Treebupachatsakul, Treesukon

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Treebupachatsakul, T.

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School of Engineering

treesukon.tr@kmitl.ac.th

Now showing 1 - 10 of 11

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Multi-parameter vital sign telemedicine system using web socket for covid-19 pandemics
(2021-03-01)
Pintavirooj, Chuchart
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Keatsamarn, Tanapon
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Treebupachatsakul, Treesukon
Telemedicine has become an increasingly important part of the modern healthcare infras-tructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.
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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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Generalized figure of merit for plasmonic dip measurement-based surface plasmon resonance sensors
(2022-04-01)
Treebupachatsakul, Treesukon
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Boosamalee, Apivitch
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Chaithatwanitch, Kamejira
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Pechprasarn, Suejit
We propose a theoretical framework to analyze quantitative sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip position, and figure of merits for different surface plasmon operating conditions for a Kretschmann configuration. Several definitions and expressions of the figure of merit have been reported in the literature. Moreover, the optimal operating conditions for each figure of merit are, in fact, different. In addition, there is still no direct figure of merit comparison between different expressions and definitions to identify which definition provides a more accurate performance prediction. Here shot-noise model and Monte Carlo simulation mimicking the noise behavior in SPR experiments have been applied to quantify standard deviation in the SPR plasmonic dip measurements to evaluate the performance responses of the figure of merits. Here, we propose and formulate a generalized figure of merit definition providing a good performance estimation to the detection limit. The measurement parameters employed in the figure of merit formulation are identified by principal component analysis and machine learning. We also show that the proposed figure of merit can provide a good estimation for the surface plasmon resonance performance of plasmonic materials, including gold and aluminum, with no need for a resource-demanding computation.
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The Study of Image Quality Effect on Model Performance for Bacteria Classification
(2025-01-31)
Treebupachatsakul, Treesukon
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Chomkwah, Wanwalee
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Tanpatanan, Tananan
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Poomrittigul, Suvit
One of the key requirements for supervised learning in deep learning model construction is the dataset for training and validation. For gathering the dataset, obtaining various image qualities from different resources is unavoidable, and this has been considered to affect the supervised model performance. This research proposes to demonstrate the effect of image quality involving high and standard datasets obtained from 2 different resources on the performance of models. The various cell characteristics with gram-positive and gram-negative bacteria datasets were challenged for trial. These different datasets were matched and contributed to 5 cases; case 1: train and test with high-quality images, case 2: train with high-quality images and test with standard quality images, case 3: train and test with images of standard quality, case 4: train with standard-quality images and test with high-quality images, and case 5: train and test with combining these two image qualities. Pre-trained CNN models were implemented to prove the purpose with and without stratified K-fold cross-validation. The results of retrained models showed that the high-performance models require high-quality datasets obtained from the same resource as the testing set, which yield more than 90% of all performance evaluation metrics when tested on challenging unseen datasets. This study provides valuable insights for building high-performance models that can be applied to automate microbiology diagnostics, impacting public health and clinical practice.
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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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Gelatin-Based Microfluidic Channel for Quantitative E. Coli Detection Using Blue Fluorescence of 4-Methyl-Umbelliferone Product and a Smartphone Camera
(2022-07-01)
Treebupachatsakul, Treesukon
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Lochotinunt, Chanin
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Teechot, Thitirat
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Pensupa, Nattha
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Pechprasarn, Suejit
Escherichia coli (E. coli) is a foodborne pathogen that can produce potent toxins, causing severe illnesses due to contaminated food and water consumption. This research has utilized a fluorescence measurement to quantify E. coli colonies from blue fluorescence emitted by 4-methyl-umbelliferone (4MU). The 4MU is the product of the catalytic reaction between beta-D-glucuronidase (GUD) secreted by multiple strains of Escherichia coli and its substrate 4-methylumbelliferyl-beta-D-glucuronide (MUG). Here, we apply the 4MU enzymatic reaction and propose simple instrumentation for label-free, real-time, in-situ, and quantitative E. coli measurement. The detection platform consists of a smartphone camera, an ultraviolet light source for fluorescence excitation, and MUG suspended microfluidic channels. The underlining mechanism for the proposed E. coli measurement is the passive diffusion process of the MUG secreted by E. coli and the GUD suspended in the gelatin, forming the blue fluorescence 4MU product in the channels. We have also proposed a cost-effective and eco-friendly fabrication method for preparing the MUG suspended gelatin microfluidic channels using a laser printer. Gelatin is an ultraviolet light-absorbing material in nature, providing an embedded optical filter. Here, we demonstrate that a smartphone camera can be utilized to image the fluorescence emission of the 4MU excited by the ultraviolet light in the gelatin film. The proposed E. coli detection technique allows the amount of E. coli colonies to be quantified without liquid sampling, cell-culturing, inoculation, and sophisticated equipment. Furthermore, the proposed method has a trade-off between response time and detection limit.
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Antibacterial characterization of ciprofloxacin-doped electrospun of low molecular weight polyethylene oxide (PEO) and sodium alginate (NaAlg) nanofibers
(2026-02-23)
Niyomchon, Phuphinee
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Treebupachatsakul, Treesukon
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Srirussamee, Kasama
Producing nanofibers using the electrospinning technique is a developed method that is widely used and of significant interest nowadays. This technique can be applied using various types of polymers. This research aimed to investigate the antibacterial PEO-NaAlg nanofiber fabrication. The fiber fabrication was examined under various viscosities of electrospinning solution. The electrospun nanofiber fabrication focuses on blending polyethylene oxide (PEO) with a molecular weight of 200-300 kDa, mixed with sodium alginate (NaAlg) of three different viscosities: 150 cP, 300 cP, and 730 cP to study how the viscosity of the solution affects the morphology of electrospun nanofibers. The PEO-NaAlg electrospun nanofiber was enhanced for water insolubility by crosslinking with calcium chloride (CaCl₂). The additional antibacterial property of the nanofiber by loading an antibacterial agent potentially against the growth of bacteria, was investigated. Antibacterial drug, ciprofloxacin at varying amounts of 0.05%w/v, 0.20%w/v, and up to 0.25%w/v was loaded to PEO-NaAlg solution and conducted electrospinning. The effectiveness of the antibacterial electrospun nanofiber was evaluated by testing its ability to inhibit the growth of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The inhibition area before and after crosslinking was observed. The results showed that the acquired nanofibber formation required 7%w/v of 200 kDa to 300 kDa of PEO, and blending 1%w/v NaAlg of 150 cP can certainly retain fiber morphology after crosslinking. Moreover, nanofibers loaded with ciprofloxacin effectively inhibit the growth of E. coli.
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Spatiotemporal variations of sand hydraulic conductivity by microbial application methods
(2024-01-01)
Kamchoom, Viroon
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Khattiwong, Thiti
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Treebupachatsakul, Treesukon
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Keawsawasvong, Suraparb
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Leung, Anthony Kwan
The spatiotemporal distributions of microbes in soil by different methods could affect the efficacy of the microbes to reduce the soil hydraulic conductivity. In this study, the specimens of bio-mediated sands were prepared using three different methods, i.e. injecting, mixing, and pouring a given microbial solution onto compacted sand specimens. The hydraulic conductivity was measured by constant-head tests, while any soil microstructural changes due to addition of the microbes were observed by scanning electron microscope (SEM) and mercury intrusion porosimetry (MIP) tests. The amount of dextran concentration produced by microbes in each type of specimen was quantified by a refractometer. Results show that dextran production increased exponentially after 5–7 d of microbial settling with the supply of culture medium. The injection and mixing methods resulted in a similar amount and uniform distribution of dextran in the specimens. The pouring method, however, produced a nonuniform distribution, with a higher concentration near the specimen surface. As the supply of culture medium discontinued, the dextran content near the surface produced by the pouring method decreased dramatically due to high competition for nutrients with foreign colonies. Average dextran concentration was negatively and correlated with hydraulic conductivity of bio-mediated soils exponentially, due to the clogging of large soil pores by dextran. The hydraulic conductivity of the injection and mixing cases did not change significantly when the supply of culture medium was absent.
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Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images
(2025-12-01)
Khovidhunkit, Siribang on Piboonniyom
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Phosri, Kunchidsong
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Thanathornwong, Bhornsawan
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Rungraungrayabkul, Dulyapong
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Poomrittigul, Suvit
Computer vision adjunctive technology for oral lesion diagnoses has been developed to detect and identify Oral Potentially Malignant Disorders (OPMDs) and non-OPMDs. The early detection of OPMDs can reduce the risk of oral cancer development, improving the survival rate of the patients. This study aims to evaluate the computer vision technique in the white oral lesion domain within the scope of photographic images. Deep learning techniques for the classification of Convolution Neural Networks (CNNs) and transformer neural networks, and one-stage models of YOLOv7 and YOLOv8 were utilized to classify and detect five classes of OPMDs and non-OPMDs oral white lesions including oral leukoplakia, oral lichen planus, pseudomembranous candidiasis, oral ulcers covered with pseudomembrane and other white benign oral lesions. From the evaluation results of classification, the IFormerBase model achieves overperformance compared to CNN models with accuracy, precision, and F1 score of more than 80% on the test set. The best model for object detection is YOLOv7 with 84.5% mean Average Precision (mAP) at Intersection over Union (IoU) threshold of 0.3 and 74.5% at IoU of 0.5 on the test set. Object detection results reveal promising automatic oral lesion identification, which can be further developed to enhance the lesion screening system.
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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.

Treebupachatsakul, Treesukon

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