Efficient Trigonometric Function Approximation for Embedded Systems Using Parallel Computation of Multiple Iterations

Trigonometric functions such as sine and cosine are commonly used in signal analysis, but traditional methods for computing these functions can be too resource-intensive for systems with limited computing power. In this paper, algorithms for approximating sine and cosine are investigated that enable efficient signal analysis with lower computational complexity. The proposed algorithms use iterative approaches to achieve a precise approximation of trigonometric functions, which facilitates their real-time application in embedded systems. Instead of a linear iterative approach, the parallel computation of multiple iterations (PCMI) in a single operation is proposed. Time-domain signal analysis, which aims to detect frequency and phase changes, is a crucial component in many embedded system applications, such as vibration measurement, phase shift detection in communication, EEG signal analysis, data preprocessing for machine learning, and many others.