Efficient capacitive touch sensing using structured matrices

Abstract

Compressive sensing is a technique used in signal processing applications to reduce sampling time. This paper talks about an efficient sampling framework based on compressive sensing for capacitive touch technology. We aim to minimize the number of measurements required during capacitance touch sensing process and in order to achieve this, we use structured matrices which can be used as a driving sensing framework for a touch controller. The novel contribution of this research is that we have modelled our recovery algorithm according to the structure of our sampling matrix, thus making it extremely efficient and simple to implement in a practical application. In this paper, we exploit the structure of the sensing matrix and conduct experiments to test the robustness of our proposed algorithm. Calculations of the floating point multiplication operations for the reconstruction algorithm and sensing matrix have also been looked into detail.