Treebupachatsakul, Treesukon

Preferred name

Alternative Name

Treebupachatsakul, T.

Main Affiliation

School of Engineering

treesukon.tr@kmitl.ac.th

Now showing 1 - 10 of 31

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Permeability and setting time of bio-mediated soil under various medium concentrations
(2021-04-01)
Treebupachatsakul, Treesukon
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Kamchoom, Viroon
The bio-clogging using bacteria can be an eco-friendly and sustainable alternative to conventional grouting methods for seepage control. However, it remains unclear to date how the dilute concentration of bacterium and medium during field installation can affect the setting time of bacterium and its correlation with permeability reduction. In this study, the setting time of bacterium and its effectiveness in permeability reduction were addressed through experimental and theoretical investigations. A series of sand column was cultivated using different concentrations of Leuconostoc mesenteroides and culture medium. The distribution and composition of the bacterial product (i.e. dextran) were observed by refractometer, scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDS). Soil permeability was recorded using a constant head test. The results revealed that bacterium was effective to produce dextran at the setting time of about 5 d after installation. This dextran can reduce the permeability of bio-mediated soil by two orders of magnitude, even without culture medium supply. In general, the dextran production decreased proportionally with increase of bacterium and medium concentration. However, at 50% bacterium and medium concentration by weight, it still has a significant influence on permeability reduction with similar setting time, compared to 100% concentration.
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Image Processing for X-ray Calibration Phantom
(2019-01-10)
Wanluk, Nutthanan
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Pintavirooj, C.
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Treebupachatsakul, T.
Medical device calibration is the process of ensuring that there is accuracy before using. X-ray imaging device is one of the high-risk medical devices that must be calibrated. The calibration process required these values to be measured; center ray, focal object distance (FOD), resolution, and linearity of the imaging system. But the devices used to measure each of these are expensive. In this paper, a design of the x-ray calibration phantom that is affordable and yet effective is presented. An aluminum rectangular phantom consists of a center ray calibration unit, focal object distance (FOD) calibration unit and Linearity calibration unit. The x-ray image of all the phantom unit can be analyzed automatically by using the digital image processing.
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Preprocessing Technique for Oral Lesion Classification using U-NET Segmentation
(2023-01-01)
Dissorn, Pun
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Treebupachatsakul, Treesukon
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Phosri, Kunchidsong
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Thanathornwong, Bhornsawan
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Khovidhunkit, Siribang On Piboonniyom
This research aims to use deep learning techniques to segment oral lesions in medical images for use as a preprocessing step in a classification model. Due to the complexity of oral lesions with undefined margins and dynamic shapes, and the limited amount of data certified by dentists, this approach was found to be underfitting and unsatisfactory. To improve the accuracy of the model, a new approach was proposed to segment interferences such as teeth from the images. This allows the model to better focus on the oral lesions. To achieve this goal, we implemented U-net models with different additional Convolutional Neural Networks (CNN) backbones, including DenseNet 121, EfficientNet B3, VGG 19, ResNet 18, SE-ResNet 18, ResNeXt 50, Inception V3, Mobilenet V2 and SE-ResNeXt 50. A segmentation model was trained with five classes of oral lesions: leukoplakia, pseudomembranous candidiasis, lichen planus, ulcer, and other white lesions. The results showed that DenseUNet and EfficientUNet achieved the highest validation and Intersection over Union (IoU) scores of 98% and 92%, respectively. Our proposed approach effectively segmented the interferences from the images, demonstrating the success of these models in handling the approach. Subsequently, a CNN model of DenseNet 121 was employed for classification. The training accuracy achieved 99.1%, while the validation and test accuracies reached 86.1% and 75.5%, respectively.
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Detection Limit of Surface Plasmon Resonance Sensor for Quantitative Foodborne E.coli Detection Using Effective Refractive Index Theory : The theoretical limit of E.coli detection of surface plasmon resonance
(2021-01-01)
Pensupa, Nattha
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Treebupachatsakul, Treesukon
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Lochotinunt, Chanin
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Pechprasarn, Suejit
Surface plasmon resonance has been a gold standard for label-free biomedical and biochemical measurements, such as protein binding kinetics, protein-protein interactions. The surface plasmon resonance has also been utilized in food safety screening, including Escherichia coli and Salmonella detection. The theory of surface plasmon resonance has been well established and demonstrated its ultra-sensitivity to detect small nucleotides, proteins, and molecules. However, the results, so far, for the E.coli detection under the surface plasmon resonance have not shown an impressive detection limit. The typical detection limit of E. c o l i under the conventional surface plasmon detection platform is around 103CFUml. The detection limit can be enhanced using a secondary binding agent to E.coli, including conjugated nanoparticles. Here, we propose a theoretical framework using effective refractive index theory to explain the detection mechanism and give an insight into the underlining obstacles that degrades the detection limit of the surface plasmon resonance.
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Performance Analysis of Machine Learning Models for Angular Interrogation of Surface Plasmon Resonance
(2022-01-01)
Shinnakerdchoke, Siratchakrit
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Thadson, Kitsada
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Pechprasarn, Suejit
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Treebupachatsakul, Treesukon
Surface plasmon resonance (SPR) paves the way for several cutting-edge sensing technologies well-known for being label-free and real-time monitoring. The angular scanning technique, one of the most common SPR applications, was performed by illuminating the SPR-based sensor with multiple incident angles of a single-wavelength laser beam. For refractive index sensing, the optical reflectance is absorbed in a specific angle, known as a plasmonic angle, which can be observed as a dark band when captured using a camera. Various methods have been proposed to locate the plasmonic position based on the detected image. This manuscript presented an analysis of the performance of machine learning on the identification of plasmonic angles based on the reflectance spectra for refractive index sensing. The reflectance curves are generated using Fresnel equations and the transfer matrix method with shot noise. After training and validating, the rational quadratic gaussian process regression model provides the most accurate model for predicting the plasmonic angle positions. The model can predict the plasmonic angles accurately for all studied refractive indices with a root mean square error of 3.83 \times 10^{\mathbf{-4}} RIU. Furthermore, the analysis of noise performance illustrated that a low number of photons could significantly degrade the model's accuracy and precision. The theoretical performance can be achieved at the photon energy level of 8.14 pJ.
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Auto focusing ophthalmoscope for smartphone
(2020-10-29)
Navaitthiporn, Nitipon
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Rithcharung, Preeyarat
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Wongpa, Chuttima
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Treebupachatsakul, Treesukon
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Pechprasarn, Suejit
Several retinal pathologies can cause severe damages and may lead to permanent vision loss. Early diagnosis is highly recommended to take a precautionary measure to reduce the risk of ocular diseases. Therefore, an eye test by an ophthalmologist plays a crucial role. This does, however, require paying visit to a hospital, which becomes a burden for elders and patients with physical disabilities. Consequently, these patients usually get a late diagnosis and treatment, which the symptoms may have progressed and developed. Here, we develop an optical toolkit with engineered software for smartphones enabling the smartphone to take images of human retina as a direct ophthalmoscope. Thanks to the modern smartphone specifications, which usually come with high resolution cameras and sufficient computing power for the software. The optical toolkit illuminates an eye with an appropriate wavelength and light intensity and it has been provisionally tested for health and safety based on the ISO-10942 and IEC 62471 standards. An image processing algorithm is embedded in the software providing an auto focusing capability.
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Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection
(2023-04-01)
Treebupachatsakul, Treesukon
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Shinnakerdchoke, Siratchakrit
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Pechprasarn, Suejit
Surface plasmon resonance (SPR) has been utilized in various optical applications, including biosensors. The SPR-based sensor is a gold standard for protein kinetic measurement due to its ultrasensitivity on the plasmonic metal surface. However, a slight change in the surface morphology, such as roughness or pattern, can significantly impact its performance. This study proposes a theoretical framework to explain sensing mechanisms and quantify sensing performance parameters of angular surface plasmon resonance detection for binding kinetic sensing at different levels of surface roughness. The theoretical investigation utilized two models, a protein layer coating on a rough plasmonic surface with and without sidewall coatings. The two models enable us to separate and quantify the enhancement factors due to the localized surface plasmon polaritons at sharp edges of the rough surfaces and the increased surface area for protein binding due to roughness. The Gaussian random surface technique was employed to create rough metal surfaces. Reflectance spectra and quantitative performance parameters were simulated and quantified using rigorous coupled-wave analysis and Monte Carlo simulation. These parameters include sensitivity, plasmonic dip position, intensity contrast, full width at half maximum, plasmonic angle, and figure of merit. Roughness can significantly impact the intensity measurement of binding kinetics, positively or negatively, depending on the roughness levels. Due to the increased scattering loss, a tradeoff between sensitivity and increased roughness leads to a widened plasmonic reflectance dip. Some roughness profiles can give a negative and enhanced sensitivity without broadening the SPR spectra. We also discuss how the improved sensitivity of rough surfaces is predominantly due to the localized surface wave, not the increased density of the binding domain.
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Analysis of effects of surface roughness on sensing performance of surface plasmon resonance detection for refractive index sensing application
(2021-09-01)
Treebupachatsakul, Treesukon
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Shinnakerdchoke, Siratchakrit
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Pechprasarn, Suejit
This paper provides a theoretical framework to analyze and quantify roughness effects on sensing performance parameters of surface plasmon resonance measurements. Rigorous coupled-wave analysis and the Monte Carlo method were applied to compute plasmonic reflectance spectra for different surface roughness profiles. The rough surfaces were generated using the low pass frequency filtering method. Different coating and surface treatments and their reported root‐mean-square roughness in the literature were extracted and investigated in this study to calculate the refractive index sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip intensity, plasmonic dip position, and figure of merit. Here, we propose a figure‐of-merit equation considering optical intensity contrast and signal‐to‐noise ratio. The proposed figure-of‐merit equation could predict a similar refractive index sensing performance compared to experimental results reported in the literature. The surface roughness height strongly affected all the performance parameters, resulting in a degraded figure of merit for surface plasmon resonance measurement.
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Cuff-Less Blood Pressure Prediction from ECG and PPG Signals Using Fourier Transformation and Amplitude Randomization Preprocessing for Context Aggregation Network Training
(2022-03-01)
Treebupachatsakul, Treesukon
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Boosamalee, Apivitch
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Shinnakerdchoke, Siratchakrit
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Pechprasarn, Suejit
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Thongpance, Nuntachai
This research proposes an algorithm to preprocess photoplethysmography (PPG) and electrocardiogram (ECG) signals and apply the processed signals to the context aggregation network-based deep learning to achieve higher accuracy of continuous systolic and diastolic blood pressure monitoring than other reported algorithms. The preprocessing method consists of the following steps: (1) acquiring the PPG and ECG signals for a two second window at a sampling rate of 125 Hz; (2) separating the signals into an array of 250 data points corresponding to a 2 s data window; (3) randomizing the amplitude of the PPG and ECG signals by multiplying the 2 s frames by a random amplitude constant to ensure that the neural network can only learn from the frequency information accommodating the signal fluctuation due to instrument attachment and installation; (4) Fourier transforming the windowed PPG and ECG signals obtaining both amplitude and phase data; (5) normalizing both the amplitude and the phase of PPG and ECG signals using z-score normalization; and (6) training the neural network using four input channels (the amplitude and the phase of PPG and the amplitude and the phase of ECG), and arterial blood pressure signal in time-domain as the label for supervised learning. As a result, the network can achieve a high continuous blood pressure monitoring accuracy, with the systolic blood pressure root mean square error of 7 mmHg and the diastolic root mean square error of 6 mmHg. These values are within the error range reported in the literature. Note that other methods rely only on mathematical models for the systolic and diastolic values, whereas the proposed method can predict the continuous signal without degrading the measurement performance and relying on a mathematical model.
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Pressure Swing Absorption Oxygen Concentrator equipped with Remote Monitoring Pulse Oximeter
(2021-01-01)
Ongtrakul, Salila
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Thitiratannapong, Anyarin
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Pintavirooj, Chuchart
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Treebupachatsakul, Treesukon
The new COVID-19 disease was first identified in China at the end of 2019 and has spread rapidly all over the world. It has been projected that, by March 2021, the number of infections could reach 300 million cases and over two million deaths. One of the main implications for COVID-19 patients is pneumonia where the lung is infected, hence patients suffering from insufficient oxygen in the blood. As the number of COVID-19 cases have significantly increased, the demand for oxygen generators have also skyrocketed. This research concerns the design and construction of emergency low-cost oxygen concentrators used for mild COVID-19 symptoms, of which are forced to be treated at home. Our absorption-based oxygen concentrator uses zeolite packed in a sieve canister. An Oil-free compressor is then used to pump air in. Zeolite will absorb nitrogen from the air leaving oxygen free to travel towards the outlet. To evaluate the treatment, we have equipped the system with a pulse oximeter to measure the percent saturation oxygen, pulse rate and temperature. To prevent COVID-19 infections between patients and caretakers, we have designed an android application to remotely control the oxygen concentrator. Experiment has shown that our emergency low-cost oxygen concentrator can supply oxygen with an 85% purity rate.
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Treebupachatsakul, Treesukon

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