Noroviruses (NoVs) cause over 90% of non-bacterial gastroenteritis outbreaks in adults and children in developed countries. Therefore, there is a need for approaches to mitigate the transmission of noroviruses in workplaces to reduce their substantial health burden. We developed and validated a low-cost, autonomous robot called the UVBot to disinfect occupational spaces using ultraviolet (UV) lamps. The total cost of the UVBOT is less than USD 1000, which is much lower than existing commercial robots that cost as much as USD 35,000. The user-friendly desktop application allows users to control the robot remotely, check the disinfection map, and add virtual walls to the map. A 2D LiDAR and a simultaneous localization and mapping (SLAM) algorithm was used to generate a map of the space being disinfected. Tulane virus (TV), a human norovirus surrogate, was used to validate the UVBot’s effectiveness. TV was deposited on a painted drywall and exposed to UV radiation at different doses. A 3-log (99.9%) reduction of TV infectivity was achieved at a UV dose of 45 mJ/cm2. We further calculated the sanitizing speed as 3.5 cm/s and the efficient sanitizing distance reached up to 40 cm from the UV bulb. The design, software, and environment test data are available to the public so that any organization with minimal engineering capabilities can reproduce the UVBot system.

In this paper, we propose a total fractional-order variation model for multiplicative noise removal and contrast enhancement of real SAR images. Inspired by the high dynamic intensity range of SAR images, the full content of the SAR images is preserved by normalizing the original data in this model. Then, we propose a degradation model based on the nonlinear transformation to adjust the intensity of image pixel values. With MAP estimator, a corresponding fidelity term is introduced into the model, which is beneficial for contrast enhancement and bias correction in the denoising process. For the regularization term, a gray level indicator is used as a weighted matrix to make the model adaptive. We first apply the scalar auxiliary variable algorithm to solve the proposed model and prove the convergence of the algorithm. By virtue of the discrete Fourier transform (DFT), the model is solved by an iterative scheme in the frequency domain. Experimental results show that the proposed model can enhance the contrast of natural and SAR images while removing multiplicative noise.