Signal and Information Processing is an international open access journal that publishes original research articles and reviews related to all fields related to signal and information processing. The journal includes digital signal processing, filtering and signal processing, compressed signal processing, artificial intelligence, neural networks, hardware implementation, data filtering and processing, human-computer interaction, signal processing applications, data fusion and remote sensing.
Damage Identification of Frame Structures Based on Compressed Signal Processing
In view of the huge amount of data generated in the process of structural health monitoring and the huge burden on data storage, a structural damage identification method based on compressed sensing is proposed. This method first reduces the dimension of the original signal by means of compressive perception theory and characterizes the original damage signal by replacing the original signal with the compressed signal. Then, the compressed signal is wavelet packet decomposition and damage identification is performed by constructing energy eigenvectors. In order to better reflect the advantages of compressive perception applied to structural damage identification, the processed compressed signal is compared with the original signal for damage identification. A three-story reinforced concrete frame is simulated by finite element software ABAQUS, which is verified by the damage identification method and numerical examples. The results show that the proposed method can accurately identify structural damage using compressed signals and obtain more accurate recognition results while reducing the computational load.
Design and Practice of Advanced and Innovative Experimental Schemes for the “Digital Signal Processing” Course
“Digital Signal Processing” is a practical basic course in electronic information majors, and its experimental practice is a powerful complement to classroom teaching. There is an urgent need to add challenging advanced experiments based on existing experiments to enhance students’ innovation abilities. This paper integrates the course content, designs advanced and comprehensive experiments, and combines independent development with group cooperation in an orderly manner according to the teaching arrangement. The results show that this scheme can solve the problem of the loose connection between theory and application in teaching, effectively improve students’ practical skills, and meet the requirements of the new engineering education philosophy and engineering professional certification specifications.
Application of Digital Signal Processing Technology in Electronic Information Engineering
With the continuous development of the social economy, people have put forward higher requirements for improving the application level of digital signal processing technology in electronic information engineering. This needs to be combined with actual work, fully utilize the advantages of digital signal processing technology, and carry out targeted innovation and optimization, thereby improving the application level of digital signal processing technology. Based on this, this article starts with the application principles of digital signal processing technology, analyzes the status quo of its application in electronic information engineering, and proposes corresponding improvement suggestions.
Research Progress and Application of Approximate Computing Technology in Digital Signal Processing
Approximate computing technology has attracted significant attention in the field of signal processing. Complex algorithms and massive data volumes have limited processing speeds and increased hardware consumption in various applications. However, due to the redundancy of signals, precise results are not always necessary, and acceptable results for users are often sufficient. Therefore, the adoption of approximate computing technology can effectively reduce computational load, improve computational efficiency, and enhance system performance. This paper delves into different design levels of approximate computing technology. Firstly, it introduces the characteristics of signal processing applications. Then, it reviews recent research progress in approximate computing technology at both the algorithmic and circuit levels. Additionally, it explores approximate computing solutions in signal processing areas such as communications, video imaging, and radar. Finally, the paper discusses and provides an outlook on the future development directions in this field, offering insights to promote the application of approximate computing technology in signal processing.