Affiliations: [a] School of computer, Qinghai Normal University, Xining, Qinghai, China
| [b] Departments of Mathematics, Qinghai College of Architectural Technology, Xining, China
Correspondence:
[*]
Corresponding author. Yongfei Liu is a lecturer in the Department of Mathematics at Qinghai College of Architectural Technology, China, and is now a Ph.D Student in the School of computer, Qinghai Normal University. His research interests include compressed sensing, digital signal processing, algorithm design. E-mail: lyfltf0109@126.com.
Abstract: The improved Sparse Signal Reconstruction (SR) algorithm for Trusted Artificial Intelligence (AI) and Distributed Compressed Sensing (DCS) technology was thoroughly investigated. The study verified its effectiveness and advantages in trusted AI and DCS systems, which have significant implications for enhancing the credibility, security, and performance of signal processing and AI algorithms. The reconstruction performance was evaluated using Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), and Least Absolute Shrinkage and Selection Operator (LASSO). The analysis primarily focused on runtime, refactoring errors, and the number of successful reconstruction attempts. When K = 4, K = 6, K = 8, and K = 10, OMP outperformed BP and LASSO in terms of successful reconstructions, demonstrating better performance and higher reconstruction precision.
Keywords: Trusted artificial intelligence, distributed compressed sensing technology, sparse signal reconstruction algorithm, orthogonal matching pursuit, basis pursuit, least absolute shrinkage and selection operator
