Sapienza - University of  Rome

DIET -  Department of Information Engineering, Electronics and Telecommunications  


Course Info: 

Adaptive Algorithms and Parallel Computing (Algoritmi Adattativi e Calcolo Parallelo)

Prof. Aurelio Uncini  (info: aurel AT ieee DOT org)

Laurea Magistrale: Ing. Comunicazioni, Ing. Informatica, Ing. Elettronica


Possible prosecution for Thesis, (also in companies, or in collaboration with other research centers at foreign universities).
For information contact the Teacher (or Dr. Scarpiniti) at his office.


Procedures for the final examination:
The exam consists of an oral pre-test on the whole program and a dissertation (home-work project) that relates to a specific part of the program.




Course beginning: February 24, 2015
Duration 13 weeks.


Lesson timetable T.B.C.

Wednesday 10:15 - 13:30
Thursday  8:30 - 11:45

Room  25



International labs link



The usability of adaptive algorithm methods to the solution of real problems is extensive and represents a paradigm for many strategic applications. Example can be found in multimodal and multimedia communications, the biological and biomedical areas, economic model, environmental sciences, acoustics, telecommunications, remote sensing, monitoring, and, in general, modeling and prediction of complex physical phenomena. Adaptive processing methods are also used in economic and financial sciences, in engineering and social sciences,  neuroscience, and in many other areas of high strategic interest.

Adaptive signal processing is also a very active field of study and research, that, for a thorough understanding, requires advanced interdisciplinary knowledge.

Objectives of the Course

The aim of this course is to provide students advanced theoretical and practical tools for the study and determination of circuit structures, and robust adaptive algorithms, in different application scenarios. In particular, in addition to presenting the fundamental theoretical base concepts, the most important adaptive algorithms are introduced, while also providing tools to evaluate the algorithms’ performance.

The student, in addition to acquiring the basic theories, will be able to design and implement the algorithms and evaluate their performance for specific applications even in the presence of parallel and distributed computing environment.

Main topics

Brief review of basic concepts of the nonlinear programming: fundamental concepts of the unconstrained and the constrained optimization methods.

The Wiener optimal filtering theory. The normal equations and the optimal Wiener filter in discrete time. Type 1, 2 and 3 multi-channel notations, and its multi-input-output optimal filter generalization are presented. Are also discussed corollaries, and presented some applications related to the random sequences prediction and estimation.

Principle of least squares (LS). The normal equations in the Yule-Walker formulation. Minimum variance optimal estimators; the normal equations weighing techniques, the regularization LS approach, the linearly constrained and the nonlinear LS techniques. Methods of matrix decomposition for solving the LS systems in the cases and of over/under-determined case. Singular value decomposition in the solution of the LS systems.

Method of Lyapunov attractor for the iterative LS solution. Total least squares (TLS), Matching pursuit algorithms for underdetermined sparse LS systems.

Stochastic gradient paradigm and LMS algorithm. Methods for performance evaluation of adaptation algorithms: convergence speed and tracking analysis. LMS algorithm variants, (NLMS, multi-channel, delayed learning algorithms, filtered-x LMS the method of the adjoint network).

Second order LS algorithms: the Newton’s method, the recursive least squares (RLS), the affine projection algorithm (APA), the Kalman filter. General adaptation low based on natural gradient approach in presence of sparsity constraints, LASSO.

Transformed domain adaptive algorithms. Frequency domain adaptive filters (FDAF). Partitioned FDAF Transformed domain adaptive filtering. Multirate methods and the subband adaptive filters (SAF).

Forward and backward linear prediction. Order recursive algorithms. Implementative issues with particular robustness and efficiency properties. In connection with this last aspect, the subject of the filter circuit structure and the adaptation algorithm is introduced, in relation to the problems of noise control, scaling and efficient computation, and effects due to coefficients quantization.

Space-time domain adaptive filtering. Anechoic and echoic wave propagation model,  sensors directivity functions,  array signal model, steering vectors of some typical array geometries. Characteristics of noise field, array quality indices. Conventional LS beamforming. Super directive methods. Adaptive on-line beamforming operating non stationary signal condition. Time-delay estimation (TDE), direction of arrival (DOA) estimation in the case of free-field narrow-band signals and in the case broadband signals in reverberant environment.

Fundamentals of parallel distributed processing (PDP) model. Computational and Biological inspired PDP model. Supervised and Unsupervised Learning Algorithm. Learning in distributed environment. Machine Learning for Signal Processing.

In addition to the theoretical part, is provided for the implementation of some of the described algorithms on parallel and distributed architecture.



  • Aurelio Uncini, "Fundamentals of Adaptive Signal Processing" - Springer, ISBN 978-3-319-02806-4, Febbraio 2015.

  • Aurelio Uncini, “Algoritmi Adattativi per l'Elaborazione dei Segnali”, Ed. Esculapio, ISBN 978-88-7488-840-5, Febbraio 2015..

  • Aurelio Uncini, “Algoritmi adattativi per circuiti intelligenti”, dispense disponibili presso i centri fotocopie.

  • S. Scardapane, D. Comminniello, M. Scarpiniti, A. Uncini, “Designing Lerge Machine Learning Simulations Using the Lynx Toolbox".

Other recommended reading

  • Ian Foster, "Designing & Building Parallel Programs: Concepts & Tools for Parallel Software Engineering," Addison-Wesley, 1995, online:

  • Dimitri P. Bertsekas and John N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, ISBN 1-886529-01-9

  • Rumelhart, D.E., Hinton, G.E., & McClelland, J.L. (1986). A General Framework for Parallel Distributed Processing. In Rumelhart, D.E., & McClelland, J.L. and the PDP Research Group (1986) Eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. MIT Press: Cambridge, MA.