Dr. Abdullahi Usman is a Nigerian researcher at Chulalongkorn University, Thailand, with diverse research interests, including optical sensors with image-processing applications, optical waveguides, optical thin-film imaging, digital holography, integrated optics, and optical communication. He has published several research articles in these fields and has served as a reviewer for several journals from prestigious publishers such as Elsevier, Wiley, Optica, and others. Before his arrival in Thailand, he had been an academic staff member in the Department of Physics with Electronics at the Federal University Birnin Kebbi (FUBK), Nigeria, since 2015.
Dr. Abdullahi Usman is acknowledged as the first member of the FUBK community to gain admission into a graduate program in Thailand. Dr. Usman received an international scholarship in 2020 to pursue a Ph.D. in Electrical and Computer Engineering at King Mongkut’s University of Technology Thonburi (KMUTT) in Bangkok. As part of his scholarship, he also participated in a fully funded research exchange at Shibaura Institute of Technology in Tokyo, Japan, where he focused on integrated optics (referred to as an optical IC) in the design and fabrication of an optical waveguide for telecommunication applications. Despite the disruptions caused by the COVID-19 pandemic, he completed the program in just two and a half years.
Dr. Usman’s PhD thesis centered on experimental optics, with a focus on thin-film thickness measurement using light interference. The first phase of his study involved designing a modified Sagnac interferometer with a self-referenced polarization and phase-shifting mechanism for real-time thin-film thickness measurement of materials such as WO₃, TiO₂, and others. Validation was carried out using conventional techniques like FE-SEM.
The second phase expanded to the use of a CCD for microscopic applications. In this experiment, the morphological structure of Spirulina cells was viewed through the interference pattern, and the result was further processed using image-processing methods. The reconstructed image was then compared to a reference image viewed through a conventional microscope. This setup demonstrated the potential of optical sensors compared to conventional microscopy. After returning from Japan, he was appointed as a postdoctoral fellow in the same department where he earned his Ph.D., continuing his research activities and supervising graduate students.
As part of his postdoctoral work, he studied fingerprint pattern detection using digital holography with a Mach-Zehnder interferometer. Fingerprints serve as an excellent means of individual identification. This study employed transmission-mode image detection using a Mach-Zehnder interferometer to produce two-dimensional fingerprint images. The research aimed to develop a fingerprint pattern recognition system by integrating novel algorithms for ridge orientation analysis. The fingerprint patterns considered in this study included loops, arches, and whorls. The objective was to achieve high-resolution fingerprint patterns to analyze fingerprint variability, distinguish foreground from background regions, and trace ridge line orientations. The recognition algorithm was implemented using image-processing techniques, with a preprocessing workflow that included binarization, resizing, and histogram equalization for image enhancement. After preprocessing, the image underwent ridge orientation analysis using the gradient method, and both similarity scores and pattern categorization were successfully achieved. The algorithm’s performance was evaluated through MATLAB simulations, with results demonstrating the proposed system’s accuracy and reliability across various fingerprint samples. The interferometric setup was also adapted to recognize microorganisms within a microfluidic channel, highlighting its potential applications in biometrics, forensics, and medical diagnostics. In addition, the use of the Mach-Zehnder interferometer on fingerprints was further investigated to predict the probability of human skin diseases and compared with the conventional use of Optical Coherence Tomography (OCT).
For the fingerprint image extraction, a suitable extraction algorithm was utilized to analyze features from the region of interest, and this was validated using conventional methods.
Understanding the increasing global relevance of solar cell technology, particularly its role in mitigating climate change and improving renewable energy access in Nigeria, Dr. Usman sought to broaden his research into this area. However, limited research funding in Nigeria made experimental solar cell investigations difficult. As a result, numerical simulations became a practical and cost-effective method for preliminary verification before laboratory implementation.
This led him to look for alternative solutions that would allow him to continue contributing meaningfully to research and academic training, especially for final-year students working on solar-cell-related projects. During his postdoctoral fellowship, he adopted SCAPS (Solar Cell Capacitance Simulator), an open-source and user-friendly tool developed at Ghent University. Drawing on his strong background in materials science, he quickly mastered the software, using it to analyze experimental data, validate measured parameters, and optimize device performance within realistic limits, offering valuable guidance for experimental benchmarks.
More recently, machine learning has become a key component of his work. Since Density Functional Theory (DFT) often produces inaccurate bandgap predictions, Dr. Usman integrated machine learning models to achieve more reliable and experimentally consistent results. This line of research earned him the Second-Best Paper Award at the International Conference on Photonics Solutions in Bangkok, Thailand, for his study titled “SVM Machine Learning Model for Bandgap Estimation of Doped TiO₂ for Numerical Modeling of Perovskite Solar Cell Applications”.
