Kyukwang Kim

Sensor Node for Remote Monitoring of Waterborne Disease-Causing Bacteria

A sensor node for sampling water and checking for the presence of harmful bacteria such as E. coli in water sources was developed in this research. A chromogenic enzyme substrate assay method was used to easily detect coliform bacteria by monitoring the color change of the sampled water mixed with a reagent. Live webcam image streaming to the web browser of the end user with a Wi-Fi connected sensor node shows the water color changes in real time. The liquid can be manipulated on the web-based user interface, and also can be observed by webcam feeds. Image streaming and web console servers run on an embedded processor with an expansion board. The UART channel of the expansion board is connected to an external Arduino board and a motor driver to control self-priming water pumps to sample the water, mix the reagent, and remove the water sample after the test is completed. The sensor node can repeat water testing until the test reagent is depleted. The authors anticipate that the use of the sensor node developed in this research can decrease the cost and required labor for testing samples in a factory environment and checking the water quality of local water sources in developing countries.

Vision Marker-Based In Situ Examination of Bacterial Growth in Liquid Culture Media.

The detection of bacterial growth in liquid media is an essential process in determining antibiotic susceptibility or the level of bacterial presence for clinical or research purposes. We have developed a system, which enables simplified and automated detection using a camera and a striped pattern marker. The quantification of bacterial growth is possible as the bacterial growth in the culturing vessel blurs the marker image, which is placed on the back of the vessel, and the blurring results in a decrease in the high-frequency spectrum region of the marker image. The experiment results show that the FFT (fast Fourier transform)-based growth detection method is robust to the variations in the type of bacterial carrier and vessels ranging from the culture tubes to the microfluidic devices. Moreover, the automated incubator and image acquisition system are developed to be used as a comprehensive in situ detection system. We expect that this result can be applied in the automation of biological experiments, such as the Antibiotics Susceptibility Test or toxicity measurement. Furthermore, the simple framework of the proposed growth measurement method may be further utilized as an effective and convenient method for building point-of-care devices for developing countries.

Light Emitting Marker for Robust Vision-Based On-The-Spot Bacterial Growth Detection

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 culture broth can disturb the detection process. This paper proposes a modified version of marker-FFT-based growth detection that uses a light emitting diode (LED) array as a marker. Since the marker itself can emit the light, the measurements can be performed even when there is no light source or the bacteria are cultured in a large volume of darkly colored broth. In addition, an LED marker can function as a region of interest (ROI) indicator in the image. We expect that the proposed LED-based marker system will allow more robust growth detection compared to conventional methods.

Visual Estimation of Bacterial Growth Level in Microfluidic Culture Systems

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.

Development of Algal Bloom Removal System Using Unmanned Aerial Vehicle and Surface Vehicle

Recently, owing to changes in weather conditions, cyanobacterial blooms, also known as harmful algal blooms (HABs), have caused serious damage to the ecosystems of rivers and lakes by producing cyanotoxins. In this paper, for the removal of HABs, an algal bloom removal robotic system (ARROS) is proposed. The ARROS has been designed with a catamaran-type unmanned surface vehicle (USV) and an algae-removal device attached below. In addition, electrical control systems and a guidance, navigation, and control (GNC) system are implemented on the ARROS to remove the algal bloom autonomously. Moreover, to increase the efficiency of the work, an unmanned aerial vehicle (UAV) is utilized and the system detects algal blooms with an image-based detection algorithm, which is known as a local binary pattern. The overall mission begins with a command from a server when the UAV detects an algal bloom, and the USV follows the given path autonomously generated by a coverage path planning algorithm. Subsequently, with an electrocoagulation and floatation reactor under the USV, HABs are removed. The performance of the algal bloom detection and HABs removal is verified through outdoor field tests in Daecheong Dam, South Korea.

A Low Cost/Low Power Open Source Sensor System for Automated Tuberculosis Drug Susceptibility Testing

In this research an open source, low power sensor node was developed to check the growth of mycobacteria in a culture bottle with a nitrate reductase assay method for a drug susceptibility test. The sensor system reports the temperature and color sensor output frequency change of the culture bottle when the device is triggered. After the culture process is finished, a nitrite ion detecting solution based on a commercial nitrite ion detection kit is injected into the culture bottle by a syringe pump to check bacterial growth by the formation of a pigment by the reaction between the solution and the color sensor. Sensor status and NRA results are broadcasted via a Bluetooth low energy beacon. An Android application was developed to collect the broadcasted data, classify the status of cultured samples from multiple devices, and visualize the data for the end users, circumventing the need to examine each culture bottle manually during a long culture period. The authors expect that usage of the developed sensor will decrease the cost and required labor for handling large amounts of patient samples in local health centers in developing countries. All 3D-printerable hardware parts, a circuit diagram, and software are available online.

MineLoC: A Rapid Production of Lab-on-a-Chip Biosensors Using 3D Printer and the Sandbox Game, Minecraft

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.

Adaptive Planar Vision Marker Composed of LED Arrays for Sensing Under Low Visibility

In image processing and robotic applications, two-dimensional (2D) black and white patterned planar markers are widely used. However, these markers are not detectable in low visibility environment and they are not changeable. This research proposes an active and adaptive marker node, which displays 2D marker patterns using light emitting diode (LED) arrays for easier recognition in the foggy or turbid underwater environments. Because each node is made to blink at a different frequency, active LED marker nodes were distinguishable from each other from a long distance without increasing the size of the marker. We expect that the proposed system can be used in various harsh conditions where the conventional marker systems are not applicable because of low visibility issues. The proposed system is still compatible with the conventional marker as the displayed patterns are identical.

Vertical thrusting unmanned surface vehicle for stable and close inspection of bridge structure

Inspection of the structural health of the bridge column is very important task but near-water surface region is dangerous for structural health monitoring robots due to water tides and spray. The unmanned surface vehicle with vertical thrusting function was developed to attach bridge column regardless of the surface status or material for close inspection and minimizing turbulence or external flow disturbing the positioning. Also, localization of the USV between the bridge columns were simulated.

Calibration of the drift error in GPS using optical flow and fixed reference station

In this paper, a sensor system using feature point tracking and optical flow to measure visual odometry is proposed to remove the GPS drift error of the hovering or landed UAVs without using an expensive INS sensor. A method of feature point tracking closest to the center of the field of view is used to reduce computational load. Measured pixel difference from the center and tracking point is converted to real displacement distance with aid of the ultrasonic range sensor. Tracking single point at a fixed position is done to measure internal error of the sensor. Whole system fully operates with low error range under a meter scale with average of 31 fps on ARM Coretex-A7 CPU-based commercial low-cost embedded Linux board. Sensor fusion with the GPS and the reference station installed at a fixed, known point helps initialization of the visual odometry, which successfully removes GPS drift errors.