What I Do
I am actively researching Human-Computer Interaction, Artificial Intelligence, Virtual Reality, and Computer Vision, with a keen interest in creating user-friendly interfaces and exploring AI applications. I also enjoy hands-on AI experimentation and stay updated through conferences and collaborations, driven by a passion for innovation in technology.
My approach centers on continuous learning and drawing from real-life experiences for innovation. I identify challenges, conduct user studies, and research extensively. Then, I leverage cutting-edge techniques to develop user-centric solutions, ensuring that my work is both technically advanced and impactful.
My mission is to leverage cutting-edge technology to improve people’s lives significantly. Through dedicated research, I aim to simplify everyday experiences, enhance accessibility, and spread knowledge widely, ensuring that technology benefits everyone. My ultimate goal is to create a more inclusive and accessible future for all.
In the field of human-computer interaction, I research air-writing, gesture recognition, and gesture-based writing systems. These technologies provide innovative and accessible means of computer interaction, benefiting individuals, especially those with disabilities, by offering alternative input methods and improving overall accessibility to digital tools and information.
My research in artificial intelligence centers on using CNN, LSTM, GRU, and network fusion to create human-computer interaction applications, such as gesture recognition and air writing. These technologies aim to facilitate intuitive and accessible interactions between humans and computers, enhancing communication and usability in various contexts.
I conduct research in virtual reality, focusing on a project that teaches American Sign Language (ASL) through gamification. A signing avatar within the virtual environment instructs and provides real-time feedback on the accuracy of ASL signs produced, creating an engaging and interactive learning experience for users.
In my computer vision research, I applied deep learning algorithms, including a proposed Generative Adversarial Network (GAN), to perform image super-resolution for integral imaging microscopy. This technique significantly increased image resolution by eight times beyond what optical lenses could achieve, enhancing the detail and applicability of microscopic imaging in scientific and medical contexts.
Top 4 High-Performing Research
- M. S. Alam, K. -C. Kwon and N. Kim, “Implementation of a Character Recognition System Based on Finger-Joint Tracking Using a Depth Camera,” in IEEE Transactions on Human-Machine Systems, vol. 51, no. 3, pp. 229-241, June 2021, doi: https://doi.org/10.1109/THMS.2021.3066854
- M. S. Alam, K. -C. Kwon and N. Kim, “Implementation of a Character Recognition System Based on Finger-Joint Tracking Using a Depth Camera,” in IEEE Transactions on Human-Machine Systems, vol. 51, no. 3, pp. 229-241, June 2021, doi: https://doi.org/10.1109/THMS.2021.3066854.
- M. S. Alam, K. -C. Kwon, M. -U. Erdenebat, M. Y. Abbass; M. A. Alam, and N. Kim, “Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy” in Sensors 2021, 21, 2164. https://doi.org/10.3390/s21062164.
- M.S. Alam, K.-C. Kwon; M.A. Alam, M.Y. Abbass, S.M. Imtiaz, N. Kim, “Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor,” in Sensors 2020, 20, 376. https://doi.org/10.3390/s20020376
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