<p>Maintaining the quality of the water quality is one of the important aspects that play a substantial effect on the aquaculture industry especially in the tilapia industry. The quality of the water needs to be continuously monitored as any deviation from the allowed critical parameters such as water temperature and potential of hydrogen (pH) can cause unwanted scenarios such as disease, stress, higher mortality rate and profit loss. Currently, the monitoring process adopted by most fish breeders is done manually by using a portable sensor. This approach is found to be very tedious, ineffective use of manpower and time consuming particularly for the large-scale aquaculture industry. Hence, this research focuses on developing a simple, low-cost automated water quality monitoring system for the tilapia industry via LabVIEW software. The developed system will be able to monitor the parameter in real-time continuously with the capability of record and analyze each reading in a more efficient way. A data acquisition (DAQ) of NI myRIO-1900 is used as an interface between sensors and a monitoring station equipped with LabVIEW. Additionally, the developed system is equipped with an alarm system to alert the user when any deviation of the parameters occurs. Result shows that the system has a small range of average relative error of 4.28% and 6.22% for temperature and pH level respectively as compare to the portable sensor. Note that the errors are down to the selection of sensors. Furthermore, the developed prototype of the monitoring system has advantages in terms of its flexibility in extending the system with more sensors and allows a longer period of data collection without human intervention. The system is also upgradable with the integration of a control element to control the parameter when the monitored parameter is exceeded the threshold value. Succinctly, the system offers lots of advantages to the aquaculture industries with further improvement leads to better performance.</p>
Traffic congestion has become a serious issue due to the growing number of vehicles in Malaysia. Traffic light control system is widely used to control the flow of road junction. Currently, most of the traffic light system used pre-time and count down timers to control traffic flow. Due to the fixed-time setting, often the system unable to handle unexpected heavy traffic flows and cause traffic jam. Thus, there is a need of adaptive traffic signals which are able to do real time monitoring to control traffic light signal based on traffic density. This study proposed an adaptive traffic light control system that uses image processing and image matching technique in controlling the traffic in an effective manner by taking images of each lane at a junction. The density of traffic in the images at each junction are compared. Results show that more time are allocated for the vehicles on the densest road to pass compared to other less dense road. Edge operation detector is used to detect the density of traffic at each lane. In this study, a comparison study was carried out by applying five different edge detectors namely Roberts, Sobel, Log, Canny and active contour. Among these detectors, Canny edge operation detector has found to be the best as it could extract actual edges with average time of 0.453 second.
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