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Ioannis D. Schizas received the diploma in Computer Engineering and Informatics (with honors) from the University of Patras, Greece, in 2004, the M.Sc. in Electrical and Computer Engineering from the University of Minnesota, Minneapolis, in 2007 and the Ph. D. in Electrical and Computer Engineering from the University of Minnesota, Minneapolis, in June 2011. Since August 2011 he has been an Assistant Professor at the Electrical Engineering department at the University of Texas at Arlington. His general research interests lie in the areas of statistical signal processing, wireless sensor networks and data dimensionality reduction. His latest research efforts focus on designing information processing techniques for handling and analyzing large amounts of spatially scattered data. His long term goal is to introduce efficient, robust and intelligent data processing algorithms that can deal with i) the constantly growing volume of data; ii) the wide spread of decentralized storage units, as well as sensing and processing systems; and iii) the heterogeneity that the available data exhibit. 

University of Minnesota
PhD
University of Minnesota
MSc
University of Patras, Greece
5-year Diploma
Computer Engineering and Informatics
September 2011
Ongoing
Assist Professor
University of Texas at Arlington
July 2013
Ongoing
Member
IEEE Geoscience and Remote Sensing
September 2011
Ongoing
Member
IEEE SPS
September 2011
Ongoing
Member
IEEE
A Framework for Exploring Data in Heterogeneous Sensor Networks:

The vision of this project is the development and analysis of an algorithmic framework that has the ability to learn the unknown structure of a monitored field and enable the mining and exploration of information in heterogeneous sensor data. The techniques developed in this project will enable learning of the sensed field while effectively reducing the usually large amount of sensor data that need to be processed by removing irrelevant and non-informative sensor measurements.  

Distributed identification of information-bearing sensing units in a sensor network

This research project involves the development and analysis of efficient algorithms that uncover sparse structures in the sensor data covariance, and determine in a distributed fashion which sensors acquire informative measurements and have to remain active. Identifying the ‘informative’ sensors can lead to significant energy savings. The research focuses on the novel utilization of covariance sparsity in developing distributed informative sensor selection algorithms that have the ability to adaptively learn the statistical behavior of the sensed field, without relying on a priori known data models. 

Distributed statistical signal processing

Development of distributed signal processing algorithms with applications to statistical inference, denoising, dimensionality reduction and compression.

Intelligent-distributed multi-threat target tracking:

An innovative engagement of stochastic filtering techniques with sparse matrix factorization. The hybrid framework stemming from this exciting blending is applied to a wide spectrum of multi-threat localization and tracking applications ranging from defense to biochemical threat scenarios. A two-level multi-threat sparsity-aware tracking framework is being developed. In the first level, novel techniques, relying on norm-1 regularization, are being designed to analyze the sensor data covariance matrix into sparse factors. The support of these factors will be used to identify the threat-informative sensors. Once the informative sensors are identified and their corresponding measurements acquired, they will be in turn used, at the second level of our framework to accurately track the threats. The second level entails the design of majorly improved particle filtering techniques that are capable of simultaneously tracking multiple threats. Drift homotopy tools will be employed to devise improved particle sampling techniques. 

Wireless sensor networks

Development and analysis of distributed power-aware algorithms for multi-target tracking and denoising using networks of sensors.

A. Malhotra*, G. Binetti, A. Davoudi and I. D. Schizas, “Distributed Power Profile Tracking for Heterogeneous Charging of Electric Vehicles,” IEEE PES Transactions on Smart Grid, to appear 2016. DOI: 10.1109/TSG.2016.2515616. 
Journal Article
Published
G. Ren*, V. Maroulas and I. D. Schizas, “Exploiting Sensor Mobility and Sparse Covariances for Distributed Tracking of Multiple Targets,” EURASIP Journal on Advances in Signal Processing, vol. 53, pp. 1-15, May 2016. DOI: 10.1186/s13634-016-0354-y. 
Journal Article
Published
S. Abhinav, I. D. Schizas, F. L. Lewis and A. Davoudi, “Distributed Noise Resilient Networked Synchrony of Active Distribution Systems,” IEEE PES Transactions on Smart Grid, to appear 2016. DOI: 10.1109/TSG.2016.2569602. 
Journal Article
Published
2016
J. Chen* and I. D. Schizas, “Online Distributed Sparsity-Aware Canonical Correlation Analysis,’’ IEEE Transactions on Signal Processing, vol. 64, no. 3, pp. 688-703, February 2016. DOI: 10.1109/TSP.2015.2481861. 
Journal Article
Published
2016
G. Ren*, V. Maroulas and I. D. Schizas, “Decentralized Sparsity-Based Multi-Source Association and State Tracking,” Elsevier Signal Processing, vol. 120, pp. 627-643, March 2016. DOI: 10.1016/j.sigpro.2015.10.013. 
Journal Article
Published
2016
J. Chen* and I. D. Schizas, “Distributed Information-Based Clustering of Heterogeneous Sensor Data,’’ Elsevier Signal Processing, vol. 126, pp. 35-51, September 2016. DOI:10.1016/j.sigpro.2015.12.017. 
Journal Article
Published
2016
G. Ren, I. D. Schizas and V. Maroulas, “Sparsity Based Multi-Target Tracking Using Mobile Sensors,” IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, pp. 4578-4582, March 20-25, 2016. 
Conference Paper
Published
2016
K. Kang, V. Maroulas, I. D. Schizas and E. Blasch, “An Enhanced Sequential Monte Carlo Filter and its Application to Multi-Target Tracking,” Proc. of the Intl. Conf. on Information Fusion, Heidelberg, Germany, July 5-8, 2016. 
Conference Paper
Published
2016
A. Malhotra, N. Erdogan, I. D. Schizas, A. Davoudi and G. Binetti, “Impact of Charging Interruptions in Coordinated Electric Vehicle Charging,” Proc. of the IEEE Global Conference on Signal and Information Processing, Washington, DC, Dec. 2016. (invited) 
Conference Paper
Under Review
2015
G. Ren*, V. Maroulas and I. D. Schizas, “Distributed Spatio-Temporal Association and Tracking of Multiple Targets Using Multiple Sensors,’’ IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 4, pp. 2570-2589, April 2015. 
Journal Article
Published
2015
I. D. Schizas and V. Maroulas, “Dynamic Data Driven Sensor Network Selection and Tracking,’’ Elsevier Procedia Computer Science, vol. 51, pp. 2583--2592, 2015. 
Journal Article
Published
2015
I. D. Schizas and A. Aduroja*, “A Distributed Framework for Dimensionality Reduction and Denoising,’’ IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6379-6394, Dec. 2015. DOI:10.1109/TSP.2015.2465300. 
Journal Article
Published
2015
 G. Ren*, I. D. Schizas and V. Maroulas, “Distributed Spatio-Temporal Multi-Target Association and Tracking,” Proc. of IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, pp. 4010—4014, 2015. 
Conference Paper
Published
2015
J. Chen* and I. D. Schizas, “Regularized Canonical Correlations for Sensor Data Information Clustering,” Proc. of IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, pp. 3601—3605, 2015. 
Conference Paper
Published
2015
V. Maroulas, K. Kang, I. D. Schizas and M. W. Berry, “A Learning Drift Homotopy Particle Filter,” Proc. of the Intl. Conf. on Information Fusion, Washington, DC, pp. 1930—1937, July 6-9, 2015. 
Conference Paper
Published
2015
V. Metsis, I. D. Schizas and G.Marshall, “Real-time Subspace Denoising of Polysomnographic Data,” Proc. of 8th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA’15), Corfu, Greece, July 2015. DOI: http://dx.doi.org/10.1145/2769493.2769584 
Conference Paper
Published
2015
S. Abhinav*, I. D. Schizas and A. Davoudi, “Noise-Resilient Synchrony of AC Microgrids,” International Symposium on Resilient Control Systems, Philadelphia, PA, pp. 1—6, Aug. 2015. 
Conference Paper
Published
2015
J. Chen*, A. Malhotra* and I. D. Schizas, “Information-Based Clustering and Filtering for Field Reconstruction,” Proc. of the Asilomar Conf. on Signals, Systems and Comp., Pacific Grove, CA, pp. 576-580, Nov. 8-11, 2015. 
Conference Paper
Published
2014
K. Kang, V. Maroulas and I. D. Schizas, “Drift Homotopy Particle Filter for non-Gaussian Multi- target Tracking,” Proc. of the Intl. Conference on Information Fusion, Salamanca, Spain, pp. 1—7, July 7-10, 2014. 
Conference Paper
Published
2014
J. Chen* and I. D. Schizas, “Adaptive Regularized Canonical Correlations in Clustering Sensor Data,” Proc. of of the Asilomar Conf. on Signals, Systems and Comp., Pacific Grove, CA, pp. 1611— 1615, Nov. 2-5, 2014. 
Conference Paper
Published
2014
G. Ren* and I. D. Schizas, “Joint Sensors-Sources Association and Tracking under Power Constraints,” Proc. of the IEEE Global Conference on Signal and Information Processing, Atlanta, GA, Dec. 3-5, pp. 754—758, 2014. 
Conference Paper
Published
2014
G. Ren*, I. D. Schizas and V. Maroulas, “Joint Sensors-Sources Association and Tracking,’’ Proc. of the IEEE Sensor Array and Multichannel Signal Processing Workshop, A Coruna, Spain, pp. 754—758, June 22-25, 2014. 
Conference Paper
Published
2013
A. Aduroja, I. D. Schizas and V. Maroulas, ``Distributed Principal Component Analysis in Sensor Networks,'' Proc. of IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) , May 26-31, 2013.
Conference Paper
Published
2013
I. D. Schizas, `` Adaptive Distributed Sparsity-Aware Matrix Decomposition,'' Proc. of IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) , May 26-31, 2013.
Conference Paper
Published
2013
I. D. Schizas, `` Distributed Informative-Sensor Identification using Sparsity-Aware Matrix Factorization,'' IEEE Trans. on Sig. Proc., vol. 61, no. 18, pp. 4610--4624, Sep. 2013. 
Journal Article
Published
2013
J. Chen and I. D. Schizas, ``Distributed Sparse Canonical Correlation Analysis in Clustering Sensor Data,'' Proc. of the Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, Nov. 2013
Conference Paper
Published
2013
I. D. Schizas, ``Distributed Data Cleansing via a Low-Rank Decomposition,'' Proc. of IEEE Global Conf. on Signal and Information Processing (GlobalSIP), Dec.  3-5, 2013.
Conference Paper
Published
2013
G. Ren and I. D. Schizas, ``Distributed Sensor-Informative Tracking of Targets,'' Proc. of the IEEE Intl. Workshop on Comp. Advances in Multi-Sensor Adaptive Processing, Saint Martin, Dec. 2013.
Conference Paper
Published
2012
I. D. Schizas and G. B. Giannakis, ``Covariance-Domain Sparsity for Data Compression and Denoising,'' IEEE Transactions on Signal Processing, May 2012.
Journal Article
Published
2012
I. D. Schizas, ``Distributed Informative-Sensor Determination via  Sparsity-Cognizant Matrix Decomposition,'' Proc. of IEEE Workshop on Statistical Signal Processing, August 5-8, 2012.
Conference Paper
Published
2011
 I. D. Schizas and G. B. Giannakis, `` Eigenspace Sparsity for Compression and Denoising,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Prague, Czech Republic, May 22-27, 2011. 
Conference Paper
Published
2010
A. Ribeiro, I. D. Schizas, S. I. Roumeliotis and G. B. Giannakis, ``Kalman Filtering in Wireless Sensor Networks: Incorporating communication cost in state estimation problems,'' IEEE Control Systems Magazine, April 2010.
Journal Article
Published
2009
H. Zhu, I. D. Schizas and G. B. Giannakis, ``Power-Efficient Dimensionality Reduction for Distributed Channel-Aware Kalman Tracking Using Wireless Sensor Networks,'' IEEE Transactions on Signal Processing, August 2009. 
Journal Article
Published
2009
I. D. Schizas, G. Mateos and G. B. Giannakis, `` Distributed LMS for Consensus-Based In-Network Adaptive Processing,'' IEEE Transactions on Signal Processing, June 2009.
Journal Article
Published
2009
G. Mateos, I. D. Schizas and G. B. Giannakis, ``Distributed Recursive Least-Squares for Consensus-Based In-Network Adaptive Estimation,'' IEEE Transactions on Signal Processing, Nov. 2009.
Journal Article
Published
2009
G. Mateos, I.D. Schizas and G. B. Giannakis, ``Performance Analysis of the Consensus-Based Distributed LMS Algorithm,'' EURASIP Journal on Advances in Signal Processing, Oct. 2009.
Journal Article
Published
2009
G. Mateos, I. D. Schizas and G. B. Giannakis, `` Closed-form MSE perfomance of the distributed LMS algorithm,''Proc. of 13th DSP Workshop, Marco Island, FL, January 4-7, 2009.
Conference Paper
Published
2009
I. D. Schizas, G. B. Giannakis and N. D. Sidiropoulos, ``Exploiting Covariance-domain Sparsity for Dimensionality Reduction,'' Proc. of 3rd Intl. Workshop on Comp. Advances in Multi-Sensor Adapt. Proc., ArubaIsland, Dec. 13-16, 2009.
Conference Paper
Published
2008
I. D. Schizas, G. B. Giannakis and N. Jindal, ``Distortion-Rate Bounds for Distributed Estimation with Wireless Sensor Networks, EURASIP Journal on Advances in Signal Processing, 2008.
Journal Article
Published
2008
I. D. Schizas, A. Ribeiro and G. B. Giannakis, `` Consensus in Ad Hoc WSNs with Noisy Links - Part I: Distributed Estimation of Deterministic Signals,'' IEEE Transactions on Signal Processing, January 2008.
Journal Article
Published
2008
I. D. Schizas, G. B. Giannakis, S. I. Roumeliotis and A. Ribeiro, ``Consensus in Ad Hoc WSNs with Noisy Links - Part II: Distributed Estimation and Smoothing of Random Signals,'' IEEE Transactions on Signal Processing, April 2008.
Journal Article
Published
2008
I. D. Schizas,G. Mateos and G. B. Giannakis, `` Stability analysis of the consensus-based distributed LMS algorithm,''Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Las Vegas, NV, March 30-April 4, 2008.
Conference Paper
Published
2007
A. Ribeiro,I.D. Schizas, J.-J. Xiao, G. B. Giannakis, and Z.-Q. Luo '' Distributed Estimation Under Bandwidth and Energy Constraints,'' in Wireless Sensor Networks: Signal Processing and Communications Perspectives (A. Swami, Q. Zhao, Y. Hong, and L. Tong, eds.), Wiley, February 2007.
Book Chapter
Published
2007
I. D. Schizas, G. B. Giannakis and Z.-Q. Luo, ``Distributed Estimation Using Reduced-Dimensionality Sensor Observations,'' IEEE Transactions on Signal Processing, August 2007.
Journal Article
Published
2007
I. D. Schizas,A. Ribeiro and G. B. Giannakis, `` Consensus-Based Distributed Parameter Estimation in Ad Hoc Wireless Sensor Networks with Noisy Links,''Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Honolulu, HI, April 15-20, 2007.
Conference Paper
Published
2007
I. D. Schizas,G. B. Giannakis and A. Ribeiro, ``Distributed MAP and LMMSE Estimation of Random Signals Using Ad Hoc Wireless Sensor Networks with Noisy Links,'' Proc. of SPAWC, Helsinki, Finland, June 17- 20, 2007.
Conference Paper
Published
2007
 I. D. Schizas,G. B. Giannakis, Stergios I. Roumeliotis and A. Ribeiro, ``Any-time Optimal Distributed Kalman Filtering and Smoothing,'' Proc. of Wrkshp. on Statistical Signal Processing, Madison, WI, August 26-29, 2007.
Conference Paper
Published
2007
 H. Zhu, I. D. Schizas andG. B. Giannakis, `` Power-Efficient Dimensionality Reduction for Distributed Channel-Aware Kalman Tracking Using Wireless Sensor Networks,'' Proc. of Wrkshp. on Statistical Signal Processing, Madison, WI, August 26-29, 2007.
Conference Paper
Published
2007
 G. Mateos, I. D. Schizas and G. B. Giannakis, ``Distributed Least-Mean Square Algorithm Uisng Wireless Ad Hoc Networks,'' Proc. of 45th Allerton Conf., Univ. of Illinois at U-C, Monticello, IL, Sept. 26-28, 2007.
Conference Paper
Published
2007
I. D. Schizas, G. Mateos and G. B. Giannakis, ``Distributed Recursive Least-Squares Using Wireless Ad Hoc Sensor Networks,'' Proc.of 41st Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 4-7, 2007.
Conference Paper
Published
2006
I. D. Schizas, A. Ribeiro, and G. B. Giannakis '' Dimensionality Reduction, Compression and Quantization for Distributed Estimation with Wireless Sensor Networks,'' in Wireless Communications (P. Agrawal, D. M. Andrews, P. J. Fleming, G. Yin, and L. Zhang, eds.), vol. 143 of IMA Volumes in Mathematics and its Applications, pp. 259--296, Springer, New York, 2006. 
Book Chapter
Published
2006
I. D. Schizas, G. B. Giannakis and Z.-Q. Luo, ``Optimal Dimensionality Reduction for Multi-Sensor Fusion in the Presence of Fading and Noise,'' Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Toulouse, France, May 15-19, 2006. 
Conference Paper
Published
2006
I. D. Schizas, A. Ribeiro and G. B. Giannakis, ``Distributed Estimation with Ad-Hoc Wireless Sensor Networks,'' Proc. of XIV EuropeanConf. Signal Processing Conference, Florence, Italy, Sep. 4-8, 2006.
Conference Paper
Published
2006
I. D. Schizas and G. B. Giannakis, `` Consensus-Based Distributed Estimation of Random Signals with Wireless Sensor Networks,'' Proc. of 40th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 29-Nov. 1, 2006.
Conference Paper
Published
2005
I. D. Schizas, G. B. Giannakis and N. Jindal, ``Distortion-Rate Analysis for Distributed Estimation with Wireless Sensor Networks,'' Proc. Of 43rd Allerton Conf., Univ. of Illinois at U-C, Monticello, IL, Sept. 28-30, 2005.
Conference Paper
Published
2005
I. D. Schizas, G. B. Giannakis and Z.-Q. Luo, ``Distributed Estimation Using Reduced Dimensionality Sensor Observations,'' Proc. of 39th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 30-Nov. 2, 2005.
Conference Paper
Published
November 2015
Distributed Information Discovery in Heterogeneous Data

Center for Intelligent Systems and Machine Learning (CISML), University of Tennessee at Knoxville, November 2015. 

Invited
March 2015
Distributed Data Clustering and Cleansing in Sensor Networks

Electrical and Computer Engineering, University of Texas at San Antonio, March 2015. 

Invited
February 2015
Distributed Sensor Data Clustering and Cleansing

Electrical Engineering, University of North Texas, February 2015. 

Invited
November 2013

Distributed Informative-Sensor Identification and Tracking via Sparsity-Aware Covariance Factorization

IEEE
Antennas and Propagation Society Technical Seminar, Fort Worth Chapter, Nov. 2013.

Invited
April 2013

Distributed Determination of Informative Network Nodes via Sparsity-Cognizant Covariance Decomposition

Invited
March 2013

Distributed Informative-Sensor Identification via Sparsity-Aware Covariance Decomposition

Invited
July 2015 -
June 2018
A Framework for Exploring Data in Heterogeneous Sensor Networks
$150,000
February 2015 -
September 2017
A Distributed Dynamic Data Driven Applications System (DDDAS) for Multi-Threat Tracking
$225,000
September 2012 -
August 2016
Distributed Informative Sensor Selection via Sparse Covariance Factorization
$205,665
June 2012 -
May 2013
Energy-Efficient Sensor Networks via Distributed Active Sensor Selection
$10,000
Ongoing
Big data signal processing
Doctoral
Ongoing
Guohua is working on distributed multi-target tracking problems.
Doctoral
May 2016
Distributed sparse-based signal processing
Master's
May 2016
Jia is working on sparsity-aware signal processing with applications to big data processing.
Doctoral
December 2013
Distributed data dimensionality reduction
Master's
Fall 2016
EE 5362 - DIGITAL COMMUNICATIONS
Office Hours (also by appointment)
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 16, 2016, 5:50 pm
No Documents Attached
Fall 2016
EE 5362 - DIGITAL COMMUNICATIONS
Office Hours (also by appointment)
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 16, 2016, 5:50 pm
No Documents Attached
Fall 2016
EE 4328 - DIGITAL COMMUNICATIONS
Office Hours (also by appointment)
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 16, 2016, 5:50 pm
No Documents Attached
Spring 2016
EE 5362 - Digital Communication
Office Hours
DayStartEnd
Tuesday5:00PM6:00PM
Thursday5:00PM6:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on January 12, 2016, 1:02 pm
No Documents Attached
Spring 2016
EE 5362 - Digital Communication
Office Hours
DayStartEnd
Tuesday5:00PM6:00PM
Thursday5:00PM6:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on January 12, 2016, 1:02 pm
No Documents Attached
Spring 2016
EE 4328 - Digital Communication
Office Hours
DayStartEnd
Tuesday5:00PM6:00PM
Thursday5:00PM6:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on January 12, 2016, 1:02 pm
No Documents Attached
Fall 2015
EE 5362 - DIGITAL COMMUNICATIONS
Office Hours
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 24, 2015, 2:11 pm
No Documents Attached
Fall 2015
EE 5362 - DIGITAL COMMUNICATIONS
Office Hours
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 24, 2015, 2:11 pm
No Documents Attached
Spring 2015
EE 5369 - Distributed Estimation Theory
Office Hours (also by appointment)
DayStartEnd
Tuesday3:30PM4:30PM
Thursday3:30PM4:30PM
The course presents major theoretical toolboxes for designing estimators and analyzing their performance. Specifically, the course will touch upon Cramer-Rao bound theory and present important estimators such as the maximum likelihood estimator, least-squares estimator and minimum mean-square error estimator to name a few. Tracking of time-varying processes and online estimation techniques will also be considered. After traditional theory is covered, the focus will move in distributed estimation with applications in sensor networks. Decentralized optimization tools such as the alternating direction method of multipliers will be studied and applied in deriving distributed estimators and tracking algorithms. Different modern distributed techniques will be considered and compared along with applications in sensing, data compression, data denoising and multi-target tracking.  The goal of this course is to help graduate students acquire the necessary theoretical background to tackle estimation problems that appear in many engineering applications, especially in networks of sensors.
Last Updated on December 31, 2014, 2:04 pm
No Documents Attached
Fall 2014
EE 5362 - Digital Communications
Office Hours (also by appointment)
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 8, 2014, 11:54 am
No Documents Attached
Spring 2014
EE 5369 - Ee 5369-001
The course presents major theoretical toolboxes for designing estimators and analyzing their performance. Specifically, the course will touch upon Cramer-Rao bound theory and present important estimators such as the maximum likelihood estimator, least-squares estimator and minimum mean-square error estimator to name a few. Tracking of time-varying processes and online estimation techniques will also be considered. After traditional theory is covered, the focus will move in distributed estimation with applications in sensor networks. Decentralized optimization tools such as the alternating direction method of multipliers will be studied and applied in deriving distributed estimators and tracking algorithms. Different modern distributed techniques will be considered and compared along with applications in sensing, data compression, data denoising and multi-target tracking.  The goal of this course is to help graduate students acquire the necessary theoretical background to tackle estimation problems that appear in many engineering applications, especially in networks of sensors.
Last Updated on April 24, 2014, 5:08 pm
No Documents Attached
Fall 2013
EE 5362 - Digital Communications
Office Hours
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 7, 2013, 4:36 pm
No Documents Attached
Fall 2013
EE 5362 - Digital Communications-Web Session
Office Hours
DayStartEnd
Tuesday3:00PM4:00PM
Thursday3:00PM4:00PM
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 7, 2013, 4:38 pm
No Documents Attached
Spring 2013
EE 5362 - DIGITAL COMMUNICATIONS
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 7, 2013, 4:03 pm
No Documents Attached
Fall 2012
EE 5368 - WIRELESS COMMUNICATION SYSTEMS
The course presents fundamental principles underlying the wireless transmission and reception of information, and studies the different parts of a modern wireless communication system. Specifically, the course will touch upon different digital modulation schemes, as well as the design and performance analysis of a transmission and reception end. The concept of diversity and its impact on reception performance (probability of symbol detection error) will be discussed. Channel capacity and channel coding will also be studied. Further, techniques for adaptive modulation and channel equalization used in state-of-the-art wireless systems will be presented. Communication using orthogonal frequency division multiplexing (OFDM), as well as spread spectrum techniques will also explored. Topics in multi-user systems, random access, cellular systems and ad hoc networks will also be covered. The goal of this course is to help graduate students to i) learn about different wireless communication technologies; ii) understand the basic components of a wireless communication system; iii) be able to design basic components in a wireless communication system; and iv) analyze its performance both analytically and numerically.
Last Updated on August 7, 2013, 4:03 pm
No Documents Attached
Spring 2012
EE 5362 - DIGITAL COMMUNICATIONS
The course presents fundamental principles underlying the transmission and reception of digital information, and studies the different parts of a modern digital communication system. Specifically, the course will touch upon different digital modulation schemes, as well as design and performance analysis of optimum receivers for additive white Gaussian noise (AWGN) channels. Some concepts of information theory and channel coding will also be studied. Further, techniques for carrier and symbol synchronization will be presented. Communication over bandlimited channels will also be explored, and the effects of intersymbol interference (ISI) and channel equalization techniques will be studied. The goal of this course is to help graduate students acquire the necessary theoretical background to i) understand the components of a digital communication system, ii) be able to design a digital communication system, and iii) analyze its performance both analytically and numerically.
Last Updated on August 7, 2013, 4:03 pm
No Documents Attached
Fall 2011
EE 5369 - Estimation Theory
The course presents major theoretical toolboxes for designing estimators and analyzing their performance. Specifically, the course will touch upon Cramer-Rao bound theory and present important estimators such as the maximum likelihood estimator, least-squares estimator and minimum mean-square error estimator to name a few. Tracking of time-varying processes and online estimation techniques will also be considered. The goal of this course is to help graduate students acquire the necessary theoretical background to tackle estimation problems that appear in many engineering applications.
Last Updated on August 7, 2013, 4:03 pm
No Documents Attached
August 2004
Ongoing
Reviewer for conferences

- IEEE International Conference on Communications

- IEEE Conference on Decision and Control (CDC)
- European Signal Processing Conference (EUSIPCO)
-IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive 

Processing (CAMSAP 2013)
- IEEE Global Conference on Signal and Information Processing
- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 

Volunteered
August 2004
Ongoing
Reviewer for Journals

  - IEEE Transactions on Signal Processing

-  IEEE Transactions on Information Theory

-  IEEE Signal Processing Letters

-  IEEE Signal Processing Magazine

-  IEEE Journal On Selected Areas in Communication

-  IEEE Journal of Selected Topics in Signal Processing

-  EURASIP Journal on Wireless Communications and Networking

-  IEEE Transactions on Communications

-  IEEE Transactions on Wireless Communications

-  IEEE Transactions on Image Processing

-  IEEE Communications Letters

-  IEEE Transactions on Automatic Control

-  IEEE Transactions on Robotics

-  Elsevier Signal Processing

-  EURASIP Journal on Advances in Signal Processing

-  Elsevier E-Reference Signal Processing

-  IEEE Transactions on Industrial Informatics

-  Elsevier Journal of The Franklin Institute

Volunteered
December -
Reviewer for Journals
IEEE Transactions on Signal Processing, IEEE Journal Of Selected Topics of Signal Processing IEEE Transactions on Image Processing, IEEE Transactions on Information Theory, IEEE Transactions on Communications, IEEE Journal on Selected Areas in Communications, IEEE Communications Letters, IEEE Signal Processing Letters, IEEE Signal Processing Magazine, IEEE Transactions on Robotics, Elsevier Signal Processing, EURASIP Journal on Advances in Signal Processing, EURASIP Journal on Wireless Communications and Networking
Uncategorized