Predictive Maintenance (PdM) is increasingly adopted in safety-critical aviation systems, including landing gear systems, where unexpected failures can compromise mission completion and increase operational costs. Recent studies have tackled this issue by developing separate solutions for Anomaly Detection, Failure Dia...
Francesco Biondani, Luigi Capogrosso, Mattia Bortolaso et al.· IEEE International Conferenc...· 0 citations
Industrial anomaly detection is essential for automated quality inspection, yet collecting representative defect samples for every product and failure mode remains costly and often impractical. In this article, we propose TIPSter, a Zero-Shot Anomaly Detection (ZSAD) framework that leverages language-steered anomaly-in...
Uzair Khan, Luigi Capogrosso, Michele Magno et al.· Forum on Specification and D...· 0 citations
Ultra-low-bit scalar quantization offers a regular arithmetic structure for efficient deployment of large language models, but preserving predictive behavior at extreme precision remains challenging: with only a few reconstruction levels, accurate weight representation must translate into consistent predictions from a...
Yu-Jie Chen, Wei-Yan Zheng, Yi-Wu Yao et al.· 0 citations
The paper presents a real-time landmark-based pipeline for two-hand static gesture recognition targeting smart glasses applications. The proposed system extends a MediaPipe-inspired hand landmark extraction module with a lightweight multilayer perceptron classifier operating on a fixed two-hand landmark representation,...
Rosanna Manzo, Pietro Bonazzi, Francesco Caputo et al.· Coins· 0 citations
Autonomous racing requires accurate trajectory tracking near the handling limits of a vehicle while maintaining low computational latency. Geometric controllers are computationally efficient, but the Ackermann steering geometry becomes invalid under limit-handling conditions. Model- and Acceleration-based Pursuit (MAP)...
Wei-Zhang Huang, Cheng Hu, Edoardo Ghignone et al.· 0 citations
The relentless proliferation of the Internet of Things (IoT), wearable bioelectronics, and cyber-physical infrastructure has rendered the conventional electrochemical battery the single most prohibitive bottleneck to long-term, maintenance-free autonomous microsystems. Energy harvesting, i.e. the conversion of ambient...
Lyuye Lin, Zhi-Fu Feng, E. Miele et al.· 0 citations
This paper proposes FPGN, an end-to-end physically-aware framework that closes the gap between LUT-native learning and latency-optimized FPGA implementation, and addresses challenges through a hardware-aligned differentiable formulation for training FPGA-native LUT neurons.
Jia-Wei Liang, Haotong Qin, Lin-Feng Du et al.· arXiv.org· 1 citation
Large-scale video diffusion models (V-DMs) have achieved remarkable text-to-video generation quality, yet their massive computational complexity makes deployment costly. Post-Training Quantization (PTQ) offers an appealing route to accelerate inference without retraining, but existing diffusion PTQ methods remain fragi...
Wei-Lun Feng, Chuan-Guang Yang, Haotong Qin et al.· IEEE Transactions on Pattern...· 4 citations
This work presents an energy-proportional, context-aware vision IoT node that addresses this challenge through a heterogeneous multimodal dual-camera architecture, enabling always-on visual monitoring in a place-and-forget scenario through autonomous edge intelligence.
Julian Moosmann, P. Mayer, Luca Benini et al.· 0 citations
Experiments on captioning, question answering, and unseen-action generalization show that mmMind consistently outperforms existing radar-language baselines, while ablations confirm the importance of pose-guided pretraining.
Duo Zhang, Zhe-Hui Yin, Zhi-Yun Yao et al.· 1 citation
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