Wiangtong, Theerayod

Preferred name

Alternative Name

Wiangtong, T.

Main Affiliation

School of Engineering

theerayod.wi@kmitl.ac.th

Now showing 1 - 10 of 39

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Set-membership adaptive reduced-rank affine projection algorithm
(2019-03-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
Set-membership filtering approach with the method of adaptive reduced-rank affine projection algorithm is presented. The distance between the present tap-weight vector and the update is used to accelerate the convergence and decreasing the update rates of proposed algorithm. For the error upper bound constraint, the adaptive averaging threshold parameter is introduced using the estimated auto-correlation between present and previous estimated error vector for controlling the update step-size. Simulation results of proposed algorithm verify the good performance concerning to the amount of updates and convergence rate compared with existing algorithm.
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A heuristic approach for scheduling in heterogeneous distributed embedded systems
(2020-02-01)
Prongnuch, Sethakarn
;
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper presents a heuristic approach for workflow scheduling in heterogeneous distributed embedded system (HDES). A genetic algorithm (GA) and ant colony optimization (ACO) modified with the greedy algorithm introduced to the system contains multiple heterogeneous embedded machines (HEMs) working as a cluster. Users can remotely access and utilize their computational power. The communications on different types of buses are taken into account to find an optimal solution. New meta-heuristic information based on forwarding dependency is proposed to build probability for ACO to generate task priorities. Besides, a greedy algorithm for machine allocation is incorporated to complete task scheduling. Experiments based on random task graphs running in the HEM cluster demonstrate the effectiveness of the modified greedy ant colony optimization algorithm which outperforms the others by 33% more result quality.
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Adaptive Orthogonal Gradient Algorithm Based on Fair Cost Function
(2024-01-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper presents an adaptive orthogonal gradient algorithm with the unconstrained Fair cost function. An adaptive orthogonal gradient-based algorithm is investigated with the help of orthogonal projection mechanism to update the approximate tap-weight vector for the convergence enhancement. Fair cost function is preferable with a smooth points that is able to detect the statistical characteristics of error. Objective of this work is to present an adaptive orthogonal gradient algorithm using Fair cost function (OGA-Fair) to enhance the performance. Simulation results show that proposed adaptive OGA-Fair algorithm can perform with the fast convergence rate and robustness better than the existing method.
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Set-Membership Orthogonal Gradient Adaptive Algorithm on Least Mean Fourth Criterion
(2024-01-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
;
Pookaiyaudom, Panavy
In this paper, we introduce a set-membership mechanism on the orthogonal gradient adaptive (OGA) algorithm based on least mean fourth (LMF) algorithm. In accordance with the fast convergence, the OG A-based scheme is conducted based on LMF algorithm that is described with the fourth power of the estimated error. The organisation of proposed algorithm composes with a brief of set-membership adaptive filtering and taking an advantage of OGA algorithm based on LMF algorithm. In order to make a smooth, an adaptive learning rate of algorithm and threshold value are furnished for convergence rate performance. There are some experimental simulations from proposed algorithm that show the mean square error trends can converge remarkably to the existing algorithm for system identification.
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Spline adaptive filtering based on normalised orthogonal gradient adaptive algorithm
(2019-07-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper proposes a normalised orthogonal gradient adaptive algorithm based on spline adaptive filtering. According to improve the convergence characteristics, the orthogonal gradient adaptive algorithm has been proposed using the orthogonal projection along with the filtered gradient adaptive algorithm. Simulation results demonstrates that the proposed algorithm exhibits more robust performance compared with the conventional spline adaptive filtering algorithms.
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Multitask Diffusion Adaptive Tuning Affine Projection Algorithm for Distributed Model
(2025-01-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper presents combine-then-adapt diffusion model on affine projection adaptive algorithm with adaptive tuning mechanism. An adaptive affine projection algorithm is suggested in the distributed network. Diffusion strategy involves with the combination stage and adaptation stage. By adding the extra-stage in the combination stage, the combination between nodes and inside nodes are built with the global cost function for multitask model to eliminate unknown data. As dealt with fast convergence, the adaptive tuning parameters are assigned in terms of step-size value. Experimental results reveal that this proposed algorithm is able to contribute the approving results for system identifications.
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Reduced complexity widely-linear adaptive forgetting-factor inverse square-root RLS algorithm
(2018-07-02)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
A reduced complexity inverse square-root recursive least squares algorithm based on widely-linear mechanism is introduced by adaptive forgetting-factor algorithm. The proposed reduced complexity widely-linear approaches based on inverse square-root recursive least squares algorithm is presented for a relation between widely-linear and reduced complexity scheme. By means of mean square deviation approach, an optimal forgetting-factor scheme is proposed in terms of optimal gain sequence. Adaptive forgetting-factor inverse square-root recursive least squares algorithm is used with regard to an optimal forgetting-factor algorithm. Results of simulation depict that the performance of proposed algorithm is shown similar to widely-linear scheme comparison with the existing algorithm.
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FPGA based-IC design for inverter with vector modulation technique
(2000-01-01)
Wiangtong, Theerayod
;
Sangchai, Worranart
;
Lumyong, Pichit
This paper presents an application of an ALTERA FPGA Device, in the FLEX10K family, producing Pulse Width Modulation (PWM) signals with the vector modulation technique for an IGBT inverter. Using a single FPGA chip for the practical implementation of the modulator, rather than a system consisting of microprocessor and external memory, has many advantages including less use of power and space, short design time, greater speed and reliability. The designed circuit can generate PWM signals at many different frequencies, and also, the input values used to adjust output signal may be obtained through either a 4×4 keypad or microprocessor port.
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Performance of Hammerstein Spline Adaptive Filtering Based on Fair Cost Function for Denoising Electrocardiogram Signals
(2025-12-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person’s cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF–APA–Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals.
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Performance analysis of cascade spline adaptive filtering based on normalized orthogonal gradient adaptive algorithm
(2024-12-01)
Wiangtong, Theerayod
;
Sitjongsataporn, Suchada
In this paper, the cascade architecture of spline adaptive filtering (CSAF) for nonlinear systems is presented with the normalized version of orthogonal gradient adaptive (NOGA) algorithm. Spline adaptive filtering comprises a sandwich of the first linear adaptive filtering (LAF) and nonlinear adaptive look-up table. In this cascading architecture, SAF is connected to the second LAF. NOGA is considered as the fast convergence applied by stochastic gradient-based approach. Convergence properties of the proposed NOGA-CSAF algorithm in terms of instantaneous errors can be derived by using Taylor series expansion. Experimental results demonstrate the effectiveness of proposed NOGA-CSAF algorithm using the mean square error scheme. It clearly outperforms the traditional least mean square algorithm on CSAF model in the nonlinear identification system.

Wiangtong, Theerayod

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