Separation of Weak Signals from White Noises Using Python-Based Software

Weak signals are available from various applications like, geological explorations, biomedical sciences, military, aerospace and other fields. Detection of such signals is important for further studies. However, these signals are often mixed with high-frequency noises. These noises have very high-frequency, zero expectation value and zero average value. Various fields of control theory like, convolution time domain, frequency domain, and time domain analysis are required along with different nonlinear theoretical tools, like, chaos, stochastic resonance etc. These theoretical algorithms are used to develop an instrument called a lock-in amplifier which has a strong detection ability and high reliability of many weak signals. Before actually developing the amplifier, a simulation model is required to be developed. In this paper, the authors have developed a simulated model for the separation of weak input signals from white noises. For this process, Google Colab notebook and Python software have been used. The main advantage of this work is that the authors have used Python-based software which is open source software and takes very less computer space.