Coronavirus Disease 19 or well known asCovid-19 is infectious disease that attack human respirator system. This virus is contagious due to its fast transmission from small droplets that come to people that sneezing, coughing, or even talking. This virus can stay in air for long time and it also can survive on the inanimate surface things. During this time, some places like hospital, mall, and station are places where crowds gathered. People in those places have possibility to spread the virus not only through close contact but also due to touching the infected surfaces. That is why device that able to clean the infected surfaces needed. The Internet of Things based robots may give big impact in combating the coronavirus that stay on inanimate surfaces. The proposed system is the robot that able to disinfect the surfaces of things using UV-C lights. The implementation of UV disinfectant robot will indeed help health authorities in reducing the transmission.

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The massive number of medical images produced by fluoroscopic and other conventional diagnostic imaging devices demand a considerable amount of space for data storage. This paper proposes an effective method for lossless compression of fluoroscopic images. The main contribution in this paper is the extraction of the regions of interest (ROI) in fluoroscopic images using appropriate shapes. The extracted ROI is then effectively compressed using customized correlation and the combination of Run Length and Huffman coding, to increase compression ratio. The experimental results achieved show that the proposed method is able to improve the compression ratio by 400 % as compared to that of traditional methods.

Image de-noising is a core operation in image processing and computer vision. In this paper, combination of two popular methods in image de-noising bilateral and anisotropic-diffusion filtering is investigated to reduce the noise in medical images, while preserving the clarity of images. The proposed method experimented on 23 MRI images. The results obtained from the proposed method gained higher peak signal to noise ratio (PSNR) and compression ratio (CR) performance in comparison with the traditional methods by more than 8%.

<p><span>The field of image compression became a mandatory tool to face the increasing and advancing production of medical images, besides the inevitable need for smaller size of medical images in telemedicine systems. In spite of its simplicity, run-length encoding (RLE) technique is a considerably effective and practical tool in the field of lossless image compression. Such that, it is widely recommended for 2D space that utilizes common searching techniques like linear and zigzag. This paper adopts a new algorithm taking advantage of the potential simplicity of the run-length algorithm to contribute a volumetric RLE approach for binary medical data in the 3D form. The proposed volumetric-RLE (VRLE) algorithm differs from the 2D RLE approach utilizing correlations of intra-slice only, which is used for compressing binary medical data utilizing voxel-correlations of inter-slice. Furthermore, several forms of scanning are used to extending proposed technique like Hilbert and Perimeter, which determines the best possible procedure of scanning suitable for data morphology considering the segmented organ. This work employs proposed algorithm on four image datasets to get as sufficient as possible evaluation. Experimental results and benchmarking illustrate that the performance of the proposed technique surpasses other state-of-the-art techniques with 1:30 enhancement on average.</span></p>