• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.454-468
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• 1997

This paper presents a bitwidth optimization algorithm for efficient hardware sharing in digital signal processing system. The proposed algorithm determines the fixed-point representation for each signal through bitwidth optimization to generate the hardware requiring less area. To reduce the operator area, the algorithm partitions the abstract operations in the design description into several groups, such that the operations in the same group can share an operator. The partitioning result are fed to a high-level synthesis system to generate the pipelined fixed-point datapaths. The proposed algorithm has been implemented in SODAS-DSP an automatic synthesis system for fixed-point DSP hardware. Accepting the models of DSP algorithms in schematics, the system automatically generates the fixed-point datapath and controller satisfying the design constraints in area, speed, and SNR(Signal-to-Noise Ratio). Experimental results show that the efficiency of the proposed algorithm by generates the area-efficient DSP hardwares satisfying performance constraints.

• Journal of applied mathematics & informatics
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• v.28 no.3_4
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• pp.783-796
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• 2010

We introduce a new iterative algorithm for equilibrium and fixed point problems of three hemi-relatively nonexpansive mappings by the CQ hybrid method in Banach spaces, Our results improve and extend the corresponding results announced by Xiaolong Qin, Yeol Je Cho, Shin Min Kang [Xiaolong Qin, Yeol Je Cho, Shin Min Kang, Convergence theorems of common elements for equilibrium problems and fixed point problems in Banach spaces, Journal of Computational and Applied Mathematics 225 (2009) 20-30], P. Kumam, K. Wattanawitoon [P. Kumam, K. Wattanawitoon, Convergence theorems of a hybrid algorithm for equilibrium problems, Nonlinear Analysis: Hybrid Systems (2009), doi:10.1016/j.nahs.2009.02.006], W. Takahashi, K. Zembayashi [W. Takahashi, K. Zembayashi, Strong convergence theorem by a new hybrid method for equilibrium problems and relatively nonexpansive mappings, Fixed Point Theory Appl. (2008) doi:10.1155/2008/528476] and others therein.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.331-341
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• 1991

The discrete cosine transform (DCT) is widely used in many signal processing areas, including image and speech data compression. In this paper, we investigate a fixed-point error analysis for fast DCT algorithms, namely, Lee [6], Hou [7] and Vetterli [8]. A statistical model for fixed-point error is analyzed to predict the output noise due to the fixed-point implementation. This paper deals with two's complement fixed-point data representation with truncation and rounding. For a comparison purpose, we also investigate the direct form DCT algorithm. We also propose a suitable scaling model for the fixed-point implementation to avoid an overflow occurring in the addition operation. Computer simulation results reveal that there is a close agreement between the theoretical and the experimental results. The result shows that Vetterli's algorithm is better than the other algorithms in terms of SNR.

• Korean Journal of Mathematics
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• v.24 no.3
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• pp.495-524
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• 2016

In this paper, we propose an iterative algorithm for finding a common solution of a system of generalized equilibrium problems, a split equilibrium problem and a hierarchical fixed point problem over the common fixed points set of a finite family of nonexpansive mappings in Hilbert spaces. Furthermore, we prove that the proposed iterative method has strong convergence under some mild conditions imposed on algorithm parameters. The results presented in this paper improve and extend the corresponding results reported by some authors recently.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.1
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• pp.23-29
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• 2003

This paper proposes an efficient feature extraction of the images by using independent component analysis(ICA) based on neural networks of the hybrid learning algorithm. The proposed learning algorithm is the fixed point(FP) algorithm based on Newton method and moment. The Newton method, which uses to the tangent line for estimating the root of function, is applied for fast updating the inverse mixing matrix. The moment is also applied for getting the better speed-up by restraining an oscillation due to compute the tangent line. The proposed algorithm has been applied to the 10,000 image patches of $12{\times}12$-pixel that are extracted from 13 natural images. The 144 features of $12{\times}12$-pixel and the 160 features of $16{\times}16$-pixel have been extracted from all patches, respectively. The simulation results show that the extracted features have a localized characteristics being included in the images in space, as well as in frequency and orientation. And the proposed algorithm has better performances of the learning speed than those using the conventional FP algorithm based on Newton method.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.293-301
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• 2022

Direction of arrival (DOA) estimation of space signals is a basic problem in array signal processing. DOA estimation based on the multiple signal classification (MUSIC) algorithm can theoretically overcome the Rayleigh limit and achieve super resolution. However, owing to its inadequate real-time performance and accuracy in practical engineering applications, its applications are limited. To address this problem, in this study, a DOA estimation algorithm with high parallelism and precision based on an analysis of the characteristics of complex matrix eigenvalue decomposition and the coordinate rotation digital computer (CORDIC) algorithm is proposed. For parallel and single precision, floating-point numbers are used to construct an orthogonal identity matrix. Thus, the efficiency and accuracy of the algorithm are guaranteed. Furthermore, the accuracy and computation of the fixed-point algorithm, double-precision floating-point algorithm, and proposed algorithm are compared. Without increasing complexity, the proposed algorithm can achieve remarkably higher accuracy and efficiency than the fixed-point algorithm and double-precision floating-point calculations, respectively.

• Journal of applied mathematics & informatics
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• v.34 no.5_6
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• pp.467-478
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• 2016

In this paper, we propose an Ishikawa-type extra-gradient iterative algorithm for finding a solution of split feasibility, fixed point problems and equilibrium problems of quasi-nonexpansive mappings. It is proven that under suitable conditions, the sequences generated by the proposed iterative algorithms converge weakly to a solution of the split feasibility, fixed point problems and equilibrium problems. An example is given to illustrate the main result of this paper.

• Kyungpook Mathematical Journal
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• v.61 no.2
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• pp.257-267
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• 2021

In this paper, we develop an iterative algorithm for obtaining common solutions to the Cayley inclusion problem and the set of fixed points of a non-expansive mapping in Hilbert spaces. A numerical example is given for the justification of our claim.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.95-103
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• 2009

We improve the performance of WCDMA repeater by cancelling the feedback interference radio signal under the fixed point implementation. Floating-point DSP or FPGA to implement the ICS algorithm may have an disadvantage of high cost, To solve this problem, we suggest the ICS algorithm based on LMS under fixed point operation, and show the validity of our results by comparing with the floating-point results through numerical simulation.

• Journal of Advanced Navigation Technology
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• v.4 no.1
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• pp.45-49
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• 2000

A GPS measurement solution, in general, is obtained as a least squares solution since the measurement includes errors such as clock errors, ionospheric and tropospheric delays, multipath effect etc. Because of the nonlinearity of the measurement equation, we utilize the nonlinear Newton algorithm to obtain a least squares solution, or mostly, use its linearized algorithm which is more convenient and effective. In this study we developed a fixed point algorithm and proved its availability to replace the nonlinear Newton algorithm and the linearized algorithm. A nonlinear Newton algorithm and a linearized algorithm have the advantage of fast convergence, while their initial values have to be near the unknown solution. On the contrary, the fixed point algorithm provides more reliable but slower convergence even if the initial values are quite far from the solution. Therefore, two types of algorithms may be combined to achieve better performance.