http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/356 Sun, 19 Apr 2026 15:43:08 GMT 2026-04-19T15:43:08Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1671 A study of brain wave pattern with the touching learning of blinded people Wachira Lawpradit Thongchai Yooyativong Dissertation (Ph.D.) -- Computer Engineering, School of Information Technology. Mae Fah Luang University, 2021 Fri, 01 Jan 2021 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1671 2021-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1205 Adaptive genetic algorithms for particle filtering improvement Chanin Kuptametee Nattapol Aunsri Particle filtering is a scheme under sequential Bayesian framework widely employed to estimate state of desired information from the observation data outputted from non-linear, non-Gaussian systems. We proposed an adaptive genetic algorithm-based scheme to enhance quality of the drawn sample vectors of state variables (called particles). Each low-weight parent pairs with a randomly selected high-weight parent. The newly created offspring particle is allowed to replace its low-weight parent only if the weight of the offspring is higher than the weight of the low-weight parent. The accepted offspring particles with high weights can also be paired with the other low-weight parents in order to promote particle diversity. Simulation results show that the new method is superior to state-of-the-art algorithms in estimating one-dimensional and multidimensional state estimation. The new method is also tested in an application under the multiple-model particle filter (MMPF) framework of spectrum and dispersion curve estimation of a time-varying acoustics propagated through an ocean waveguide. The new method still can perform well in capturing the modal frequency. However, the new method is also sensitive to high-intensity time-domain noise where such severe noise causes false frequency contents to be more likely to be misidentified as modal frequency. Such a pilot study of testing the new method on the MMPF indicates that further research and improvements of GAs still be needed. Dissertation (Ph.D.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2025 Wed, 01 Jan 2025 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1205 2025-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1190 EEG-based detection of induced relaxation Bunyarat Umsura Roungsan Chaisricharoen Relaxation, defined as the absence of stress, is important for health, as prolonged periods of tension can lead to illness. This study detects induced relaxation through electroencephalography (EEG) for real-time monitoring of brain activities. This study employed the Multivariate Empirical Mode Decomposition with Dynamic Phase-Synchronized Hilbert-Huang Transform (MEMD-DPS-HHT) algorithm to extract dynamic patterns of brain waves. The dataset of essential oil blends was preprocessed to remove artifacts using independent component analysis (ICA), bandpass filtering in the 4-12 Hz frequency range, and normalization. The EEG data were decomposed into five Intrinsic Mode Functions (IMFs) utilizing the MEMD method. The average frequencies of IMF1 during the eyes-closed and eyes-open conditions are 8.56 Hz and 10.77 Hz, respectively. For IMF2, the frequencies are 4.91 Hz and 6.72 Hz, respectively. Phase synchronization analysis across channels revealed a maximum coherence of 0.87. The dynamic Hilbert spectra and relaxation indices, pre- and post-stimulation, averaged a maximum of 0.16. This finding was consistent with the paired t-test and factorial design experiment results, both of which showed no significant differences in theta and alpha bands pre- and post-stimulation. The findings indicate that MEMD-DPS-HHT effectively analyzes cross-channel, multidimensional, and dynamic EEG data related to relaxation. Dissertation (Ph.D.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2025 Wed, 01 Jan 2025 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1190 2025-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1081 Ensemble deep learning for real-bogus classification with sky survey images Pakpoom Prommool Sirikan Chucherd The detection of astronomical transient events—short-lived phenomena such as supernovae, gamma-ray bursts, and stellar flares—has become a major focus in contemporary astrophysical research. These events are often linked to extreme cosmic processes and provide essential insights into stellar evolution and the dynamic nature of the universe. However, identifying such events within the vast and rapidly expanding datasets generated by modern sky surveys presents significant challenges, especially as manual inspection becomes infeasible. The Gravitational-wave Optical Transient Observer (GOTO) project exemplifies this complexity. Designed to detect optical counterparts to gravitational-wave sources, GOTO produces hundreds of sky survey images per night, with each image containing tens of thousands of celestial objects. The scale and frequency of this data render traditional analysis methods—such as manual feature extraction and visual inspection—insufficient and inefficient, leading to missed opportunities in capturing rapidly fading events. This research proposes the use of Deep Learning techniques, particularly Convolutional Neural Networks (CNNs), to enhance the classification of transient objects. Unlike traditional methods, CNNs can automatically learn discriminative features directly from raw image data, making them well-suited for large-scale, high-dimensional image classification tasks. To improve model performance and robustness, the study employs Transfer Learning and Fine-Tuning strategies using pre-trained models on ImageNet, adapting them to the specific characteristics of astronomical imagery. It also integrates various Data Augmentation techniques—including image rotation, flipping, and noise injection—to increase data diversity. Additionally, the research investigates the role of Dropout in preventing overfitting and evaluates the effect of varying Batch Sizes on model training and generalization. To further enhance classification performance, the study incorporates Ensemble Learning techniques such as Soft Voting and Weighted Voting, combining multiple CNN models to produce more reliable predictions. The results demonstrate that this integrated approach significantly improves the accuracy and reliability of transient classification within large-scale datasets, offering a practical and scalable solution for real-time astronomical event detection in projects like GOTO. Dissertation (Ph.D.) -- Computer Engineering, School of Applied Digital Technology. Mae Fah Luang University, 2025 Wed, 01 Jan 2025 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1081 2025-01-01T00:00:00Z