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pro vyhledávání: '"Bourdoux, A"'
In Frequency Modulated Continuous Waveform (FMCW) radar systems, the phase noise from the Phase-Locked Loop (PLL) can increase the noise floor in the Range-Doppler map. The adverse effects of phase noise on close targets can be mitigated if the trans
Externí odkaz:
http://arxiv.org/abs/2405.09680
Autor:
Safa, Ali, Verbelen, Tim, Keuninckx, Lars, Ocket, Ilja, Bourdoux, André, Catthoor, Francky, Gielen, Georges, Cauwenberghs, Gert
This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents. A generative model capturing the environment dynamics is learned by a network compos
Externí odkaz:
http://arxiv.org/abs/2306.05053
Autor:
Safa, Ali, Verbelen, Tim, Catal, Ozan, Van de Maele, Toon, Hartmann, Matthias, Dhoedt, Bart, Bourdoux, André
Frequency-modulated continuous-wave (FMCW) radar is a promising sensor technology for indoor drones as it provides range, angular as well as Doppler-velocity information about obstacles in the environment. Recently, deep learning approaches have been
Externí odkaz:
http://arxiv.org/abs/2301.02451
Autor:
Safa, Ali, Verbelen, Tim, Ocket, Ilja, Bourdoux, André, Sahli, Hichem, Catthoor, Francky, Gielen, Georges
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is processed by a bio-inspired Spiking Neural Network (SNN) with continual
Externí odkaz:
http://arxiv.org/abs/2210.04236
Autor:
Safa, Ali, Verbelen, Tim, Ocket, Ilja, Bourdoux, André, Sahli, Hichem, Catthoor, Francky, Gielen, Georges
Learning to safely navigate in unknown environments is an important task for autonomous drones used in surveillance and rescue operations. In recent years, a number of learning-based Simultaneous Localisation and Mapping (SLAM) systems relying on dee
Externí odkaz:
http://arxiv.org/abs/2208.12997
This paper demonstrates for the first time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) can continuously learn to detect walking people on the fly using retina-inspired, event-based
Externí odkaz:
http://arxiv.org/abs/2202.08023
We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs). In contrast to empirical parameter search used in most previous works, we provide novel theoretical grounds f
Externí odkaz:
http://arxiv.org/abs/2111.00791
Autor:
Safa, Ali, Verbelen, Tim, Ocket, Ilja, Bourdoux, André, Catthoor, Francky, Gielen, Georges G. E.
Drones are currently being explored for safety-critical applications where human agents are expected to evolve in their vicinity. In such applications, robust people avoidance must be provided by fusing a number of sensing modalities in order to avoi
Externí odkaz:
http://arxiv.org/abs/2109.13666
Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction. Compared to traditional energy- and area-hungry deep learning methods, SNNs are significantly
Externí odkaz:
http://arxiv.org/abs/2108.02669
Autor:
Safa, Ali, Verbelen, Tim, Keuninckx, Lars, Ocket, Ilja, Hartmann, Matthias, Bourdoux, André, Catthoor, Franky, Gielen, Georges
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neig
Externí odkaz:
http://arxiv.org/abs/2107.07250