After several decades of continuously optimizing computing systems, the Moore's law is reaching its end. However, there is an increasing demand for fast and efficient processing systems that can handle large streams of data while decreasing system footprints. Neuromorphic computing answers this need by creating decentralized architectures that communicate with binary events over time. Despite its rapid growth in the last few years, novel algorithms are needed that can leverage the potential of this emerging computing paradigm and can stimulate the design of advanced neuromorphic chips. In this work, we propose a time-based spiking neural network that is mathematically equivalent to the Fourier transform. We implemented the network in the neuromorphic chip Loihi and conducted experiments on five different real scenarios with an automotive frequency modulated continuous wave radar. Experimental results validate the algorithm, and we hope they prompt the design of ad hoc neuromorphic chips that can improve the efficiency of state-of-the-art digital signal processors and encourage research on neuromorphic computing for signal processing.

Education in industrial electronics-related subjects is very challenging because of the many different technological topics and application domains to be addressed. Mobile robotics is a very interesting area in this context, because it requires a wide coverage of many diverse topics and, at the same time, it is typically very appealing for engineering students. Therefore, mobile robot systems are very suitable platforms for the development of Project Based Learning solutions. This paper presents UviSpace, a modular, open-source intelligent space with Unmanned Ground Vehicles, which is being successfully applied for hands-on training of students in real-world problem solving in industrial electronics-related subjects. The hardware and software modules of the system are described, and student projects are discussed to demonstrate the usefulness and versatility of UviSpace as educational platform.