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In this paper, image noise is removed by using a hybrid model of wiener and fuzzy filters. It is a challenging task to remove Gaussian noise (GN) from an image and to protect the image’s edges. The Fuzzy-Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results proved that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.

This paper proposes an adaptive learning control and monitoring of oxygen for patients with breathing complexities and respiratory diseases. By recording the oxygen saturation levels in real-time, this system uses an adaptive learning controller (ALC) to vary the oxygen delivered to the patient and maintains it in an optimum range. In the presented approach, the PID controller gain is tuned with the learning technique to provide improved response time and a proactive approach to oxygen control for the patient. A case study is performed by monitoring the time varying health vitals across different age groups to gain a better understanding of the relationship between these parameters for COVID-19 patients. This information is then used to improve the standard of care provided to patients and reducing the time to recovery. Results show that ALC controls the oxygen saturation within the target range of 90% to 94% SpO2, 77% and 80.1% of the time in patients of age groups 40-50 years old and 50-60 years old, respectively. It also had faster time to recovery to target SpO2 range when the concentration dropped rapidly or when the patient becomes hypoxic as compared to the manual control of the oxygen saturation by the healthcare staff.

This study aims to identify and predict objects underwater using the autoregressive moving average with exogenous excitation (ARMX) model in such a way that the outcome of the model is similar to actual measurements. It is used for parameter estimation. This model is validated by comparing results in actual model with ARMX model, autoregressive with an exogenous variables, and Box Jenkins (BJ) model. The results are analyzed in frequency and time domain by using mean square error criterion. Initial results show that ARMX predicts the acoustic scattering response with an accuracy of 96%, while ARX provides an accuracy of 78%, and BJ model poorly estimates the signal with an accuracy of 35%. ARMX also provides higher accuracy of detection by 7-8% as compared to the existing techniques.