Motivated by the energy-efficient computation of the brain, an energy-efficient wireless
sensor network infrastructure is built for solving the pattern recognition task. In this work …

Artificial neural networks showed astonishing results in the last decades. However, they tend
to consume large amounts of energy which is problematic on edge devices such as …

Event-based neural networks are currently being explored as efficient solutions for
performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural …

This paper proposes a biologically plausible network architecture with spiking neurons for
sequence recognition. This architecture is a unified and consistent system with functional …

Spiking neural networks (SNN) are a promising approach for low-power edge AI (artificial
intelligence), especially when run on dedicated neuromorphic hardware. In this work we set …

One of the main challenges in developing embedded radar-based gesture recognition
systems is the requirement of energy efficiency. To facilitate this, we present an embedded …

Gesture recognition using luminance invariant radar sensors is vital due to its extensive use
in human-machine interfaces. However, the necessity for computationally expensive radar …

A multi-sensor network usually produces a large scale of data, some of which represent
specific meaningful events. For event-driven multi-sensor networks, event classification is …

In this paper, we propose a novel unsupervised learning approach for spatio-temporal
pattern classification. We use a spike timing neural network with axonal conductance delays …

The biological brain is capable of processing temporal information at an incredible
efficiency. Even with modern computing resources, traditional learning-based approaches …