Particle Image Velocimetry (PIV) is a widely adopted non-invasive imaging technique that tracks the motion of tracer particles across image sequences to capture the velocity distribution of fluid flows. It is commonly employed to analyze complex flow structures and validate numerical simulations. This study explores the untapped potential of spike cameras—ultra-high-speed, high-dynamic-range vision sensors—in high-speed fluid velocimetry. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), tailored for high-resolution fluid motion estimation. To enhance the network’s performance, we design three novel modules specifically adapted to the characteristics of fluid dynamics and spike streams: the Detail-Preserving Hierarchical Transform (DPHT), the Graph Encoder (GE), and the Multi-scale Velocity Refinement (MSVR). Furthermore, we introduce a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which contains labeled samples from three representative fluid-dynamics scenarios: steady turbulence, high-speed flow, and high-dynamic-range conditions. Our proposed method outperforms existing baselines across all these scenarios, demonstrating its effectiveness.

Spike cameras, a novel neuromorphic visual sensor, can capture full-time spatial information through spike stream, offering ultra-high temporal resolution and an extensive dy-namic range. Autofocus control (AC) plays a pivotal role in a camera to efficiently capture information in challenging real-world scenarios. Nevertheless, due to disparities in data modality and information characteristics compared to frame stream and event stream, the current lack of effi-cient AC methods has made it challenging for spike cam-eras to adapt to intricate real-world conditions. To ad-dress this challenge, we introduce a spike-based autofo-cus framework that includes a spike-specific focus measure called spike dispersion (SD), which effectively mitigates the influence of variations in scene light intensity during the focusing process by leveraging the spike camera's ability to record full-time spatial light intensity. Additionally, the framework integrates a fast search strategy called spike-based goldenfast search (SGFS), allowing rapidfocal positioning without the need for a complete focus range traver-sal. To validate the performance of our method, we have collected a spike-based autofocus dataset (SAD) containing synthetic data and real-world data under varying scene brightness and motion scenarios. Experimental results on these datasets demonstrate that our method offers state-of-the-art accuracy and efficiency. Furthermore, experiments with data captured under varying scene brightness levels illustrate the robustness of our method to changes in light intensity during the focusing process.

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. Cdoe and datasets are avaliable at: https://github.com/Onetism/RTDE

Nuclei detection is one of the most important issues in the field of histopathology image analysis. Recent studies exploit convolutional neural networks to either locate the centroids of nuclei (counting-based approaches) or isolate them with sophisticated boundaries (segmentation-based algorithms). However, the training of these two methods demands considerable and laborious pixel-wise annotations labeled by pathology experts. To reduce the annotation cost, we propose a novel local integral regression network (LIRNet) that allows both strongly and weakly supervised learning (SSL/WSL) frameworks on nuclei detection. In addition, the LIRNet is able to predict a refined density map of nuclei, in which locating each nucleus is barely affected by the post-processing algorithms. Quantitative experimental results demonstrate that the SSL version of the LIRNet achieves state-of-the-art performance compared with counterparts. Besides, the WSL version possesses a competitive detection performance and an effortless data annotation that dramatically reduces annotation cost by 74.3%.