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In this paper, an experimental imaging flow cytometer using an event-based CMOS camera is presented, with data processed by adaptive feedforward and recurrent spiking neural networks. PMMA particles flowing in a microfluidic channel are classified, and analysis of experimental data shows that spiking recurrent networks, including LSTM and GRU models, achieve high accuracy by leveraging temporal dependencies. Adaptation mechanisms in lightweight feedforward spiking networks further improve performance. This work provides a roadmap for neuromorphic-assisted biomedical applications, enhancing classification while maintaining low latency and sparsity.

In this paper, a novel probabilistic model is proposed that leverages the stochastic distribution of events along moving edges. A lightweight, patch-based algorithm is introduced that employs a linear combination of event spatial coordinates, making it highly suitable for specialized hardware. The approach scales linearly with dimensionality, making it compatible with emerging event-based 3D sensors such as Light-Field DVS (LF-DVS). Experimental results demonstrate the efficiency and scalability of the method, establishing a solid foundation for real-time, ultra-efficient 2D and 3D motion estimation in event-based sensing systems.

This paper presents a novel approach that combines a photonic neuromorphic spiking computing scheme with a bio-inspired event-based image sensor. Designed for real-time processing of sparse image data, the system uses a time-delayed spiking extreme learning machine implemented via a two-section laser. Tested on high-flow imaging cytometry data, it classifies artificial particles of varying sizes with 97.1% accuracy while reducing parameters by a factor of 6.25 compared to conventional neural networks. These results highlight the potential of fast, low-power event-based neuromorphic systems for biomedical analysis, environmental monitoring, and smart sensing.

In this paper, a new compact vision sensor consisting of two fisheye event cameras mounted back to back is presented, offering a full 360-degree view of the surrounding environment. The optical design, projection model, and practical calibration using incoming event streams of the novel stereo camera called SFERA are described. Its potential for real-time target tracking is evaluated using a Bayesian estimator adapted to the sphere’s geometry. Real-world experiments with a prototype including two synchronized Prophesee EVK4 cameras and a DJI Mavic Air 2 quadrotor demonstrate the system’s effectiveness for aerial surveillance.

In this paper, the Asynchronous Event Multi-Object Tracking (AEMOT) algorithm is presented for detecting and tracking multiple objects by processing individual raw events asynchronously. AEMOT detects salient event blob features by identifying regions of consistent optical flow using a novel Field of Active Flow Directions built from the Surface of Active Events. Detected features are tracked as candidate objects using the recently proposed Asynchronous Event Blob (AEB) tracker to construct small intensity patches of each candidate object.

The Florence RGB-Event Drone dataset (FRED) is a novel multimodal dataset specifically designed for drone detection, tracking, and trajectory forecasting, combining RGB video and event streams. FRED features more than 7 hours of densely annotated drone trajectories, using five different drone models and including challenging scenarios such as rain and adverse lighting conditions.