The traditional de-noising method used by classical hardware equipments can’t achieve successful de-noising effect and the software-only method never meets a high real time capability. Based on the Undecimated Wavelet Transform (UWT) which is an effective technique for de-noising signals corrupted by non-stationary noises, we propose implementing the UWT method on the field programmable gate array (FPGA) to realize a digital electronic circuit to de-noise the airflow of mechanical ventilation. The experiment results obtained was done regarding signal to noise ratio (SNR) and the requirement of real-time signal processing.
non-invasive fetal electrocardiogram (FECG) signal extraction from signals recorded at abdominal area of mother is a challenging problem for the biomedical and signal processing communities. In this paper, we improve the FECG extraction approaches which consist to find the relation-ships between the cardiac potentials generated at the heart level of mother and the potentials recorded on the abdominal area. We used an efficient signal processing method combining a hybrid learning algorithm based on the fuzzy adaptive resonance theory and the hybrid soft computing technique called Adaptive Neuro Fuzzy Inference System (ANFIS) trained with modified Particle Swarm Optimization (PSO) endowed with an initialization strategy to adjust the antecedent parameters of fuzzy rules. We implemented our algorithm and other algorithm on simulated signals, and we found that the proposed ANFIS with hybrid learning algorithm achieves superior performance in learning accuracy and allowed yielding best processing results to extract the FECG signal.
Show abstract
Full Text
