Nicholas J. Cotton

Papers

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About

Nicholas J. Cotton is a researcher specializing in embedded systems, sensor linearization, and the practical application of neural networks on low-cost microcontrollers. His major contribution lies in developing efficient, real-time methods for compensating nonlinearities in sensors—a critical challenge in measurement and control systems. Cotton’s most-cited work, “Compensation of Nonlinearities Using Neural Networks Implemented on Inexpensive Microcontrollers” (2010, 58 citations), introduces a neuron-by-neuron processing approach implemented in assembly language, enabling complex neural network architectures to run on 8-bit microcontrollers with minimal code and maximum speed. A related paper (2010, 16 citations) further demonstrates how powerful neural networks can be embedded on ultra-low-cost hardware, making advanced compensation accessible for widespread industrial and consumer applications. Cotton’s work bridges the gap between sophisticated machine learning techniques and resource-constrained embedded platforms, offering practical, deployable solutions. His achievements highlight a rare combination of theoretical understanding and hands-on optimization, making his research highly cited by engineers and academics working on sensor calibration, embedded AI, and cost-effective automation.