2012
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71
An Efficient Scheme for PAPR Reduction of OFDM based on Selected Mapping without Side Information
An Efficient Scheme for PAPR Reduction of OFDM based on Selected Mapping without Side Information
2
2
Orthogonal frequency division multiplexing (OFDM) has become a promising method for many wireless communication applications. However, one main drawback of OFDM systems is the high peaktoaverage power ratio (PAPR). Selected mapping (SLM) is a wellknown technique to decrease the problem of high PAPR in OFDM systems. In this method, transmitter is obliged to send some bits named side information (SI) for each data block. Such side information causes bandwidth efficiency to be decreased; in addition, incorrect detection of SI in receiver side make whole data block be lost. In this paper, we propose a technique by using linear feedback shift register (LFSR) in which side information bits are not explicitly sent. By considering an OFDM system through the use of 16QAM modulation as an illustrated example, it is shown that the proposed technique from the view point of bit error rate (BER), probability of detection failure ( Pdf) and PAPR reduction performs very well.
1
Orthogonal frequency division multiplexing (OFDM) has become a promising method for many wireless communication applications. However, one main drawback of OFDM systems is the high peaktoaverage power ratio (PAPR). Selected mapping (SLM) is a wellknown technique to decrease the problem of high PAPR in OFDM systems. In this method, transmitter is obliged to send some bits named side information (SI) for each data block. Such side information causes bandwidth efficiency to be decreased; in addition, incorrect detection of SI in receiver side make whole data block be lost. In this paper, we propose a technique by using linear feedback shift register (LFSR) in which side information bits are not explicitly sent. By considering an OFDM system through the use of 16QAM modulation as an illustrated example, it is shown that the proposed technique from the view point of bit error rate (BER), probability of detection failure ( Pdf) and PAPR reduction performs very well.
1
9
صابر
میمنت آبادی
Saber
Meymanatabadi
Assistant Professor, Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Assistant Professor, Department of Electrical
Iran
smeymanat@tabrizu.ac.ir


Javad
Musevi Niya
Associate Professor, Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Associate Professor, Department of Electrical
Iran
niya@tabrizu.ac.ir
بهزاد
مظفری تازه کند
behzad
mozaffari tazehkand
Associate Professor, Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Associate Professor, Department of Electrical
Iran
mozaffary@tabrizu.ac.ir
Orthogonal frequency division multiplexing (OFDM)
peaktoaverage power ratio (PAPR)
selected mapping
Side Information
[R. van. Nee, G. Awater, M. Morikura, H. Takanashi, M. Webster, K. W. Halford, “New highrate wireless LAN standards”, IEEE Commun. Mag, vol. 37, no. 12, pp. 82– 88, 1999.##P. H. Moose, D. Roderick, R. North, M. Geile, “A COFDMbased radio for HDR LOS networked communications”, in Proc. IEEE ICC, vol. 1, pp. 187– 192, 1999.##I. Koffman, V. Roman, “Broadband wireless access solutions based on OFDM access in IEEE 802.16”, IEEE Commun. Mag, vol. 40, no. 4, pp. 96– 103, 2002.##H. Yang, “A road to future broadband wireless access: MIMOOFDM based air interface”, IEEE Commun. Mag, vol. 43, no. 1, pp. 53– 60, 2005.##S. B. Weinstein, P. M. Ebert, “Data Transmission by FrequencyDivision Multiplexing using the Discrete Fourier Transform”, IEEE Trans. on Communications, vol. 19, no. 5, pp. 628– 634, 1971.##R. Nee, R. Prasad, “OFDM for Wireless Multimedia Communications”, Boston, Artech House, 2000.##L. Nuaymi, WiMAXTechnology for Broadband Wireless Access, West Sussex, John Wiley & Sons, 2008.##X. Li and L. J. Cimini, “Effects of clipping and filtering on the performance of OFDM”, IEEE Commun. Lett., vol. 2, no. 5, pp. 131– 133, May, 1998.##N. Carson and T. A. Gulliver, “Peaktoaverage power ratio reduction of OFDM using repeataccumulate codes and selective mapping”, in Proc. IEEE Int. Symp. Inf. Theory, Jun.–Jul., pp. 244–244, 2002.##S. H. Han and J. H. Lee, “An overview of peaktoaverage power ratio reduction techniques for multicarrier transmission”, IEEE Wireless Communications, vol. 12, no. 2, pp. 56– 65, Apr, 2005.##S. B. Weinstein and P. M. Ebert, “Data transmission by frequency division multiplexing using the discrete Fourier transform”, IEEE Trans.##Comm. Technology, vol. COM19, no. 15, Oct, 1971.##H. Ochiai and H. Imai, “Performance analysis of deliberately clipped OFDM signals”, IEEE Trans. Commun., vol. 50, no. 1, pp. 89– 101, Jan, 2002.##S. H. Muller and J. B. Hubber, “OFDM with reduced peaktoaverage power ratio by optimum combination of partial transmit sequences”, Electron. Lett., vol. 33, no. 5, pp. 368– 369, Feb, 1997.##B. S. Krongold and D. L. Jones, “PAR reduction in OFDM via active constellation extension”, IEEE Trans. Broadcast., vol. 49, no. 3, pp. 258– 268, Sep, 2003.##R. W. Bauml, R. F. H. Fischer, J. B. Huber, “Reducing the peak toaverage power ratio of multicarrier modulation by selected mapping”, Electron. Lett, vol. 32, no. 22, pp. 2056 2057, 1996.##M. Breiling, S. H. MullerWeinfurtner, J. B. Hubber, “SLM peakpower reduction without explicit side information”, IEEE Commun. Lett, vol. 5, no. 6, pp. 239– 241, 2001.##K. Yang, S. Chang, “Peaktoaverage power control in OFDM using standard arrays of linear block codes”, IEEE Commun. Lett, vol. 7, no. 4, pp. 174– 176, 2003.##A. D. S. Jayalath, C. Tellambura, “SLM and PTS peakpower reduction of OFDM signals without side information”, IEEE Trans. Wireless Commun, vol. 4, no. 5, pp. 2006– 2013, 2005.##N. Chen, G. T. Zhou, “Peaktoaverage power ratio reduction in OFDM with blind selected pilot tone modulation”, IEEE Trans. Wireless Commun., vol. 5, no. 8, pp. 2210 2216, 2006.##S. Y. L. Goff, B. K. Khoo, C. C. Tsimenidis, B. S. Sharif, “A Novel Selected Mapping Technique for PAPR Reduction in OFDM Systems”, IEEE Transactions on Communication, vol. 56, no. 11, November, 2008.##Saber. Meymanatabadi, Javad Musevi Niya, Behzad Mozaffari, “Selected Mapping Technique for PAPR Reduction Without Side Information Based on mSequence”, Wireless Personal Communication, vol. 71, no. 4, pp. 2523 2534, August, 2013.##S. Shepherd, J. Orriss, S. Barton, “Asymptotic limits in peak envelope power reduction by redundant coding in orthogonal frequencydivision multiplex modulation”, IEEE Trans. Communications, vol. 46, no. 1, pp. 5– 10, Jan, 1998. ##R. van Nee, A. deWild, “Reducing the peak to average power ratio of OFDM”, in Proceedings of the 48th IEEE Semiannual Vehicular Technology Conference, vol. 3, pp. 2072– 2076, May, 1998.##S. Q. Wei, D. L. Goeckel, P. E. Kelly, “A modem extreme value theory approach to calculating the distribution of the peaktoaverage power ratio in OFDM Systems”, in IEEE International Conference on Communications, vol. 3, pp. 1686–1690, Apr, 2002.##J. G. Proakis., “Digital Communications”, 4th ed. New York, USA: McGrawHill, 2001.##Golomb, S. W., Gong, G., “Signal Design for Good Correlation”, Cambridge University Press. 2005.##Simon, M. K. Spread Spectrum Communications Handbook. Boston: McGrawHill. 1994.##Don, T., “Principles of Spread Spectrum Communication Systems”, Springer. 2004.##http://en.wikipedia.org/wiki/Primitive_polynomial.##Williams, M., Sloane, F.J., “Pseudorandom sequences and arrays”, Proceedings of the IEEE, pp.1715 1729, 1976.##C. Tellambura, “Computation of the continuoustime PAR of an OFDM signal with BPSK subcarriers”, IEEE Commun. Lett, vol. 5, no. 5, pp. 185 187, 2001.##]
A New Compact Ultrawideband Linear Antenna Array for Target Detection Applications
A New Compact Ultrawideband Linear Antenna Array for Target Detection Applications
2
2
This paper presents a lowcost compact planar microstripfed monopole antenna and its fourelement array design for ultrawideband (UWB) wireless communication and target detection applications, respectively, operating in the frequency span of 3 GHz to 11 GHz. A prototype was fabricated and then measured based on optimal parameters. The results of reflection coefficient (S11) and radiation patterns are shown and discussed. There is good consistency between the simulated S11 and the measured one. In addition, a 1 × 4 linear array design with the size of 100 mm × 34 mm has been proposed to achieve a higher gain. Simulation shows that the array gain is increased about 6 dBi in comparison to the single element through the whole UWB frequency range. The proposed array has an average of 15 dB side lobe level (SLL) in the mentioned range. And also, a 23 dB SLL has been achieved by applying DolphChebyshev amplitude distribution at 6 GHz. Simulation results confirm that the antenna exhibits a constant bidirectional radiation pattern with a high and flat gain in case of the array design.
1
This paper presents a lowcost compact planar microstripfed monopole antenna and its fourelement array design for ultrawideband (UWB) wireless communication and target detection applications, respectively, operating in the frequency span of 3 GHz to 11 GHz. A prototype was fabricated and then measured based on optimal parameters. The results of reflection coefficient (S11) and radiation patterns are shown and discussed. There is good consistency between the simulated S11 and the measured one. In addition, a 1 × 4 linear array design with the size of 100 mm × 34 mm has been proposed to achieve a higher gain. Simulation shows that the array gain is increased about 6 dBi in comparison to the single element through the whole UWB frequency range. The proposed array has an average of 15 dB side lobe level (SLL) in the mentioned range. And also, a 23 dB SLL has been achieved by applying DolphChebyshev amplitude distribution at 6 GHz. Simulation results confirm that the antenna exhibits a constant bidirectional radiation pattern with a high and flat gain in case of the array design.
11
19
رضا
غلامی
reza
gholami
M.Sc. Student, Department of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran
M.Sc. Student, Department of Electrical Engineerin
Iran
reza214412@yahoo.com
بیژن
ذاکری
Bijan
Zakeri
Assistant Professor, Department of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran
Assistant Professor, Department of Electrical
Iran
zakeri@nit.ac.ir


hossein
Mehrpour Bernety
M.Sc. Student, Department of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran
M.Sc. Student, Department of Electrical Engineerin
Iran
hossein_mehrpourbernety@ieee.org
crosspolar pattern
element spacing
reflection coefficient
UWB antenna
and UWB array
[Federal Communication Commission, “FCC 0248– First Report and Order: Revision of Part 15 of the Commission’s Rules Regarding UltraWideband Transmission Systems”, Washington, DC, released April 22, 2002.##J. Liang, C. C. Chiau, X. D. Chen, and C. G. Parini, “Study of a Printed Circular Disc Monopole Antenna for UWB Systems”, IEEE Transactions on Antennas and Propagation, vol. 53, no. 11, pp. 3500– 3504, Nov, 2005.##Eldek, A. A., “Numerical Analysis of a Small Ultrawideband Microstripfed Tap Monopole Antenna”, Progress in Electromagnetics Research, PIER 66, pp. 199– 212, 2006.##R. Zaker, Ch. Ghobadi, and J. Nourinia, “A Modified Microstripfed twostep Tapered Monopole Antenna for UWB AND WLAN Applications”, Progress in Electromagnetics Research, PIER 77, pp. 137– 148, 2007.##N. Prombutr, P. Kirawanich, and P. Akkaraekthalin, “Bandwidth Enhancement of UWB Microstrip Antenna with a Modified Ground Plane”, International Journal of Microwave Science and Technology, vol. Article ID 821515, 7 pages, 2009.##Chen, D. and C.H. Cheng, “A Novel Compact Ultrawideband (UWB) Wide Slot Antenna with via Holes”, ProgressIn Electromagnetics Research, Vol. 94, pp. 343– 349, 2009.##M. Ojaroudi, Sh. Yazdanifard, N. Ojaroudi, and M. NaserMoghaddasi, “Small Square Monopole Antenna With Enhanced Bandwidth by Using Inverted TShaped Slot and ConductorBacked Plane”, IEEE Transactions on Antennas and Propagation,vol. 59, no. 2, pp. 670– 674, February, 2011.##Klemm M. et al, “RadarBased Breast Cancer Detection Using a Hemispherical Antenna Array–Experimental Results”, IEEE Transactions on Antennas and Propagation, Vol. 57, No. 6, pp. 1692–1704, 2009.##H. J. Lam, J. Bornemann, “UltraWideband PrintedCircuit Array Antenna for Medical Monitoring Applications”, IEEE International Conference on UltraWideband (ICUWB), pp. 506–510, September, 2009. ##K. SHAMBAVI, Z. C. ALEX, T. N. P. KRISHNA, “Design and Analysis of High Gain MillimeterWave Microstrip Antenna Array for WirelessApplications”, Journal of Theoretical and Applied Information Technology, Vol. 7, No.2, pp. 159–164, 2010.##C.Lakshmipriya, A. P. Nithyapriya, R. Nivedha, “DualBand Microstrip Patch Array Antenna Design for LTE and WLAN Application”, International Journal of Communications and Engineering, Vol. 3, No.3, pp. 11– 14, 2012.##M. Garbaruk, “Analysis of ultrawideband linear antenna arrays”, PrzegladElektrotechniczny (Electrical Review), Vol., No. 8, pp. 75–76, 2012.##B. Kasi, C. K. Chakrabarty, “ULTRAWideband Antenna Array Design for Target Detection”, Progress In Electromagnetics Research C, Vol. 25, pp. 67– 79, 2012.##Bin Huang, YingxinXu, “Analysis and Design of A Novel UWB Antenna Array”, IEEE International Conference on Microwave and Millimeter Wave Technology (ICMMT), pp. 313– 316, May, 2010.##R. D’Errico, A. Sibille, “Mutual Coupling in UWB Compact Arrays”, European Cooperation in the Field of Scientific and Technical Research (EuroCOST), Valencia, Spain, May, 2009.##O. Ahmed, A. Sebak, “Mutual Coupling Effect on Ultrawideband Linear Antenna Array Performance”, International Journal of Antennas and Propagation, Vol. 2011, 11 pages, 2011.##T. Y. Yun, and K. Chang, “A LowCost 8 to 26.5 GHz Phased Array Antenna Using a Piezoelectric Transducer Controlled Phase Shifter”, IEEE Trans. Antennas Propagat. , vol. 49, No. 9, Sept, 2001.##M. Y. W. Chia, T. H. Lim, J. K. Yin, P. Y. Chee, S. W. Leong, and C. K. Sim, “Electronic BeamSteering Design for UWB Phased Array”, IEEE Trans. Microw. Theory Tech., vol. 54, No. 6, June, 2006.##B. Allen, “Ultra Wideband Antennas and Propagation for Communications, Radar and Imaging”, John Wiley & Sons Inc, ISBN 978 0470 03255 8.##K. Bahadori, Y. RahmatSamii, “A Miniaturized EllipticCard UWB Antenna With WLAN Band Rejection forWireless Communications”, IEEE Transactions on Antennas and Propagation, Vol. 55, No. 11, 2007.##R. C. Hanson, “Phased Array Antennas, Second Edition”, New Jersey: John Wiley & Sons Inc.,2009.##O. Ahmed, A. R. Sebak, “Mutual Coupling Effect on Ultrawideband Linear Antenna Array Performance”, International Journal of Antennas and Propagation, Vol, 11 pages, Article ID 142581, 2011.##]
Magnetic Saturation Impacts on Fault Analysis of SquirrelCage Induction Motors using Winding Function Approach
Magnetic Saturation Impacts on Fault Analysis of SquirrelCage Induction Motors using Winding Function Approach
2
2
Multiple coupled circuit modeling of squirrelcage induction motors, or winding function approach is the most detailed and complete analytical model used to analyze the performance of the faulty induction motors. This paper extends the abovementioned model to a saturable model including variable degrees of the saturation effects using an appropriate air gap function and novel techniques for locating the angular position of the air gap flux density and for estimating the saturation factor. Comparing simulated and experimental magnetization characteristics and noload current waveforms, accuracy of the new saturable model is verified. Using saturable and nonsaturable models, various simulations are carried out on faulty induction motors, and then, by comparing the results, the impacts of the saturation on the performance of the faulty motor become evident. Also, comparisons are made with the experimental and/or finite elements results, which confirm the higher accuracy of the saturable model versus nonsaturable model.
1

21
31
منصور
اوجاقی
mansour
ojaghi
زنجان  خ صفا کوچه شهید علوی  سمت راست  پ 7
زنجان  خ صفا کوچه شهید علوی  سمت راست
Iran
mojaghi@znu.ac.ir


Mahdi
Sabouri
Iran
m.sabouri@znu.ac.ir
induction motors
Failure analysis
Saturation magnetization
Modeling
Computer simulation
Statistical Variation Analysis of Formant and Pitch Frequencies in Anger and Happiness Emotional Sentences in Farsi Language
Statistical Variation Analysis of Formant and Pitch Frequencies in Anger and Happiness Emotional Sentences in Farsi Language
2
2
Setup of an emotion recognition or emotional speech recognition system is directly related to how emotion changes the speech features. In this research, the influence of emotion on the anger and happiness was evaluated and the results were compared with the neutral speech. So the pitch frequency and the first three formant frequencies were used. The experimental results showed that there are logical and reasonable relations between the emotions and variations of these speech features. These results were also used to confirm our previous research about emotion recognition and emotional speech recognition.
1
Setup of an emotion recognition or emotional speech recognition system is directly related to how emotion changes the speech features. In this research, the influence of emotion on the anger and happiness was evaluated and the results were compared with the neutral speech. So the pitch frequency and the first three formant frequencies were used. The experimental results showed that there are logical and reasonable relations between the emotions and variations of these speech features. These results were also used to confirm our previous research about emotion recognition and emotional speech recognition.
33
45


Davood
Gharavian
Assistant Professor, Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Assistant Professor, Department of Electrical
Iran
dgharavian@gmail.com
Emotion
Emotion Recognition
Emotional Speech Recognition
statistical analysis
Formant and Pitch Frequencies
[Rong, J., Li, G. and Chen, Y. P., “Acoustic Feature Selection for Automatic Emotion Recognition from Speech”, Information Processing and Management, 45 (3), pp. 315 328, doi:10.1016/j.ipm.2008.09.003, 2009.##Batliner, A., Steidi, S., Schuller, B., Seppi, D., Vogt, T., Wagner, J., Devillers, L., Vidrascu, L., Aharonson, V., Kessous, L. and Amir, N., “Whodunnit Searching for the Most Important Feature Types Signalling EmotionRelated User States in Speech”, Computer Speech and Language, 25(1), pp. 4 28, doi:10.1016/j.csl.2009.12.003, 2010.##Polzehl, T., Schmitt, A., Metze F., and Wagner, M., “Anger Recognition in Speech Using Acoustic and##Linguistic Cues”, Speech Communication, 53 (910), pp. 1198 1209, doi: 10.1016/j.specom2011.05.002, 2011.##Bozkurt, E., Erdem, C. E., Erdem, A. T. and Erzin, E., “Formant Position Based Weighted Spectral Features for Emotion Recognition”, Speech Communication Journal, 53(910), pp. 1186 1197, 2011.##Petridis, S., and Pantic, M., “Audiovisual Discrimination between Laughter and Speech”, in Proc Int. Conf. on Acoustic, Speech and Signal Processing, pp. 5117 5120, 2008.##Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., Rose, R., Tyagi, V., and Wellekens, C., “Automatic Speech Recognition and Speech Variability, A Review”, Speech Communication, vol.49, pp. 763 786, doi:10.1016/j.specom, 02, 006, 2007.##Zhang, C., Weijer, J. V. D., Cui, J., “Intra and InterSpeaker Variations of Formant Patter for Lateral Syllables in Standard Chinese”, Journal of Forensic Science International, 158 (23), pp. 117 124, doi:101016/j.forsciint.2005.04.043, 2005.##Hagenaars, M. A., and Minnen, A. V., “The Effect of Fear on Paralinguistic Aspects of Speech in Patients with Panic Disorder with Agoraphobia”, In journal of Anxiety Disorder, 19(5), pp. 521 537, 2005.##Lakshminarayanan, K., Shalom, D. B., Wassenhove, V. V., Orbelo, D., Houde, J., and Poeppel, D., “The Effect of Spectral Manipulations on the Identification of Affective and Linguistic Prosody”, Brain and Language, 84 (2), pp. 250 263, 2003.##Steidl, S., Batliner, A., Seppi, D., and Schuller, B., “On the Impact of Children’s Emotional Speech on Acoustic and Language Models”, EURASIP Journal on Audio, Speech and Music Processing, doi:10.1155/2010/783954, 2010.##Toivanen, J., Vayrynen, E., and Seppanen, T., “Automatic Discrimination of Emotion from Spoken Finnish”, Language and Speech Journal, 47 (4), pp. 383 412, doi:10.1177/00238309040470040301, 2004.##Pell , M. D., Paulmann, S., Dara, C., Alasseri, A., and Kotz, S. A., “Factors in the Recognition of Vocally Expressed Emotions: A Comparison of Four Languages”, Journal of Phonetics, 37 (4), pp. 417436, 2009.##Jong, K. D., “Stress, Lexical focus, and segmental focus in English: Patterns of Variation in Vowel Duration”, Journal of Phonetics, 32 (4), pp. 493516, 2004.##Gharavian, D., Sheikhzadeh, H. and Ahadi, S. M., “An Experimental MultiSpeaker Study on Farsi Phoneme Duration Rules Using Automatic Alignment”, in Proc. 8th Australian International Conference on Speech Science and Technology, pp. 186191, 2000.##Gharavian, D. and Ahadi, S. M., “Statistical Evaluation of the Influence of Stress on Pitch Frequency and Phoneme Durations in Farsi Language”, in Proc 8th European Conference on Speech Communication and Technology, pp. 1 4, 2003.##Gharavian, D. and Ahadi, S. M., “Evaluation of the Effect of Stress on Formants in Farsi Vowels”, in Proc. 2004 International Conference on Acoustics, Speech, and Signal Processing, pp. 661 664, 2004.##Gharavian, D., “Prosody in Farsi Language and Its Use in Recognition of Intonation and Speech”, PhD Thesis, Elec. Eng. Dept., Amirkabir University, Tehran, 2004.##Gharavian, D. and Ahadi, S. M., “Use of Formants in Stressed and Unstressed Continuous Speech Recognition”, in Proc. 8th International Conference on Spoken Language Processing, pp. 1 4, 2004.##Gharavian, D. and Ahadi, S.M., “Statistical Evaluation of Stress in Farsi and Its Effect on Vowel Pitch Frequencies, Durations and Energies”, Amirkabir Scientific Research Journal, 15 (58A), pp. 258 268, Spring, 2004.##Gharavian, D., Sheikhan, M. and Janipour, M., “Pitch in Emotional Speech and Emotional Speech Recognition Using Pitch Frequency”, Majlesi Journal of Electrical Engineering, 4(1), pp. 19 24, 2010.##Gharavian, D. and Sheikhan, M., “Emotion Recognition and Emotion Spotting Improvement Using FormantRelated Features”, Majlesi Journal of Electrical Engineering, 4(1), pp. 1 8, 2010.##Sheikhan, M., Gharavian, D. and Ashoftedel, F., “Using DTWNeural Based MFCC Warping to Improve Emotional Speech Recognition”, Neural Computing and Applications Journal, 21 (7), pp. 1765 1773, doi: 10.1007/s00521 011 0620 8, 2012.##Gharavian, D., Sheikhan, M., Nazerieh, A. and Garoucy, S., “Speech Emotion Recognition Using FCBF Feature Selection Method and GA Optimized Fuzzy ARTMAP Neural Network”, Neural Computing and Applications Journal, 21(8), pp. 112, doi:10.1007/s00521 011 0643 1, 2011.##Gharavian, D. and Sheikhan, M., “GMMBased Emotion Recognition in Farsi Language Using Feature Selection Algorithms”, World Applied Science Journal, 14(4), pp. 626 638, 2011.##Gharavian, D., Sheikhan, M. and Ashoftedel, F., “Using Neutralized Formant Frequencies to Improve Emotional Speech Recognition”, IEICE Electronic Express, 8(14), pp. 1155 1160, 2011.##Sheikhan, M., Bejani, M. and Gharavian, D., “Modular NeuralSVM Scheme for Speech Recognition Using ANOVA Feature Selection Method”, Neural Computing and Applications Journal, pp. 113. doi:10.1007/s00521 012 0814 8, 2012.##Gharavian, D., Sheikhan, M. and Ashoftedel, F., “Emotion Recognition Improvement Using Normalized Formant Supplementary Features by Hybrid of DTWMLPGMM”, Neural Computing and Applications Journal, pp. 111, doi: 10.1007/s00521 012 0884 7, 2012.##Bijankhan, M., Sheikhzadegan, J., Roohani, M. R., Samareh, Y., Lucas, C. and Tebiani, M., “The Speech Database of Farsi Spoken Language”, in Proc. 1994 5th Australian Int. Conf. on Speech Science and Technology, pp. 826 83, 1994.##Young, S. J., Evermann, G., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V. and Woodland, P., The HTK Book (ver 3.2), Cambridge University Eng. Dept, 2002.##]
The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways
The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways
2
2
The performance of many traffic control strategies depends on how much the traffic flow models are accurately calibrated. One of the most applicable traffic flow model in traffic control and management is LWR or METANET model. Practically, key parameters in LWR model, including free flow speed and critical density, are parameterized using flow and speed measurements gathered by inductive loop detectors and ClosedCircuit TV. The challenging problem here is continuous changes in these parameters due to traffic conditions (traffic composition, incidents) and environmental factors (dense fog, strong wind, snow) and missing data. In this paper Maximum Likelihood approaches are developed to the LWR model identification while inaccurate observations are available at the traffic control center. A Maximum Likelihood method is accomplished via the employment of an Expectation Maximization algorithm. To approximate first and second derivatives of optimal filter without sticking in analytical complexities, The EM algorithm is implemented based on particle filters and smoothers. Two convincing simulation results for two sets of field traffic data are used to demonstrate the effectiveness of the proposed approaches.
1
The performance of many traffic control strategies depends on how much the traffic flow models are accurately calibrated. One of the most applicable traffic flow model in traffic control and management is LWR or METANET model. Practically, key parameters in LWR model, including free flow speed and critical density, are parameterized using flow and speed measurements gathered by inductive loop detectors and ClosedCircuit TV. The challenging problem here is continuous changes in these parameters due to traffic conditions (traffic composition, incidents) and environmental factors (dense fog, strong wind, snow) and missing data. In this paper Maximum Likelihood approaches are developed to the LWR model identification while inaccurate observations are available at the traffic control center. A Maximum Likelihood method is accomplished via the employment of an Expectation Maximization algorithm. To approximate first and second derivatives of optimal filter without sticking in analytical complexities, The EM algorithm is implemented based on particle filters and smoothers. Two convincing simulation results for two sets of field traffic data are used to demonstrate the effectiveness of the proposed approaches.
47
58


Amin
Ramezani
Assistant Professor, Control and Intelligent Processing Center of Excellence, School of ELec & Comp, Engineering, University of Tehran, Tehran, Iran
Assistant Professor, Control and Intelligent
Iran
ramezani@modares.ac.ir


Behzad
Moshiri
Professor, Control and Intelligent Processing Center of Excellence, School of ELec, & Comp. Engineering, University of Tehran, Tehran, Iran
Professor, Control and Intelligent Processing
Iran
moshiri@ut.ac.ir


Ashkan
Rahimi Kian
Associate Professor, Control and Intelligent Processing Center of Excellence, School of ELec & Comp, Engineering, University of Tehran, Tehran, Iran
Associate Professor, Control and Intelligent
Iran
arkian@ut.ac.ir
Maximum Likelihood Estimation
Free Speed
Critical Density
Expectation Maximization Algorithm
[[1] M. Papageorgiou, J. Blosseviller, and H. HadjSalem, “Modelling and realtime control of traffic flow on the southern part of boulevard peripherique in paris: Part i: modelling”, Transportation Research, vol. 24A, no. 5, pp. 345– 359, 1990.##[2] Y. Wang, M. Papageorgiou, and A. Messmer, “A realtime freeway network traffic surveillance tool”, IEEE Trans. Control Systems Technology, vol. 14, no. 1, pp. 18– 32, 2006.##[3] L. Ljung., “System identification, Theory for the user. System sciences series”, Prentice Hall, Upper Saddle River, NJ, USA, second edition, 1999.##[4] T. Soderstrom and P. Stoica, “System identification. Systems and Control Engineering”, Prentice Hall, 1989.##[5] Doucet, A., de Freitas, J.F.G. and Gordon N.J. (eds.), “Sequential Monte Carlo Methods in Practice”, New York: Springer Verlag, 2001.##[6] Guyader, A., LeGland, F. and Oudjane, N., “A particle implementation of the recursive MLE for partially observed diffusions”, Proceedings of the 13th IFAC Symposium on System Identfication, 1305 1310, 2003.##[7] Cerou F., LeGland F. and Newton N.J., “Stochastic particle methods for linear tangent equations in Optimal Control and PDE's Innovations and Applications”, eds. J. Menaldi, E. Rofman, 2001.##[8] Benveniste, A., Metivier, M. and Priouret, P..Adaptive Algorithms and Stochastic Approximation. New York: Springer Verlag, 1990.##[9] Spall J. C., “Adaptive stochastic approximation by the simultaneous perturbation method”, IEEE Trans. Autom. Contr., vol. 45, pp. 1839 1853, 2000.##[10] Bertsekas D., “Nonlinear Programming”, 2nd Edition, Athena Scientific,1999.##[11] A. Coquelin, R. Deguest, and R. Munos, “Numerical methods for sensitivity analysis of Feynman Kac models”, Technical Report INRIA 00125427, INRIA, France, January, 2007.##[12] Doucet, A., de Freitas, N. and Gordon, N. (Eds.), “Sequential Monte Carlo Methods in Practice”, Springer Verlag, 2001.##[13] M. Papageorgiou, J. Blosseviller, and H. HadjSalem, “Modelling and realtime control of traffic flow on the southern part of boulevard peripherique in paris: Parti: modelling”, Transportation Research, vol. 24A, no. 5, pp. 345–359, 1990.##[14] Doucet, A. and Tadic, V.B., “Parameter estimation in general statespace models using particle methods”, Ann. Inst. Stat. Math., 55, 409 422, 2003.##[15] N. J. Gordon, D. J. Salmond, and A. F. M. Smith., “Novel approach to nonlinear/nonGaussian Bayesian state estimation”, In IEE Proceedings on Radar and Signal Processing, volume 140, pages 107– 113, 1993.##[16] B. Coifman, D. Lyddy, and A. Sabardonis, “The berkeley highway laboratorybuilding on the i80 field experiment”, in Proc. IEEE ITS Council Annual Meeting, pp. 5– 10, 2000.##]
Optimum Design of BrushLess DC Motor with Minimum Torque Pulsation using FEM & PSO
Optimum Design of BrushLess DC Motor with Minimum Torque Pulsation using FEM & PSO
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Despite many advantages of BrushLess DC motor, torque pulsation is one of the most important disadvantages of it. At first, an optimum primary design of BLDC motor is done using PSO algorithm. Then, other parameters for reducing the torque pulsation are considered by finite element method. It is shown that fractional slot structure has many advantages than conventional one. Both cogging and ripple torque have been reduced for fractional structure. In the next steps, other important torque pulsation parameters, such as magnet embrace, offset and skew are considered. Proper embrace, skew and offset are achieved for reducing torque pulsation by nonlinear finiteelement analysis.
1
Despite many advantages of BrushLess DC motor, torque pulsation is one of the most important disadvantages of it. At first, an optimum primary design of BLDC motor is done using PSO algorithm. Then, other parameters for reducing the torque pulsation are considered by finite element method. It is shown that fractional slot structure has many advantages than conventional one. Both cogging and ripple torque have been reduced for fractional structure. In the next steps, other important torque pulsation parameters, such as magnet embrace, offset and skew are considered. Proper embrace, skew and offset are achieved for reducing torque pulsation by nonlinear finiteelement analysis.
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Mojtaba
Pourjafari
M.Sc. Student, Department of Engineering, University of Guilan, Rasht, Iran
M.Sc. Student, Department of Engineering,
Iran
m_en_p@yahoo.com
اسماعیل
فلاح چولابی
Esmael
Fallah Choolabi
Assistant Professor, Department of Engineering, University of Guilan, Rasht, Iran
Assistant Professor, Department of Engineering,
Iran
fallah_e@guilan.ac.ir


mehrdad
jafar bolan
Associated Professor, Department of Electrical Engineering, Malek Ashtar University of Technology, Shahin shahr, Iran
Associated Professor, Department of Electrical
Iran
j_mehrdad405@hotmail.com
BrushLess DC Motor
Torque pulsation
Finite Element
PSO algorithm
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