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A neuromorphic camera for tracking passive and active matter with lower data throughput

2025-01-13 · Gabriel Britto Monteiro, Megan Lim, Tiffany Cheow Yuen Tan, Avinash Upadhya, Zhuo Liang, Benjamin Agnew, Tomonori Hu, Benjamin J. Eggleton, Christopher Perrella, Kylie Dunning, Kishan Dholakia

We demonstrate the merits of using a neuromorphic, or event-based camera (EBC), for tracking of both passive and active matter. For passive matter, we tracked the Brownian motion of different micro-particles and estimated their diffusion coefficient. For active matter, we explored the case of tracking murine spermatozoa and extracted motility parameters from the motion of cells. This has applications in enhancing outcomes for clinical fertility treatments. Using the EBC, we obtain results equivalent to those from an sCMOS camera, yet achieve a reduction in file size of up to two orders of magnitude. This is important in the modern computer era, as it reduces data throughput, and is well-aligned with edge-computing applications. We believe the EBC is an excellent choice, particularly for long-term studies of active matter.

📄 PDF Abstract BibTeX arXiv:2501.07230

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Edge-computing

Methods 이 논문이 사용한 방법론

Diffusion Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound…
EBC Traditional methods are based on block-wise regression. This framework, Enhanced Blockwise Classification (EBC), however, is based on the idea that aims to classify the count…

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