Project Details
Description
This project investigates the theory and applications of threshold based sampling in signal processing. It addresses the combination of threshold based sampling approaches and reconstruction algorithms, especially in the context of estimation problems. One of its aims is to design reconstruction algorithms tailored for specific engineering problems and to optimize the algorithms in terms of estimation performance as well as computational complexity.
| Status | Finished |
|---|---|
| Effective start/end date | 01.01.2019 → 31.12.2022 |
Collaborative partners
- Johannes Kepler University Linz (lead)
- SCCH - Software Competence Center Hagenberg (Project partner)
Fields of science
- 202017 Embedded systems
- 202028 Microelectronics
- 202027 Mechatronics
- 202015 Electronics
- 202037 Signal processing
- 202036 Sensor systems
- 202 Electrical Engineering, Electronics, Information Engineering
- 202023 Integrated circuits
- 202022 Information technology
- 202041 Computer engineering
- 202034 Control engineering
- 202030 Communication engineering
- 102019 Machine learning
- 202040 Transmission technology
- 202025 Power electronics
JKU Focus areas
- Digital Transformation
Research output
- 1 Conference proceedings
-
Robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series Analysis
Lunglmayr, M., Lindorfer, G. & Moser, B. A., 2021, Database and Expert Systems Applications - DEXA 2021 Workshops. Kotsis, G., Tjoa, A. M., Khalil, I., Moser, B., Mashkoor, A., Sametinger, J., Fensel, A., Martinez-Gil, J., Fischer, L., Czech, G., Sobieczky, F. & Khan, S. (eds.). Cham: Springer International Publishing, p. 119-126 8 p. (Communications in Computer and Information Science; vol. 1479 CCIS).Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review
Activities
- 1 Contributed talk
-
Robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series Analysis
Lunglmayr, M. (Speaker)
30 Sept 2021Activity: Talk or presentation › Contributed talk › science-to-science