anpalagan, Alagan
    • Journal of Communications and Networks
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    • v.16 no.5
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    • pp.523-533
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    • 2014
  • Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling. In this paper, we propose the use of composite differential evolution (CoDE) algorithm to allocate the subchannels. The CoDE algorithm is population-based where a set of potential solutions evolves to approach a near-optimal solution for the problem under study. CoDE uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. In CoDE, three trial vectors are generated for each target vector unlike other differential evolution (DE) techniques where only a single trial vector is generated. Then the best one enters the next generation if it is better than its target vector. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity.

Performance of Magnitude Sum Correlation and Vector Sum Correlation Methods for Robust Frame Synchronization Under Low Signal-to-Noise Ratios (낮은 신호 대 잡음 비에서 강건한 프레임 동기를 위한 크기 합 상관 및 벡터 합 상관 방식의 성능 평가)

  • Lee, Dong-Uk;Kim, Sang-Tae;Sung, Won-Jin
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.45 no.7
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    • pp.32-37
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    • 2008
  • Satellite communication systems including the DVB-S2 (Digital Video Broadcasting - Satellite Version 2) system require operations under low signal-to-noise ratio (SNR) and large frequency offset values, and the initial frame synchronization process necessitates a robust correlation method. While a variety of conventional correlation structures exist for the initial synchronization, each method has different characteristics and performance in different channel environments. In this paper, we propose new correlation methods which exhibit enhanced performance in low SNR and large frequency offsets, and analyze their performance. The proposed methods use the magnitude sum and vector sum of extended differential correlation values, to maximize the correlation between the received signal and the synchronization sequence by using the spanned differential correlation result. The magnitude sum correlation method has better performance compared to conventional methods including the approximated ML (Maximum likelihood) method for SNR values below 4 dB with or without frequency offsets. The vector sum correlation method has improved performance over the magnitude sum method for channels with relatively small frequency offsets.

GENERALIZED QUADRATIC MAPPINGS IN 2d VARIABLES

  • Cho, Yeol Je;Lee, Sang Han;Park, Choonkil
    • Korean Journal of Mathematics
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    • v.19 no.1
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    • pp.17-24
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    • 2011
  • Let X, Y be vector spaces. It is shown that if an even mapping $f:X{\rightarrow}Y$ satisfies f(0) = 0, and $$2(_{2d-2}C_{d-1}-_{2d-2}C_d)f\({\sum_{j=1}^{2d}}x_j\)+{\sum_{{\iota}(j)=0,1,{{\small\sum}_{j=1}^{2d}}{\iota}(j)=d}}\;f\({\sum_{j=1}^{2d}}(-1)^{{\iota}(j)}x_j\)=2(_{2d-1}C_d+_{2d-2}C_{d-1}-_{2d-2}C_d){\sum_{j=1}^{2d}}f(x_j)$$ for all $x_1$, ${\cdots}$, $x_{2d}{\in}X$, then the even mapping $f:X{\rightarrow}Y$ is quadratic. Furthermore, we prove the Hyers-Ulam stability of the above functional equation in Banach spaces.

A Study on the New Learning Method to Improve Noise Tolerance in Fuzzy ART (퍼지 ART에서 잡음 여유도를 개선하기 위한 새로운 학습방법의 연구)

  • 이창주;이상윤;이충웅
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.10
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    • pp.1358-1363
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    • 1995
  • This paper presents a new learning method for a noise tolerant Fuzzy ART. In the conventional Fuzzy ART, the top-down and bottom-up weight vectors have the same value. They are updated by a fuzzy AND operation between the input vector and the current value of the top-down or bottom- up weight vectors. However, it can not prevent the abrupt change of the weight vector and can not achieve good performance for a noisy input vector. To solve the problems, we updated using the weighted sum of the input vector and the current value of the top-down vector. To achieve stability, the bottom-up weight vector is updated using the fuzzy AND operation between the newly learned top-down vector and the current value of the bottom-up vector. Computer simulations show that the proposed method prominently resolves the category proliferation problem without increasing the training epoch for stabilization in noisy environments.

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QUANTITATIVE WEIGHTED BOUNDS FOR THE VECTOR-VALUED SINGULAR INTEGRAL OPERATORS WITH NONSMOOTH KERNELS

  • Hu, Guoen
    • Bulletin of the Korean Mathematical Society
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    • v.55 no.6
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    • pp.1791-1809
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    • 2018
  • Let T be the singular integral operator with nonsmooth kernel which was introduced by Duong and McIntosh, and $T_q(q{\in}(1,{\infty}))$ be the vector-valued operator defined by $T_qf(x)=({\sum}_{k=1}^{\infty}{\mid}T\;f_k(x){\mid}^q)^{1/q}$. In this paper, by proving certain weak type endpoint estimate of L log L type for the grand maximal operator of T, the author establishes some quantitative weighted bounds for $T_q$ and the corresponding vector-valued maximal singular integral operator.

Multivariate CUSUM Charts with Correlated Observations

  • Cho, Gyo-Young;Ahn, Young-Sun
    • Journal of the Korean Data and Information Science Society
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    • v.12 no.1
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    • pp.127-133
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    • 2001
  • In this article we establish multivariate cumulative sum (CUSUM) control charts based on residual vector with correlated observations. We first find the residual vector and its expectation and variance-covariance matrix and then evaluate the average run length (ARL) of the control charts.

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