Jeddah, Saudi Arabia

Maaz Mahadi


Average Co-Inventor Count = 5.0

ph-index = 1


Company Filing History:


Years Active: 2022

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2 patents (USPTO):Explore Patents

Title: The Innovative Contributions of Maaz Mahadi

Introduction

Maaz Mahadi is a prominent inventor based in Jeddah, Saudi Arabia. He has made significant contributions to the field of beamforming technology, holding 2 patents that showcase his innovative approach to solving complex problems in signal processing.

Latest Patents

One of his latest patents is titled "Robust linearly constrained minimum power (LCMP) beamformer with limited snapshots." This invention involves advanced beamformers and beamforming methods that utilize diagonal loading for regularization. A key feature of this patent is the automatic determination of the regularization parameter using a linearly constrained minimum power (LCMP) bounded perturbation regularization (BPR) approach.

Another notable patent by Mahadi is "Low-complexity robust beamforming for a moving source." This invention also employs diagonal loading and includes significant features such as the automatic determination of the regularization parameter using the LCMP BPR approach. This method is particularly advantageous for efficient beamforming when the desired signal originates from a moving source. After establishing an initial regularization parameter, each data window utilizes the prior window's regularization parameter to recursively determine the next one.

Career Highlights

Maaz Mahadi is affiliated with King Abdulaziz University, where he contributes to research and development in his field. His work is characterized by a commitment to advancing technology and improving methodologies in beamforming.

Collaborations

He collaborates with notable colleagues, including Muhammad Moinuddin and Ubaid M Al-Saggaf, who share his passion for innovation and research.

Conclusion

Maaz Mahadi's contributions to beamforming technology reflect his dedication to innovation and excellence in engineering. His patents not only advance the field but also demonstrate the potential for future developments in signal processing.

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