IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 1, JANUARY 2007
27
A Novel MDL-based Compression Method
for Power Quality Applications
Moisés V. Ribeiro, Member, IEEE, Seop Hyeong Park, Member, IEEE,
João Marcos T. Romano, Senior Member, IEEE, and Sanjit K. Mitra, Life Fellow, IEEE
Abstract—This paper introduces a novel source coding method
for voltage and current signals, called fundamental, harmonic and
transient coding method (FHTCM), which is a generalization of
the enhanced disturbance compression method (EDCM). The proposed method makes use of notch filtering–warped discrete Fourier
transform (NF–WDFT) technique for estimating the parameters
(amplitude, frequency, and phase) of the fundamental and harmonic components acquired from power lines so that only the transient components are compressed with wavelet transform (WT)
coding technique. For the WT-based compression of transient components, we formulate a minimum description length (MDL) criterion, taking into account the selection of wavelet bases in a dictionary, wavelet decomposition structure, and quantization. Computational simulations have verified that the proposed method outperforms the EDCM as well as the traditional WT-based compression techniques.
Index Terms—Data compression, fundamental and harmonics,
minimum description length, notch filtering, parameter quantization, power quality, warped discrete Fourier transform (DFT),
wavelet transforms (WTs).
I. INTRODUCTION
R
ECENT advances in the signal processing field have stimulated a great deal of interest in the use of digital signal
processing techniques for the monitoring and analysis of events
in power systems. This has led to the development of feasible
and efficient signal processing techniques for the estimation of
harmonic parameters; the detection, compression, and classification of events; and the localization of sources of power-quality
(PQ) problems.
The compression of PQ events has necessitated the development of efficient and low-complexity algorithms. Let us conManuscript received December 1, 2004; revised February 3, 2006. This work
was supported in part by CAPES under Grant BEX2418/03-7, in part by CNPq
under Grants 552371/01-7 and 150064/2005-5, and in part by FAPESP under
Grant 01/08513-0, all from Brazil. Paper no. TPWRD-00571-2004.
Moisés V. Ribeiro was with the Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106 USA. He is now
with the Department of Electrical Circuit, Federal University of Juiz de Fora,
Juiz de Fora, MG 36 036 330, Brazil (e-mail: mribeiro@ieee.org).
S. H. Park is with the Department of Electronic Engineering, Hallym University, Chuncheon, Gangwon-do, 200-702, Korea (e-mail: spark@hallym.ac.kr).
João Marcos T. Romano is with the Department of Communications, School
of Electrical and Computer Engineering, University of Campinas, Campinas,
SP 13 081 970, Brazil (e-mail: romano@decom.fee.unicamp.br).
S. K. Mitra is with the Department of Electrical and Computer Engineering,
University of California, Santa Barbara, CA 93106 USA (e-mail: mitra@ece.
ucsb.edu).
Color versions of Figs. 4, 6, 8, 10, 12, and 14 are available online at http://
ieeexplore.org.
Digital Object Identifier 10.1109/TPWRD.2006.887091
sider, for example, the installation of 1000 pieces of monitoring
equipment in a distribution system. If the analog-to-digital converter (ADC) uses 16-bit word length and a sampling rate of 15
360 Hz for the acquisition of power line signals, then the bandwidth required for the transmission of all monitored signals to a
processing center is as high as 234 375 Mbits/s. To get around
this bandwidth requirement, it is necessary to compress the PQ
events [1].
The wavelet transform (WT) is suitable for the compression
of wideband signals like PQ events since WT has good localization in both time and frequency domains [2]. It has also been
shown that various wavelet thresholding schemes have near-optimal denoising properties [3]. For these reasons, a variety of
compression techniques based on WT or wavelet packet transform (WPT) [4] have been successfully applied so far to PQ
events compression [5]–[18].
For an effective reduction of the redundancy in events, we
need to select the best basis representation. In [19]–[24], this
topic has been addressed by considering a statistical model for
the distribution of wavelet coefficients and by using the minimum description length (MDL) criterion introduced by Rissanen [25], [26].
Hamid and Kawasaki [5] have applied Saito’s MDL criterion [19] to disturbance event compression to select the optimal
bases of WT and keep intact the best number of wavelet coefficients. However, the MDL used in [5] does not take into account
quantization in its formulation [19].
Yu et al. [22] and Chang et al. [23] used the MDL criterion taking into account the quantization for the compression
of image signals. However, both methods are not suitable for
the compression of events because the image statistical models
are not practical to represent the events in power line signals.
Hsieh et al. proposed a WT-based approach with a sinusoidal
reference signal subtraction [7]. However, their approach did not
provide the detailed method for generating the reference sinusoidal signal.
Riberio et al. advanced EDCM which makes use of the
Kalman filter for the estimation of the fundamental sinusoidal
component and then separate it from the events [12], [13].
In the EDCM, the deterministic sinusoidal component and
the residual signal are compressed by the parameter quantization and WT-based compression techniques, respectively.
Simulation results [13] have verified that it outperforms other
well-known WT-based event compression methods.
However, there is room for improvement in the performance
of EDCM. First, the fundamental component is not the only deterministic component included in the power signal. The har-
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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 1, JANUARY 2007
monics can also be modeled as deterministic sinusoidal functions, which can be compressed better by parameter quantization, rather than by WT compression techniques. Second, the
WT compression technique in EDCM also needs to be optimized by a strategy for the best basis representation, the choice
of a suitable filter bank with sparse representation property, and
optimal quantization.
The above considerations have led to a new source coding
method for current and voltage signals, to be called fundamental, harmonic and transient component coding method
(FHTCM) [27] proposed in this paper. The main features of
FHTCM are as follows: 1) it generalizes the EDCM by the use
of notch filter-warped discrete Fourier transform (NF–WDFT)
technique [27], [28] for the detection, estimation and subtraction of the fundamental and harmonic components; and 2) it
provides a new MDL criterion that considers a dictionary of
wavelet bases, adaptive tree-structured decomposition, and the
quantization of wavelet coefficients. A preliminary version of
this method has appeared recently [18].
This paper is organized as follows. Section II describes the
proposed compression method for PQ events. Section III reports numerical simulation results and discussions. Finally, concluding remarks and possible directions for future work are included in Section IV.
II. PROPOSED METHOD FOR CODING VOLTAGE
AND CURRENT SIGNALS
A. Motivation
In our method, the discrete version of monitored power line
signal is divided into non-overlapped frames of samples and
the discrete sequence in a frame can be expressed as an additive
contribution of several types of phenomena
(1)
where
is the sampling rate, and
, and
represent the power supply
signal (or fundamental component), harmonics, inter-harmonics, transient, and background noise, respectively, defined
as follows:
(2)
(3)
(4)
(5)
with
being independently and identically distributed
distribution and indepen(i.i.d.) noise with a normal
dent of
, and
.
, and
represent the magnitude, fundamental
In (2),
frequency, and phase of the power supply signal, respectively.
and
are the -th harmonic and the
In (3) and (4),
-th inter-harmonic, respectively, each of which is defined as
(6)
(7)
and
are the magnitude and phase of
respectively. In (6),
the -th harmonic, respectively. In (7),
, and
are
the magnitude, frequency, and phase of the -th inter-harmonic,
in (5) is the -th transient component.
respectively.
Letting
(8)
and
(9)
(1) reduces to
(10)
Even though the inter-harmonic components are sinusoidal
ones, they tend to have much shorter periods and lower power
than the fundamental and harmonic components. Taking this
into account, in this paper, we consider them as transient components.
Then, our goal can be stated as follows: To find the best algoand
, and to remove
.
rithm to compress
One of the most promising approach to this problem might be
the divide and conquer approach, that is, to split the monitored
power line signal into deterministic sinusoidal and residual transient components and then apply different compression methods
to each of the components in order to obtain better compression performance. For example, unlike speech, music, image,
and video signals, sinusoidal functions can be compressed best
with scalar quantization of the sinusoidal parameters. On the
other hand, the transient components can be compressed well
with WT compression techniques. This approach is attractive
not only because the monitored signal can be compressed with
fewer bits but also important information about fundamental and
harmonic components are provided for the PQ assessment. The
traditional disturbance compression techniques in [5]–[9] and
[10]–[17] are not able to provide such information.
B. Fundamental, Harmonics, and Transient Separation
The separation of
from
is equivalent to the
estimation and subtraction of
from
. In general,
, where
and
are the variances of
and
, respectively. The overall distortion due to the compresmainly depends on the estimation error of the
sion of
sinusoidal parameters. Thus, development of an efficient technique for the estimation of fundamental and harmonic parameters is of importance.
For the purpose of detecting sinusoidal functions, various
methods have been presented in the literature, some of which
are discrete Fourier transform (DFT), phase-locked loop (PLL),
notch filtering [28]–[33]. In this paper, we used the NF–WDFT
RIBEIRO et al.: A NOVEL MDL-BASED COMPRESSION METHOD FOR POWER QUALITY APPLICATIONS
29
signal, so that only the transient signal is submitted to WT compression. The decision criterion used in our method is the fundamental-to-harmonics ratio (FHR). The FHR of the -th component,
, is defined as
(11)
Fig. 1. Block diagram for the NF–WDFT method for estimating the parameters
of fundamental and harmonic components.
Fig. 2. Block diagram of the proposed method to compress and reconstruct the
monitored signal.
method [28] for the estimation of the fundamental and harmonic
components in the monitored power signal because of the following reasons: First, it does not demand the use of a long sequence for the estimation of fundamental and harmonic parameters as FFT does; Second, it provides better sinusoidal parameters estimation than the well-established fast Fourier transform
(FFT) for PQ applications (see [28]).
Fig. 1 shows a block diagram of the NF–WDFT method combined with multi-layer perceptron neural network (MLPNN)
to estimate the parameters of fundamental and harmonic components. In Fig. 1, the fundamental and harmonic parameters
.
are estimated from the input frame
, represents notch filter eliminating the
sinusoidal component with frequency
.
, and
represent the estimates of
, and , respectively. Further
details about this method can be found in [28].
C. Adaptive Compression Scheme
Since the disturbance event occurs at irregular intervals and
its spectral content is broadband, the proposed method makes
use of an adaptive compression scheme.
Fig. 2 illustrates a block diagram of the proposed method to
compress and reconstruct the monitored signal.
is responsible for
1) Disturbance Detection: The block
the detection of the disturbance event, that is, it makes a decision whether or not the current frame has transient components
that need to be compressed. Also, it makes use of parameters
of the fundamental and harmonic components to generate the
sinusoidal signals that are to be subtracted from the monitored
and
represents the estimations of
and
where
, respectively. These sinusoidal functions can be generated
by a fast and low-complex sinusoid generator technique, which
can be found in [34], [35].
Based on numerical simulations, satisfactory performance
has been obtained using the following steps.
.
Step 1) Set
Step 2) If
, then the estimated parameters
of all sinusoidal components are quantized, the resulting transient components are compressed, and
exit.
.
Step 3) Set
, then exit
If
else go to Step 2).
is set to be 40, which is a very conserIn our simulation,
vative value that performs well for a wide range of events.
The MDL block performs the proposed MDL criterion discussed in Section II-D.
denotes the vector composed of the previous esIn Fig. 2,
timated fundamental and harmonic parameters,
and
refer
to the scalar quantization and the inverse process of the coeffi, respectively.
cients in
2) WT-Based Compression of Transient Components and
perBackground Noise Reduction: In Fig. 2, the block
forms a WT with a set of wavelet bases in the dictionary
, where is the number of wavelet
denote an
bases in the dictionary. Let
approximation of
, where
and
is expressed as
(12)
where
and
are quantized values of
and ,
is the number of sinusoidal components
respectively, and
detected by the decision block .
Let
be a vector representation of the
, where
. If we define
sequence
and
, then
(13)
The wavelet coefficient of generated by
decomposition structure is given by
with a specific
(14)
(15)
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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 1, JANUARY 2007
where
is the transform matrix of
and
, respectively.
, be a vector
Let
representation of the -th sub-band signal obtained by the
wavelet decomposition , where is the number of sub-bands
. Note that
in the decomposition structure and
for all . Then,
.
The block
, is responsible for the quantization of all wavelet coefficients in the -th sub-band. In typical
WT-based compression methods, uniform threshold quantization (UTQ) is often used, either implicitly or explicitly [37], because the dead-zone corresponds to a threshold for de-noising
[23]. In this paper, UTQ has been used for the quantization of
wavelet coefficients.
, performs a
The UTQ for
mapping by which a partition of is mapped into the chosen
is partitioned into cells
alphabet, . Here,
. More specifically,
is defined as
if
if
, is i.i.d.
Since it is assumed that the background noise,
, the WT coefnormal with mean 0 and variance
, is also i.i.d. normal
and
ficients of
. If
is estimated accurately and
quantized with as many bits as needed to make
, where
is the variance of the estimation error of
, then
(19)
The length of the description of wavelet coefficients and the
model can be expressed as the sum of the code-lengths to describe: 1) two integer numbers for wavelet basis and decom; 2)
real numbers for threshold and
position structure
for each sub-band and integer numbers for
step size
given
; and 3) the indexes of quantized wavelet coefficients.
Given the observation , the MDL principle finds a model
to minimize the code-length
(16)
if
(20)
and
are the step size and the dead-zone threshold
where
of the -th sub-band, respectively. Let be the number of quantizer intervals in the positive side, then the total number of quanbecause of the symmetry and the
tizer intervals is
dead-zone of UTQ. In our approach we consider
,
is a estimate of the background noise variance.
where
is specified by a code-book
The dequantizer
. The optimal value of
is the centroid of
the corresponding quantizer region and depends on the statis.
tical distribution of the wavelet coefficients,
The blocks and
represent an entropy encoder and an
entropy decoder, respectively. The design of a specific entropy
encoder and decoder is out of the scope of this paper. However, it
should be noted that there are several entropy coding techniques
applicable to this part. For example, Lempel–Ziv–Welch (LZW)
and Huffman coding techniques [36] can be applied (as done in
[13] and [15]).
to reconThe block performs the inverse transform of
struct . The reconstruction of is given by
The block
where
, and
is the code-length for
is the code-length for
based on
.
For the convenience of computation, the integer requirement
in (20) is
for the code-length is ignored. Then,
given by
(21)
In (21),
constant, and
can be ignored in the minimization since it is a
is given by
(22)
(23)
(24)
(17)
and are the number of bits needed to code a real
where
number and an integer number, respectively.
in (21) is given by [23]
(18)
(25)
reconstructs the monitored signal by
D. Proposed MDL Criterion
In order to formulate a new MDL criterion, we consider the
use of a dictionary of wavelet bases, adaptive tree-structured de. The
composition, and scalar quantization with
centroid of the quantizer region is reconstructed assuming a generalized Gaussian distribution (GGD).
where
(26)
where
and
represents the number of the occurrence of
.
RIBEIRO et al.: A NOVEL MDL-BASED COMPRESSION METHOD FOR POWER QUALITY APPLICATIONS
31
in (20) is given by [23]
(27)
(28)
denotes the quantized
. The second term
where
in (28) is a constant, thus it is ignored in the minimization.
Then the MDL criterion to select the model can be stated as
follows:
Fig. 3. Plot of the event I. This waveform represents a voltage signal corrupted
by harmonics and switching capacitor transient.
(29)
The above criterion is more general than Saito’s [19] and
Chang’s [23] criteria because it takes into account the wavelet
bases dictionary, adaptive tree-structured decomposition, and
scalar quantization in its formulation.
Given specific wavelet basis, the following bottom-up
strategy [21] is considered to prune the best tree-structured
decomposition: Each sub-band is assumed to be associated
with specific cost value for its representation, which is given by
(20) If the sum of the cost values of two children sub-bands are
lower than that of the parent sub-band, the children sub-bands
are selected, otherwise the parent sub-band is selected and
children sub-bands are discarded.
III. SIMULATION RESULTS AND DISCUSSION
In this section, we consider six typical monitored signals for
analyzing the performance of the proposed method. The first
four signals were synthetically generated to simulate some frequent disturbances in power systems. These signals were samHz and quantized with 12 bits. The
pled at
last two signals are selected from the database at the IEEE PES
Working Group P1433 Power Quality web-site [39].
The parameters set in our simulation are as follows: The
number of bits for sinusoidal parameter quantization is 24,
the maximum number of the stages in adaptive tree-structured
decomposition is 4, the wavelet bases dictionary consists of
Symlet, Daubechies and Meyer wavelets, the number of bits
to code the dead-zone threshold and step-size in UTQ is 24,
respectively, the number of bits to code the number of quantizer
regions in each sub-band is 8, the number of bits to code the
tree-structured decomposition is 6, and the numbers of samples
in synthetic and real power line signals are 2560 and 1536,
respectively.
It is noted that the overall performance of the proposed algorithm is partly controlled by the construction of the dictionary. The more the number of the wavelets, the less the compression error while the compression complexity increases in
proportional to the number of the wavelets. The wavelets in the
dictionary should be carefully selected so that each wavelet may
represent a transient component with different spectral trait. The
dictionary construction problem is very closely related to the
classification of events and requires a further in-depth study.
For the purpose of performance comparison, each of five signals is compressed by the following six methods: 1) FHTCM;
2) SDCM–MDL (WT-based compression of the original signal
based on the proposed MDL criterion without best basis representation); 3) EDCM–MDL (WT-based compression of the
signal after the fundamental component extraction based on the
proposed MDL criterion without best basis representation); 4)
Saito’s method (SAITO) [5], [19]; 5) EDCM [13]; and 6) SDCM
[13].
A. Synthetic Disturbance Event I
Fig. 3 shows a synthetic disturbance event generated in accordance with [38] and corrupted by a narrow-band disturbance
due to oscillator transients. The signal is given by
(30)
Fig. 4 compares the performance of six source coding
methods in terms of the mean square error (MSE) at various
bit rates. This synthetic signal represents a typical signal with
dB. From
high-power harmonic presence. Note that
Fig. 4, it is clear that FHTCM shows the best performance. This
is mainly because FHTCM successfully estimates all sinusoidal
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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 1, JANUARY 2007
Fig. 4. Performance comparison of the six compression methods in terms of
MSE at various bit rates. These results refer to the input signal shown in Fig. 3.
Fig. 6. Performance comparison of the six compression methods in terms of
MSE at various bit rates. These results refer to the input signal shown in Fig. 5.
Fig. 5. Plot of event II. This waveform corresponds to a signal corrupted by
notches.
Fig. 7. Plot of the event III. This signal illustrates a voltage signal corrupted by
transient capacitor switching and ac–dc conversion.
components and compresses them with less bits by parameter
quantization.
Comparing the MSE plots of SDCM–MDL with SDCM
and EDCM–MDL with EDCM, we see that MDL-combined
methods result in less MSEs. This shows the effectiveness of
the proposed MDL criteria when they are combined with other
WT compression methods.
tendency to FHTCM performance highlighted in Fig. 4. As this
signal does not have any harmonics, the EDCM shows comparable performance to that of the FHTCM.
B. Synthetic Disturbance Event II
Fig. 5 shows a synthetic signal with a fundamental components and notch disturbances, where
dB. The analysis of this signal is quite important because notch disturbance
is the most common one in the power systems as a result of
ac–dc conversions. Fig. 6 shows the MSE obtained by the six
source coding methods at various bit rates. Regarding FHTCM
performance, it can be noted that this graph shows a very similar
C. Synthetic Disturbance Event III
Fig. 7 shows a synthetic signal containing both narrow-band
and wide-band disturbances, which is typically produced by capacitor switching and ac–dc conversion. Note that
dB. Fig. 8 shows the MSE plots at various bit rates obtained by
the six source coding methods. This signal also does not have
any harmonics so that EDCM–MDL shows similar performance
to that of FHTCM.
D. Synthetic Disturbance Event IV
Fig. 9 shows a synthetic signal with harmonic and
inter-harmonics components containing both narrow-band
RIBEIRO et al.: A NOVEL MDL-BASED COMPRESSION METHOD FOR POWER QUALITY APPLICATIONS
Fig. 8. Performance comparison of the six compression methods in terms of
MSE at various bit rates. These results refer to the input signal portrayed in
Fig. 7.
33
Fig. 10. Performance comparison of the six compression methods in terms of
MSE at various bit rates. These results refer to the input signal shown in Fig. 9.
Fig. 11. Plot of the disturbance event I from the IEEE PES database.
Fig. 9. Plot of the event III. This signal illustrates a voltage signal corrupted by
transient capacitor switching and ac–dc conversion.
and wide-band disturbances, which is typically produced by
capacitor switching and ac–dc conversion. The signal can be
expressed by
are the narrow-band and wide-band disturdB. Fig. 10 shows the MSE plots at
bances, and
various bit rates obtained by the six source coding methods.
The presented results reveal that FHTCM surpasses the performance of the other methods.
E. Disturbance Event I From the IEEE PES Database
(31)
Fig. 11 shows a plot of a monitored signal available in [39],
which is a typical sag disturbance in power systems. Fig. 12
shows the MSE plots at various bit rates yielded by the six compression methods. For this signal, FHTCM outperforms in most
of the cases all of the other compression methods.
F. Disturbance Event II From the IEEE PES Database
where
rad/s,
rad/s,
rad/s, and
rad/s are
the angular frequencies of the inter-harmonics components,
Fig. 13 shows a plot of another signal obtained from [39],
which contains a frequently observed distortion in power systems. Fig. 14 shows the MSE plots at various bit rates yielded by
34
IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 1, JANUARY 2007
Fig. 12. Performance comparison of the six compression methods in terms
of MSE at various bit rates. These results refer to the input signal depicted in
Fig. 11.
Fig. 13. Plot of the disturbance event II from the IEEE Power Engineering
Society database.
the six compression methods. Again, it can be seen that FHTCM
surpasses the performance of the others methods in the range
that is appropriate for waveform compression of voltage signals.
Based upon the simulation results, it can be seen that the proposed method outperforms all the other source coding methods
for typical events in power systems. The critical improvement
comes from the combination of fundamental and harmonic subtraction as well as the proposed MDL criterion. However, it is
worth stating that the performance of HFTCM depends on the
signal characteristics.
Fig. 14. Performance comparison of the six compression methods in terms of
MSE at various bit rates. These results refer to the input signal portrayed in
Fig. 13.
three different groups of components: 1) the deterministic fundamental and harmonic components; 2) the non-deterministic
inter-harmonic and transient components; and 3) background
noise. Based on the above assumption, this paper presents
a new compression technique for the power events called
FHTCM. FHTCM splits the monitored signal into deterministic fundamental, harmonic components and non-deterministic
components and applies different source coding techniques to
each group of signals. For the estimation of the fundamental
and harmonic components, we make use of the recently developed NF–WDFT method. These sinusoidal components are
compressed via parameter quantization, which is much more
efficient than waveform compression techniques like WT-based
one.
The remaining signal after fundamental and harmonic component extraction consists of transient signal and background
noise. In order to compress the transient signal with as less
bits as possible as well as to remove the background noise, this
paper applies an adaptive WT compression technique with adaptive wavelet basis, adaptive tree-structured decomposition, and
adaptive bit allocation to each sub-band. As a criterion to select
the optimal compression scheme, this paper introduces a MDL
criterion considering these factors.
We have shown through computer simulations that the proposed FHTCM outperforms in terms of performance source
coding methods previously introduced in the literature.
Further studies to improve the performance of FHTCM are
being considered. The UTQ used in this paper is not optimal
in the sense of MSE. This is mainly because there is no valid
statistical model of the distribution of the wavelet coefficients
of the transient components. Finding a model and the design
of an optimal scalar quantizer is being carried out. The scalar
quantizers can be replaced with vector quantizers.
IV. CONCLUDING REMARKS
ACKNOWLEDGMENT
In this paper, it is assumed that, in a short duration, a monitored power line signal can be modeled as a summation of
The authors are very thankful to the anonymous reviewers for
providing valuable comments and suggestions.
RIBEIRO et al.: A NOVEL MDL-BASED COMPRESSION METHOD FOR POWER QUALITY APPLICATIONS
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Moisés V. Ribeiro (S’03–M’05) was born in Três
Rios, Brazil, in 1974. He received the B.S. degree
in electrical engineering from the Federal University
of Juiz de Fora (UFJF), Juiz de Fora, Brazil, in
1999, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Campinas
(UNICAMP), Campinas, Brazil, in 2001 and 2005,
respectively.
Currently, he is an Assistant Professor at UFJF.
He was a Visiting Researcher in the Image and
Signal Processing Laboratory of the University of
California, Santa Barbara, in 2004, a Postdoctoral Researcher at UNICAMP,
in 2005, and at UFJF from 2005 to 2006. He is Guest Editor for special issues
on emerging signal processing techniques for power quality applications and
on advanced signal processing and computational intelligence techniques
for power-line communications for the EURASIP Journal on Applied Signal
Processing and a Reviewer of international journals. He has been the author of
many journal and conference papers and holds six patents. His research interests include computational intelligence, digital and adaptive signal processing,
power quality, power-line communication, and digital communications.
Dr. Ribeiro has been the recipient of nine scholarships from the Brazilian
government agencies. He received student awards from IECON’01 and ISIE’03.
He is a member of the technical program committee of the ISPLC06, ISPLC07,
CERMA06, and ANDESCOM06, and a member of the IEEE ComSoc Technical
Committee on Power Line Communications.
36
Seop Hyeong Park (S’85–M’91) was born in 1961
in Seoul, Korea. He received the B.S., M.S., and
Ph.D. degrees in electrical engineering from the
Department of Control and Instrumentation Engineering, Seoul National University, Seoul, in 1984,
1986, and 1990, respectively.
From 1990 to 1992, he was with the HDTV Development Center at the Korean Academy of Industrial
Technology (KAITECH), Seoul, where he worked on
the design and implementation of an HDTV decoder.
From 1992 to 1998, he was with Korea Telecom, Daejeon, where he worked on digital video compression, multimedia service-management software, multimedia service over broadband integrated services digital network (ISDN), including video on demand and videoconferencing. In
1993, he was a Visiting Researcher at the NTT Human Interface Laboratory,
Yokosuka, Japan, where he worked on postprocessing of compressed HDTV
video signals. He joined Hallym University, Chuncheon, Gangwon-do, Korea,
in 1998, where he is currently a Professor in the Department of Electronic Engineering and Dean of College of Information and Electronic Engineering. He
was a Visiting Scholar in the Image and Signal Processing Laboratory of the
University of California, Santa Barbara, from 2004 to 2005. His research interests are signal processing, compression of speech, audio and video signals, and
wireless multimedia communication systems.
João Marcos T. Romano (M’88–SM’02) was born
in Rio de Janeiro in 1960. He received the B.S.
and M.S. degrees in electrical engineering from the
University of Campinas (UNICAMP), Campinas,
Brazil, in 1981 and 1984, respectively, and the
Ph.D. degree from the University of Paris-XI, Paris,
France, in 1987.
In 1988, he joined the Communications Department of the Faculty of Electrical and Computer Engineering at UNICAMP, where he is currently Professor. He served as an Invited Professor in the University René Descartes, Paris, France, in 1999, and in the Communications and
Electronic Laboratory in Conservatoire National des Arts et Métiers (CNAM),
Paris, France, in 2002. He is responsible for the Signal Processing for Communications Laboratory at UNICAMP. His research interests are adaptive and
intelligent signal processing and its applications in telecommunications problems, such as channel equalization and smart antennas. Since 1988, he has been
a recipient of the Research Fellowship of CNPq-Brazil. From 2000 to 2004, he
was the President of the Brazilian Communications Society (SBrT), a sister society of ComSoc-IEEE. He has been the Associated Director of the School of
Electrical and Computer Engineering at UNICAMP since 2003.
Dr. Romano is a member of the IEEE Electronics and Signal Processing
Technical Committee.
IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 1, JANUARY 2007
Sanjit K. Mitra (S’59–M’63–SM’69–F’74–LF’00)
is a Professor of Electrical and Computer Engineering at the University of California, Santa
Barbara, where he served as Chairman of the Department from 1979 to 1982. He has published more
than 600 papers in signal and image processing, 12
books, and holds five patents.
Dr. Mitra has served IEEE in various capacities,
including service as the President of the IEEE
Circuits and Systems Society in 1986, and has held
visiting appointments in Australia, Croatia, Finland,
India, Japan, Singapore, Turkey, and the U.K. He is currently on the editorial
board of three international journals. He is the recipient of the 1973 F. E.
Terman Award and the 1985 AT&T Foundation Award of the American Society
of Engineering Education, the 1989 Education Award, and the 2000 Mac Van
Valkenburg Society Award of the IEEE Circuits and Systems Society, the
Distinguished Senior U.S. Scientist Award from the Alexander von Humboldt
Foundation of Germany in 1989, the 1996 Technical Achievement Award,
and the 2001 Society Award of the IEEE Signal Processing Society, the
IEEE Millennium Medal in 2000, the McGraw-Hill/Jacob Millman Award of
the IEEE Education Society in 2001, and the 2002 Technical Achievement
Award of the European Association for Signal Processing (EURASIP). He
is the Co-Recipient of the 2000 Blumlein–Browne–Willans Premium of
the Institution of Electrical Engineers (London, U.K.) and the 2001 IEEE
Transactions on Circuits and Systems for Video Technology Best Paper Award.
He is an Academician of the Academy of Finland, a member of the US
National Academy of Engineering, a member of the Norwegian Academy of
Technological Sciences, and a foreign member of the Croatian Academy of
Sciences and Arts. Dr. Mitra is a Fellow of the AAAS and SPIE.