6 papers
Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Training
Aurora Pia Ghiardelli, Guangzhi Tang, Tao Sun
We propose a reliable and energy-efficient framework for 3D brain tumor segmentation using spiking neural networks (SNNs). A multi-view ensemble of sagittal, coronal, and axial SNN…
Full Integer Arithmetic Online Training for Spiking Neural Networks
Ismael Gomez, Guangzhi Tang
Spiking Neural Networks (SNNs) are promising for neuromorphic computing due to their biological plausibility and energy efficiency. However, training methods like Backpropagation T…
A Segmented Robot Grasping Perception Neural Network for Edge AI
Casper Bröcheler, Thomas Vroom, Derrick Timmermans +4
Robotic grasping, the ability of robots to reliably secure and manipulate objects of varying shapes, sizes and orientations, is a complex task that requires precise perception and…
Predicting the Lifespan of Industrial Printheads with Survival Analysis
Dan Parii, Evelyne Janssen, Guangzhi Tang +2
Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in bot…
SteelBlastQC: Shot-blasted Steel Surface Dataset with Interpretable Detection of Surface Defects
Irina Ruzavina, Lisa Sophie Theis, Jesse Lemeer +6
Automating the quality control of shot-blasted steel surfaces is crucial for improving manufacturing efficiency and consistency. This study presents a dataset of 1654 labeled RGB i…
Adaptively Pruned Spiking Neural Networks for Energy-Efficient Intracortical Neural Decoding
Francesca Rivelli, Martin Popov, Charalampos S. Kouzinopoulos +1
Intracortical brain-machine interfaces demand low-latency, energy-efficient solutions for neural decoding. Spiking Neural Networks (SNNs) deployed on neuromorphic hardware have dem…