Real-time parameter evaluation of high-speed microfluidic droplets using continuous spike streams

Bo Xiong, Changqing Su, Zihan Lin, Yanqin Chen, You Zhou, Zhen Cheng, Zhaofei Yu, Tiejun Huang

Abstract: Droplet-based microfluidic devices, with their high throughput and low power consumption, have found wide-ranging applications in the life sciences, such as drug discovery and cancer detection. However, the lack of real-time methods for accurately estimating droplet generation parameters has resulted in droplet microfluidic systems remaining largely offline-controlled, making it challenging to achieve efficient feedback in droplet generation. To meet the real-time requirements, it's imperative to minimize the data throughput of the collection system while employing parameter estimation algorithms that are both resource-efficient and highly effective. Spike camera, as an innovative form of neuromorphic camera, facilitates high temporal resolution scene capture with comparatively low data throughput. In this paper, we propose a real-time evaluation method for high-speed droplet parameters based on spike-based microfluidic flow-focusing, named RTDE, that integrates spike camera into the droplet collection system to efficiently capture information using spike stream. To process the spike stream effectively, we develop a spike-based estimation algorithm for real-time droplet generation parameters. To validate the performance of our method, we collected spike-based droplet datasets (SDD), comprising synthetic and real data with varying flow velocities, frequencies, and droplet sizes. Experiments result on these datasets consistently demonstrate that our method achieves parameter estimations that closely match the ground truth values, showcasing high precision. Furthermore, comparative experiments with image-based parameter estimation methods highlight the superior time efficiency of our method, enabling real-time calculation of parameter estimations.

Primary Subject Area: [Generation] Multimedia Foundation Models

Secondary Subject Area: [Generation] Multimedia Foundation Models

Relevance To Conference: Video streams serve as primary conduits for multimedia information, encapsulating abundant spatio-temporal data and boasting extensive applications in target monitoring and analysis. However, due to limitations in imaging speed and transmission bandwidth of acquisition systems, the temporal resolution of video streams is often constrained, rendering them ineffective for capturing high-speed scenes. Spike cameras, emerging as a novel type of neural-inspired sensor, achieve continuous scene recording utilizing spike streams, boasting remarkably high temporal resolution. This work proposes, for the first time, a droplet parameter estimation method based on spike streams, which significantly reduces system bandwidth requirements compared to traditional video-based estimation methods, enabling real-time feedback in droplet microfluidics. Additionally, while maintaining commendable performance, it substantially diminishes data transmission and storage costs. Representing the pioneering exploration of spike streams replacing video streams, we anticipate that this research will offer a fresh perspective on information carriers within the multimedia community.

Submission Number: 3650