European Journal of Molecular & Clinical Medicine

In the domain of Internet of things Reconstruction of images from compression is very important. Transferal, store house and receiving of a set of different images in different technologies such as wireless, bigdata, machine learning, medical etc. is playing very important role to get desired output. This paper gives more information about Design of a Reconstruction from compression for Dynamic Magnetic Resonance Images Imaging. In this paper author has worked extension of segmentation and compression work on IPPFRFT and IDWT. Hunch responsiveness to the images of dynamic nature becomes heir to the various parameters from the Pseudo-Polar Trajectory of PPFRFT methods. Peak Signal to Noise Ratio, Structural Similarity Index Measures, Mean Square Error etc. take place performance parameters evaluated by simulation and comparison results commonly masterful as compare to existing methods in terms of reconstruction and compression.

The QR (Quick Response) Code became popular nowadays between us such as on flyers, posters, magazines, and so on. The QR Code can be printed in a smaller space that can store more information, more character types, also allowing users to interact with the world using their smartphone. Due to this pandemic era, users have to scan the QR Code once to enter some places to allows the owner to keep track of each customer that enters their premises. Therefore, the main objective of this article is to enhanced data storage of colored QR Code using the C3M technique to ensure that every data can be store safely on the server. This article will briefly explain how to use compression, multiplexing, multilayer, and multicolor techniques. The multicolor technique will use a combination of eight colored QR Code, which is known as Black, White, Red, Green, Blue, Cyan, Magenta, and Yellow (BWRGBCMY) and as a result, the storage capacity will be increased three times more than the original QR Code