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The sensor enables real-time monitoring of finger joint bending (55% resistance variation at 90° bending) and wrist movements (64% resistance variation) through a 9 × 9 orthogonal electrode grid and ...
Built a deep learning model trained on MNIST dataset achieving 98% accuracy. Utilized CNNs for feature extraction, implemented image preprocessing and normalization. Evaluated model performance using ...
This project focuses on building a robust handwritten digit recognition system by combining the predictive strengths of Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random ...
General performance across different handwriting styles is ensured by using machine learning algorithms, especially neural networks trained on big datasets. For improved performance and accuracy in ...
Handwritten digit identification is an area that requires substantial attention. It is necessary to take into account the differences in handwritten digits’ size, thickness, position, and orientation ...
Citation: Vallejo-Mancero B, Madrenas J and Zapata M (2024) Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware.
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