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College of Electrical & Mechanical Engineering  (CEME)
Dr. Muhammad Omer Bin Saeed
Assistant Professor
Department of Computer & Software Engineering

CE&ME
National University of Sciences and Technology (NUST)
Peshawar Road Rawalpindi
Tel :  

Specialization
Digital Signal Processing, Adaptive Filtering, Optimization

Education
PhD (Electrical Engineering) Saudi Arabia.

Dr Muhammad Omer Bin Saeed received the BE and MS degrees in Electrical Engineering from College of E&ME, National University of Science and Technology (NUST), Pakistan in 2003 and 2005, respectively, and PhD in Electrical Engineering from King Fahd University of Petroleum & Minerals, Saudi Arabia, in 2011. Currently, he is an Assistant Professor in Department of Computer Engineering, CEME, NUST, Pakistan since May, 2016. He has various publications in international journals and conferences and has also been invited as a reviewer of several international journals and conferences. His research interests include Digital Signal Processing, Adaptive Systems, Optimization, Error Control Coding, Communications, Image Processing and Machine Learning.​omersaeed@ceme.nust.edu.pk​​​

DSP, Adaptive Systems, Communications, Optimization, Image Processing and Machine Learning

​Journals:

1.      M. A. Kousa, M. O. Bin Saeed and M. A. I. Aabed, “Exact enumeration of dominant unrecoverable erasure patterns in SPCPC schemes,” in Proc. Of Arabian Journal of Science & Engineering, vol. 35, no. 2B, Oct. 2010, pp. 185-197.

2.      M. O. Bin Saeed, A. Zerguine and S. A. Zummo, “A noise constrained algorithm for estimation over adaptive networks,” in International Jounal of Adaptive Control and Signal Processing, DOI: 10.1002/acs.2358, 2013.

3.      M. O. Bin Saeed, A. Zerguine and S. A. Zummo, “A variable step size strategy for distributed estimation over adaptive networks,” inEURASIP Journal on Advances in Signal Processing, 2013:135  doi:10.1186/1687-6180-2013-135, 2013.

4.      M. O. Bin Saeed, A. Zerguine and S. A. Zummo, “Blind distributed estimation algorithms for adaptive networks,” in EURASIP Journal on Advances in Signal Processing, 2014:136  doi:10.1186/1687-6180-2014-136, 2014.

5.      M. O. Bin Saeed, M. S. Sohail, S. Z. Rizvi, M. Shoaib and A. U. H. Sheikh, “An accelerated CLPSO algorithm,” available online athttp://arxiv.org/abs/1304.3892, 2013.

6.      M. S. Sohail, M. O. Bin Saeed, S. Z. Rizvi, M. Shoaib and A. U. H. Sheikh, “Low complexity particle swarm optimization for time-critical applications,” available online at http://arxiv.org/abs/1401.0546, 2014.

7.      M. O. Bin Saeed, “LMS-based variable step-size algorithms: a unified analysis approach,” Arabian Journal for Science and Engineering, vol. 42, no. 7, pp. 2809-2816, Jul. 2017.

8.      S. Tehsin, S. Rehman, M. O. Bin Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young and M. S. Alam, “Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition,” IEEE Access, vol. 5, no. 1, pp. 24495-24502, Oct. 2017.

9.      M. O. Bin Saeed, W. Ejaz, S. Rehman, A. Zerguine, A. Anpalagan and H. Song, “A Unified Analytical Framework for Distributed Variable Step Size LMS Algorithms in Sensor Networks for IoT,” accepted in Telecommunication Systems.

 Conferences:

1.      M. O. Bin Saeed, A. Zerguine and S. A. Zummo, “Variable step-size least mean square algorithms over adaptive networks,” in Proc. of ISSPA 2010, Kuala Lumpur, Malaysia, pp. 381-384, 2010.

2.      M. O. Bin Saeed, A. Zerguine and S. A. Zummo, “Noise constrained diffusion least mean squares over adaptive networks,” in Proc. of PIMRC 2010, Istanbul, Turkey, pp. 288-292, 2010.

3.      M. O. Bin Saeed, A. Zerguine and S. A. Zummo, “A robust LMS adaptive algorithm over distributed networks,” in Proc. of ASILOMAR 2011, Pacific Grove, CA, pp. 547-550, 2011.

4.      M. O. Bin Saeed and A. Zerguine, “A new variable step-size strategy for adaptive networks,” in Proc. of ASILOMAR 2011, Pacific Grove, CA, pp. 312-315, 2011.

5.      S. H. Arastu, A. Zerguine, M. O. Bin Saeed and A. T. Al-Awami, “Cooperative parameter estimation using PSO in ad-hoc WSN,” in Proc. of EUSIPCO 2012, Bucharest, Romania, pp. 779-783, 2012.

6.      M. O. Bin Saeed and A. U. H. Sheikh, “A new LMS strategy for sparse estimation in adaptive networks,” in Proc. of PIMRC 2012, Sydney, Australia, pp. 1728-1733, 2012.

7.      M. O. Bin Saeed, A. Zerguine, S. A. Zummo and A. H. Sayed, “Unsupervised algorithms for distributed estimation over adaptive networks,” inProc. of ASILOMAR 2012, Pacific Grove, CA, pp. 1780-1783, 2012.

8.      M. O. Bin Saeed and A. U. H. Sheikh, “Sparse system identification over adaptive networks,” in Proc. of ICCSPA 2013, Sharjah, UAE, pp. 1-5, 2013.

9.      M. O. Bin Saeed and A. Zerguine, “A variable step size strategy for sparse system identification,” in Proc. of SSD 2013, Hammamet, Tunisia, pp. 1-4, 2013.

10.  S. Abdul Baqi, A. Zerguine and M. O. Bin Saeed, “Diffusion normalized least mean squares over wireless sensor networks,” in Proc. of IWCMC 2013, Cagliari, Italy, pp. 1454-1457, 2013.

11.  K. Mahmood, S. M. Asad, M. Moinuddin, M. O. Bin Saeed and A. Zerguine, “Rayleigh fading channel estimation using MMSE estimator for MIMO-CDMA system,” in Proc. of ICCSPA ’15, Sharjah, UAE, pp. 1-4, 2015.

12.  M. O. Bin Saeed, A. Zerguine, M. S. Sohail, S. Rehman, W. Ejaz and A. Anpalagan, “Variable step-size strategy for distributed parameter estimation of compressible systems in WSNs,” in Proc. of IEEE CAMAD ’16, pp. 1-5, 2016.

 Patents:

1.      A. Zerguine, M. O. Bin Saeed and S. A. Zummo, “Noise-constrained diffusion least mean square method for estimation in adaptive networks,” Patent Publication No. US 8462892, USPTO.

2.      S. A. Zummo, M. O. Bin Saeed and A. Zerguine, “Variable step-size least mean square method for estimation in adaptive networks,” Patent Publication No. 8547854, USPTO.

3.      M. O. Bin Saeed and A. Zerguine, “Variable step-size least mean square method for estimation in adaptive networks,” Patent Publication No. US 8903685, USPTO.

4.      M. O. Bin Saeed and A. Zerguine, “Adaptive filter for system identification,” U.S. Patent filed under US20140310326 A1.

​Courses Taught:

​Course Code ​Course Title
​EC 102 ​Computer System and Programming
​EC 105 ​Introduction to Computing
​EC 431 ​Digital Communication
​CE 847 ​Digital Communication