Company Filing History:
Years Active: 2021-2025
Title: Mahdi H Al-Badrawi: Innovator in Empirical Mode Decomposition Techniques
Introduction
Mahdi H Al-Badrawi is a notable inventor based in Durham, NH (US), recognized for his contributions to the field of signal processing. He holds three patents that focus on advanced techniques for noise estimation and signal de-noising using empirical mode decomposition (EMD). His work has significant implications for improving the accuracy and efficiency of signal processing systems.
Latest Patents
Al-Badrawi's latest patents include innovative methods for EMD-based noise estimation and signal de-noising. The first patent describes a process for blind estimations of noise power for a given signal under test. This non-parametric and adaptive EMD-based noise estimation process operates without prior knowledge of the received signal, enhancing the capabilities of existing spectrum sensing approaches. The second patent outlines techniques for EMD-based signal de-noising that utilize statistical properties of intrinsic mode functions (IMFs). This framework allows for the identification of information-carrying IMFs, enabling the reconstruction of a de-noised output signal by distinguishing between noise and signal contributions.
Career Highlights
Al-Badrawi is affiliated with the University of New Hampshire, where he continues to advance research in signal processing. His work has garnered attention for its innovative approach to handling noise in signals, which is crucial for various applications in telecommunications and data analysis.
Collaborations
Some of his notable coworkers include Nicholas J Kirsch and Bessam Z Al-Jewad, who have collaborated with him on various research projects related to signal processing and empirical mode decomposition techniques.
Conclusion
Mahdi H Al-Badrawi's contributions to the field of signal processing through his patents on empirical mode decomposition techniques highlight his innovative spirit and dedication to advancing technology. His work continues to influence the development of more effective noise estimation and signal de-noising methods.