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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Effects of Equatorial Plasma Bubbles over Real-Time Kinematic Positioning in Low-Latitude Region
(2023-01-01)
Thu, Phyo C.
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Supnithi, Pornchai
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Myint, Lin Min Min
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Budtho, Jirapoom
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Saito, Susumu
Equatorial plasma bubbles (EPBs) refer to ionospheric irregularities in low-latitude regions, commonly observed after sunset. They originate at the magnetic equator and then potentially spread to mid-latitude region. As cm-level positioning techniques are increasingly important to various segments of society, the performance degradation of these systems due to EPB at low latitudes needs to be investigated. In this work, we analyze the EPB effects on the performances of real-time kinematic (RTK) positioning at the short, medium, and long baselines at low-latitude stations in Thailand. The low-latitudes local ionospheric disturbances such EPBs are shown to degrade the positioning accuracy of RTK in different seasons in 2022. It is found that the positioning errors are higher during the disturbance periods and more severe at the long baselines than the shorter ones, especially during the equinoctial months.
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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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Study of Detectors with Noise Predictor for High Areal Density BPMR Systems
(2019-04-10)
Myint, Lin Min Min
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Supnithi, Pornchai
In this paper, we study the performance of Viterbi detectors embedded with noise predictor for the 2-D interference channel of high areal density bit patterned media recording (BPMR) system. After the readback signal is processed by a equalizer, the received signal at the input of detector is corrupted by the colored/correlated noise. Therefore, the noise whitening process using a noise predictor is required to improve the performance of the detector. When the noise predictor is incorporated into the detector, it needs to extend the structure of trellis in the detector. For the 2-D interference channel of BPMR system, the complexity of the detector will grow beyond the practical level. Therefore, we design a detector embedded by a noise prediction technique without extending the trellis. In the proposed technique, the most likely noise samples generated from the branch metric calculation are stored along the surviving path of the trellis in Viterbi algorithm. The proposed technique requires less number of memory and arithmetic operations at each state of the trellis compared to conventional technique. We consider both finite impulse response (FIR) filter and infinite impulse response (IIR) filter for the noise prediction filter. The simulation results show that the proposed noise prediction can improve the performance of detector, especially for the high areal density at 4 Tbit/in<sup>2</sup>.
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Ionospheric Scintillation Prediction Using Decision Tree and Rainforest Techniques
(2024-01-01)
Trachuentong, Sirasake
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Supnithi, Pornchai
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Myint, Lin Min Min
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Saito, Susumu
The ionosphere contains electron density variation. When radio signals transmitted from global navigation satellite systems (GNSS) pass through such medium, additional delays are added. With ionospheric irregularity, fluctuation in GNSS signals known as scintillation are often observed resulting in reduced number of tracked satellites then degrade positioning performances. At present, scintillation is considered random, hence, the ability to detect or predict such phenomenon is crucial to efficient system operation. In this research, we design machine learning algorithm for scintillation prediction. Both Decision Tree (DT) and Random Decision Forest (RF), are implemented to predict daily ionospheric scintillation at King Mongkut's Institute of Technology Ladkrabang (KMITL) station in Thailand (13.73 ° E, 100.77° N). The rate of total electron content change index (ROTI) is also used. Modeling is carried out for four months in March (equinox), June (solstice), September (equinox), and December (solstice) in 2022, representing different seasons in space weather study. The prediction results are evaluated using the S 4 index observations at KMITL station and then compared between DT and RF methods. The designed model has a high potential for scintillation prediction.
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Track mis-registration detection using correlation functions in magnetic recording
(2012-12-01)
Myint, L.
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Supnithi, P.
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Puttarak, N.
Track mis-registration (TMR) causes the asymmetric inter-track interference which degrades the system performance. In this work, we propose a TMR detection method based on the cross-correlation functions in bit patterned media (BPM) recording systems. Firstly, the coefficients of the channel are predicted with the cross-correlation functions between the readback signals and the recorded bit sequences on the main track as well as the interfering tracks. Using the ratio of the channel coefficients, the presence and direction of TMR and can be predicted more accurately. © 2012 DSI.
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Equatorial Plasma Bubble Detection by Support Vector Machine at Chumphon Station, Thailand
(2022-01-01)
Thanakulketsarat, Thananphat
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Supnithi, Pornchai
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Myint, Lin Min Min
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Hozumi, Kornyanat
Equatorial Plasma Bubble (EPB) is a phenomenon in which depletion of plasma density occurs in the ionosphere particularly in the equatorial region. It can degrade the performances of the navigation system and satellite communication. In this work, we analyze EPB based on the very-high frequency (VHF) radar images at Chumphon station, Thailand. Then an EPB detection system using the support vector machine (SVM) technique is developed, and the accuracies of the systems using different kernels: linear kernel, the polynomial kernel, the radial basic functions kernel (RBF), and the sigmoid kernel are compared. Among the different kernels, we find that the RBF kernel gives the highest accuracy in prediction at 86.67 percent.
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Off-track detection based on the readback signals in magnetic recording
(2012-10-29)
Myint, Lin Min Min
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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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An early termination technique of polar codes for IR-HARQ scheme
(2020-10-08)
Mueadkhunthod, Krittiyaporn
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Phakphisut, Watid
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Myint, Lin Min Min
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Supnithi, Pornchai
In this work, we focus on polar codes in mobile communication systems where a retransmission strategy, namely, the incremental redundancy hybrid automatic repeat request (IR-HARQ) scheme, is performed. We propose an early termination (ET) of polar codes using the interleaved cyclic redundancy check (CRC) codes and the parity check (PC) codes. The parity-check equations of CRC and PC codes are used to detect the incorrectly decoded bits during the polar decoding. The simulation results verify that the proposed ET techniques can detect the erroneous bits in initial transmission and retransmission. The proposed technique provides the ET rate about 4-90%, and the block error rate (BLER) performance degradation is less than 2 dB.

Myint, Lin Min

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