| Title |
Compressive Sensing Solution Optimization Method In Sensing-Transmission-Calculation Integrated System |
| ID_Doc |
15357 |
| Authors |
Ma L.; Zhang Z.; Li Y.; Fu Y.; Ma D. |
| Year |
2022 |
| Published |
Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022 |
| DOI |
http://dx.doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00312 |
| Abstract |
The Internet of Everything is a necessary link for building smart cities, smart manufacturing, and other scenarios. And the demand for many forms of data such as high definition and high precision for the Internet of Everything puts great pressure on the storage and transmission of data. Starting from the end of the Internet of Things (IoT), this paper investigates a compressed sensing algorithm adapted to the end of the IoT sensing and computing system to reduce the power consumption and data communication volume of the end sensing and computing system and proposes an optimization algorithm for iteratively updating the measurement matrix and sparse dictionary. First, fix the sparse dictionary and use adaptive gradient descent to make the Gram matrix infinitely approximate the unit matrix and the matrix determined by the sparse dictionary structure to obtain the optimized measurement matrix; then, use the sample data obtained from this matrix to perform sparse dictionary learning and use the results as the dictionary input for the next round of measurement matrix; finally, improve the measurement matrix and dictionary performance through continuous iterative updating. Simulation experiments show that the measurement matrix and sparse dictionary obtained by this method have better performance for image signal acquisition reconstruction in a low sampling rate environment compared with traditional optimization algorithms. © 2022 IEEE. |
| Author Keywords |
compressive sensing; measurement matrix; sparse dictionary learning |
