Eventos - Mini-Cursos

5th LNCC Meeting on Computational Modeling

Minicurso 1 - Data Selective Learning

Minicurso 2 - Discontinuous Petrov-Galerkin (DPG) Method with Optimal Test Functions


Minicurso 1 - Data Selective Learning

Professor: Paulo Diniz (COPPE - UFRJ)
Período: 17/07/2012 a 19/07/2012
Horário: terça - quarta - quinta   de 08:30 hs às 09:30 hs
Local: Auditorio A

Resumo:
I. Courses Focus: Data-Selective Learning
II. Instructor: Dr. Paulo S.R. Diniz (Universidade Federal do Rio de Janeiro)
III. Description:
This course is intended to present fundamental, as well as some advanced concepts that are
important to understand the principles of data-selective algorithms. These type of algorithms
utilize environment data for updating only when they bring new information. As a result the
obtained parameter estimations become more accurate without sacrificing the learning speed. The
data-selective algorithm are specially suitable for applications where computational resources are
limited and/or power saving is a requirement. Typical examples are many communication systems
where the growing demand for services has increased the interest in employing adaptive techniques
as part of the receivers.
In other modern communication systems, such as wireless and multiuser systems, there are
two major sources of interference: intersymbol interference due to multipath fading and co-channel
interference due to multiple-user communications. In addition, in a wireless system such as land
mobile radio, the fast time-varying fading calls for adaptive solutions. Recent studies have also
shown that sensor arrays is another application where the data-selective algorithms will play a key
role, given the limited power resources and transmission bandwidth of the sensors. Some of these
applications are used as example to illustrate the usefulness of data-selective learning.
Course: Data-Selective Learning
1. An Introduction to Adaptive Signal Processing
2. Wiener Filtering, Newton’s Algorithm and Steepest Descent Algorithm, Least-mean-squares
(LMS) method, Transform-Domain LMS, Sign LMS, Normalized, Binormalized LMS, and
Affine Projection Algorithms
3. Data-Selective Adaptive Filtering
4. Set-Membership Filtering
5. Set-Membership Normalized LMS Algorithm
6. Set-Membership Affine Projection Algorithm
• Trivial Choice
• Simple Vector
• Reducing the Complexity in the Simplified SM-AP Algorithm
7. Set-Membership Binormalized LMS Algorithms
1COPPE/EE, Universidade Federal do Rio de Janeiro, Programa de Engenharia Eletrica, Caixa Postal 68504, Rio
de Janeiro, R.J., Brazil, e-mail: diniz@lps.ufrj.br
1
• SM-BNLMS Algorithm 1
• SM-BNLMS Algorithm 2
8. Computational Complexity
9. Time-Varying Threshold
10. Partial-Update Adaptive Filtering Set-Membership Partial-Update NLMS Algorithm
11. Applications
• System Identification Simulations
• Echo Cancellation Environment
• Wireless Channel Environment
12. Blind Adaptive Filtering
• Affine Projection CM Algorithm
• Blind SIMO Equalizers
13. Analysis of Set-Membership Affine Projection Algorithm
• Probability of Update
• Misadjustment in the Simplified SM-AP Algorithm
• Transient Behavior
References:
1. P. S. R. Diniz, “ADAPTIVE FILTERING: Algorithms and Practical Implementation,” Springer,
New York, NY, Fourth edition, 2012 (668 pages).
2. Werner, S., and Diniz, P. S. R., “Set-Membership Affine Projection Algorithm,” IEEE Signal
Processing Letters, Vol. 8, pp. 231-235, August 2001.
3. Diniz, P. S. R., and Werner, S., “ Set-Membership Binormalized Data Reusing LMS Algorithms,”
IEEE Trans. on Signal Processing, Vol. 51, pp. 124-134, Jan. 2003.
4. Werner, S., de Campos, M. L. R., and Diniz, P. S. R., “Partial-Update with Data-Selective
Updating,” IEEE Trans. on Signal Processing, Vol. 52, pp. 938-949, April 2004.
5. Werner, S., Apolin´ario Jr., J. A., and Diniz, P. S. R., “Set Membership Proportionate Affine
Projection Algorithms,” EURASIP Journal on Audio, Speech, and Music Processing, Vol.
2007, pp. 1-10, Article ID 34242, 2007.
6. de Lamare, R. C., and Diniz, P. S. R., “Set-Membership Adaptive Algorithms based on Time-
Varying Error Bounds for CDMA Interference Suppression,” IEEE Transactions on Vehicular
Technology, Vol. 58, pp. 644-654, Feb. 2009.
2
7. Diniz, P. S. R., “Convergence Performance of the Simplified Set-Membership AffineProjection
Algorithm,” Circuits, Systems and Signal Processing, Birkh¨auser, MA, Vol. 30, pp. 439-462,
April 2011. DOI information: DOI 10.1007/s00034-010-9219-z.
8. de Lamare, R. C., and Diniz, P. S. R., “Blind Constrained Set-Membership Algorithms with
Time-Varying Bounds for CDMA Interference Suppression,” Proc. 2006 IEEE Intern. Conf.
on Acoust. Speech and Signal Processing, Toulouse, France, pp. IV-617 - IV-620, May 2006.
9. Diniz, P. S. R., Lima, M. V. S., and Martins, W. A., “Semi-Blind Data-Selective Algorithms
for Channel Equalization,” Proc. 2008 IEEE Intern. Symposium on Circuits and Systems,
Seattle, WA, pp. 53-56, May 2008.
10. Martins, W. A., Lima, M. V. S., and Diniz, P. S. R., “Semi-Blind Data-Selective Equalizers for
QAM,” Proc. SPAWC-2008: The Ninth IEEE International Workshop on Signal Processing
Advances in Wireless Communications, Recife, Brazil, pp. 501-505, July 2008. 978-1-4244-
2046-9
11. Lima, M. V. S., and Diniz, P. S. R., “Steady-State Analysis of the Set-Membership Affine
Projection Algorithm,” Proc. 2010 IEEE Intern. Conf. on Acoust. Speech and Signal
Processing, Dallas, Texas, USA, pp. 3802-3805, March 2010.
12. Martins, W. A., and Diniz, P. S. R., “Semi-Blind Data-Selective Algorithms for Channel
Equalization,” Proc. 2010 IEEE Intern. Symposium on Circuits and Systems, Paris, France,
pp. 3112-3115, May 2010.
13. Lima, M. V. S., and Diniz, P. S. R., “On the Steady-State MSE Performance of the Set-
Membership NLMS Algorithm,” Proc. 7th Intern. Symposium on Wireless Wireless Communications
Systems (ISWCS), York, UK, pp. 389-393, September 2010.
3
IV. Biosketch of Lecturer:
Paulo S. R. Diniz was born in Niter´oi, Brazil. He received the Electronics Eng. degree
(Cum Laude) from the Federal University of Rio de Janeiro (UFRJ) in 1978, the M.Sc. degree
from COPPE/UFRJ in 1981, and the Ph.D. from Concordia University, Montreal, P.Q., Canada,
in 1984, all in electrical engineering.
Since 1979 he has been with the Department of Electronic Engineering (the undergraduate
dept.) UFRJ. He has also been with the Program of Electrical Engineering (the graduate studies
dept.), COPPE/UFRJ, since 1984, where he is presently a Professor. He served as Undergraduate
Course Coordinator and as Chairman of the Graduate Department. He is one of the three senior
researchers and coordinators of the National Excellence Center in Signal Processing. He has also
received the Rio de Janeiro State Scientist award, from the Governor of Rio de Janeiro state.
From January 1991 to July 1992, he was a visiting Research Associate in the Department of
Electrical and Computer Engineering of University of Victoria, Victoria, B.C., Canada. He also
holds a Docent position at Helsinki University of Technology. From January 2002 to June 2002, he
was a Melchor Chair Professor in the Department of Electrical Engineering of University of Notre
Dame, Notre Dame, IN, USA. His teaching and research interests are in analog and digital signal
processing, adaptive signal processing, digital communications, wireless communications, multirate
systems, stochastic processes, and electronic circuits. He has published several refereed papers in
some of these areas and wrote ADAPTIVE FILTERING: Algorithms Practical Implementation,
Springer, Fourth Edition 2012, and DIGITAL SIGNAL PROCESSING: System Analysis and Design,”
Cambridge University Press, Cambridge, UK, Second Edition 2010 (with E. A. B. da Silva
and S. L. Netto).
He was the Technical Program Chair of the 1995 IEEE MWSCAS and IEEE SPAWC both held
in Brazil. He has been on the technical committee of several international conferences including
ISCAS, ICECS, EUSIPCO and MWSCAS. He has served Vice President for region 9 of the IEEE
Circuits and Systems Society and as Chairman of the DSP technical committee of the same Society.
He is also a Fellow of IEEE (for fundamental contributions to the design and implementation of
fixed and adaptive filters and Electrical Engineering Education). He has served as associate editor
for the following Journals: IEEE Transactions on Circuits and Systems II: Analog and Digital
Signal Processing from 1996 to 1999, IEEE Transactions on Signal Processing from 1999 to 2002,
and the Circuits, Systems and Signal Processing Journal from 1998 to 2002. He was a distinguished
lecturer of the IEEE Circuits and Systems Society for the year 2000 to 2001. In 2004 he served
as distinguished lecturer of the IEEE Signal Processing Society and received the 2004 Education
Award of the IEEE Circuits and Systems Society.

Prof. Paulo S. R. Diniz, Ph. D.
Programa de Engenharia El´etrica
COPPE/Federal University of Rio de Janeiro

Ementa:
I. Courses Focus: Data-Selective Learning
II. Instructor: Dr. Paulo S.R. Diniz (Universidade Federal do Rio de Janeiro)
III. Description:
This course is intended to present fundamental, as well as some advanced concepts that are
important to understand the principles of data-selective algorithms. These type of algorithms
utilize environment data for updating only when they bring new information. As a result the
obtained parameter estimations become more accurate without sacrificing the learning speed. The
data-selective algorithm are specially suitable for applications where computational resources are
limited and/or power saving is a requirement. Typical examples are many communication systems
where the growing demand for services has increased the interest in employing adaptive techniques
as part of the receivers.
In other modern communication systems, such as wireless and multiuser systems, there are
two major sources of interference: intersymbol interference due to multipath fading and co-channel
interference due to multiple-user communications. In addition, in a wireless system such as land
mobile radio, the fast time-varying fading calls for adaptive solutions. Recent studies have also
shown that sensor arrays is another application where the data-selective algorithms will play a key
role, given the limited power resources and transmission bandwidth of the sensors. Some of these
applications are used as example to illustrate the usefulness of data-selective learning.
Course: Data-Selective Learning
1. An Introduction to Adaptive Signal Processing
2. Wiener Filtering, Newton’s Algorithm and Steepest Descent Algorithm, Least-mean-squares
(LMS) method, Transform-Domain LMS, Sign LMS, Normalized, Binormalized LMS, and
Affine Projection Algorithms
3. Data-Selective Adaptive Filtering
4. Set-Membership Filtering
5. Set-Membership Normalized LMS Algorithm
6. Set-Membership Affine Projection Algorithm
• Trivial Choice
• Simple Vector
• Reducing the Complexity in the Simplified SM-AP Algorithm
7. Set-Membership Binormalized LMS Algorithms
1COPPE/EE, Universidade Federal do Rio de Janeiro, Programa de Engenharia Eletrica, Caixa Postal 68504, Rio
de Janeiro, R.J., Brazil, e-mail: diniz@lps.ufrj.br
1
• SM-BNLMS Algorithm 1
• SM-BNLMS Algorithm 2
8. Computational Complexity
9. Time-Varying Threshold
10. Partial-Update Adaptive Filtering Set-Membership Partial-Update NLMS Algorithm
11. Applications
• System Identification Simulations
• Echo Cancellation Environment
• Wireless Channel Environment
12. Blind Adaptive Filtering
• Affine Projection CM Algorithm
• Blind SIMO Equalizers
13. Analysis of Set-Membership Affine Projection Algorithm
• Probability of Update
• Misadjustment in the Simplified SM-AP Algorithm
• Transient Behavior
References:
1. P. S. R. Diniz, “ADAPTIVE FILTERING: Algorithms and Practical Implementation,” Springer,
New York, NY, Fourth edition, 2012 (668 pages).
2. Werner, S., and Diniz, P. S. R., “Set-Membership Affine Projection Algorithm,” IEEE Signal
Processing Letters, Vol. 8, pp. 231-235, August 2001.
3. Diniz, P. S. R., and Werner, S., “ Set-Membership Binormalized Data Reusing LMS Algorithms,”
IEEE Trans. on Signal Processing, Vol. 51, pp. 124-134, Jan. 2003.
4. Werner, S., de Campos, M. L. R., and Diniz, P. S. R., “Partial-Update with Data-Selective
Updating,” IEEE Trans. on Signal Processing, Vol. 52, pp. 938-949, April 2004.
5. Werner, S., Apolin´ario Jr., J. A., and Diniz, P. S. R., “Set Membership Proportionate Affine
Projection Algorithms,” EURASIP Journal on Audio, Speech, and Music Processing, Vol.
2007, pp. 1-10, Article ID 34242, 2007.
6. de Lamare, R. C., and Diniz, P. S. R., “Set-Membership Adaptive Algorithms based on Time-
Varying Error Bounds for CDMA Interference Suppression,” IEEE Transactions on Vehicular
Technology, Vol. 58, pp. 644-654, Feb. 2009.
2
7. Diniz, P. S. R., “Convergence Performance of the Simplified Set-Membership AffineProjection
Algorithm,” Circuits, Systems and Signal Processing, Birkh¨auser, MA, Vol. 30, pp. 439-462,
April 2011. DOI information: DOI 10.1007/s00034-010-9219-z.
8. de Lamare, R. C., and Diniz, P. S. R., “Blind Constrained Set-Membership Algorithms with
Time-Varying Bounds for CDMA Interference Suppression,” Proc. 2006 IEEE Intern. Conf.
on Acoust. Speech and Signal Processing, Toulouse, France, pp. IV-617 - IV-620, May 2006.
9. Diniz, P. S. R., Lima, M. V. S., and Martins, W. A., “Semi-Blind Data-Selective Algorithms
for Channel Equalization,” Proc. 2008 IEEE Intern. Symposium on Circuits and Systems,
Seattle, WA, pp. 53-56, May 2008.
10. Martins, W. A., Lima, M. V. S., and Diniz, P. S. R., “Semi-Blind Data-Selective Equalizers for
QAM,” Proc. SPAWC-2008: The Ninth IEEE International Workshop on Signal Processing
Advances in Wireless Communications, Recife, Brazil, pp. 501-505, July 2008. 978-1-4244-
2046-9
11. Lima, M. V. S., and Diniz, P. S. R., “Steady-State Analysis of the Set-Membership Affine
Projection Algorithm,” Proc. 2010 IEEE Intern. Conf. on Acoust. Speech and Signal
Processing, Dallas, Texas, USA, pp. 3802-3805, March 2010.
12. Martins, W. A., and Diniz, P. S. R., “Semi-Blind Data-Selective Algorithms for Channel
Equalization,” Proc. 2010 IEEE Intern. Symposium on Circuits and Systems, Paris, France,
pp. 3112-3115, May 2010.
13. Lima, M. V. S., and Diniz, P. S. R., “On the Steady-State MSE Performance of the Set-
Membership NLMS Algorithm,” Proc. 7th Intern. Symposium on Wireless Wireless Communications
Systems (ISWCS), York, UK, pp. 389-393, September 2010.
3
IV. Biosketch of Lecturer:
Paulo S. R. Diniz was born in Niter´oi, Brazil. He received the Electronics Eng. degree
(Cum Laude) from the Federal University of Rio de Janeiro (UFRJ) in 1978, the M.Sc. degree
from COPPE/UFRJ in 1981, and the Ph.D. from Concordia University, Montreal, P.Q., Canada,
in 1984, all in electrical engineering.
Since 1979 he has been with the Department of Electronic Engineering (the undergraduate
dept.) UFRJ. He has also been with the Program of Electrical Engineering (the graduate studies
dept.), COPPE/UFRJ, since 1984, where he is presently a Professor. He served as Undergraduate
Course Coordinator and as Chairman of the Graduate Department. He is one of the three senior
researchers and coordinators of the National Excellence Center in Signal Processing. He has also
received the Rio de Janeiro State Scientist award, from the Governor of Rio de Janeiro state.
From January 1991 to July 1992, he was a visiting Research Associate in the Department of
Electrical and Computer Engineering of University of Victoria, Victoria, B.C., Canada. He also
holds a Docent position at Helsinki University of Technology. From January 2002 to June 2002, he
was a Melchor Chair Professor in the Department of Electrical Engineering of University of Notre
Dame, Notre Dame, IN, USA. His teaching and research interests are in analog and digital signal
processing, adaptive signal processing, digital communications, wireless communications, multirate
systems, stochastic processes, and electronic circuits. He has published several refereed papers in
some of these areas and wrote ADAPTIVE FILTERING: Algorithms Practical Implementation,
Springer, Fourth Edition 2012, and DIGITAL SIGNAL PROCESSING: System Analysis and Design,”
Cambridge University Press, Cambridge, UK, Second Edition 2010 (with E. A. B. da Silva
and S. L. Netto).
He was the Technical Program Chair of the 1995 IEEE MWSCAS and IEEE SPAWC both held
in Brazil. He has been on the technical committee of several international conferences including
ISCAS, ICECS, EUSIPCO and MWSCAS. He has served Vice President for region 9 of the IEEE
Circuits and Systems Society and as Chairman of the DSP technical committee of the same Society.
He is also a Fellow of IEEE (for fundamental contributions to the design and implementation of
fixed and adaptive filters and Electrical Engineering Education). He has served as associate editor
for the following Journals: IEEE Transactions on Circuits and Systems II: Analog and Digital
Signal Processing from 1996 to 1999, IEEE Transactions on Signal Processing from 1999 to 2002,
and the Circuits, Systems and Signal Processing Journal from 1998 to 2002. He was a distinguished
lecturer of the IEEE Circuits and Systems Society for the year 2000 to 2001. In 2004 he served
as distinguished lecturer of the IEEE Signal Processing Society and received the 2004 Education
Award of the IEEE Circuits and Systems Society.

Prof. Paulo S. R. Diniz, Ph. D.
Programa de Engenharia El´etrica
COPPE/Federal University of Rio de Janeiro


Minicurso 2 - Discontinuous Petrov-Galerkin (DPG) Method with Optimal Test Functions

Professor: Leszek Demkowicz (ICES)
Período: 17/07/2012 a 19/07/2012
Horário: terça - quarta - quinta   de 08:30 hs às 09:30 hs
Local: Auditorio B

Resumo:
The three hour course will be devoted to an overview of a new Discontinuous Petrov-Galerkin (DPG) method proposed recently by Demkowicz and Gopalakrishnan [1,2]. The main idea of the method lies in using test functions that are NOT predefined but COMPUTED on the fly, by inverting element by element the Riesz map corresponding to a specific choice of a test norm. The method can be reinterpreted as a least squares method with a twist: the residual lives in a dual space and is computed using a dual norm. The methodology guarantees stability for any linear PDEs and elements of arbitrary order, extending thus dramatically the applicability of higher order discretizations. The method is especially suitable for singular perturbation problems where one strives not only for the stability but also for ROBUSTNESS, i.e. uniform stability with respect to the perturbation parameter. I will use two important singular perturbation problems as an illustration: convection dominated diffusion [3] and linear acoustics [4].

Lectures:
1. Concept of Petrov-Galerkin Method with Optimal Test Functions.
2. Ultra-weak variational formulation and the DPG method for the convection-dominated diffusion.
3. The DPG method fo linear acoustics.

[1] L. Demkowicz, J. Gopalakrishnan, ``A Class of Discontinuous Petrov-Galerkin Methods. Part II: Optimal Test Functions'', Numer. Meth. Part. D. E., 2011, Vol 27, 70-105, see also ICES Report 9/16.
[2] L. Demkowicz and J. Gopalakrishnan. A New Paradigm for Discretizing Difficult Problems: Discontinuous Petrov Galerkin Method with Optimal Test Functions. Expressions (publication of International Association for Computational Mechanics), November 2010.
[3] L. Demkowicz, N. Heuer, Robust DPG Method for Convection-Dominated Diffusion Problems. ICES Report 2011-33, submitted to SIAM J. Num. Anal. [4] L. Demkowicz, J. Gopalakrishnan, I. Muga, and J. Zitelli. Wavenumber Explicit Analysis for a DPG Method for the Multidimensional Helmholtz Equation'', ICES Report 2011-24, CMAME, in print.



 
 



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