Nono Heryana | Searcharxiv

arXiv2019

Mapping and Monitoring Pollution Levels of Carbon Monoxide (CO) using Arduino and Location-Based Service

Air pollution is a serious issue in the world. Around 98% of cities with a population of over 100,000 people in low and middle-income countries do not meet air quality standards, while in high-income countries, the number has decreased by 52%. Industries and motor vehicles are the biggest contributors to carbon monoxide (CO).Air pollution is a severe issue in the world. The object of research to investigate is the detection of carbon monoxide (CO) levels in an area mapped based on the current CO levels along with the location coordinates via GPS and LBS technology with a microcontroller and sensor-based device.The results of testing the CO level detection devices found that high CO levels are in the afternoon with an average CO level of 49.59656 which means classified as dangerous if we are outdoors more than 30 minutes, can interfere with heart function.

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arXiv2019

Reduce Noise in Computed Tomography Image using Adaptive Gaussian Filter

One image processing application that is very helpful for humans is to improve image quality, poor image quality makes the image more difficult to interpret because the information conveyed by the image is reduced. In the process of the acquisition of medical images, the resulting image has decreased quality (degraded) due to external factors and medical equipment used. For this reason, it is necessary to have an image processing process to improve the quality of medical images, so that later it is expected to help facilitate medical personnel in analyzing and translating medical images, which will lead to an improvement in the quality of diagnosis. In this study, an analysis will be carried out to improve the quality of medical images with noise reduction with the Gaussian Filter Method. Next, it is carried out, and tested against medical images, in this case, the lung photo image. The test image is given noise in the form of impulse salt & pepper and adaptive Gaussian then analyzed its performance qualitatively by comparing the output filter image, noise image, and the original image by naked eye.

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