arXiv subjects

Daniel Yunge

Publications and source records attributed to Daniel Yunge.

3 recordsLinked to original sources

arXiv2026

Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs

This work presents the development of hybrid models that integrate spiking neural networks (SNNs) with components of convolutional neural networks (CNNs) to learn from simulated event-based camera data (Dynamic Vision Sensor, DVS) generated from conventional smartphone videos. Aimed primarily at human fall detection, the approach leverages the energy efficiency and spatio-temporal processing capabilities of SNNs by converting video frames into event-based data. The proposed models are evaluated through simulations on multiple datasets, comparing their performance to that of traditional machine learning models. Results demonstrate significant gains in efficiency without sacrificing accuracy, underscoring the potential of combining SNNs and DVS technology for complex tasks in real-world environments.

cs.LG↗

arXiv2026

FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection

The escalating congestion in orbital space demands advanced monitoring solutions. This work presents a comprehensive open-source framework for neuromorphic resident space object (RSO) detection, adapting the foundational grid clustering algorithm for FPGA acceleration. The system integrates a single event-based camera (EBC) with a custom, distributed processing architecture, where rapid spatial quantization is executed in programmable logic (FPGA) and cluster formation is managed by a software client. We validate this architecture through systematic sampling of night-sky observations from the EVAS dataset, demonstrating 97% detection accuracy for RSOs. The implementation, which serves as a foundational toolkit for event-based FPGA processing, achieves efficient throughput with a total power consumption of 8.5 W and deterministic processing latencies below 62 ms. The architecture's energy efficiency and high-precision detection position it as a viable solution for distributed space surveillance networks.

cs.AR↗

arXiv2014

Precise Energy Modeling for the Bluetooth Low Energy Protocol

Bluetooth Low Energy (BLE) is a wireless protocol well suited for ultra-low-power sensors running on small batteries. BLE is described as a new protocol in the official Bluetooth 4.0 specification. To design energy-efficient devices, the protocol provides a number of parameters that need to be optimized within an energy, latency and throughput design space. To minimize power consumption, the protocol parameters have to be optimized for a given application. Therefore, an energy-model that can predict the energy consumption of a BLE-based wireless device for different parameter value settings, is needed. As BLE differs from the original Bluetooth significantly, models for Bluetooth cannot be easily applied to the BLE protocol. Since the last one year, there have been a couple of proposals on energy models for BLE. However, none of them can model all the operating modes of the protocol. This paper presents a precise energy model of the BLE protocol, that allows the computation of a device's power consumption in all possible operating modes. To the best of our knowledge, our proposed model is not only one of the most accurate ones known so far (because it accounts for all protocol parameters), but it is also the only one that models all the operating modes of BLE. Furthermore, we present a sensitivity analysis of the different parameters on the energy consumption and evaluate the accuracy of the model using both discrete event simulation and actual measurements. Based on this model, guidelines for system designers are presented, that help choosing the right parameters for optimizing the energy consumption for a given application.

cs.NI↗