A touch panel is an input device for human computer interaction. It consists of a network of sensors, a sampling circuit and a micro controller for detecting and locating a touch input. Touch input can come from either finger or stylus depending upon the type of touch technology. These touch panels provide an intuitive and collaborative workspace so that people can perform various tasks with the use of their fingers instead of traditional input devices like keyboard and mouse. Touch sensing technology is not new. At the time of this writing, various technologies are available in the market and this paper reviews the most common ones. We review traditional designs and sensing algorithms for touch technology. We also observe that due to its various strengths, capacitive touch will dominate the large-scale touch panel industry in years to come. In the end, we discuss the motivation for doing academic research on large-scale panels.
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.
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