Myint, Lin Min

Preferred name

Myint, Lin Min

Alternative Name

Myint, L. M.

Myint, L.

Myint, Lin Min Min

Myint, Lin M.

Main Affiliation

School of Engineering

linminmin.my@kmitl.ac.th

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Equatorial spread-F forecasting model with local factors using the long short-term memory network
(2023-12-01)
Thammavongsy, Phimmasone
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Supnithi, Pornchai
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Myint, Lin Min Min
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Hozumi, Kornyanat
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Lakanchanh, Donekeo
The predictability of the nighttime equatorial spread-F (ESF) occurrences is essential to the ionospheric disturbance warning system. In this work, we propose ESF forecasting models using two deep learning techniques: artificial neural network (ANN) and long short-term memory (LSTM). The ANN and LSTM models are trained with the ionogram data from equinoctial months in 2008 to 2018 at Chumphon station (CPN), Thailand near the magnetic equator, where the ESF onset typically occurs, and they are tested with the ionogram data from 2019. These models are trained especially with new local input parameters such as vertical drift velocity of the F-layer height (Vd) and atmospheric gravity waves (AGW) collected at CPN station together with global parameters of solar and geomagnetic activity. We analyze the ESF forecasting models in terms of monthly probability, daily probability and occurrence, and diurnal predictions. The proposed LSTM model can achieve the 85.4% accuracy when the local parameters: Vd and AGW are utilized. The LSTM model outperforms the ANN, particularly in February, March, April, and October. The results show that the AGW parameter plays a significant role in improvements of the LSTM model during post-midnight. When compared to the IRI-2016 model, the proposed LSTM model can provide lower discrepancies from observational data. Graphical Abstract: [Figure not available: see fulltext.].
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Off-track detection based on the readback signals in magnetic recording
(2012-10-29)
Myint, Lin Min Min
;
Supnithi, Pornchai
Off-track condition in magnetic recording systems degrades the system performance. It is typically detected and adjusted by the servo control loop. In this work, we propose an off-track detection based on the readback signals and improve the bit error performance using an asymmetric target depending on the detected off-track direction. Specifically, we investigate the effects of off-track events on the target-shaping equalizer coefficients when the generalized partial-response target (GPR) is fixed. For a 3 × 3 channel matrix of bit patterned media recording (BPMR) system, the asymmetric targets offer the gain of about 1 to 2 dB at BER = 10 <sup>-4</sup> for the TMR level of 20% to 25%. © 2012 IEEE.
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Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms
(2025-01-01)
Mutasov, Gleb
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Supnithi, Pornchai
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Budtho, Jirapoom
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Tongkasem, Napat
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Nishioka, Michi
Equatorial ionospheric irregularities, particularly those associated with equatorial plasma bubbles (EPB), can significantly disrupt satellite navigation and communication systems. As the demand for reliable Global Navigation Satellite System (GNSS) and communication services grows, the prediction of ionospheric irregularities becomes critical. A key step in the prediction process is to identify distinct spatiotemporal patterns of irregularities, including day-to-day, longitudinal, and seasonal variations. However, with large datasets, manually classification or identification of these irregularities is a complex and challenging task. In this work, we propose unsupervised machine learning techniques to recognize and group irregularity patterns in large, unlabeled Rate of Total Electron Content (TEC) Index (ROTI) keograms. Specifically, two machine learning models: Gaussian Mixture Model and k-means clustering are employed. The ROTI keograms are constructed using GNSS data from two low-latitude receiver stations in Thailand. To reduce redundancy in the keogram images, three feature extraction techniques are applied before the clustering process. A comparative analysis is performed to determine the optimal number of clusters using these models. Based on the results, the optimal combination of feature extraction and clustering technique is determined for the proposed clustering model. The resulting k-means model with contour extractor classifies five distinct patterns of ionospheric irregularity patterns, providing valuable insights for enhancing EPB prediction models and deepening our understanding of ionospheric dynamics. Furthermore, these five irregularity patterns are analyzed in relation to space weather parameters such as the solar radio flux index (F10.7), and the geomagnetic index (Kp). The findings contribute to the development of robust prediction models, improving the reliability of satellite-based communication and navigation systems.
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Modified graph-based detection methods for two-dimensional interference channels
(2012-10-29)
Sopon, Thanomsak
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Myint, Lin Min Min
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Supnithi, Pornchai
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Vichienchom, Kasin
Two-dimensional (2-D) interference channels with inter-symbol interference (ISI) and inter-track interference (ITI) exist in the magnetic recording systems at high areal density. A number of 2-D detection methods have recently been proposed for the multi-track processing of the 2-D channels. Graph-based detector with the belief propagation algorithm appears as an alternative method, but at a degraded performance and high complexity level. In this work, we propose two methods to modify the graph-based (GB) detector. One applies a serial scheduling to the GB detector, while the other modifies the GB detection by ignoring some connections during one direction of the reliability updates in the factor graph leading to the reduction of short cycles. The simulation results show that the proposed GB detectors give better bit error rate performances than the other GB detectors. © 2012 IEEE.
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Simultaneous equatorial plasma bubble observation using amplitude scintillations from GNSS and LEO satellites in low-latitude region
(2023-12-01)
Seechai, Khanitin
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Myint, Lin Min Min
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Hozumi, Kornyanat
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Nishioka, Michi
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Saito, Susumu
This study estimates the scale sizes of the plasma density irregularities and the longitudinal width associated with equatorial plasma bubbles (EPBs) in equatorial and low-latitude regions. By analyzing amplitude scintillation S<inf>4</inf> indices and total electron content (TEC) measured from low earth orbit (LEO) satellite’s beacon signals with 400 MHz and Global Navigation Satellite System (GNSS) L1/E1 signals with 1575.42 MHz, recorded by receivers at the KMITL station in Bangkok, Thailand (geographic; 13.73° N, 100.77°E, magnetic: 7.26°N), we investigate the characteristics of these irregularities. We collected data of 154 LEO satellite pass events during nighttime on 21 disturbed days in four equinoctial months in 2021. Based on the presence or absence of the scintillation effects on GNSS and LEO beacon signals, the events are categorized into four classes to estimate the scale size of the plasma density irregularities. The analysis suggests that events with both GNSS and LEO scintillations, as well as events with GNSS scintillation alone, occur predominantly before midnight assuming the presence of the small-scale size of the irregularities within EPB. However, events with only LEO scintillation occur throughout the whole night and some events are observed before the events with both GNSS and LEO scintillations. Post-sunset LEO scintillation alone may be attributed to the onset of EPBs developing at low altitude, while post-midnight LEO scintillation events near the magnetic equator, observed during periods of low GNSS Rate of TEC Index (ROTI) values, are associated with bottom-side ionospheric irregularities but are not linked with EPB. The findings are consistent with previous researches on the generation and decay of electron density irregularities within plasma bubbles. However, this study provides new insights by using specific data sets and analysis techniques, offering a more comprehensive understanding of the association of LEO scintillations with bottom-side ionospheric irregularities near the magnetic equator, not observed in the ROTI map. Graphical Abstract: [Figure not available: see fulltext.]
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Single-track equalization method with TMR correction system based on cross correlation functions for a patterned media recording system
(2017-01-01)
Myint, Lin Min Min
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Warisarn, Chanon
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Busyatras, Wiparat
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Kovintavewat, Piya
Bit-patterned media recording (BPMR) is a promising technology for ultra-high density media, however, there are some challenges that need to be addressed including two-dimensional (2D) interference, and track mis-registration (TMR). The system can experience TMR due to misalignment of the head and the track center. Conventionally, TMR is tackled using a servo system in which the head position offset is estimated by processing the overhead servo sequences before reading the data sequences. However, TMR impairment can also occur when the head is reading data sequences that are beyond the servo mechanism. To address this problem, we proposed TMR detection and correction techniques based on our previous work involving a single-track equalization method for a BPMR system using cross-correlation functions between the single readback signal and each of the training sequences from three adjacent tracks. In this proposed technique, the presence and level of TMR is detected and estimated based on the changes in the value of mean square error (MSE) between the equalized and feedback signals from the detectors after passing through a one-dimensional (1D) target for each sequence. Then, the estimated TMR levels are used in selecting the appropriate equalizer and the generalized partial response (GPR) target pair to tackle the TMR from the readback signal. The simulation results show improvement in the data recovery of a BPMR system using the proposed method when the system is experiencing 2D interference and TMR impairment.
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Deep learning-based prediction models for the vertical total electron content using GNSS satellite observations
(2026-08-01)
Mutasov, Gleb
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Myint, Lin Min Min
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Budtho, Jirapoom
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Perwitasari, Septi
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Nishioka, Michi
Ionospheric Total Electron Content (TEC) is a crucial parameter for characterizing the state of the ionosphere and assessing its impact on satellite-based navigation systems and on communication technologies. In equatorial and low-latitude regions, ionospheric irregularities, particularly equatorial plasma bubbles (EPBs), pose significant challenges for satellite navigation and communication due to their capacity to cause rapid TEC fluctuations and signal degradation. Since these effects are especially pronounced during ionospheric and geomagnetic disturbances, making accurate TEC prediction is an essential task for improving the reliability of GNSS-based positioning and space weather applications. This study presents a machine learning-based framework for one-day-ahead prediction of TEC with a 30-min resolution over the magnetic equator and low-latitude regions, with a focus on Southeast Asia. Unlike global models, our approach is tailored to local GNSS observations and directly predicts TEC values along specific satellite-receiver paths, defined by geographic location and satellite visibility. We integrate ionospheric pierce point (IPP) coordinates, geomagnetic indices, and solar activity indicators as features to enhance temporal and spatial forecasting accuracy. To address the nonlinear and nonstationary nature of TEC variations, we investigate and compare three deep learning architectures: a Transformer-based time-series model, a Temporal Kolmogorov–Arnold Network (TKAN), and a Long Short-Term Memory (LSTM). Additionally, the predictions are benchmarked against the empirical IRI-2020 model and a persistence baseline. The results demonstrate that both the Transformer and TKAN models outperform the LSTM and empirical approaches, particularly during different geomagnetic and ionospheric conditions, showing improved robustness and generalization. The proposed framework highlights the potential for accurate, resource-efficient TEC prediction in low-latitude regions and opens a pathway for further improvements by integrating multi-GNSS observations and additional space weather parameters.
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Soft-information flipper based on long-short term memory networks for ultra-high density magnetic recording
(2021-01-01)
Rueangnetr, N.
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Myint, Lin M.
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Warisarn, C.
Currently, researchers have been developing new ultra-high density magnetic recording technologies to meet the exponential growth of data storage demand. One of the main prospective technologies is bit-patterned media recording (BPMR) technology which is expected to upgrade the areal density (AD) up to 4.0 Terabit per square inch (Tb/in2). To achieve the expected high AD, the distance between each magnetic island in BPMR medium must; however, be reduced significantly, and it will enhance the two-dimensional (2-D) interference, namely inter-symbol interference (ISI) and inter-track interference (ITI). These two effects need to be probably handled to maintain overall system performance. Therefore, we propose a soft-information flipper based on long-short term memory (LSTM) networks combined with the rate-5/6 2-D modulation code in the coded three-track/three-head BPMR systems. In the proposed system, three soft-information sequences produced by the multiple 2-D soft-output Viterbi algorithms are employed as LSTM network inputs to generate the coded data sequences. During the supervised learning process, the known values of the coded data sequences are used as the targets at the output stage of LSTM network. The simulation results indicate that, at the same user density of 2.5 Tb/in2, the proposed system can provide bit-error-rate performance over both the soft-information flipping scheme based on a priori log-likelihood ratios summation and conventional uncoded systems. Moreover, the results also reveal that the proposed system is more robust to the media noise compared to other systems.
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Estimation and Validation of Vertical Total Electron Content Using Standalone Single-Frequency Observations
(2022-01-01)
Tongkasem, Napat
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Myint, Lin Min Min
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Supnithi, Pornchai
Nonuniform ionospheric delay is a well-known cause of degradations in radio wave propagations such as in satellite communication and positioning. In general, the ionospheric delay can be estimated using the Global Navigation Satellite System (GNSS) data from dual or multiple-frequency receivers; however, satellite differential code biases (DCBs) must be downloaded via network connection. For positioning based on standalone single-frequency receivers, the Klobuchar model, a well-known model in the GPS positioning system, is used to estimate the ionospheric delay based on solar activity, season, or region by using the eight coefficients in the broadcast navigation message. Although this model can reduce positioning errors by about 50 percent, the low-latitude disturbances such as the equatorial plasma bubble (EPB) phenomenon, significantly diminishes the accuracy of modeled delay estimation. In this work, we propose an ionospheric delay estimation technique based on observed single-frequency GPS data without requiring network-based corrections for DCB. The ionospheric delays estimated by the proposed method are compared with those from the GPS dual-frequency observation, the broadcast/network models in 2014 (high solar activity) and 2020 (low solar activity). According to the results, the proposed ionospheric delay estimation can correct the ionosphere errors better than the well-known Klobuchar model, by about 9.98 percent and 6.77 percent in 2014 and 2020, respectively. The proposed model increases the ionospheric error correction efficiency in vertical positioning by up to 81 percent in 2014 and 79 percent in 2020.
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Classification of the equatorial plasma bubbles using convolutional neural network and support vector machine techniques
(2023-12-01)
Thanakulketsarat, Thananphat
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Supnithi, Pornchai
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Myint, Lin Min Min
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Hozumi, Kornyanat
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Nishioka, Michi
Equatorial plasma bubble (EPB) is a phenomenon characterized by depletions in ionospheric plasma density being formed during post-sunset hours. The ionospheric irregularities can lead to disruptions in trans-ionospheric radio systems, navigation systems and satellite communications. Real-time detection and classification of EPBs are crucial for the space weather community. Since 2020, the Prachomklao radar station, a very high frequency (VHF) radar station, has been installed at Chumphon station (Geographic: 10.72° N, 99.73° E and Geomagnetic: 1.33° N) and started to produce radar images ever since. In this work, we propose two real-time plasma bubble detection systems based on support vector machine techniques. Two designs are made with the convolutional neural network (CNN) and singular value decomposition (SVD) used for feature extraction, the connected to the support vector machine (SVM) for EPB classification. The proposed models are trained using quick look (QL) plot images from the VHF radar system at the Chumphon station, Thailand, in 2017. The experimental results show that the combined CNN-SVM model, using the RBF kernel, achieves the highest accuracy of 93.08% while the model using the polynomial kernel achieved an accuracy of 92.14%. On the other hand, the combined SVD-SVM models yield the accuracies of 88.37% and 85.00% for RBF and polynomial kernels of SVM, respectively. Graphical Abstract: [Figure not available: see fulltext.].

Myint, Lin Min

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