20 citations · 27 across the 23 of their papers we have counts for
7 papers · 1 filter
LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing
Uzair Khan, Luigi Capogrosso, Muhammad Aqeel +3
In modern high-throughput industrial production lines, product configurations and visual characteristics frequently change, making it impractical to collect and annotate data for e…
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
Uzair Khan, Luigi Capogrosso, Francesco Biondani +4
Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across diffe…
Event-Based Vision in Space: Applications, Trends, and Future Directions
Luigi Capogrosso, Pietro Bonazzi, Michele Magno
Earth Observation (EO) is undergoing a significant transformation driven by the deployment of novel sensing technologies. Traditional frame-based optical sensors often struggle wit…
Exploiting In-Sensor Computing for Energy-Efficient Earth Observation
Luigi Capogrosso, Pietro Bonazzi, Loris Hoxhaj +1
The rapid growth of the satellite industry has driven a significant increase in geospatial data acquisition, highlighting a critical bottleneck: the severe disparity between the vo…
TinyML Enhances CubeSat Mission Capabilities
Luigi Capogrosso, Michele Magno
Earth observation (EO) missions traditionally rely on transmitting raw or minimally processed imagery from satellites to ground stations for computationally intensive analysis. Thi…
TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection
Pietro Bonazzi, Rafael Sutter, Luigi Capogrosso +2
Anomaly detection plays a key role in industrial quality control, where defects must be identified despite the scarcity of labeled faulty samples. Recent self-supervised approaches…
Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review
Luigi Capogrosso, Pietro Bonazzi, Michele Magno
This review examines the rapidly evolving landscape of ultra-low-power edge processors, covering heterogeneous Systems-on-Chips (SoCs), neural accelerators, near-sensor and in-sens…