Statistical signal and data processing (SSigPro)

The research group applies tools for the characterization, generation and processing of signals and data that focus and exploit the statistical features of the information to manage.

The approach is very general and considers tools from classical statistical analysis to statistical theory of chaotic dynamical systems, signal modulation techniques, discrete an continuous optimization, data-driven methods exploiting machine learning. This allows to tackle a wide range of applications, even not strictly in the IT field, in those disciplines in which the gathering and management of information brings benefit.

Many of the solutions developed are implemented as physical subsystems either in digital platforms or in application specific integrated circuits designed ad hoc.

As examples, the research group has addressed and/or is currently addressing these applications

  • Optimization of CDMA codes for asynchronous access

  • Reduction and management of electro-magnetic interference due to synchronization signals in electronic systems

  • Re-use of A/D building blocks as generators of high-performance HW generator of true random bits

  • Optimization of pulse-width modulations to enhance spectral purity

  • Discrete optimization methods in the design of delta-sigma converters

  • Powerline communication by adaptation of DC-DC converters

  • Nonconventional design flow for DC-DC converters

  • Compressed sensing of signal from physical and biological sensors

  • Diagnosis and predictive maintenance for railway application, structural health monitoring, automotive and satellite

  • Simulation and optimization of TX/RX chains by neural surrogates

SSigPro is active in several collaborations (Accademia and Industry):

  • Politecnico di Torino

  • Politecnico di Milano

  • Gruppo Ferrovie dello Stato Italiano, RFI

  • Thales Alenia Italia Space, TASI

  • HPE COXA

The group is currently investigating the impact of Quantum Machine Learning on the variational version of the neural architectures employed in industrial applications and the implementation of post-quantum cryptographic primitives.

ERC Fields

  • PE1_14 Statistics
  • PE1_21 Application of mathematic to industry and society
  • PE6_11 Machine learning, statistical data processing and applications using signalprocessing
  • PE7_7 Signal Processing

Scientific Coordinator: Prof. Riccardo Rovatti

Faculty

Sergio Callegari

Associate Professor

Mauro Mangia

Associate Professor

Alex Marchioni

Junior assistant professor (fixed-term)

Riccardo Rovatti

Full Professor

PhD Students and Research Fellows

Andriy Enttsel

PhD Student

Teaching tutor