Digital signal processing in microcontrollers: An experimental approach to optimization
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Abstract
Introduction: Digital signal processing in microcontrollers is key in embedded systems, but faces limitations in terms of memory, computing power, and energy. Algorithmic optimization can improve performance without compromising accuracy.
Objective: To evaluate optimization strategies to increase computational efficiency in microcontrollers. To reduce resource consumption while maintaining accuracy in signal processing.
Method: Floating-point and fixed-point implementations were compared on an-Arduino Uno, with measurements of time, memory, and accuracy. Fifty iterations were performed per variant, and the results were transmitted wirelessly via an HC-05.
Results: The optimized version reduced execution time by more than 40% and decreased SRAM and Flash memory usage. Implementing the FFT reduces computational complexity while maintaining adequate accuracy.
Conclusions: Microcontroller optimization improves efficiency, reduces energy consumption, and preserves spectral accuracy. This validates its application in IoT, edge computing, and low-cost embedded systems.
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