anomaly detection

Data-driven Anomaly Detection in Industrial Processes

Fouzi Harrou, Senior Research Scientist, Statistics

Feb 12, 12:00 - 13:00

B9 L2 R2325

anomaly detection multivariate statistics artificial intelligence AI

This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.

Circuits, Systems, and Algorithms for Low-power Signal Processing in IoT Nodes Implementation

Wed, Jul 17 2024

IoT compressed sensing anomaly detection

In the IoT paradigm the low-power signal processing, either analog of digital, is a key-enabling technology. Many unconventional processing techniques, either based on a statistical analysis or not, have been introduced in the effort of being able to complete a task with the lowest possible energy. A first example is given by the Compressed Sensing, an acquisition technique which relies on the sparsity of the underlying signals, to enable sampling below the classical Nyquist rate. The advantages with respect to the above “classical” technique is to transfer complexity from the acquisition