Implementation of Temperature Data Denoising Operator Using Steepest Descent Method
Chien‐Cheng Tseng, Su‐Ling Lee · 2021
In this paper, a distributed implementation of temperature data denoising operator using the steepest descent method is presented. First, the smoothness-based denoising method using normalized Laplacian matrix is described and the conventional Neumann series implementation is reviewed briefly. Then, the steepest descent method is applied to develop a distributed implementation of denoising operator and its convergence condition is studied. It can be also shown that the Neumann series method is a special case of steepest descent method. Next, the momentum term is added to the update term of the steepest descent method for speeding up the convergence of implementation method. Finally, the real temperature data collected from the sensor network at Taiwan is used to demonstrate the effectiveness of the proposed denoising method and some discussions are made.