| Modeling the active neuron separation in the compressed sensing and finite rate of innovation framework T Schnier, C Bockelmann, A Dekorsy IEEE Transactions on Signal Processing 67 (17), 4521-4534, 2019 | 6 | 2019 |
| Minimum measurement deterministic compressed sensing based on complex reed solomon decoding T Schnier, C Bockelmann, A Dekorsy 2016 24th European Signal Processing Conference (EUSIPCO), 359-363, 2016 | 3 | 2016 |
| SparkDict: A fast dictionary learning algorithm T Schnier, C Bockelmann, A Dekorsy 2017 25th European Signal Processing Conference (EUSIPCO), 1564-1568, 2017 | 2 | 2017 |
| RSCS: Minimum measurement MMV deterministic compressed sensing based on complex reed solomon coding T Schnier, C Bockelmann, A Dekorsy 2015 49th Asilomar Conference on Signals, Systems and Computers, 483-487, 2015 | 2 | 2015 |
| A theoretical analysis of the spatial multi channel compressed sensing model T Schnier, C Bockelmann, A Dekorsy 2017 IEEE 18th International Workshop on Signal Processing Advances in …, 2017 | 1 | 2017 |
| Acquisition and Reconstruction of Compressed Signals with Applications in Wireless Neural Systems T Schnier Shaker Verlag, 2019 | | 2019 |
| Reduction of necessary data rate for neural data through exponential and sinusoidal spline decomposition using the Finite Rate of Innovation framework T Schnier, C Bockelmann, A Dekorsy 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | | 2017 |
