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Information Science Lab Research Group

Welcome to the Information Science Lab! Professor Al-Naffouri's research interests lie in adaptive, sparse, and statistical signal processing and its applications. These applications include communications, ultrawideband and ultrasound-based localization and tracking. His research also extends to the design and analysis of wireless networks. Please follow the links below to learn more about the currently ongoing projects and the internship opportunities within the team. As you can see by looking at the people profiles, the group is extremely diverse, and we always welcome bright and hard

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About

‌ Professor Al-Naffouri's research interests lie in the areas of adaptive, sparse and statistical signal processing and their applications. These applications include communications, ultrawideband and ultrasound-based localization and tracking. His research also extends to the design and analysis of wireless networks. Projects Aeronautical Data Aggregation and Field Estimation in IoT Networks: Hovering and Traveling Time Dilemma of UAVs The Internet of things (IoT) advancement relies on connecting a huge number of sensors that are inexpensive, simple structured and self-powered. Massive MIMO

Collaborators

Ahmad Bahai National Semiconductor Aldo Vesnaver Earth Sciences Department, King Fahd University of Petroleum & Minerals Ali H. Sayed Electrical Engineering Department, University of California, Los Angeles Amir Dana Qualcomm Inc. Arogyaswami Paulraj Information Systems Laboratory (ISL), Stanford University Azzedine Zerguine Electrical Engineering Department, King Fahd University of Petroleum & Minerals Babak Hassibi Electrical Engineering, California Institute of Technology Ghazi Al-Rawi Eastgate Capital Group, United Arab Emirates Giuseppe Caire

Conference Publications ISL

Conference Publications 166. R. Alghamdi, N. Saeed, H. Dahrouj, M. Alouini and T. Y. Al-Naffouri, "Towards Ultra-Reliable Low-Latency Underwater Optical Wireless Communications," 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA, pp. 1-6, Sep. 2019. 165. H. Chen, T. Ballal, X. Liu and T. Y. Al-Naffouri, "Realtime 2-D DOA Estimation using Phase-Difference Projection (PDP)," 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain, pp. 1-5, Sep. 2019. 164. A. A. Ahmad et al., "Rate Splitting and Common Message Decoding for MIMO C-RAN Systems,"

Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

Software Description Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. This software is a new regularization approach and a new regularization parameter selection approach for linear least squares discrete ill-posed problems. It is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data

Former Members from KFUPM

Ph.D. Students: Hussain Ali - did a M.S. Telecommunication Engineering, 2010–2013, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, and Ph.D. in Electrical Engineering, 2013–2017, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. Now is a Research Engineer at Dhahran Techno Valley, KFUPM, Dhahran, Saudi Arabia. Alam Zaib - PhD student at KFUPM, Sep 2011 – Dec 2015. Now is an Assistant Professor / COMSATS institute of information technology, Abbottabad Mostafa Mohamed Lotfy Emara - Ph.D. in Electrical Engineering, KFUPM, Sep 2014 – Apr 2018, Dhahran

Funding

Online Development of the Undergraduate Communication Engineering Course, funded by Deanship of Academic Development, King Fahd University of Petroleum & Minerals, May 2006 – Jun. 2007. (Co-investigator) Establishing Entrepreneurial and Value-added Programs, A Project of KFUPM’s Strategic Plan, Mar. 2006 – Jun. 2007. (Co-investigator) Frequency Domain Estimation of Time Variant Channels in OFDM, Junior Faculty Project, funded by Deanship of Scientific Research, King Fahd University of Petroleum & Minerals, Sep. 2006 – Aug. 2007. (Principal Investigator) Enhancing Student
KAUST CEMSE ISL CST Course 2024 at Riyadh

ISL Talks

On Regularization Tuning Electrical & Computer Engineering Department, University of Toronto, Toronto, Canada, Jul 29, 2019. Distributed Agnostic Structured Sparsity Recovery: Algorithms and Applications Department de geenie electrique, Ecole de technologie superieure, Montreal, Canada, Jul 24, 2019. Aerial Data Aggregation in IoT Networks: A Novel Paradigm with Application to Forest Fire Fighting KAUST Research Conference on Sustainable Urban-Enviornmental Future, Thuwal, Saudi Arabia, Apr 3, 2019 Detection and Estimation Using Regularized Least Squares: Performance Analysis and Optimal

Journal Publications ISL

113. A. Douik, H. Dahrouj, T. Y. Al-Naffouri and M. Alouini, "A Tutorial on Clique Problems in Communications and Signal Processing," in Proceedings of the IEEE, vol. 108, no. 4, pp. 583-608, Apr. 2020. 112. H. Ali, S. Ahmed, T. Y. Al-Naffouri, M. S. Sharawi,, and M.-S. Alouini, “Reduced Complexity DOA and DOD Estimation for Moving Target in Bistatic MIMO Radar”, Submitted to IEEE Transactions on Signal Processing. 111. K. Elkhalil, A. Kammoun, R. Couillet, T. Y. Al-Naffouri and M. Alouini, "A Large Dimensional Study of Regularized Discriminant Analysis," in IEEE Transactions on Signal

On the Distribution of Indefinite Quadratic Forms in Gaussian Random Variables

Software Description: This software evaluate the CDF and PDF of indefinite quadratic forms in Gaussian random variables. Such a quantity appears in many applications in communications, signal processing, information theory, and adaptive filtering. For example, this quantity appears in the mean-square-error (MSE) analysis of the normalized least-mean square (NLMS) adaptive algorithm, and SINR associated with each beam in beam forming applications. The trick of this software is to replace inequalities that appear in the CDF calculation with unit step functions and to use complex integral

Patents ISL

24. T. Y. Al-Naffouri, and Taha Bouchoucha, “Direct Closed-Form Finite Alphabet Constant-Envelope Waveform Design for Planar Array Beampatterns”, US patent submitted. 23. T. Y. Al-Naffouri, and Mohamed Saadeldin, “Smart Parking System”, US patent submitted. 22. T. Y. Al-Naffouri, and Hui Chen, “An Ultrasound Based Air-Writing System”, US patent submitted. 21. T. Y. Al-Naffouri, and S. Valaee, “RSS-Based Indoor Localization with No Deployment nor Update Efforts”, US patent submitted. 20. T. Y. Al-Naffouri, and Mohamed Saadeldin, “A Gesture Recognition System Using Ultrasonic Angle-of-Arrival

Qawam

Boost Your Confidence and Improve Your Health with Qawam's AI-Powered Posture Monitoring Solutions Qawam is addressing the pervasive issue of poor posture resulting from the increasing use of screens in daily life. As people spend more time on computers, smartphones, and other digital devices, they often develop a slouched posture, which can lead to chronic pain, particularly in the back, neck, and shoulders. Poor posture is not just a physical issue; it can also affect the confidence and mental well-being of a total of 6.5 million office workers in Saudi Arabia. By leveraging AI technology

Service

Member of SPCOM TC (Signal Processing for Communications Technical Committee). Associate Editor for IEEE Transactions on Signal Processing (2013 - 2017). Executive member of IEEE Education Society, Gulf Section (2007-2012). IEEE KFUPM Student Branch Counselor (2007-2012). Reviewer for: IEEE Transactions Signal Processing IEEE Transactions on Communications IEEE Transactions on Wireless Communications IEEE Transactions on Selected Areas in Communications IEEE Transactions on Vehicular Technology IEEE Signal Processing Letters IEEE Communication Letters TPC Member for the following conferences

SNR Estimation in Linear Systems With Gaussian Matrices

Software Description: This work proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation while unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate

Support agnostic Bayesian Matching pursuit for block sparse signals

Software Description: Some signal processing problems can be modeled as finding a sparse solution to an under-determined linear system of equations. There are a number of ways to find the sparse solution. We considered a Matching Pursuit approach that is Bayesian in nature and at the same time agnostic to the distribution of the unknowns. Our formulation involves projection of observations onto the orthogonal complement space of the regression matrix elements. The proposed greedy approach and efficient computation strategies resulted in an algorithm that is faster than many similar algorithms