A Novel MDL-based Compression Method for Power Quality ... - CERN

IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 22, NO. 1, JANUARY 2007

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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: [email protected]). S. H. Park is with the Department of Electronic Engineering, Hallym University, Chuncheon, Gangwon-do, 200-702, Korea (e-mail: [email protected]). 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: [email protected]). 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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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

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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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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 .


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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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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. 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


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.

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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

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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.


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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.

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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.


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.