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Time constraint is one of the important issues for the patients suffering from heart diseases. Delay in getting treatment can be life threatening. Essential data collection requires a great deal of efforts and time to collect and analyze the information. Such processes are usually slow and may become error-prone if done in a hurry. This situation limits the clinical diagnostics and monitoring capabilities. This paper presents the development of a remote monitoring system for ECG signals. Proposed solution is based on the concept of cloud computing and wireless networks. Analog signal is acquired with the ADC unit of the PIC24 controller, this information in terms of ECG is transferred through ZigBee network connected to the cloud and finally examination and storing are carried out. The signals obtained from the patients can be monitored simultaneously by the experts. Cloud provides availability, reliability.

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In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the Rider Optimization Algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.