Image smoothing with edge preservation in the presence of outliers is a challenge in image processing. Anisotropic diffusion smoothing is a well-established paradigm in digital image smoothing with edge preservation. Anisotropic diffusion smoothing filters are not robust to impulse noise. They employ two or more stages for robustness in the presence of outliers, and, therefore, they demand significantly increased arithmetic operations for robustness; consequently, they are not power efficient. This paper introduces a low arithmetic complexity image smoothing model and proposes an intrinsically robust and power efficient algorithm for anisotropic diffusion smoothing of images. The algorithm outperforms the foundational robust smoothing algorithms in terms of the standard performance metrics and visual quality.

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Purpose The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis. Design/methodology/approach The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries. Findings Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle. Originality/value The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.