We describe a new way to apply spatial filter gridded data from models or observations, focusing on low-pass filters. The method is analogous smoothing via diffusion, and its implementation requires only discrete Laplacian operator appropriate the data. can approximate arbitrary shapes, including Gaussian filters, be extended spatially varying anisotropic diffusion-based smoother's properties a...

State estimation in cyber-physical systems is a challenging task involving integrating physical models and measurements to estimate dynamic states accurately practical machine-to-machine IoT deployments. However, advanced wireless communication intelligent has increased vulnerability of external intrusion through centralized server. This study addresses the challenge Gaussian filtering for spec...

In this article we introduce line Smoothness-Increasing Accuracy-Conserving Multi-Resolution Analysis. This is a procedure for exploiting convolution kernel post-processors obtaining more accurate multidimensional multiresolution analysis in terms of error reduction. filtering-projection tool allows the transition data between different resolutions while simultaneously decreasing errors fine gr...

Noise amplitude in original time domain data is usually discrete and sparse. This article presents a digital filter denoising method based on statistical frequencies of the signal values. The effective noise are identified by comparing frequency value each pixelin with preset validity discrimination threshold. Signals recognized as valid will be output directly, while signals replaced mean thei...