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Simple methods using the striped pattern paper marker and FFT (fast Fourier transformation) have been proposed as alternatives to measuring the optical density for determining the level of bacterial growth. The marker-based method can be easily automated, but due to image-processing-base of the method, the presence of light or the color of the cult...
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... Especially in the case of the structure, 24-h monitoring is essential but because the input data of the VDS is the wavelength of the visible light region, the VDS has a disadvantage: the reliability of the data is drastically lowered at night, when the visible ray is lacking. Research using LED has been conducted to overcome this disadvantage [34] but it poses problems such as reduction of the accuracy of the displacement data due to the LED light leaks, in addition to the aforementioned problem caused by the marker. ...
Vision-based displacement sensors (VDSs) have the potential to be widely used in the structural health monitoring field, because the VDSs are generally easier to install and have higher applicability to the existing structures compared to the other conventional displacement sensors. However, the VDS also has disadvantages, in that ancillary markers are needed for extracting displacement data and data reliability is significantly lowered at night. In this study, a night vision displacement sensor (NVDS) was proposed to overcome the aforementioned two limitations. First, a non-contact NVDS system is developed with the installation of the infrared (IR) pass filter. Since it utilizes the wavelength of the infrared region and it is not sensitive to the change of a visible ray, it can precisely extract the shape information of the structure even at night. Second, a technique to extract the feature points from the images without any ancillary marker was formulated through an image convex hull optimization. Finally, the experimental tests of a three-story scaled model were performed to investigate the effectiveness of proposed NVDS at night. The results demonstrate that the NVDS has sufficiently high accuracy even at night and it can precisely measure the dynamic characteristics such as mode shapes and natural frequencies of the structure. The proposed system and formulation would extend the applicability of vision sensor not only into night-time measure but also marker-free measure.
... Moreover, the results were compared with previously reported data using a conventionally fabricated chip of the same chip design. Kim et al. [21,22], in the previous report, proposed a vision-based method to measure bacterial The result showed that the area of the non-overlapping region (error) was 2965 pixels while the area of the total region is 21,100 pixels. The accuracy of 86% was achieved by the proposed method. ...
... Moreover, the results were compared with previously reported data using a conventionally fabricated chip of the same chip design. Kim et al. [21,22], in the previous report, proposed a vision-based method to measure bacterial ...
... Moreover, the results were compared with previously reported data using a conventionally fabricated chip of the same chip design. Kim et al. [21,22], in the previous report, proposed a vision-based method to measure bacterial growth level in the culture vessel or micro/millifluidic devices. The striped patterned marker placed on the back side of the culture chamber and the camera was used to take the picture of the marker. ...
Here, MineLoC is described as a pipeline developed to generate 3D printable models of master templates for Lab-on-a-Chip (LoC) by using a popular multi-player sandbox game “Minecraft”. The user can draw a simple diagram describing the channels and chambers of the Lab-on-a-Chip devices with pre-registered color codes which indicate the height of the generated structure. MineLoC converts the diagram into large chunks of blocks (equal sized cube units composing every object in the game) in the game world. The user and co-workers can simultaneously access the game and edit, modify, or review, which is a feature not generally supported by conventional design software. Once the review is complete, the resultant structure can be exported into a stereolithography (STL) file which can be used in additive manufacturing. Then, the Lab-on-a-Chip device can be fabricated by the standard protocol to produce a Lab-on-a-Chip. The simple polydimethylsiloxane (PDMS) device for the bacterial growth measurement used in the previous research was copied by the proposed method. The error calculation by a 3D model comparison showed an accuracy of 86%. It is anticipated that this work will facilitate more use of 3D printer-based Lab-on-a-Chip fabrication, which greatly lowers the entry barrier in the field of Lab-on-a-Chip research.
... Recently, bacterial growth estimation has been performed for a macro/millifluidic scale culture using a fast Fourier transform (FFT)-based imaging method, as previously reported by our group [10,11]. The FFT method was used to measure the magnitude of the marker concealment, and the blurring of the broth due to cell growth was easily detectable [12]. ...
Microfluidic devices are an emerging platform for a variety of experiments involving bacterial cell culture, and has advantages including cost and convenience. One inevitable step during bacterial cell culture is the measurement of cell concentration in the channel. The optical density measurement technique is generally used for bacterial growth estimation, but it is not applicable to microfluidic devices due to the small sample volumes in microfluidics. Alternately, cell counting or colony-forming unit methods may be applied, but these do not work in situ; nor do these methods show measurement results immediately. To this end, we present a new vision-based method to estimate the growth level of the bacteria in microfluidic channels. We use Fast Fourier transform (FFT) to detect the frequency level change of the microscopic image, focusing on the fact that the microscopic image becomes rough as the number of cells in the field of view increases, adding high frequencies to the spectrum of the image. Two types of microfluidic devices are used to culture bacteria in liquid and agar gel medium, and time-lapsed images are captured. The images obtained are analyzed using FFT, resulting in an increase in high-frequency noise proportional to the time passed. Furthermore, we apply the developed method in the microfluidic antibiotics susceptibility test by recognizing the regional concentration change of the bacteria that are cultured in the antibiotics gradient. Finally, a deep learning-based data regression is performed on the data obtained by the proposed vision-based method for robust reporting of data.
