Fernando Manuel Quintana

• Doctor of Philosophy
• PostDoc Position at University of Groningen

Developing dedicated neuromorphic computing platforms optimized for embedded or edge-computing applications requires time-consuming design, fabrication, and deployment of full-custom neuromorphic processors.bTo ensure that initial prototyping efforts, exploring the properties of different network architectures and parameter settings, lead to realis...

Neuromorphic perception with event-based sensors, asynchronous hardware, and spiking neurons shows promise for real-time, energy-efficient inference in embedded systems. Brain-inspired computing aims to enable adaptation to changes at the edge with online learning. However, the parallel and distributed architectures of neuromorphic hardware based o...

Dielectric Elastomer Actuators (DEAs) can be seen as flexible capacitors, where an incompressible elastomer is sandwiched between two compliant electrodes. From the elastomer non-linear and time-dependent phenomena arise during the activation of the actuator. This results in the need for appropriate control methods, especially when they are used in...

Spiking Neuron Networks (SNNs), also known as the third generation of neural networks, are inspired from natural computing in the brain and recent advances in neuroscience. SNNs can overcome the computational power of neural networks made of threshold or sigmoidal units. Recent advances on event-based devices along with their great power, consideri...

Most of widely used datasets are not suitable for Spiking Neural Networks (SNNs) due to the need to encode the static data into spike trains and then put them into the network. In addition, the majority of these datasets have been generated to classify objects and can not be used to solve object tracking problems. Therefore, we propose a new neurom...

The neuromorphic engineering field aims to mimic complex biological structures by means of mathematical models, implementing them in either analog or digital circuits. These models include the dynamic characteristics of biological neurons and synapses, which are interconnected creating spiking neural networks. These are first simulated using softwa...

Neuromorphic perception with event-based sensors, asynchronous hardware and spiking neurons is showing promising results for real-time and energy-efficient inference in embedded systems. The next promise of brain-inspired computing is to enable adaptation to changes at the edge with online learning. However, the parallel and distributed architectur...

Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) is a learning method for Spiking Neural Network (SNN) that makes use of an external learning signal to modulate the synaptic plasticity produced by Spike-Timing-Dependent Plasticity (STDP). Combining the advantages of reinforcement learning and the biological plausibility of STDP, online l...

Computer vision is gaining more and more importance in the world of industrial robotics, since it is necessary to carry outincreasingly precise and autonomous tasks, which is why a more exact positioning of the robot is needed. This requires the supportof a vision system that is the one that gives the robot precision in its pose, calibrating said s...

Fillet welding is one of the most widespread types of welding in the industry, which is still carried out manually or automated by contact. This paper aims to describe an online programming system for noncontact fillet welding robots with U and L shaped structures, which responds to the needs of the Fourth Industrial Revolution. In this paper, the...

Purpose Fillet welding is one of the most widespread types of welding in the industry, which is still carried out manually or automated by contact. This paper aims to describe an online programming system for noncontact fillet welding robots with “U”- and “L”-shaped structures, which responds to the needs of the Fourth Industrial Revolution. Desig...