| Title | Reducing the Root-Mean-Square Error at Signal Restoration using Discrete and Random Changes in the Sampling Rate for the Compressed Sensing Problem |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Kozhemyak, Olesya A., Stukach, Oleg V. |
| Conference Name | 2021 International Siberian Conference on Control and Communications (SIBCON) |
| Date Published | may |
| Keywords | composability, compressive sampling, compressive sensing, Image coding, machine learning algorithms, Market research, process control, pubcrawl, random sampling, Real-time Systems, resilience, Resiliency, signal reconstruction, Signal restoration |
| Abstract | The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information. |
| DOI | 10.1109/SIBCON50419.2021.9438937 |
| Citation Key | kozhemyak_reducing_2021 |
Reducing the Root-Mean-Square Error at Signal Restoration using Discrete and Random Changes in the Sampling Rate for the Compressed Sensing Problem