System & Board Gallery

This system is an on-device module used to classify PCB waste through computer vision in an electronic waste recycling system. It detects PCB objects using YOLO, and the detected information is transmitted to a robot in real time for classification. To ensure real-time performance, this module is equipped with an NPU to accelerate artificial intelligence.

This IoT-based device integrates advanced sensors, embedded processors, and AI-driven algorithms to continuously measure key air quality parameters, including particulate matter (PM2.5, PM10), carbon dioxide (CO₂), volatile organic compounds (VOCs), temperature, and humidity. 

This study is to develop a system that measures and analyzes low-frequency noise below 1 kHz occured by inter-floor noise and living noise. For analysis, the FFT results can be monitored on the display, and the measured noise and sensing information are transmitted to the cloud in real time and used for big data analysis.

The drop counter accurately records the number of drops of titrant added during the titration, which is automatically converted into volume. It continuously irradiates infrared rays, and the photodetector measures the amount of infrared light scattered when the drop of the solution falls, thereby counting the solution. If it goes beyond the range set by the nurse, it is a system that notifies via an alarm.

Transparent objects are extensively utilized in industrial automation and daily activities. However, achieving robust visual recognition and perception of transparent objects has consistently posed a significant challenge. We have successfully developed a depth extraction system tailored for transparent objects such as plastic bottles, glass bottles, and more. 

The system is employed to monitor the health status of human exhalations. When an individual's health deteriorates, the concentration of anions in exhaled breath increases. The measured results are transmitted to the smartphone via Bluetooth Low Energy (LE). Additionally, we have developed an Android application to support this functionality. 

This board serves as the main component of the air purifier system installed in elementary school classrooms. The air purifiers we have developed connect wirelessly to a central PC, which controls multiple units simultaneously. Additionally, we have developed dedicated software to run on the central PC for effective management of the air purification system." 

Air pollution, encompassing hazardous substances such as nitrogen, carbon, particulates, and toxic gases, is a worldwide issue. Specifically, Korea faces challenges from particulate matter and yellow dust originating from China. Our device is capable of detecting and monitoring ambient particulate matter concentrations ranging from PM1.0 to PM10.0, aiding in addressing this pressing environmental concern 

Hematocrit (HCT) levels present challenges in accurately measuring blood glucose concentration. This device is designed to measure HCT levels and subsequently correct errors induced by HCT during blood glucose measurements, ensuring more accurate results. 

Anemia is a condition characterized by a deficiency of healthy red blood cells, resulting in insufficient oxygen transport to the body's tissues. This system can diagnose anemia by measuring the hemoglobin levels in the blood. 

The NFC-based blood glucose meter does not contain its own battery; instead, it harvests energy from the NFC frequency transmitted by the smartphone to power its operations. The smartphone's display is utilized to provide the graphical user interface (GUI). 

This study presents the design and implementation of an indoor ambient noise monitoring system aimed at detecting and recording information concerning indoor ambient noise within a building. The proposed system is capable of measuring the noise level and estimating both the source and direction of the noise. 

Rental cars are often shared by multiple customers, and occasionally, some may leave the car dirty by smoking inside. This system is designed to swiftly detect smoking occurrences within the car.

The system employs lightweight AI technology to diagnose faults in industrial robots. It utilizes datasets obtained simultaneously from sound and vibration sensors interfaced with the system. 

Volatile organic compounds (VOCs), particulate matter (PM), mites, mold, and indoor noise are well-known sources of indoor pollution. Additionally, noise generated on upper floors and property loss caused by fire and malicious intruders are significant social concerns in many countries. The system is designed to address all the aforementioned issues. 

In recent times, air pollution, comprising hazardous substances such as nitrogen, carbon, particulates, and toxic gases, has become a global problem. The small dongle system, which connects to a smartphone, is equipped with multiple sensors capable of detecting air pollution. The smartphone serves as the user interface for managing the dongle system. 

We propose a lightweight smart insole designed to assist in the analysis of human gait. The smart insole utilizes a force-sensitive resistor (FSR) array to measure each step with low power consumption. A smartphone application gathers sensor data from both insoles via Bluetooth Low Energy (LE) and calculates the walking pattern. 

Semiconductor-based gas sensors are commonly employed for detecting volatile organic compounds (VOC) gases. However, they often lack selectivity in detecting specific gases or mixed gases. To address this limitation, we have developed a device utilizing a VOC sensor array for selective gas detection. A smartphone application is available to monitor the real-time status of all sensors.

The system is designed for novel walking speed estimation for wrist-worn devices. The proposed method consists of five main phases: data preprocessing and filtering, feature calculation from the IMU data, detection of the user's sensor-carrying mode using a pre-trained TreeBagger model, and calculation of the matching function for walking speed estimation.