Optimizing EMA Weights for Minimizing MSE in Compression

In this article, we propose a novel method for selecting the optimal Exponential Moving Average (EMA) weights for Compressive Sensing (CS). CS is a technique that enables the efficient acquisition of sparse signals by exploiting the sparsity of the signal in a transform domain. The EMA weight selection method is crucial for optimizing the performance of CS systems.
The proposed method uses an online algorithm to dynamically adjust the weights based on the input signal. The algorithm minimizes the Mean Squared Error (MSE) between the original signal and the reconstructed signal using a combination of EMA filters. The selection of the optimal EMA weights is crucial, as it can significantly impact the performance of the CS system.
To address this challenge, we propose an iterative minimization algorithm that optimizes the EMA weights based on the MSE criterion. The algorithm updates the weights using a quasi-Newton optimization method, which is efficient and robust for high-dimensional problems.
The key idea behind our approach is to use an adaptive weight selection strategy that can adjust the EMA filters in real-time to optimize the performance of the CS system. By minimizing the MSE between the original signal and the reconstructed signal, we can ensure that the resulting signal is as accurate as possible.
To illustrate the effectiveness of our proposed method, we conduct extensive simulations using a variety of signals. Our results show that our approach outperforms traditional EMA weight selection methods in terms of reconstruction accuracy and computational efficiency.
In summary, this article presents an innovative method for optimizing Exponential Moving Average (EMA) weights for Compressive Sensing (CS) systems. The proposed method uses an iterative minimization algorithm to dynamically adjust the EMA weights based on the input signal, resulting in improved reconstruction accuracy and reduced computational complexity. Our approach has important implications for a wide range of applications, including image and video compression, sensor networks, and machine learning. By using adaptive weight selection strategies, we can significantly improve the performance of CS systems and enable real-time signal processing with high accuracy and efficiency.