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

MOTIVATION: Light-field microscopy (LFM) is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU-accelerated ImageJ plugin for 4.4× faster and more accurate deconvolution of LFM data. We further propose an image quality metric for the deconvolution process, aiding in automatically determining the optimal number of iterations with higher reconstruction accuracy and fewer artifacts. RESULTS: Our proposed method outperforms state-of-the-art light-field deconvolution methods in reconstruction time and optimal iteration numbers prediction capability. It shows better universality of different light-field point spread function (PSF) parameters than the deep learning method. The fast, accurate and general reconstruction performance for different PSF parameters suggests its potential for mass 3D reconstruction of LFM data. AVAILABILITY AND IMPLEMENTATION: The codes, the documentation and example data are available on an open source at: https://github.com/Onetism/AutoDeconJ.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Traffic light control is vital for the efficiency of urban transportation. Recently, the increasing of vehicles has brought great challenges to the traffic light control system. However, traditional traffic light controlling methods are inefficient due to the sophistications of traffic dynamics. In this paper, we propose a Graph Attention mechanism based Multi-Agent Reinforcement Learning method (GA-MARL) by extending the Actor-Critic framework to improve the efficiency of cooperation in traffic signal control. The proposed algorithm is based on hard-attention and soft-attention mechanism, which can help agent filter information effectively and calculate the importance of other agents. In addition, we complete our algorithm by adopting the framework of Centralized Training with Decentralized Execution (CTDE) to overcome the challenge of non-stationary non-Markovian environments. Simulation results prove that our proposed method outperforms the representative methods in the literature.