We demonstrate, for the first time, a spiking neural network running on neuromorphic hardware for a fully event-based flow cytometry pipeline with 98.45% testing accuracy. We open up new possibilities for online and on-chip learning in flow cytometry applications.
In this paper, we propose the first multibracket HDR pipeline combining a standard camera with an event camera. Our results show better overall robustness when using events, with improvements in PSNR by up to 5dB on synthetic data and up to 0.7dB on real-world data.
We further propose datasets captured by a high-speed camera and an RS-Event hybrid camera system for training and testing our network. Experimental results on both public and proposed datasets show a systematic performance improvement compared to state-of-the-art methods.We further propose datasets captured by a high-speed camera and an RS-Event hybrid camera system for training and testing our network.
The proposed method achieves up to 4 kbps in an indoor environment and lossless transmission over a distance of 100 meters, at a transmission rate of 500 bps, in full sunlight, demonstrating the potential of the technology in an outdoor environment.
Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging.