This course is an introduction to analysis, modeling and interpretation of biometric data for biometric person recognition, forensic biometrics, cybersecurity and behavioral biometrics in man-machine communication.


  1. Ancient cryptography: Vigenère, Enigma, Vernam cipher, Shannon theory
  2. Diffie-Hellman cryptography: algebra, Diffie-Hellman, ElGamal
  3. RSA cryptography: number theory, RSA, factoring
  4. Elliptic curve cryptography: elliptic curves over a finite field, ECDH, ECIES
  5. Symmetric encryption: block ciphers, stream ciphers, exhaustive search
  6. Integrity and authentication: hashing, MAC, birthday paradox
  7. Applications to symmetric cryptography: mobile telephony, Bluetooth, WiFi
  8. Public-key cryptography: cryptosystem, digital signature
  9. Trust establishment: secure communication, trust setups
  10. Case studies: Bluetooth, TLS, SSH, PGP, biometric passport

Establishing the theoretical basis of linear and nonlinear dynamical systems in both continuous and discrete time. Learning how to anticipate the properties of linear as well as strongly and weakly nonlinear systems.

The goal of this class is to present signal processing tools from an intuitive geometric point of view which is at the heart of all modern signal processing techniques from Fourier transforms and sampling theorems to time-frequency analysis and wavelets. The course is designed to provide the mathematical depth and rigor needed for the study of advanced topics in signal processing and also features introductions to current applications where such tools are crucial. In particular, several applications will be studied, including image compression with linear and non-linear approximation, audio coding, mobile sensing, and prediction of the stock market. 

During this course, students will:
- Master the right tools to tackle advanced signal and data processing problems
- Have an intuitive understanding of signal processing through a geometrical approach
- Get to know the applications that are of interest today
- Learn about topics that are at the forefront of signal processing research

In this course you will learn and understand the main ideas that underlie and the way networks are built and run. You will be able to apply the concepts to the smart grid. In the labs you will exercise practical configurations. You will be able to

  • Test and clarify your understanding of the networking concepts by connecting computers to form a LAN, interconnected by routers and interconnected autonomous routing domains. 
  • test the effect on performance of your TCP implementation and of traffic control settings
  • develop and test various communicating programs using sockets
  • be familiar with IPv6 as well as IPv4 and the interworking between them
  • run a virtual networking environment in your computer where and deploy real networks in an emulated environment

This is a master level course for master and PhD students