SysGpr: Sistema de Generación de Señales Sintéticas Pseudo-realistas

Signals obtained from sensors are widely used in different scientific fields. However, the resources to obtain the data are not always available due to structural constraints, physical, economic, environmental, and data collection failures, etc. It is in this scenario that the generation of synthetic data is established. The generation of synthetic data has several benefits, such as, reducing waiting times compared to the long periods required by some sensors to obtain large volumes of samples. In addition, the generated data can be as robust as users need it to be. For this reason, this paper presents a pseudo-realistic synthetic signal generation system for use in the validation of methods and design of experiments. The proposed signal generation method makes use of statistical models and the gradient of the signal to generate new data. The developed system is open for the public, available as a web tool.