As water is one of the basic needs of human beings, shortage of water we cannot live on. Water pollution and scarcity, are current global problems that require modification of water resources and guiding principles from the international level to individual wells. Water pollution is the leading cause of diseases worldwide. Previously, water quality monitoring was done through a physical collection of data; there was no smart sensor system. This led to inefficiency, especially, in real-time data access, device monitoring, and less accurate collection of data. There is a need to integrate mechanisms to monitor water quality in real-time, to ensure a safe supply of drinking water. Hence, the idea of the Internet of Things (IoT) which allows connections between various devices that can exchange and gather data emerged. To solve these issues, a low-cost real-time that is real-time platform based on the Internet of Things (IoT), and LoRa (Long Range) technology is proposed for monitoring water quality. The platform will measure and collect critical information about water quality. Such quality involves parameters like pH value, turbidity, and temperature of the surrounding atmosphere. The platform will additionally establish communication to the end-user, through LoRa Gateway and the internet. It will simultaneously provide data access, accurate data collection, and supervision of the component, whereby the user will notice if the virtual device is not receiving any data at a given time interval. The developed platform will consist of five different parts: sensor devices, IoT Board, Lora Gateway, cloud server, and user domain. The Lora Getaway will transmit the sensor data to the cloud through the internet to the cloud server. The cloud server is equipped with Matlab analysis and visualization applications which manipulate and analyze the data, to monitor the quality of the water. In the end, the useful information is visualized from the user domain for any decision-making process. The paper argues that the proposed platform is important in ensuring a safe supply of quality drinking water for people worldwide.
Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of Things devices. The station monitors parameters like temperature, humidity, and toxic gases like methane and carbon monoxide to detect any abnormalities and alert the miner via a buzzer on the helmet. The data is processed by the Thing Speak cloud, which enables users to communicate via internet-connected devices and displays a field graph of the transmitted data.
The Retina images suffer from the low gray level of contrast and less illumination in the region where it is nearby the optic disk with high brightness, while the region where it is far from the optic disk, has a lack of brightness thus can affect the extraction and can increment the computational time. This paper applies the enhancement of extraction and detection of retina images by reviewing the existing mechanisms and then performing experimental comparison of the developed solution through the integration of CLAHE (Contrast Limited Adaptive histogram equalization) and C-language techniques. The use of existing image enhancement mechanisms is with built-in function in Matlab, which is defined as adapthisteq (). The existing mechanism can enhance the contrast of the grayscale image by transforming the values using the Contrast limited adaptive histogram equalization. Based on the review, it was observed that there is still a need for a more timely and effective mechanism for enhancing the image quality in terms of its contrast and illumination. Hence, this study has an implemented image and enhanced mechanism with the use of CLAHE and C-language. In this integration, the c language codes involve the built-in function from the toolbox library of OpenCV (Open-Source Computer vision), like reading the Retina image and other functions. Then, the results produced between the existing mechanism and the new developed mechanism are compared. The difference between Matlab results and CLAHE integrated with C-language in the performed experiment shows the results for the verification of the Experimental is the developed solution with integration of CLAHE and C-language producing more enhanced quality of the image compared to the existing mechanism. Therefore, the study recommends integration of the developed mechanism in the devices used for capturing images such as retina.
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