ABSTRACT
The risk of Coronavirus disease (COVID-19) was reported to be higher in the indoor environment due to poor ventilation systems. A good and efficient ventilation system in enclosed spaces can help reduce the risk of infection. Thus, it is important to monitor the efficiency of the ventilation system. Therefore, this research aims to develop an indoor air quality (IAQ) monitoring and control system using the fuzzy logic controller (FLC). Three IAQ parameters were selected in this study (temperature, relative humidity (RH), and carbon dioxide (CO2) concentration). In addition, benchmark testing was done to test the efficiency of the IAQ monitoring and control system. The system's engine is a microcontroller, which collects data on IAQ parameters, and is equipped with an exhaust fan as the ventilation strategy. The device aids in monitoring IAQ parameters and is equipped with an exhaust fan as the ventilation strategy. The device, which aids in monitoring IAQ, was created using a machine learning technique, fuzzy logic controller. The performance of the proposed air quality monitoring and control system was also investigated and validated through several experiments. The system was tested by modifying each parameter individually while keeping the controlled parameters safe. In addition, the tests were changed to include the existence of a controller in the system to see how ventilation affects the measured metrics. The test revealed that without the controller, the parameters take a long time to return to their initial values, however with the controller, the readings return to their original values faster than normal. The system also demonstrated that by following the fuzzy rules set, it is capable of handling two parameters at the same time. © 2022 IEEE.
ABSTRACT
Electrochemical biosensors are advantageous for in-situ sensing applications measurements because of their low detection limit, good selectivity, minimum calibration requirement, and, most importantly, inexpensive cost. Nevertheless, integrated readout circuits for biosensing applications are typically cumbersome, costly, and not maintenance-free. Numerous studies have been conducted on electrochemical biosensor systems with portable readout circuits, specifically on potentiostat for diverse in-situ sensing applications. High-quality microfluidic packaging is required to build this portable sensor device. Before samples can be measured for biosensing applications, notably loop-mediated isothermal amplification (LAMP), they must undergo further treatments and heating. Therefore, in this paper, we have developed a portable biosensor application system consisting of sensor packaging, fluid flow, and a heater that can be used in the field without requiring laboratory or specialist equipment. The sensor packaging is 3D printed, and a fluid leakage test validates its performance. Two types of heaters are utilized for the heating system and compared. This portable biosensor device can be utilized for various in-situ electrochemical biosensing applications, including Covid-19 sensing, ions detection in saltwater, and heavy metals detection in water pollution. © 2022 IEEE.
ABSTRACT
Sleep problem is currently a norm for many people, especially during this Covid-19 pandemic. Due to the limited number of sleep medicine studies, most people were unaware and just ignored their sleep problems. The use of polysomnography (PSG) in sleep medicine is quite popular, but due to its disturbance towards the subjects, it may decrease the subjects' sleep quality and may affect the result accuracy since it needs to be attached to the subjects' body. This work proposed a smart alarm based on the sleep cycle using speech analysis that uses a non-contact device, which is an undirected microphone of the Google AIY Voice Kit with Raspberry Pi. The microphone will be used to record the subjects' sleep sounds and detect the subjects' sleep cycle. The system will trigger a speaker attached to the Google Voice Kit to produce a sound to wake up the subject when they complete a particular sleep cycle according to their preference. Results showed that the system could detect sounds when subjects were sleeping and show a subject's sleep pattern. Whenever the subject past specific minutes, the sound amplitude is increased by 3 dB. These results indicate that subject is likely having their REM stages, and after 10 minutes, the subject will complete a sleep cycle.