This CWTe colloquium will host two speakers presenting photonic integrated receivers for optical wireless communication and neuromorphic radar signal processing.

Here are the presenters and presentations:

  •  Mikołaj Wolny (TU/e, Electro-Optical Communication) — Photonic Integrated Receivers for Optical Wireless Communication
  •  S. Chiavazza (TU/e, Electronic Systems) — Sparse Spectral Estimation of Streaming Radar Signals with Adaptive-Frequency Resonate-and-Fire Neurons

Abstracts & Bios

Mikołaj Wolny(Postdoc, Electro-Optical Communication group, TU/e)
Title: Photonic Integrated Receivers for Optical Wireless Communication

Abstract: Optical wireless communication (OWC) offers many benefits over existing wireless technologies that rely on radio frequencies, making it a promising candidate for future wireless networks. When using narrow optical beams, OWC brings a number of advantages, including a very wide unlicensed spectrum, huge bandwidth, low latency, high security, high privacy, and high energy efficiency. Photonic integrated circuits (PIC) have great potential in OWC because they offer the possibility to separate light collection and light detection. In this way, both can be optimized separately.

Bio: Mikołaj Wolny is a postdoc in the Electro-Optical Communication group at the TU/e. His research primarily focuses on the photonic chips for free space optical telecommunications.

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Stefano Chiavazza (PhD candidate, Electronic Systems group, TU/e)

Title: Sparse Spectral Estimation of Streaming Radar Signals with Adaptive-Frequency Resonate-and-Fire Neurons

Abstract: Frequency Modulated Continuous Wave (FMCW) radar systems traditionally rely on Fourier-based methods, such as the Fast Fourier Transform (FFT), to estimate target range and velocity. While computationally efficient, these approaches require storing and processing large blocks of data, which can become a bottleneck in memory-constrained or low-latency applications. In this work, we propose a neuromorphic-inspired signal processing method based on adaptive resonate-and-fire (ARF) neurons formulated as a discrete-time dynamical system. Each neuron dynamically adjusts its internal frequency to match dominant frequency components of the input radar signal, enabling direct estimation of target ranges and velocities without computing the full frequency spectrum. The proposed model operates in a sample-by-sample fashion, resulting in memory requirements that scale with the number of tracked targets rather than the signal length. A feedback mechanism is also introduced to enable multiple neurons to lock on distinct frequency components in multi-target cases.

Results on simulated and experimental data demonstrate that the method can successfully track multiple targets. Compared to conventional FFT-based approaches, the proposed method offers reduced memory usage, making it suitable for resource-constrained and edge-based radar applications.

Bio: S. Chiavazza is a Ph.D. candidate in the Electronic Systems (ES) group at Eindhoven University of Technology (TU/e). He holds a Master’s degree in Computer Science from TU Berlin, specializing in neuromorphic computing. His doctoral research focuses on developing energy-efficient, neuromorphic radar signal processing algorithms.

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