arXiv subjects

Daniel Rossi

Publications and source records attributed to Daniel Rossi.

3 recordsLinked to original sources

arXiv2026

BoltNet: An Ultra-Lightweight Convolutional Network for On-Device Plant Species Identification

Automated plant species identification from citizen-science imagery is an established, demanding fine-grained recognition problem: large taxonomic label spaces, visually similar species, and long-tailed observations require real model capacity, while field use constrains memory, latency, and power. Model size is only part of the deployment cost: intermediate activations held in memory during inference and platformdependent execution behavior matter too, so compact recognition must be assessed on target hardware rather than through complexity metrics alone. We present BoltNet, an ultra-lightweight fully convolutional architecture combining a Spatial Redistribution Bottleneck and Logit PreSampling to improve the tradeoff between predictive performance and model size in high-cardinality classification, and report the AccuracyCompression Tradeoff as a complementary diagnostic. On Pl@ntNet300K, BoltNet reaches 0.682 F1-score with 341K parameters (1.37 MB), the highest F1-score among evaluated models below 2 MB and close to substantially larger convolutional backbones. Model-only measurements on a Raspberry Pi 5, Jetson Orin Nano, and Hailo-8 characterize execution across CPU, GPU, and NPU platforms, where BoltNet is the most consistently efficient model, with the best FPS/W on the GPU and NPU and second-best on the CPU. Results on AIDERv2 and CLRS provide secondary evidence of transfer across environmental image-classification tasks. Code available at: https://codeberg.org/danielrossi/BoltNet

cs.CV↗

arXiv2025

TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios

Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses. In this work, we introduce TakuNet, a novel light-weight architecture which employs techniques such as depth-wise convolutions and an early downsampling stem to reduce computational complexity while maintaining high accuracy. It leverages dense connections for fast convergence during training and uses 16-bit floating-point precision for optimization on embedded hardware accelerators. Experimental evaluation on two public datasets shows that TakuNet achieves near-state-of-the-art accuracy in classifying aerial images of emergency situations, despite its minimal parameter count. Real-world tests on embedded devices, namely Jetson Orin Nano and Raspberry Pi, confirm TakuNet's efficiency, achieving more than 650 fps on the 15W Jetson board, making it suitable for real-time AI processing on resource-constrained platforms and advancing the applicability of drones in emergency scenarios. The code and implementation details are publicly released.

cs.CV↗

arXiv2020

Intense Dark Exciton Emission from Strongly Quantum Confined CsPbBr$_3$ Nanocrystals

Dark ground state exciton in semiconductor nanocrystals has been a subject of much interest due to its long lifetime attractive for applications requiring long-lived electronic or spin states. Significant effort has been made recently to explore and access the dark exciton level in metal halide perovskite nanocrystals, which are emerging as a superior source of photons and charges compared to other existing semiconductor nanocrystals. However, the direct observation of long-lived photoluminescence from dark exciton has remained elusive in metal halide perovskite nanocrystals. Here, we report the observation of the intense emission from dark ground state exciton with 1-10 us lifetime in strongly quantum confined CsPbBr3 nanocrystals, which contrasts the behavior of weakly confined system explored so far. The study in CsPbBr3 nanocrystals with varying degree of confinement has revealed the crucial role of quantum confinement in enhancing the bright-dark exciton level splitting which is important for accessing the dark exciton. Our work demonstrates the future potential of strongly quantum-confined perovskite nanocrystals as a new platform to utilize dark excitons.

physics.app-ph↗