Name
Abstract | ||
|---|---|---|
This work presents a new data compression method that uses an energy operator to exploit the correlated energy in neural recording features in order to achieve adaptive sampling. This approach enhances conventional data converter topologies with the power saving of asynchronous systems while maintaining low complexity & high efficiency. The proposed scheme enables the transmission of 0.7kS/s, while preserving the features of the signal with an accuracy of 95%. It is also shown that the operation of the system is not susceptible to noise, even for signals with 1dB SNR. The whole system consumes 3.94μW with an estimated area of 0.093mm2. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISCAS.2015.7169318 | International Symposium on Circuits and Systems |
Field | DocType | ISSN |
Asynchronous communication,Energy operator,Adaptive sampling,Computer science,Signal-to-noise ratio,Real-time computing,Data conversion,Electronic engineering,Network topology,Data compression,Image compression | Conference | 0271-4302 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Faliagkas | 1 | 0 | 0.34 |
| Lieuwe B. Leene | 2 | 8 | 4.69 |
| Timothy G. Constandinou | 3 | 78 | 38.42 |