Optimization of algorithms in microcontrollers for efficient signal processing

Abstract

Algorithm optimization on microcontrollers is a crucial challenge in the field of digital signal processing applied to embedded systems, where memory, energy, and computing capacity are severely limited. This work reviews strategies and methodologies for implementing signal processing algorithms on low-power platforms, with the aim of maximizing computational and energy efficiency without compromising accuracy. Optimization techniques at both the software and hardware levels are described, including the selection of appropriate algorithms (FFT, DFT, digital filtering), the use of fixed-point arithmetic, task parallelization, and the adaptation of code to specific microcontroller architectures. The results reported in the literature show significant improvements in execution time and energy consumption in practical applications such as digital noise filtering, signal demodulation in communication systems, spectral analysis of electrical loads, and real-time pattern recognition. The discussion highlights the importance of balancing algorithmic complexity with the constraints of embedded systems, emphasizing that efficiency must be achieved without sacrificing signal processing accuracy. Finally, future trends point toward the use of low-power microcontrollers with integrated artificial intelligence capabilities, opening new possibilities for applications in IoT, energy monitoring, and intelligent control systems. This work provides a structured guide for researchers and developers interested in implementing efficient signal processing solutions in resource-constrained environments, based on experiences documented in the literature and real applications of algorithmic optimization on microcontrollers.