Adaptive Rate Sampling and Filtering for Low Power Embedded Systems
Résumé
This work is part of a large project aimed to enhance the signal processing chain required in embedded systems. The motivation is to reduce their size, cost, processing noise, electromagnetic emis-sion and especially power consumption. This can be done by smartly reorganizing their associated signal processing theory and architecture. In this context a signal's amplitude driven sampling scheme based on level crossing is employed. This sampling scheme adapts the sampling rate and so the system activity by following the input signal variations. In order to filter the non-uniformly sampled signal obtained at the output of this sampling scheme two novel adaptive rate FIR filtering techniques are devised. The principle of both techniques is to smartly combine the features of both uniform and non-uniform signal processing tools to achieve an efficient online filtering process. The computa-tional complexities of the proposed techniques are deduced and compared to the classical FIR filtering technique. The results promise a significant gain of the computational efficiency and hence of the processing power. A comparison of both proposed techniques in terms of computational complexity is finally made.