Visitsattapongse, Sarinporn
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Visitsattapongse, Sarinporn
Alternative Name
Visitsattapongse, S.
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Email
sarinporn.vi@kmitl.ac.th
5 results
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Item type:Publication, Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy(2022-05-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning(2022-12-01); Pechprasarn, SuejitAngular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10<sup>–5</sup> RIU to 10<sup>–6</sup> RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10<sup>–6</sup> RIU for the proposed artificial intelligence-based method compared to 7.03 × 10<sup>–6</sup> RIU for the cubic polynomial curve fitting and 5.59 × 10<sup>–6</sup> RIU for 2-dimensional contour fitting using Horner's method. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application(2021-12-01); Pechprasarn, SuejitA deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10<sup>–7</sup> to 10<sup>–8</sup> RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10<sup>–6</sup> RIU compared to conventional intensity measurement methods of 1.73 × 10<sup>–5</sup> RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance analysis of non‐interferometry based surface plasmon resonance microscopes(2021-08-01); Pechprasarn, SuejitSurface plasmon microscopy has been of interest to the science and engineering commu-nity and has been utilized in broad aspects of applications and studies, including biochemical sensing and biomolecular binding kinetics. The benefits of surface plasmon microscopy include label‐free detec-tion, high sensitivity, and quantitative measurements. Here, a theoretical framework to analyze and com-pare several non‐interferometric surface plasmon microscopes is proposed. The scope of the study is to (1) identify the strengths and weaknesses in each surface plasmon microscopes reported in the literature; (2) quantify their performance in terms of spatial imaging resolution, imaging contrast, sensitivity, and measurement accuracy for quantitative and non‐quantitative imaging modes of the microscopes. Six types of non‐interferometric microscopes were included in this study: annulus aperture scanning, half annulus aperture scanning, single‐point scanning, double‐point scanning, single‐point scanning, at 45 degrees azimuthal angle, and double‐point scanning at 45 degrees azimuthal angle. For non‐quantitative imaging, there is a substantial tradeoff between the image contrast and the spatial resolution. For the quantitative imaging, the half annulus aperture provided the highest sensitivity of 127.058 rad/μm<sup>2</sup> RIU<sup>−1</sup>, followed by the full annulus aperture of 126.318 rad/μm<sup>2</sup> RIU<sup>−1</sup>. There is a clear tradeoff between spatial resolution and sensitivity. The annulus aperture and half annulus aperture had an optimal resolution, sensitivity, and crosstalk compared to the other non‐interferometric surface plasmon resonance micro-scopes. The resolution depends strongly on the propagation length of the surface plasmons rather than the numerical aperture of the objective lens. For imaging and sensing purposes, the recommended mi-crofluidic channel size and protein stamping size for surface plasmon resonance experiments is at least 25 μm for accurate plasmonic measurements. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of the surface plasmon resonance interferometric imaging performance of scanning confocal surface plasmon microscopy(2021-01-01); Pechprasarn, SuejitHere, we apply rigorous coupled-wave theory to analyze the optical phase imaging performance of scanning confocal surface plasmon microscope. The scanning confocal surface plasmon resonance microscope is an embedded interferometric microscope interfering between two integrated optical beams. One beam is provided by the central part around the normal incident angle of the back focal plane, and the other beam is the incident angles beyond the critical angle, exciting the surface plasmon. Furthermore, the two beams can form an interference signal inside a confocal pinhole in the image plane, which provides a well-defined path for the surface plasmon propagation. The scanning confocal surface plasmon resonance microscope operates by scanning the sample along the optical axis z, so-called V(z). The study investigates two imaging modes: non-quantitative imaging and quantitative imaging modes. We also propose a theoretical framework to analyze the scanning confocal surface plasmon resonance microscope compared to non-interferometric surface plasmon microscopes and quantify quantitative performance parameters including spatial resolution and optical contrast for non-quantitative imaging; sensitivity and crosstalk for quantitative imaging. The scanning confocal SPR microscope can provide a higher spatial resolution, better sensitivity, and lower crosstalk measurement. The confocal SPR microscope configuration is a strong candidate for high throughput measurements since it requires a smaller sensing channel than the other SPR microscopes.